You are viewing a plain text version of this content. The canonical link for it is here.
Posted to user@kudu.apache.org by Benjamin Kim <bk...@amobee.com> on 2016/03/01 20:00:13 UTC
Re: Spark on Kudu
Hi J-D,
Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
Just curious,
Benjamin Kim
Data Solutions Architect
[a•mo•bee] (n.) the company defining digital marketing.
Mobile: +1 818 635 2900
3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com<http://www.amobee.com/>
On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
The DStream stuff isn't there at all. I'm not sure if it's needed either.
The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
J-D
On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>> wrote:
J-D,
It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
Thanks,
Ben
On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
Hope this helps,
J-D
On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>> wrote:
I see this KUDU-1214<https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
Just curious.
Cheers,
Ben
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Hi Chris,
I just read the weekly blog progress update. The UPSERT operation is now supported on the server side. Does this mean that there is no need to explicitly state “Append” or “Overwrite” when writing to a table?
Just curious.
Cheers,
Ben
> On May 18, 2016, at 9:01 AM, Chris George <Ch...@rms.com> wrote:
>
> There is some code in review that needs some more refinement.
> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>
> Example usages will look something like:
> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>
> -Chris George
>
>
> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>
> Can someone tell me what the state is of this Spark work?
>
> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>
> Thanks,
> Ben
>
>
>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>
>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>> -Chris
>>
>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>
>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>
>> MERGE INTO table_name USING table_reference ON (condition)
>> WHEN MATCHED THEN
>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>> WHEN NOT MATCHED THEN
>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>
>> Cheers,
>> Ben
>>
>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>
>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>
>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>
>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>
>>> -Chris George
>>>
>>>
>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>
>>> J-D
>>>
>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>
>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>
>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>> Mark,
>>>
>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>
>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>> Do they care being able to insert into Kudu with SparkSQL
>>>
>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>
>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>
>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>
>>>
>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>> Yup, starting to get a good idea.
>>>
>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>
>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>
>>> J-D
>>>
>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>
>>> I hope this helps you get an idea where I am coming from…
>>>
>>> Cheers,
>>> Ben
>>>
>>>
>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> J-D,
>>>>
>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>
>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>
>>>>
>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>
>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>
>>>>
>>>> I hope this helps.
>>>>
>>>> It does, thanks for nice reply.
>>>>
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>
>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>
>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>
>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>
>>>>> Thanks,
>>>>>
>>>>> J-D
>>>>>
>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> Hi J-D,
>>>>>
>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> Hi Ben,
>>>>>>
>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>
>>>>>> Regards,
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>> Hi J-D,
>>>>>>
>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>
>>>>>> Just curious,
>>>>>> Benjamin Kim
>>>>>> Data Solutions Architect
>>>>>>
>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>
>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>
>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>
>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>
>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>
>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> J-D,
>>>>>>>
>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>
>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>
>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>
>>>>>>>> Hope this helps,
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>
>>>>>>>> Just curious.
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>
>
Re: Spark on Kudu
Posted by Dan Burkert <da...@cloudera.com>.
Hi Benjamin,
The spark connector jar can be found on the Apache maven repository.
Maven Coordinates:
<dependency>
<groupId>org.apache.kudu</groupId>
<artifactId>kudu-spark_2.10</artifactId>
<version>1.0.0</version>
</dependency>
<repository>
<id>apache.releases</id>
<name>Apache Release Repository</name>
<url>https://repository.apache.org/releases</url>
</repository>
On Tue, Sep 20, 2016 at 1:18 PM, Benjamin Kim <bb...@gmail.com> wrote:
> Now that Kudu 1.0.0 is officially out and ready for production use, where
> do we find the spark connector jar for this release?
>
> Thanks,
> Ben
>
>
> On Jun 17, 2016, at 11:08 AM, Dan Burkert <da...@cloudera.com> wrote:
>
> Hi Ben,
>
> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I
> do not think we support that at this point. I haven't looked deeply into
> it, but we may hit issues specifying Kudu-specific options (partitioning,
> column encoding, etc.). Probably issues that can be worked through
> eventually, though. If you are interested in contributing to Kudu, this is
> an area that could obviously use improvement! Most or all of our Spark
> features have been completely community driven to date.
>
>
>> I am assuming that more Spark support along with semantic changes below
>> will be incorporated into Kudu 0.9.1.
>>
>
> As a rule we do not release new features in patch releases, but the good
> news is that we are releasing regularly, and our next scheduled release is
> for the August timeframe (see JD's roadmap
> <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email
> about what we are aiming to include). Also, Cloudera does publish snapshot
> versions of the Spark connector here
> <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>, so
> the jars are available if you don't mind using snapshots.
>
>
>> Anyone know of a better way to make unique primary keys other than using
>> UUID to make every row unique if there is no unique column (or combination
>> thereof) to use.
>>
>
> Not that I know of. In general it's pretty rare to have a dataset without
> a natural primary key (even if it's just all of the columns), but in those
> cases UUID is a good solution.
>
>
>> This is what I am using. I know auto incrementing is coming down the line
>> (don’t know when), but is there a way to simulate this in Kudu using Spark
>> out of curiosity?
>>
>
> To my knowledge there is no plan to have auto increment in Kudu.
> Distributed, consistent, auto incrementing counters is a difficult problem,
> and I don't think there are any known solutions that would be fast enough
> for Kudu (happy to be proven wrong, though!).
>
> - Dan
>
>
>>
>> Thanks,
>> Ben
>>
>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>>
>> I'm not sure exactly what the semantics will be, but at least one of them
>> will be upsert. These modes come from spark, and they were really designed
>> for file-backed storage and not table storage. We may want to do append =
>> upsert, and overwrite = truncate + insert. I think that may match the
>> normal spark semantics more closely.
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> Dan,
>>>
>>> Thanks for the information. That would mean both “append” and
>>> “overwrite” modes would be combined or not needed in the future.
>>>
>>> Cheers,
>>> Ben
>>>
>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>
>>> Right now append uses an update Kudu operation, which requires the row
>>> already be present in the table. Overwrite maps to insert. Kudu very
>>> recently got upsert support baked in, but it hasn't yet been integrated
>>> into the Spark connector. So pretty soon these sharp edges will get a lot
>>> better, since upsert is the way to go for most spark workloads.
>>>
>>> - Dan
>>>
>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows
>>>> in 64s. I would assume that now I can use the “overwrite” mode on existing
>>>> data. Now, I have to find answers to these questions. What would happen if
>>>> I “append” to the data in the Kudu table if the data already exists? What
>>>> would happen if I “overwrite” existing data when the DataFrame has data in
>>>> it that does not exist in the Kudu table? I need to evaluate the best way
>>>> to simulate the UPSERT behavior in HBase because this is what our use case
>>>> is.
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>
>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>>>
>>>> Hi,
>>>>
>>>> Now, I’m getting this error when trying to write to the table.
>>>>
>>>> import scala.collection.JavaConverters._
>>>> val key_seq = Seq(“my_id")
>>>> val key_list = List(“my_id”).asJava
>>>> kuduContext.createTable(tableName, df.schema, key_seq, new
>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list,
>>>> 100))
>>>>
>>>> df.write
>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>>> .mode("overwrite")
>>>> .kudu
>>>>
>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to
>>>> Kudu; sample errors: Not found: key not found (error 0)Not found: key not
>>>> found (error 0)Not found: key not found (error 0)Not found: key not found
>>>> (error 0)Not found: key not found (error 0)
>>>>
>>>> Does the key field need to be first in the DataFrame?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>
>>>>
>>>>
>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> Dan,
>>>>>
>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a
>>>>> column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>
>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>
>>>>> java.lang.IllegalArgumentException: Table partitioning must be
>>>>> specified using setRangePartitionColumns or addHashPartitions
>>>>>
>>>>
>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of
>>>> primary key columns, so in this case you have specified the single PK
>>>> column "my_id". The `addHashPartitions` call adds hash partitioning to the
>>>> table, in this case over the column "my_id" (which is good, it must be over
>>>> one or more PK columns, so in this case "my_id" is the one and only valid
>>>> combination). However, the call to `addHashPartition` also takes the
>>>> number of buckets as the second param. You shouldn't get the
>>>> IllegalArgumentException as long as you are specifying either
>>>> `addHashPartitions` or `setRangePartitionColumns`.
>>>>
>>>> - Dan
>>>>
>>>>
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>
>>>>> Looks like we're missing an import statement in that example. Could
>>>>> you try:
>>>>>
>>>>> import org.kududb.client._
>>>>>
>>>>> and try again?
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I encountered an error trying to create a table based on the
>>>>>> documentation from a DataFrame.
>>>>>>
>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>> kuduContext.createTable(tableName, df.schema,
>>>>>> Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>
>>>>>> Is there something I’m missing?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> It's only in Cloudera's maven repo: https://repository.
>>>>>> cloudera.com/cloudera/cloudera-repos/org/kududb/
>>>>>> kudu-spark_2.10/0.9.0/
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar
>>>>>>> for spark-shell to use. Can you show me where to find it?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>> wrote:
>>>>>>>
>>>>>>> What's in this doc is what's gonna get released: https://github.com/
>>>>>>> cloudera/kudu/blob/master/docs/developing.adoc#kudu-
>>>>>>> integration-with-spark
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <
>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>
>>>>>>>> It will be in 0.9.0.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi Chris,
>>>>>>>>>
>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <
>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>
>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource
>>>>>>>>> api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>>>>>
>>>>>>>>> Example usages will look something like:
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>>>>>
>>>>>>>>> -Chris George
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>
>>>>>>>>> Also, does anyone have any sample code on how to update/insert
>>>>>>>>> data in Kudu using DataFrames?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <
>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>
>>>>>>>>> SparkSQL cannot support these type of statements but we may be
>>>>>>>>> able to implement similar functionality through the api.
>>>>>>>>> -Chris
>>>>>>>>>
>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an
>>>>>>>>> “upsert” if it were to be implemented.
>>>>>>>>>
>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <
>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>
>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it
>>>>>>>>> into gerrit if you want to take a look. http://gerrit.cloudera.
>>>>>>>>> org:8080/#/c/2754/
>>>>>>>>> It does pushdown predicates which the existing input formatter
>>>>>>>>> based rdd does not.
>>>>>>>>>
>>>>>>>>> Within the next two weeks I’m planning to implement a datasource
>>>>>>>>> for spark that will have pushdown predicates and insertion/update
>>>>>>>>> functionality (need to look more at cassandra and the hbase datasource for
>>>>>>>>> best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>> Having a datasource would give us useful data frames and also make
>>>>>>>>> spark sql usable for kudu.
>>>>>>>>>
>>>>>>>>> My reasoning for having a spark datasource and not using Impala
>>>>>>>>> is: 1. We have had trouble getting impala to run fast with high concurrency
>>>>>>>>> when compared to spark 2. We interact with datasources which do not
>>>>>>>>> integrate with impala. 3. We have custom sql query planners for extended
>>>>>>>>> sql functionality.
>>>>>>>>>
>>>>>>>>> -Chris George
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>> You guys make a convincing point, although on the upsert side
>>>>>>>>> we'll need more support from the servers. Right now all you can do is an
>>>>>>>>> INSERT then, if you get a dup key, do an UPDATE. I guess we could at least
>>>>>>>>> add an API on the client side that would manage it, but it wouldn't be
>>>>>>>>> atomic.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <
>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>
>>>>>>>>>> It's pretty simple, actually. I need to support versioned
>>>>>>>>>> datasets in a Spark SQL environment. Instead of a hack on top of a Parquet
>>>>>>>>>> data store, I'm hoping (among other reasons) to be able to use Kudu's write
>>>>>>>>>> and timestamp-based read operations to support not only appending data, but
>>>>>>>>>> also updating existing data, and even some schema migration. The most
>>>>>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>>>>>> be added/appended.
>>>>>>>>>>
>>>>>>>>>> Using Kudu's Java API and developing additional functionality on
>>>>>>>>>> top of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>>>>>> decision.
>>>>>>>>>>
>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>>> Mark,
>>>>>>>>>>>
>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it
>>>>>>>>>>> caught the attention of other folks!
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <
>>>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>>>>>> to whether we adopt Kudu.
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary
>>>>>>>>>>> for you. Is it just that you currently do it that way into some database or
>>>>>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>>>>>> KuduSession be too much work?
>>>>>>>>>>>
>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your
>>>>>>>>>>> current solution? If it's not completely clear, I'd love to help you think
>>>>>>>>>>> through it.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>>
>>>>>>>>>>>>> What are your DS folks looking for in terms of functionality
>>>>>>>>>>>>> related to Spark? A SparkSQL integration that's as fully featured as
>>>>>>>>>>>>> Impala's? Do they care being able to insert into Kudu with SparkSQL or just
>>>>>>>>>>>>> being able to query real fast? Anything more specific to Spark that I'm
>>>>>>>>>>>>> missing?
>>>>>>>>>>>>>
>>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <
>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions.
>>>>>>>>>>>>>> But, since it’s not “production-ready”, upper management doesn’t want to
>>>>>>>>>>>>>> fully deploy it yet. They just want to keep an eye on it though. Kudu was
>>>>>>>>>>>>>> so much simpler and easier to use in every aspect compared to HBase. Impala
>>>>>>>>>>>>>> was great for the report writers and analysts to experiment with for the
>>>>>>>>>>>>>> short time it was up. But, once again, the only blocker was the lack of
>>>>>>>>>>>>>> Spark support for our Data Developers/Scientists. So, production-level data
>>>>>>>>>>>>>> population won’t happen until then.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> As for our company, we have been looking for an updatable
>>>>>>>>>>>>>>> data store for a long time that can be quickly queried directly either
>>>>>>>>>>>>>>> using Spark SQL or Impala or some other SQL engine and still handle TB or
>>>>>>>>>>>>>>> PB of data without performance degradation and many configuration
>>>>>>>>>>>>>>> headaches. For now, we are using HBase to take on this role with Phoenix as
>>>>>>>>>>>>>>> a fast way to directly query the data. I can see Kudu as the best way to
>>>>>>>>>>>>>>> fill this gap easily, especially being the closest thing to other
>>>>>>>>>>>>>>> relational databases out there in familiarity for the many SQL analytics
>>>>>>>>>>>>>>> people in our company. The other alternative would be to go with AWS
>>>>>>>>>>>>>>> Redshift for the same reasons, but it would come at a cost, of course. If
>>>>>>>>>>>>>>> we went with either solutions, Kudu or Redshift, it would get rid of the
>>>>>>>>>>>>>>> need to extract from HBase to parquet tables or export to PostgreSQL to
>>>>>>>>>>>>>>> support more of the SQL language using by analysts or the reporting
>>>>>>>>>>>>>>> software we use..
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off
>>>>>>>>>>>>>> with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we
>>>>>>>>>>>>>>> like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>>>>>> dying down.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> There's definitely an overlap in the use cases that
>>>>>>>>>>>>>>> Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C*
>>>>>>>>>>>>>>> is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month,
>>>>>>>>>>>>>>> it's a storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to
>>>>>>>>>>>>>>> learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>>>>>> replacement.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose.
>>>>>>>>>>>>>>>> They told me that everything was about Spark and there is a big buzz about
>>>>>>>>>>>>>>>> the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any
>>>>>>>>>>>>>>>> timeline. I know of one person on the Kudu Slack who's working on a better
>>>>>>>>>>>>>>>> RDD, but that's about it.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <
>>>>>>>>>>>>>>>> bkim@amobee.com> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to
>>>>>>>>>>>>>>>>> target a version of Kudu to begin real testing of Spark against it for our
>>>>>>>>>>>>>>>>> devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA
>>>>>>>>>>>>>>>>> 90405 | www.amobee.com
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's
>>>>>>>>>>>>>>>>> needed either.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format,
>>>>>>>>>>>>>>>>> ideally we'd use scans directly.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The goal was to provide something for others to contribute
>>>>>>>>>>>>>>>>> to. We have some basic unit tests that others can easily extend. None of us
>>>>>>>>>>>>>>>>> on the team are Spark experts, but we'd be really happy to assist one
>>>>>>>>>>>>>>>>> improve the kudu-spark code.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements
>>>>>>>>>>>>>>>>>> (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more
>>>>>>>>>>>>>>>>>> Spark SQL functionality (Dataframes) and doing the documentation, what more
>>>>>>>>>>>>>>>>>> needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark
>>>>>>>>>>>>>>>>>> with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get
>>>>>>>>>>>>>>>>>> this in for 0.7.0: https://issues.
>>>>>>>>>>>>>>>>>> cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use
>>>>>>>>>>>>>>>>>> SparkSQL on Kudu, but it will require a lot more work to make it
>>>>>>>>>>>>>>>>>> fast/useful.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>>>>>>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>>>>>>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>>>>>>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Thanks!
> On Sep 20, 2016, at 3:02 PM, Jordan Birdsell <jo...@gmail.com> wrote:
>
> http://kudu.apache.org/docs/developing.html#_kudu_integration_with_spark <http://kudu.apache.org/docs/developing.html#_kudu_integration_with_spark>
>
> On Tue, Sep 20, 2016 at 5:00 PM Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> I see that the API has changed a bit so my old code doesn’t work anymore. Can someone direct me to some code samples?
>
> Thanks,
> Ben
>
>
>> On Sep 20, 2016, at 1:44 PM, Todd Lipcon <todd@cloudera.com <ma...@cloudera.com>> wrote:
>>
>> On Tue, Sep 20, 2016 at 1:18 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Now that Kudu 1.0.0 is officially out and ready for production use, where do we find the spark connector jar for this release?
>>
>>
>> It's available in the official ASF maven repository: https://repository.apache.org/#nexus-search;quick~kudu-spark <https://repository.apache.org/#nexus-search;quick~kudu-spark>
>>
>> <dependency>
>> <groupId>org.apache.kudu</groupId>
>> <artifactId>kudu-spark_2.10</artifactId>
>> <version>1.0.0</version>
>> </dependency>
>>
>>
>> -Todd
>>
>>
>>
>>> On Jun 17, 2016, at 11:08 AM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>
>>> Hi Ben,
>>>
>>> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I do not think we support that at this point. I haven't looked deeply into it, but we may hit issues specifying Kudu-specific options (partitioning, column encoding, etc.). Probably issues that can be worked through eventually, though. If you are interested in contributing to Kudu, this is an area that could obviously use improvement! Most or all of our Spark features have been completely community driven to date.
>>>
>>> I am assuming that more Spark support along with semantic changes below will be incorporated into Kudu 0.9.1.
>>>
>>> As a rule we do not release new features in patch releases, but the good news is that we are releasing regularly, and our next scheduled release is for the August timeframe (see JD's roadmap <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email about what we are aiming to include). Also, Cloudera does publish snapshot versions of the Spark connector here <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>, so the jars are available if you don't mind using snapshots.
>>>
>>> Anyone know of a better way to make unique primary keys other than using UUID to make every row unique if there is no unique column (or combination thereof) to use.
>>>
>>> Not that I know of. In general it's pretty rare to have a dataset without a natural primary key (even if it's just all of the columns), but in those cases UUID is a good solution.
>>>
>>> This is what I am using. I know auto incrementing is coming down the line (don’t know when), but is there a way to simulate this in Kudu using Spark out of curiosity?
>>>
>>> To my knowledge there is no plan to have auto increment in Kudu. Distributed, consistent, auto incrementing counters is a difficult problem, and I don't think there are any known solutions that would be fast enough for Kudu (happy to be proven wrong, though!).
>>>
>>> - Dan
>>>
>>>
>>> Thanks,
>>> Ben
>>>
>>>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>
>>>> I'm not sure exactly what the semantics will be, but at least one of them will be upsert. These modes come from spark, and they were really designed for file-backed storage and not table storage. We may want to do append = upsert, and overwrite = truncate + insert. I think that may match the normal spark semantics more closely.
>>>>
>>>> - Dan
>>>>
>>>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Dan,
>>>>
>>>> Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>
>>>>> Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>
>>>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> Now, I’m getting this error when trying to write to the table.
>>>>>>
>>>>>> import scala.collection.JavaConverters._
>>>>>> val key_seq = Seq(“my_id")
>>>>>> val key_list = List(“my_id”).asJava
>>>>>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>>>>
>>>>>> df.write
>>>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>>>>> .mode("overwrite")
>>>>>> .kudu
>>>>>>
>>>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>>>>>>
>>>>>> Does the key field need to be first in the DataFrame?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Dan,
>>>>>>>
>>>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>>>
>>>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>>>
>>>>>>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>>>>>>
>>>>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>>>>>>
>>>>>>> - Dan
>>>>>>>
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>>>>
>>>>>>>> Looks like we're missing an import statement in that example. Could you try:
>>>>>>>>
>>>>>>>> import org.kududb.client._
>>>>>>>> and try again?
>>>>>>>>
>>>>>>>> - Dan
>>>>>>>>
>>>>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>>>>>>
>>>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>>>
>>>>>>>> Is there something I’m missing?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> Hi J-D,
>>>>>>>>>
>>>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>
>>>>>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> It will be in 0.9.0.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>> Hi Chris,
>>>>>>>>>>>
>>>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>>>>>>
>>>>>>>>>>>> Example usages will look something like:
>>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>>>>>>
>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>>>>
>>>>>>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>>>>>>> -Chris
>>>>>>>>>>>>>
>>>>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>>>>>>
>>>>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>>wrote:
>>>>>>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>>wrote:
>>>>>>>>>>>>>> Mark,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra<mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>>wrote:
>>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>>
>>
>> --
>> Todd Lipcon
>> Software Engineer, Cloudera
>
Re: Spark on Kudu
Posted by Mark Hamstra <ma...@clearstorydata.com>.
I realize that the Spark on Kudu work to date has been based on Spark 1.6,
where your statement about Spark SQL relying on Hive is true. In Spark
2.0, however, that dependency no longer exists since Spark SQL essentially
copied over the parts of Hive that were needed into Spark itself, and has
been free to diverge since then.
On Mon, Oct 10, 2016 at 4:11 PM, Dan Burkert <da...@cloudera.com> wrote:
> Hi Ben,
>
> SparkSQL relies on Hive for DDL statements, so having support for this
> requires adding support to Hive for manipulating Kudu tables. This is
> something that we would like to do in the long term, but there are no
> concrete plans (that I know of) to make it happen in the near term.
>
> - Dan
>
> On Thu, Oct 6, 2016 at 4:38 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Anyone know if the Spark package will ever allow for creating tables in
>> Spark SQL?
>>
>> Such as:
>> CREATE EXTERNAL TABLE <table-name>
>> USING org.apache.kudu.spark.kudu
>> OPTIONS (Map("kudu.master" -> “<kudu-master>", "kudu.table" ->
>> “table-name”));
>>
>> In this way, plain SQL can be used to do DDL, DML statements whether in
>> Spark SQL code or using JDBC to interface with Spark SQL Thriftserver.
>>
>> By the way, we are trying to create a DMP in Kudu with the a farm of
>> RESTful Endpoints to do cookie sync, ad serving, segmentation data
>> exchange. And, the Spark compute cluster and the Kudu cluster will reside
>> on the same racks in the same datacenter.
>>
>> Thanks,
>> Ben
>>
>> On Sep 20, 2016, at 3:02 PM, Jordan Birdsell <jo...@gmail.com>
>> wrote:
>>
>> http://kudu.apache.org/docs/developing.html#_kudu_integration_with_spark
>>
>> On Tue, Sep 20, 2016 at 5:00 PM Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> I see that the API has changed a bit so my old code doesn’t work
>>> anymore. Can someone direct me to some code samples?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>> On Sep 20, 2016, at 1:44 PM, Todd Lipcon <to...@cloudera.com> wrote:
>>>
>>> On Tue, Sep 20, 2016 at 1:18 PM, Benjamin Kim <bb...@gmail.com> wrote
>>> :
>>>
>>>> Now that Kudu 1.0.0 is officially out and ready for production use,
>>>> where do we find the spark connector jar for this release?
>>>>
>>>>
>>> It's available in the official ASF maven repository:
>>> https://repository.apache.org/#nexus-search;quick~kudu-spark
>>>
>>> <dependency>
>>> <groupId>org.apache.kudu</groupId>
>>> <artifactId>kudu-spark_2.10</artifactId>
>>> <version>1.0.0</version>
>>> </dependency>
>>>
>>>
>>> -Todd
>>>
>>>
>>>
>>>> On Jun 17, 2016, at 11:08 AM, Dan Burkert <da...@cloudera.com> wrote:
>>>>
>>>> Hi Ben,
>>>>
>>>> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL,
>>>> I do not think we support that at this point. I haven't looked deeply into
>>>> it, but we may hit issues specifying Kudu-specific options (partitioning,
>>>> column encoding, etc.). Probably issues that can be worked through
>>>> eventually, though. If you are interested in contributing to Kudu, this is
>>>> an area that could obviously use improvement! Most or all of our Spark
>>>> features have been completely community driven to date.
>>>>
>>>>
>>>>> I am assuming that more Spark support along with semantic changes
>>>>> below will be incorporated into Kudu 0.9.1.
>>>>>
>>>>
>>>> As a rule we do not release new features in patch releases, but the
>>>> good news is that we are releasing regularly, and our next scheduled
>>>> release is for the August timeframe (see JD's roadmap
>>>> <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email
>>>> about what we are aiming to include). Also, Cloudera does publish snapshot
>>>> versions of the Spark connector here
>>>> <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>,
>>>> so the jars are available if you don't mind using snapshots.
>>>>
>>>>
>>>>> Anyone know of a better way to make unique primary keys other than
>>>>> using UUID to make every row unique if there is no unique column (or
>>>>> combination thereof) to use.
>>>>>
>>>>
>>>> Not that I know of. In general it's pretty rare to have a dataset
>>>> without a natural primary key (even if it's just all of the columns), but
>>>> in those cases UUID is a good solution.
>>>>
>>>>
>>>>> This is what I am using. I know auto incrementing is coming down the
>>>>> line (don’t know when), but is there a way to simulate this in Kudu using
>>>>> Spark out of curiosity?
>>>>>
>>>>
>>>> To my knowledge there is no plan to have auto increment in Kudu.
>>>> Distributed, consistent, auto incrementing counters is a difficult problem,
>>>> and I don't think there are any known solutions that would be fast enough
>>>> for Kudu (happy to be proven wrong, though!).
>>>>
>>>> - Dan
>>>>
>>>>
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>
>>>>> I'm not sure exactly what the semantics will be, but at least one of
>>>>> them will be upsert. These modes come from spark, and they were really
>>>>> designed for file-backed storage and not table storage. We may want to do
>>>>> append = upsert, and overwrite = truncate + insert. I think that may match
>>>>> the normal spark semantics more closely.
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Dan,
>>>>>>
>>>>>> Thanks for the information. That would mean both “append” and
>>>>>> “overwrite” modes would be combined or not needed in the future.
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>>
>>>>>> Right now append uses an update Kudu operation, which requires the
>>>>>> row already be present in the table. Overwrite maps to insert. Kudu very
>>>>>> recently got upsert support baked in, but it hasn't yet been integrated
>>>>>> into the Spark connector. So pretty soon these sharp edges will get a lot
>>>>>> better, since upsert is the way to go for most spark workloads.
>>>>>>
>>>>>> - Dan
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> I tried to use the “append” mode, and it worked. Over 3.8 million
>>>>>>> rows in 64s. I would assume that now I can use the “overwrite” mode on
>>>>>>> existing data. Now, I have to find answers to these questions. What would
>>>>>>> happen if I “append” to the data in the Kudu table if the data already
>>>>>>> exists? What would happen if I “overwrite” existing data when the DataFrame
>>>>>>> has data in it that does not exist in the Kudu table? I need to evaluate
>>>>>>> the best way to simulate the UPSERT behavior in HBase because this is what
>>>>>>> our use case is.
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> Now, I’m getting this error when trying to write to the table.
>>>>>>>
>>>>>>> import scala.collection.JavaConverters._
>>>>>>> val key_seq = Seq(“my_id")
>>>>>>> val key_list = List(“my_id”).asJava
>>>>>>> kuduContext.createTable(tableName, df.schema, key_seq, new
>>>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list,
>>>>>>> 100))
>>>>>>>
>>>>>>> df.write
>>>>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" ->
>>>>>>> tableName))
>>>>>>> .mode("overwrite")
>>>>>>> .kudu
>>>>>>>
>>>>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame
>>>>>>> to Kudu; sample errors: Not found: key not found (error 0)Not found: key
>>>>>>> not found (error 0)Not found: key not found (error 0)Not found: key not
>>>>>>> found (error 0)Not found: key not found (error 0)
>>>>>>>
>>>>>>> Does the key field need to be first in the DataFrame?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Dan,
>>>>>>>>
>>>>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a
>>>>>>>> column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>>>>
>>>>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
>>>>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>>>>
>>>>>>>> java.lang.IllegalArgumentException: Table partitioning must be
>>>>>>>> specified using setRangePartitionColumns or addHashPartitions
>>>>>>>>
>>>>>>>
>>>>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of
>>>>>>> primary key columns, so in this case you have specified the single PK
>>>>>>> column "my_id". The `addHashPartitions` call adds hash partitioning to the
>>>>>>> table, in this case over the column "my_id" (which is good, it must be over
>>>>>>> one or more PK columns, so in this case "my_id" is the one and only valid
>>>>>>> combination). However, the call to `addHashPartition` also takes the
>>>>>>> number of buckets as the second param. You shouldn't get the
>>>>>>> IllegalArgumentException as long as you are specifying either
>>>>>>> `addHashPartitions` or `setRangePartitionColumns`.
>>>>>>>
>>>>>>> - Dan
>>>>>>>
>>>>>>>
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>>>>
>>>>>>>> Looks like we're missing an import statement in that example.
>>>>>>>> Could you try:
>>>>>>>>
>>>>>>>> import org.kududb.client._
>>>>>>>>
>>>>>>>> and try again?
>>>>>>>>
>>>>>>>> - Dan
>>>>>>>>
>>>>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> I encountered an error trying to create a table based on the
>>>>>>>>> documentation from a DataFrame.
>>>>>>>>>
>>>>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>>>>> kuduContext.createTable(tableName, df.schema,
>>>>>>>>> Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>>>>
>>>>>>>>> Is there something I’m missing?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>> It's only in Cloudera's maven repo: https://repository.cloud
>>>>>>>>> era.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi J-D,
>>>>>>>>>>
>>>>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark
>>>>>>>>>> jar for spark-shell to use. Can you show me where to find it?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>> What's in this doc is what's gonna get released:
>>>>>>>>>> https://github.com/cloudera/kudu/blob/master/docs/
>>>>>>>>>> developing.adoc#kudu-integration-with-spark
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <
>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>
>>>>>>>>>>> It will be in 0.9.0.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <
>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Hi Chris,
>>>>>>>>>>>>
>>>>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <
>>>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>>>>> It will allow upsert/insert from a dataframe using the
>>>>>>>>>>>> datasource api. It will also allow the creation and deletion of tables from
>>>>>>>>>>>> a dataframe
>>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>>>>>>>>
>>>>>>>>>>>> Example usages will look something like:
>>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>>>>>>>>
>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>>>>
>>>>>>>>>>>> Also, does anyone have any sample code on how to update/insert
>>>>>>>>>>>> data in Kudu using DataFrames?
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <
>>>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> SparkSQL cannot support these type of statements but we may be
>>>>>>>>>>>> able to implement similar functionality through the api.
>>>>>>>>>>>> -Chris
>>>>>>>>>>>>
>>>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an
>>>>>>>>>>>> “upsert” if it were to be implemented.
>>>>>>>>>>>>
>>>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <
>>>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it
>>>>>>>>>>>> into gerrit if you want to take a look.
>>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2754/
>>>>>>>>>>>> It does pushdown predicates which the existing input formatter
>>>>>>>>>>>> based rdd does not.
>>>>>>>>>>>>
>>>>>>>>>>>> Within the next two weeks I’m planning to implement a
>>>>>>>>>>>> datasource for spark that will have pushdown predicates and
>>>>>>>>>>>> insertion/update functionality (need to look more at cassandra and the
>>>>>>>>>>>> hbase datasource for best way to do this) I agree that server side upsert
>>>>>>>>>>>> would be helpful.
>>>>>>>>>>>> Having a datasource would give us useful data frames and also
>>>>>>>>>>>> make spark sql usable for kudu.
>>>>>>>>>>>>
>>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala
>>>>>>>>>>>> is: 1. We have had trouble getting impala to run fast with high concurrency
>>>>>>>>>>>> when compared to spark 2. We interact with datasources which do not
>>>>>>>>>>>> integrate with impala. 3. We have custom sql query planners for extended
>>>>>>>>>>>> sql functionality.
>>>>>>>>>>>>
>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> You guys make a convincing point, although on the upsert side
>>>>>>>>>>>> we'll need more support from the servers. Right now all you can do is an
>>>>>>>>>>>> INSERT then, if you get a dup key, do an UPDATE. I guess we could at least
>>>>>>>>>>>> add an API on the client side that would manage it, but it wouldn't be
>>>>>>>>>>>> atomic.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <
>>>>>>>>>>>> mark@clearstorydata.com>wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> It's pretty simple, actually. I need to support versioned
>>>>>>>>>>>>> datasets in a Spark SQL environment. Instead of a hack on top of a Parquet
>>>>>>>>>>>>> data store, I'm hoping (among other reasons) to be able to use Kudu's write
>>>>>>>>>>>>> and timestamp-based read operations to support not only appending data, but
>>>>>>>>>>>>> also updating existing data, and even some schema migration. The most
>>>>>>>>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>>>>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>>>>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>>>>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>>>>>>>>> be added/appended.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Using Kudu's Java API and developing additional functionality
>>>>>>>>>>>>> on top of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>>>>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>>>>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>>>>>>>>> decision.
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>>>>>>>>> jdcryans@apache.org>wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Mark,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it
>>>>>>>>>>>>>> caught the attention of other folks!
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra<
>>>>>>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>>>>>>>>> to whether we adopt Kudu.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary
>>>>>>>>>>>>>> for you. Is it just that you currently do it that way into some database or
>>>>>>>>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>>>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>>>>>>>>> KuduSession be too much work?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS
>>>>>>>>>>>>>> your current solution? If it's not completely clear, I'd love to help you
>>>>>>>>>>>>>> think through it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> What are your DS folks looking for in terms of
>>>>>>>>>>>>>>>> functionality related to Spark? A SparkSQL integration that's as fully
>>>>>>>>>>>>>>>> featured as Impala's? Do they care being able to insert into Kudu with
>>>>>>>>>>>>>>>> SparkSQL or just being able to query real fast? Anything more specific to
>>>>>>>>>>>>>>>> Spark that I'm missing?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall.
>>>>>>>>>>>>>>>> At Cloudera all our resources are committed to making things happen in
>>>>>>>>>>>>>>>> time, and a more fully featured Spark integration isn't in our plans during
>>>>>>>>>>>>>>>> that period. I'm really hoping someone in the community will help with
>>>>>>>>>>>>>>>> Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <
>>>>>>>>>>>>>>>> bbuild11@gmail.com>wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7
>>>>>>>>>>>>>>>>> versions. But, since it’s not “production-ready”, upper management doesn’t
>>>>>>>>>>>>>>>>> want to fully deploy it yet. They just want to keep an eye on it though.
>>>>>>>>>>>>>>>>> Kudu was so much simpler and easier to use in every aspect compared to
>>>>>>>>>>>>>>>>> HBase. Impala was great for the report writers and analysts to experiment
>>>>>>>>>>>>>>>>> with for the short time it was up. But, once again, the only blocker was
>>>>>>>>>>>>>>>>> the lack of Spark support for our Data Developers/Scientists. So,
>>>>>>>>>>>>>>>>> production-level data population won’t happen until then.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> As for our company, we have been looking for an updatable
>>>>>>>>>>>>>>>>>> data store for a long time that can be quickly queried directly either
>>>>>>>>>>>>>>>>>> using Spark SQL or Impala or some other SQL engine and still handle TB or
>>>>>>>>>>>>>>>>>> PB of data without performance degradation and many configuration
>>>>>>>>>>>>>>>>>> headaches. For now, we are using HBase to take on this role with Phoenix as
>>>>>>>>>>>>>>>>>> a fast way to directly query the data. I can see Kudu as the best way to
>>>>>>>>>>>>>>>>>> fill this gap easily, especially being the closest thing to other
>>>>>>>>>>>>>>>>>> relational databases out there in familiarity for the many SQL analytics
>>>>>>>>>>>>>>>>>> people in our company. The other alternative would be to go with AWS
>>>>>>>>>>>>>>>>>> Redshift for the same reasons, but it would come at a cost, of course. If
>>>>>>>>>>>>>>>>>> we went with either solutions, Kudu or Redshift, it would get rid of the
>>>>>>>>>>>>>>>>>> need to extract from HBase to parquet tables or export to PostgreSQL to
>>>>>>>>>>>>>>>>>> support more of the SQL language using by analysts or the reporting
>>>>>>>>>>>>>>>>>> software we use..
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far
>>>>>>>>>>>>>>>>> off with Kudu. Have you folks tried Kudu with Impala yet with those use
>>>>>>>>>>>>>>>>> cases?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we
>>>>>>>>>>>>>>>>>> like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>>>>>>>>> dying down.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> There's definitely an overlap in the use cases that
>>>>>>>>>>>>>>>>>> Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C*
>>>>>>>>>>>>>>>>>> is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month,
>>>>>>>>>>>>>>>>>> it's a storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love
>>>>>>>>>>>>>>>>>> to learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>>>>>>>>> replacement.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San
>>>>>>>>>>>>>>>>>>> Jose. They told me that everything was about Spark and there is a big buzz
>>>>>>>>>>>>>>>>>>> about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think
>>>>>>>>>>>>>>>>>>> that Cassandra is just an interim solution as a low-latency, easily queried
>>>>>>>>>>>>>>>>>>> data store. I was wondering if anything significant happened in regards to
>>>>>>>>>>>>>>>>>>> Kudu, especially on the Spark front. Plus, can you come up with your own
>>>>>>>>>>>>>>>>>>> proposed stack acronym to promote?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any
>>>>>>>>>>>>>>>>>>> timeline. I know of one person on the Kudu Slack who's working on a better
>>>>>>>>>>>>>>>>>>> RDD, but that's about it.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <
>>>>>>>>>>>>>>>>>>> bkim@amobee.com> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want
>>>>>>>>>>>>>>>>>>>> to target a version of Kudu to begin real testing of Spark against it for
>>>>>>>>>>>>>>>>>>>> our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital
>>>>>>>>>>>>>>>>>>>> marketing.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA
>>>>>>>>>>>>>>>>>>>> 90405 | www.amobee.com
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if
>>>>>>>>>>>>>>>>>>>> it's needed either.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format,
>>>>>>>>>>>>>>>>>>>> ideally we'd use scans directly.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort
>>>>>>>>>>>>>>>>>>>> of pushdown. It's really basic.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> The goal was to provide something for others to
>>>>>>>>>>>>>>>>>>>> contribute to. We have some basic unit tests that others can easily extend.
>>>>>>>>>>>>>>>>>>>> None of us on the team are Spark experts, but we'd be really happy to
>>>>>>>>>>>>>>>>>>>> assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic
>>>>>>>>>>>>>>>>>>>>> requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides
>>>>>>>>>>>>>>>>>>>>> shoring up more Spark SQL functionality (Dataframes) and doing the
>>>>>>>>>>>>>>>>>>>>> documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark
>>>>>>>>>>>>>>>>>>>>> with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to
>>>>>>>>>>>>>>>>>>>>> get this in for 0.7.0: https://issues.cloudera
>>>>>>>>>>>>>>>>>>>>> .org/browse/KUDU-1321
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use
>>>>>>>>>>>>>>>>>>>>> SparkSQL on Kudu, but it will require a lot more work to make it
>>>>>>>>>>>>>>>>>>>>> fast/useful.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>>>>>>>>>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>>>>>>>>>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>>>>>>>>>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>> Todd Lipcon
>>> Software Engineer, Cloudera
>>>
>>>
>>>
>>
>
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
On Mon, Oct 10, 2016 at 4:11 PM, Dan Burkert <da...@cloudera.com> wrote:
> Hi Ben,
>
> SparkSQL relies on Hive for DDL statements, so having support for this
> requires adding support to Hive for manipulating Kudu tables. This is
> something that we would like to do in the long term, but there are no
> concrete plans (that I know of) to make it happen in the near term.
>
To be fair there's https://issues.apache.org/jira/browse/HIVE-12971 with a
link to https://github.com/BimalTandel/HiveKudu-Handler which I think Bimal
said he was going to update soon.
But we're still far, I think, from any Kudu support in a released version
of Hive.
>
> - Dan
>
> On Thu, Oct 6, 2016 at 4:38 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Anyone know if the Spark package will ever allow for creating tables in
>> Spark SQL?
>>
>> Such as:
>> CREATE EXTERNAL TABLE <table-name>
>> USING org.apache.kudu.spark.kudu
>> OPTIONS (Map("kudu.master" -> “<kudu-master>", "kudu.table" ->
>> “table-name”));
>>
>> In this way, plain SQL can be used to do DDL, DML statements whether in
>> Spark SQL code or using JDBC to interface with Spark SQL Thriftserver.
>>
>> By the way, we are trying to create a DMP in Kudu with the a farm of
>> RESTful Endpoints to do cookie sync, ad serving, segmentation data
>> exchange. And, the Spark compute cluster and the Kudu cluster will reside
>> on the same racks in the same datacenter.
>>
>> Thanks,
>> Ben
>>
>> On Sep 20, 2016, at 3:02 PM, Jordan Birdsell <jo...@gmail.com>
>> wrote:
>>
>> http://kudu.apache.org/docs/developing.html#_kudu_integration_with_spark
>>
>> On Tue, Sep 20, 2016 at 5:00 PM Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> I see that the API has changed a bit so my old code doesn’t work
>>> anymore. Can someone direct me to some code samples?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>> On Sep 20, 2016, at 1:44 PM, Todd Lipcon <to...@cloudera.com> wrote:
>>>
>>> On Tue, Sep 20, 2016 at 1:18 PM, Benjamin Kim <bb...@gmail.com> wrote
>>> :
>>>
>>>> Now that Kudu 1.0.0 is officially out and ready for production use,
>>>> where do we find the spark connector jar for this release?
>>>>
>>>>
>>> It's available in the official ASF maven repository:
>>> https://repository.apache.org/#nexus-search;quick~kudu-spark
>>>
>>> <dependency>
>>> <groupId>org.apache.kudu</groupId>
>>> <artifactId>kudu-spark_2.10</artifactId>
>>> <version>1.0.0</version>
>>> </dependency>
>>>
>>>
>>> -Todd
>>>
>>>
>>>
>>>> On Jun 17, 2016, at 11:08 AM, Dan Burkert <da...@cloudera.com> wrote:
>>>>
>>>> Hi Ben,
>>>>
>>>> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL,
>>>> I do not think we support that at this point. I haven't looked deeply into
>>>> it, but we may hit issues specifying Kudu-specific options (partitioning,
>>>> column encoding, etc.). Probably issues that can be worked through
>>>> eventually, though. If you are interested in contributing to Kudu, this is
>>>> an area that could obviously use improvement! Most or all of our Spark
>>>> features have been completely community driven to date.
>>>>
>>>>
>>>>> I am assuming that more Spark support along with semantic changes
>>>>> below will be incorporated into Kudu 0.9.1.
>>>>>
>>>>
>>>> As a rule we do not release new features in patch releases, but the
>>>> good news is that we are releasing regularly, and our next scheduled
>>>> release is for the August timeframe (see JD's roadmap
>>>> <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email
>>>> about what we are aiming to include). Also, Cloudera does publish snapshot
>>>> versions of the Spark connector here
>>>> <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>,
>>>> so the jars are available if you don't mind using snapshots.
>>>>
>>>>
>>>>> Anyone know of a better way to make unique primary keys other than
>>>>> using UUID to make every row unique if there is no unique column (or
>>>>> combination thereof) to use.
>>>>>
>>>>
>>>> Not that I know of. In general it's pretty rare to have a dataset
>>>> without a natural primary key (even if it's just all of the columns), but
>>>> in those cases UUID is a good solution.
>>>>
>>>>
>>>>> This is what I am using. I know auto incrementing is coming down the
>>>>> line (don’t know when), but is there a way to simulate this in Kudu using
>>>>> Spark out of curiosity?
>>>>>
>>>>
>>>> To my knowledge there is no plan to have auto increment in Kudu.
>>>> Distributed, consistent, auto incrementing counters is a difficult problem,
>>>> and I don't think there are any known solutions that would be fast enough
>>>> for Kudu (happy to be proven wrong, though!).
>>>>
>>>> - Dan
>>>>
>>>>
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>
>>>>> I'm not sure exactly what the semantics will be, but at least one of
>>>>> them will be upsert. These modes come from spark, and they were really
>>>>> designed for file-backed storage and not table storage. We may want to do
>>>>> append = upsert, and overwrite = truncate + insert. I think that may match
>>>>> the normal spark semantics more closely.
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Dan,
>>>>>>
>>>>>> Thanks for the information. That would mean both “append” and
>>>>>> “overwrite” modes would be combined or not needed in the future.
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>>
>>>>>> Right now append uses an update Kudu operation, which requires the
>>>>>> row already be present in the table. Overwrite maps to insert. Kudu very
>>>>>> recently got upsert support baked in, but it hasn't yet been integrated
>>>>>> into the Spark connector. So pretty soon these sharp edges will get a lot
>>>>>> better, since upsert is the way to go for most spark workloads.
>>>>>>
>>>>>> - Dan
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> I tried to use the “append” mode, and it worked. Over 3.8 million
>>>>>>> rows in 64s. I would assume that now I can use the “overwrite” mode on
>>>>>>> existing data. Now, I have to find answers to these questions. What would
>>>>>>> happen if I “append” to the data in the Kudu table if the data already
>>>>>>> exists? What would happen if I “overwrite” existing data when the DataFrame
>>>>>>> has data in it that does not exist in the Kudu table? I need to evaluate
>>>>>>> the best way to simulate the UPSERT behavior in HBase because this is what
>>>>>>> our use case is.
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> Now, I’m getting this error when trying to write to the table.
>>>>>>>
>>>>>>> import scala.collection.JavaConverters._
>>>>>>> val key_seq = Seq(“my_id")
>>>>>>> val key_list = List(“my_id”).asJava
>>>>>>> kuduContext.createTable(tableName, df.schema, key_seq, new
>>>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list,
>>>>>>> 100))
>>>>>>>
>>>>>>> df.write
>>>>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" ->
>>>>>>> tableName))
>>>>>>> .mode("overwrite")
>>>>>>> .kudu
>>>>>>>
>>>>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame
>>>>>>> to Kudu; sample errors: Not found: key not found (error 0)Not found: key
>>>>>>> not found (error 0)Not found: key not found (error 0)Not found: key not
>>>>>>> found (error 0)Not found: key not found (error 0)
>>>>>>>
>>>>>>> Does the key field need to be first in the DataFrame?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Dan,
>>>>>>>>
>>>>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a
>>>>>>>> column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>>>>
>>>>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
>>>>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>>>>
>>>>>>>> java.lang.IllegalArgumentException: Table partitioning must be
>>>>>>>> specified using setRangePartitionColumns or addHashPartitions
>>>>>>>>
>>>>>>>
>>>>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of
>>>>>>> primary key columns, so in this case you have specified the single PK
>>>>>>> column "my_id". The `addHashPartitions` call adds hash partitioning to the
>>>>>>> table, in this case over the column "my_id" (which is good, it must be over
>>>>>>> one or more PK columns, so in this case "my_id" is the one and only valid
>>>>>>> combination). However, the call to `addHashPartition` also takes the
>>>>>>> number of buckets as the second param. You shouldn't get the
>>>>>>> IllegalArgumentException as long as you are specifying either
>>>>>>> `addHashPartitions` or `setRangePartitionColumns`.
>>>>>>>
>>>>>>> - Dan
>>>>>>>
>>>>>>>
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>>>>
>>>>>>>> Looks like we're missing an import statement in that example.
>>>>>>>> Could you try:
>>>>>>>>
>>>>>>>> import org.kududb.client._
>>>>>>>>
>>>>>>>> and try again?
>>>>>>>>
>>>>>>>> - Dan
>>>>>>>>
>>>>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> I encountered an error trying to create a table based on the
>>>>>>>>> documentation from a DataFrame.
>>>>>>>>>
>>>>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>>>>> kuduContext.createTable(tableName, df.schema,
>>>>>>>>> Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>>>>
>>>>>>>>> Is there something I’m missing?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>> It's only in Cloudera's maven repo: https://repository.cloud
>>>>>>>>> era.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi J-D,
>>>>>>>>>>
>>>>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark
>>>>>>>>>> jar for spark-shell to use. Can you show me where to find it?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>> What's in this doc is what's gonna get released:
>>>>>>>>>> https://github.com/cloudera/kudu/blob/master/docs/
>>>>>>>>>> developing.adoc#kudu-integration-with-spark
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <
>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>
>>>>>>>>>>> It will be in 0.9.0.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <
>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Hi Chris,
>>>>>>>>>>>>
>>>>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <
>>>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>>>>> It will allow upsert/insert from a dataframe using the
>>>>>>>>>>>> datasource api. It will also allow the creation and deletion of tables from
>>>>>>>>>>>> a dataframe
>>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>>>>>>>>
>>>>>>>>>>>> Example usages will look something like:
>>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>>>>>>>>
>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>>>>
>>>>>>>>>>>> Also, does anyone have any sample code on how to update/insert
>>>>>>>>>>>> data in Kudu using DataFrames?
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <
>>>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> SparkSQL cannot support these type of statements but we may be
>>>>>>>>>>>> able to implement similar functionality through the api.
>>>>>>>>>>>> -Chris
>>>>>>>>>>>>
>>>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an
>>>>>>>>>>>> “upsert” if it were to be implemented.
>>>>>>>>>>>>
>>>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <
>>>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it
>>>>>>>>>>>> into gerrit if you want to take a look.
>>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2754/
>>>>>>>>>>>> It does pushdown predicates which the existing input formatter
>>>>>>>>>>>> based rdd does not.
>>>>>>>>>>>>
>>>>>>>>>>>> Within the next two weeks I’m planning to implement a
>>>>>>>>>>>> datasource for spark that will have pushdown predicates and
>>>>>>>>>>>> insertion/update functionality (need to look more at cassandra and the
>>>>>>>>>>>> hbase datasource for best way to do this) I agree that server side upsert
>>>>>>>>>>>> would be helpful.
>>>>>>>>>>>> Having a datasource would give us useful data frames and also
>>>>>>>>>>>> make spark sql usable for kudu.
>>>>>>>>>>>>
>>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala
>>>>>>>>>>>> is: 1. We have had trouble getting impala to run fast with high concurrency
>>>>>>>>>>>> when compared to spark 2. We interact with datasources which do not
>>>>>>>>>>>> integrate with impala. 3. We have custom sql query planners for extended
>>>>>>>>>>>> sql functionality.
>>>>>>>>>>>>
>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> You guys make a convincing point, although on the upsert side
>>>>>>>>>>>> we'll need more support from the servers. Right now all you can do is an
>>>>>>>>>>>> INSERT then, if you get a dup key, do an UPDATE. I guess we could at least
>>>>>>>>>>>> add an API on the client side that would manage it, but it wouldn't be
>>>>>>>>>>>> atomic.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <
>>>>>>>>>>>> mark@clearstorydata.com>wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> It's pretty simple, actually. I need to support versioned
>>>>>>>>>>>>> datasets in a Spark SQL environment. Instead of a hack on top of a Parquet
>>>>>>>>>>>>> data store, I'm hoping (among other reasons) to be able to use Kudu's write
>>>>>>>>>>>>> and timestamp-based read operations to support not only appending data, but
>>>>>>>>>>>>> also updating existing data, and even some schema migration. The most
>>>>>>>>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>>>>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>>>>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>>>>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>>>>>>>>> be added/appended.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Using Kudu's Java API and developing additional functionality
>>>>>>>>>>>>> on top of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>>>>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>>>>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>>>>>>>>> decision.
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>>>>>>>>> jdcryans@apache.org>wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Mark,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it
>>>>>>>>>>>>>> caught the attention of other folks!
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra<
>>>>>>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>>>>>>>>> to whether we adopt Kudu.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary
>>>>>>>>>>>>>> for you. Is it just that you currently do it that way into some database or
>>>>>>>>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>>>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>>>>>>>>> KuduSession be too much work?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS
>>>>>>>>>>>>>> your current solution? If it's not completely clear, I'd love to help you
>>>>>>>>>>>>>> think through it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> What are your DS folks looking for in terms of
>>>>>>>>>>>>>>>> functionality related to Spark? A SparkSQL integration that's as fully
>>>>>>>>>>>>>>>> featured as Impala's? Do they care being able to insert into Kudu with
>>>>>>>>>>>>>>>> SparkSQL or just being able to query real fast? Anything more specific to
>>>>>>>>>>>>>>>> Spark that I'm missing?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall.
>>>>>>>>>>>>>>>> At Cloudera all our resources are committed to making things happen in
>>>>>>>>>>>>>>>> time, and a more fully featured Spark integration isn't in our plans during
>>>>>>>>>>>>>>>> that period. I'm really hoping someone in the community will help with
>>>>>>>>>>>>>>>> Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <
>>>>>>>>>>>>>>>> bbuild11@gmail.com>wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7
>>>>>>>>>>>>>>>>> versions. But, since it’s not “production-ready”, upper management doesn’t
>>>>>>>>>>>>>>>>> want to fully deploy it yet. They just want to keep an eye on it though.
>>>>>>>>>>>>>>>>> Kudu was so much simpler and easier to use in every aspect compared to
>>>>>>>>>>>>>>>>> HBase. Impala was great for the report writers and analysts to experiment
>>>>>>>>>>>>>>>>> with for the short time it was up. But, once again, the only blocker was
>>>>>>>>>>>>>>>>> the lack of Spark support for our Data Developers/Scientists. So,
>>>>>>>>>>>>>>>>> production-level data population won’t happen until then.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> As for our company, we have been looking for an updatable
>>>>>>>>>>>>>>>>>> data store for a long time that can be quickly queried directly either
>>>>>>>>>>>>>>>>>> using Spark SQL or Impala or some other SQL engine and still handle TB or
>>>>>>>>>>>>>>>>>> PB of data without performance degradation and many configuration
>>>>>>>>>>>>>>>>>> headaches. For now, we are using HBase to take on this role with Phoenix as
>>>>>>>>>>>>>>>>>> a fast way to directly query the data. I can see Kudu as the best way to
>>>>>>>>>>>>>>>>>> fill this gap easily, especially being the closest thing to other
>>>>>>>>>>>>>>>>>> relational databases out there in familiarity for the many SQL analytics
>>>>>>>>>>>>>>>>>> people in our company. The other alternative would be to go with AWS
>>>>>>>>>>>>>>>>>> Redshift for the same reasons, but it would come at a cost, of course. If
>>>>>>>>>>>>>>>>>> we went with either solutions, Kudu or Redshift, it would get rid of the
>>>>>>>>>>>>>>>>>> need to extract from HBase to parquet tables or export to PostgreSQL to
>>>>>>>>>>>>>>>>>> support more of the SQL language using by analysts or the reporting
>>>>>>>>>>>>>>>>>> software we use..
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far
>>>>>>>>>>>>>>>>> off with Kudu. Have you folks tried Kudu with Impala yet with those use
>>>>>>>>>>>>>>>>> cases?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we
>>>>>>>>>>>>>>>>>> like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>>>>>>>>> dying down.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> There's definitely an overlap in the use cases that
>>>>>>>>>>>>>>>>>> Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C*
>>>>>>>>>>>>>>>>>> is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month,
>>>>>>>>>>>>>>>>>> it's a storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love
>>>>>>>>>>>>>>>>>> to learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>>>>>>>>> replacement.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San
>>>>>>>>>>>>>>>>>>> Jose. They told me that everything was about Spark and there is a big buzz
>>>>>>>>>>>>>>>>>>> about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think
>>>>>>>>>>>>>>>>>>> that Cassandra is just an interim solution as a low-latency, easily queried
>>>>>>>>>>>>>>>>>>> data store. I was wondering if anything significant happened in regards to
>>>>>>>>>>>>>>>>>>> Kudu, especially on the Spark front. Plus, can you come up with your own
>>>>>>>>>>>>>>>>>>> proposed stack acronym to promote?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any
>>>>>>>>>>>>>>>>>>> timeline. I know of one person on the Kudu Slack who's working on a better
>>>>>>>>>>>>>>>>>>> RDD, but that's about it.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <
>>>>>>>>>>>>>>>>>>> bkim@amobee.com> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want
>>>>>>>>>>>>>>>>>>>> to target a version of Kudu to begin real testing of Spark against it for
>>>>>>>>>>>>>>>>>>>> our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital
>>>>>>>>>>>>>>>>>>>> marketing.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA
>>>>>>>>>>>>>>>>>>>> 90405 | www.amobee.com
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if
>>>>>>>>>>>>>>>>>>>> it's needed either.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format,
>>>>>>>>>>>>>>>>>>>> ideally we'd use scans directly.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort
>>>>>>>>>>>>>>>>>>>> of pushdown. It's really basic.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> The goal was to provide something for others to
>>>>>>>>>>>>>>>>>>>> contribute to. We have some basic unit tests that others can easily extend.
>>>>>>>>>>>>>>>>>>>> None of us on the team are Spark experts, but we'd be really happy to
>>>>>>>>>>>>>>>>>>>> assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic
>>>>>>>>>>>>>>>>>>>>> requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides
>>>>>>>>>>>>>>>>>>>>> shoring up more Spark SQL functionality (Dataframes) and doing the
>>>>>>>>>>>>>>>>>>>>> documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark
>>>>>>>>>>>>>>>>>>>>> with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to
>>>>>>>>>>>>>>>>>>>>> get this in for 0.7.0: https://issues.cloudera
>>>>>>>>>>>>>>>>>>>>> .org/browse/KUDU-1321
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use
>>>>>>>>>>>>>>>>>>>>> SparkSQL on Kudu, but it will require a lot more work to make it
>>>>>>>>>>>>>>>>>>>>> fast/useful.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>>>>>>>>>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>>>>>>>>>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>>>>>>>>>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>> Todd Lipcon
>>> Software Engineer, Cloudera
>>>
>>>
>>>
>>
>
Re: Spark on Kudu
Posted by Dan Burkert <da...@cloudera.com>.
Hi Ben,
SparkSQL relies on Hive for DDL statements, so having support for this
requires adding support to Hive for manipulating Kudu tables. This is
something that we would like to do in the long term, but there are no
concrete plans (that I know of) to make it happen in the near term.
- Dan
On Thu, Oct 6, 2016 at 4:38 PM, Benjamin Kim <bb...@gmail.com> wrote:
> Anyone know if the Spark package will ever allow for creating tables in
> Spark SQL?
>
> Such as:
> CREATE EXTERNAL TABLE <table-name>
> USING org.apache.kudu.spark.kudu
> OPTIONS (Map("kudu.master" -> “<kudu-master>", "kudu.table" ->
> “table-name”));
>
> In this way, plain SQL can be used to do DDL, DML statements whether in
> Spark SQL code or using JDBC to interface with Spark SQL Thriftserver.
>
> By the way, we are trying to create a DMP in Kudu with the a farm of
> RESTful Endpoints to do cookie sync, ad serving, segmentation data
> exchange. And, the Spark compute cluster and the Kudu cluster will reside
> on the same racks in the same datacenter.
>
> Thanks,
> Ben
>
> On Sep 20, 2016, at 3:02 PM, Jordan Birdsell <jo...@gmail.com>
> wrote:
>
> http://kudu.apache.org/docs/developing.html#_kudu_integration_with_spark
>
> On Tue, Sep 20, 2016 at 5:00 PM Benjamin Kim <bb...@gmail.com> wrote:
>
>> I see that the API has changed a bit so my old code doesn’t work anymore.
>> Can someone direct me to some code samples?
>>
>> Thanks,
>> Ben
>>
>>
>> On Sep 20, 2016, at 1:44 PM, Todd Lipcon <to...@cloudera.com> wrote:
>>
>> On Tue, Sep 20, 2016 at 1:18 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> Now that Kudu 1.0.0 is officially out and ready for production use,
>>> where do we find the spark connector jar for this release?
>>>
>>>
>> It's available in the official ASF maven repository:
>> https://repository.apache.org/#nexus-search;quick~kudu-spark
>>
>> <dependency>
>> <groupId>org.apache.kudu</groupId>
>> <artifactId>kudu-spark_2.10</artifactId>
>> <version>1.0.0</version>
>> </dependency>
>>
>>
>> -Todd
>>
>>
>>
>>> On Jun 17, 2016, at 11:08 AM, Dan Burkert <da...@cloudera.com> wrote:
>>>
>>> Hi Ben,
>>>
>>> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL,
>>> I do not think we support that at this point. I haven't looked deeply into
>>> it, but we may hit issues specifying Kudu-specific options (partitioning,
>>> column encoding, etc.). Probably issues that can be worked through
>>> eventually, though. If you are interested in contributing to Kudu, this is
>>> an area that could obviously use improvement! Most or all of our Spark
>>> features have been completely community driven to date.
>>>
>>>
>>>> I am assuming that more Spark support along with semantic changes below
>>>> will be incorporated into Kudu 0.9.1.
>>>>
>>>
>>> As a rule we do not release new features in patch releases, but the good
>>> news is that we are releasing regularly, and our next scheduled release is
>>> for the August timeframe (see JD's roadmap
>>> <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email
>>> about what we are aiming to include). Also, Cloudera does publish snapshot
>>> versions of the Spark connector here
>>> <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>,
>>> so the jars are available if you don't mind using snapshots.
>>>
>>>
>>>> Anyone know of a better way to make unique primary keys other than
>>>> using UUID to make every row unique if there is no unique column (or
>>>> combination thereof) to use.
>>>>
>>>
>>> Not that I know of. In general it's pretty rare to have a dataset
>>> without a natural primary key (even if it's just all of the columns), but
>>> in those cases UUID is a good solution.
>>>
>>>
>>>> This is what I am using. I know auto incrementing is coming down the
>>>> line (don’t know when), but is there a way to simulate this in Kudu using
>>>> Spark out of curiosity?
>>>>
>>>
>>> To my knowledge there is no plan to have auto increment in Kudu.
>>> Distributed, consistent, auto incrementing counters is a difficult problem,
>>> and I don't think there are any known solutions that would be fast enough
>>> for Kudu (happy to be proven wrong, though!).
>>>
>>> - Dan
>>>
>>>
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>
>>>> I'm not sure exactly what the semantics will be, but at least one of
>>>> them will be upsert. These modes come from spark, and they were really
>>>> designed for file-backed storage and not table storage. We may want to do
>>>> append = upsert, and overwrite = truncate + insert. I think that may match
>>>> the normal spark semantics more closely.
>>>>
>>>> - Dan
>>>>
>>>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> Dan,
>>>>>
>>>>> Thanks for the information. That would mean both “append” and
>>>>> “overwrite” modes would be combined or not needed in the future.
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>
>>>>> Right now append uses an update Kudu operation, which requires the row
>>>>> already be present in the table. Overwrite maps to insert. Kudu very
>>>>> recently got upsert support baked in, but it hasn't yet been integrated
>>>>> into the Spark connector. So pretty soon these sharp edges will get a lot
>>>>> better, since upsert is the way to go for most spark workloads.
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I tried to use the “append” mode, and it worked. Over 3.8 million
>>>>>> rows in 64s. I would assume that now I can use the “overwrite” mode on
>>>>>> existing data. Now, I have to find answers to these questions. What would
>>>>>> happen if I “append” to the data in the Kudu table if the data already
>>>>>> exists? What would happen if I “overwrite” existing data when the DataFrame
>>>>>> has data in it that does not exist in the Kudu table? I need to evaluate
>>>>>> the best way to simulate the UPSERT behavior in HBase because this is what
>>>>>> our use case is.
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> Now, I’m getting this error when trying to write to the table.
>>>>>>
>>>>>> import scala.collection.JavaConverters._
>>>>>> val key_seq = Seq(“my_id")
>>>>>> val key_list = List(“my_id”).asJava
>>>>>> kuduContext.createTable(tableName, df.schema, key_seq, new
>>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list,
>>>>>> 100))
>>>>>>
>>>>>> df.write
>>>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" ->
>>>>>> tableName))
>>>>>> .mode("overwrite")
>>>>>> .kudu
>>>>>>
>>>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame
>>>>>> to Kudu; sample errors: Not found: key not found (error 0)Not found: key
>>>>>> not found (error 0)Not found: key not found (error 0)Not found: key not
>>>>>> found (error 0)Not found: key not found (error 0)
>>>>>>
>>>>>> Does the key field need to be first in the DataFrame?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Dan,
>>>>>>>
>>>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a
>>>>>>> column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>>>
>>>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
>>>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>>>
>>>>>>> java.lang.IllegalArgumentException: Table partitioning must be
>>>>>>> specified using setRangePartitionColumns or addHashPartitions
>>>>>>>
>>>>>>
>>>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of
>>>>>> primary key columns, so in this case you have specified the single PK
>>>>>> column "my_id". The `addHashPartitions` call adds hash partitioning to the
>>>>>> table, in this case over the column "my_id" (which is good, it must be over
>>>>>> one or more PK columns, so in this case "my_id" is the one and only valid
>>>>>> combination). However, the call to `addHashPartition` also takes the
>>>>>> number of buckets as the second param. You shouldn't get the
>>>>>> IllegalArgumentException as long as you are specifying either
>>>>>> `addHashPartitions` or `setRangePartitionColumns`.
>>>>>>
>>>>>> - Dan
>>>>>>
>>>>>>
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>>>
>>>>>>> Looks like we're missing an import statement in that example. Could
>>>>>>> you try:
>>>>>>>
>>>>>>> import org.kududb.client._
>>>>>>>
>>>>>>> and try again?
>>>>>>>
>>>>>>> - Dan
>>>>>>>
>>>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> I encountered an error trying to create a table based on the
>>>>>>>> documentation from a DataFrame.
>>>>>>>>
>>>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>>>> kuduContext.createTable(tableName, df.schema,
>>>>>>>> Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>>>
>>>>>>>> Is there something I’m missing?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <
>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>
>>>>>>>> It's only in Cloudera's maven repo: https://repository.
>>>>>>>> cloudera.com/cloudera/cloudera-repos/org/kududb/
>>>>>>>> kudu-spark_2.10/0.9.0/
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi J-D,
>>>>>>>>>
>>>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark
>>>>>>>>> jar for spark-shell to use. Can you show me where to find it?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>> What's in this doc is what's gonna get released:
>>>>>>>>> https://github.com/cloudera/kudu/blob/master/
>>>>>>>>> docs/developing.adoc#kudu-integration-with-spark
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>> It will be in 0.9.0.
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com
>>>>>>>>>> > wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi Chris,
>>>>>>>>>>>
>>>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <
>>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>>>> It will allow upsert/insert from a dataframe using the
>>>>>>>>>>> datasource api. It will also allow the creation and deletion of tables from
>>>>>>>>>>> a dataframe
>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>>>>>>>
>>>>>>>>>>> Example usages will look something like:
>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>>>>>>>
>>>>>>>>>>> -Chris George
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>>>
>>>>>>>>>>> Also, does anyone have any sample code on how to update/insert
>>>>>>>>>>> data in Kudu using DataFrames?
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <
>>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>> SparkSQL cannot support these type of statements but we may be
>>>>>>>>>>> able to implement similar functionality through the api.
>>>>>>>>>>> -Chris
>>>>>>>>>>>
>>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an
>>>>>>>>>>> “upsert” if it were to be implemented.
>>>>>>>>>>>
>>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <
>>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it
>>>>>>>>>>> into gerrit if you want to take a look. http://gerrit.cloudera.
>>>>>>>>>>> org:8080/#/c/2754/
>>>>>>>>>>> It does pushdown predicates which the existing input formatter
>>>>>>>>>>> based rdd does not.
>>>>>>>>>>>
>>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource
>>>>>>>>>>> for spark that will have pushdown predicates and insertion/update
>>>>>>>>>>> functionality (need to look more at cassandra and the hbase datasource for
>>>>>>>>>>> best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>>> Having a datasource would give us useful data frames and also
>>>>>>>>>>> make spark sql usable for kudu.
>>>>>>>>>>>
>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala
>>>>>>>>>>> is: 1. We have had trouble getting impala to run fast with high concurrency
>>>>>>>>>>> when compared to spark 2. We interact with datasources which do not
>>>>>>>>>>> integrate with impala. 3. We have custom sql query planners for extended
>>>>>>>>>>> sql functionality.
>>>>>>>>>>>
>>>>>>>>>>> -Chris George
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>>>>>>>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> You guys make a convincing point, although on the upsert side
>>>>>>>>>>> we'll need more support from the servers. Right now all you can do is an
>>>>>>>>>>> INSERT then, if you get a dup key, do an UPDATE. I guess we could at least
>>>>>>>>>>> add an API on the client side that would manage it, but it wouldn't be
>>>>>>>>>>> atomic.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <
>>>>>>>>>>> mark@clearstorydata.com>wrote:
>>>>>>>>>>>
>>>>>>>>>>>> It's pretty simple, actually. I need to support versioned
>>>>>>>>>>>> datasets in a Spark SQL environment. Instead of a hack on top of a Parquet
>>>>>>>>>>>> data store, I'm hoping (among other reasons) to be able to use Kudu's write
>>>>>>>>>>>> and timestamp-based read operations to support not only appending data, but
>>>>>>>>>>>> also updating existing data, and even some schema migration. The most
>>>>>>>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>>>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>>>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>>>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>>>>>>>> be added/appended.
>>>>>>>>>>>>
>>>>>>>>>>>> Using Kudu's Java API and developing additional functionality
>>>>>>>>>>>> on top of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>>>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>>>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>>>>>>>> decision.
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>>>>>>>> jdcryans@apache.org>wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Mark,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it
>>>>>>>>>>>>> caught the attention of other folks!
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra<
>>>>>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>>>>>>>> to whether we adopt Kudu.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary
>>>>>>>>>>>>> for you. Is it just that you currently do it that way into some database or
>>>>>>>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>>>>>>>> KuduSession be too much work?
>>>>>>>>>>>>>
>>>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS
>>>>>>>>>>>>> your current solution? If it's not completely clear, I'd love to help you
>>>>>>>>>>>>> think through it.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> What are your DS folks looking for in terms of functionality
>>>>>>>>>>>>>>> related to Spark? A SparkSQL integration that's as fully featured as
>>>>>>>>>>>>>>> Impala's? Do they care being able to insert into Kudu with SparkSQL or just
>>>>>>>>>>>>>>> being able to query real fast? Anything more specific to Spark that I'm
>>>>>>>>>>>>>>> missing?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>>>>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>>>>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>>>>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>>>>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <
>>>>>>>>>>>>>>> bbuild11@gmail.com>wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7
>>>>>>>>>>>>>>>> versions. But, since it’s not “production-ready”, upper management doesn’t
>>>>>>>>>>>>>>>> want to fully deploy it yet. They just want to keep an eye on it though.
>>>>>>>>>>>>>>>> Kudu was so much simpler and easier to use in every aspect compared to
>>>>>>>>>>>>>>>> HBase. Impala was great for the report writers and analysts to experiment
>>>>>>>>>>>>>>>> with for the short time it was up. But, once again, the only blocker was
>>>>>>>>>>>>>>>> the lack of Spark support for our Data Developers/Scientists. So,
>>>>>>>>>>>>>>>> production-level data population won’t happen until then.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> As for our company, we have been looking for an updatable
>>>>>>>>>>>>>>>>> data store for a long time that can be quickly queried directly either
>>>>>>>>>>>>>>>>> using Spark SQL or Impala or some other SQL engine and still handle TB or
>>>>>>>>>>>>>>>>> PB of data without performance degradation and many configuration
>>>>>>>>>>>>>>>>> headaches. For now, we are using HBase to take on this role with Phoenix as
>>>>>>>>>>>>>>>>> a fast way to directly query the data. I can see Kudu as the best way to
>>>>>>>>>>>>>>>>> fill this gap easily, especially being the closest thing to other
>>>>>>>>>>>>>>>>> relational databases out there in familiarity for the many SQL analytics
>>>>>>>>>>>>>>>>> people in our company. The other alternative would be to go with AWS
>>>>>>>>>>>>>>>>> Redshift for the same reasons, but it would come at a cost, of course. If
>>>>>>>>>>>>>>>>> we went with either solutions, Kudu or Redshift, it would get rid of the
>>>>>>>>>>>>>>>>> need to extract from HBase to parquet tables or export to PostgreSQL to
>>>>>>>>>>>>>>>>> support more of the SQL language using by analysts or the reporting
>>>>>>>>>>>>>>>>> software we use..
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far
>>>>>>>>>>>>>>>> off with Kudu. Have you folks tried Kudu with Impala yet with those use
>>>>>>>>>>>>>>>> cases?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we
>>>>>>>>>>>>>>>>> like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>>>>>>>> dying down.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> There's definitely an overlap in the use cases that
>>>>>>>>>>>>>>>>> Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C*
>>>>>>>>>>>>>>>>> is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month,
>>>>>>>>>>>>>>>>> it's a storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love
>>>>>>>>>>>>>>>>> to learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>>>>>>>> replacement.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose.
>>>>>>>>>>>>>>>>>> They told me that everything was about Spark and there is a big buzz about
>>>>>>>>>>>>>>>>>> the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any
>>>>>>>>>>>>>>>>>> timeline. I know of one person on the Kudu Slack who's working on a better
>>>>>>>>>>>>>>>>>> RDD, but that's about it.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <
>>>>>>>>>>>>>>>>>> bkim@amobee.com> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to
>>>>>>>>>>>>>>>>>>> target a version of Kudu to begin real testing of Spark against it for our
>>>>>>>>>>>>>>>>>>> devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital
>>>>>>>>>>>>>>>>>>> marketing.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA
>>>>>>>>>>>>>>>>>>> 90405 | www.amobee.com
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if
>>>>>>>>>>>>>>>>>>> it's needed either.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format,
>>>>>>>>>>>>>>>>>>> ideally we'd use scans directly.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort
>>>>>>>>>>>>>>>>>>> of pushdown. It's really basic.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> The goal was to provide something for others to
>>>>>>>>>>>>>>>>>>> contribute to. We have some basic unit tests that others can easily extend.
>>>>>>>>>>>>>>>>>>> None of us on the team are Spark experts, but we'd be really happy to
>>>>>>>>>>>>>>>>>>> assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic
>>>>>>>>>>>>>>>>>>>> requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides
>>>>>>>>>>>>>>>>>>>> shoring up more Spark SQL functionality (Dataframes) and doing the
>>>>>>>>>>>>>>>>>>>> documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark
>>>>>>>>>>>>>>>>>>>> with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get
>>>>>>>>>>>>>>>>>>>> this in for 0.7.0: https://issues.
>>>>>>>>>>>>>>>>>>>> cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use
>>>>>>>>>>>>>>>>>>>> SparkSQL on Kudu, but it will require a lot more work to make it
>>>>>>>>>>>>>>>>>>>> fast/useful.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>>>>>>>>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>>>>>>>>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>>>>>>>>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>> --
>> Todd Lipcon
>> Software Engineer, Cloudera
>>
>>
>>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Anyone know if the Spark package will ever allow for creating tables in Spark SQL?
Such as:
CREATE EXTERNAL TABLE <table-name>
USING org.apache.kudu.spark.kudu
OPTIONS (Map("kudu.master" -> “<kudu-master>", "kudu.table" -> “table-name”));
In this way, plain SQL can be used to do DDL, DML statements whether in Spark SQL code or using JDBC to interface with Spark SQL Thriftserver.
By the way, we are trying to create a DMP in Kudu with the a farm of RESTful Endpoints to do cookie sync, ad serving, segmentation data exchange. And, the Spark compute cluster and the Kudu cluster will reside on the same racks in the same datacenter.
Thanks,
Ben
> On Sep 20, 2016, at 3:02 PM, Jordan Birdsell <jo...@gmail.com> wrote:
>
> http://kudu.apache.org/docs/developing.html#_kudu_integration_with_spark <http://kudu.apache.org/docs/developing.html#_kudu_integration_with_spark>
>
> On Tue, Sep 20, 2016 at 5:00 PM Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> I see that the API has changed a bit so my old code doesn’t work anymore. Can someone direct me to some code samples?
>
> Thanks,
> Ben
>
>
>> On Sep 20, 2016, at 1:44 PM, Todd Lipcon <todd@cloudera.com <ma...@cloudera.com>> wrote:
>>
>> On Tue, Sep 20, 2016 at 1:18 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Now that Kudu 1.0.0 is officially out and ready for production use, where do we find the spark connector jar for this release?
>>
>>
>> It's available in the official ASF maven repository: https://repository.apache.org/#nexus-search;quick~kudu-spark <https://repository.apache.org/#nexus-search;quick~kudu-spark>
>>
>> <dependency>
>> <groupId>org.apache.kudu</groupId>
>> <artifactId>kudu-spark_2.10</artifactId>
>> <version>1.0.0</version>
>> </dependency>
>>
>>
>> -Todd
>>
>>
>>
>>> On Jun 17, 2016, at 11:08 AM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>
>>> Hi Ben,
>>>
>>> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I do not think we support that at this point. I haven't looked deeply into it, but we may hit issues specifying Kudu-specific options (partitioning, column encoding, etc.). Probably issues that can be worked through eventually, though. If you are interested in contributing to Kudu, this is an area that could obviously use improvement! Most or all of our Spark features have been completely community driven to date.
>>>
>>> I am assuming that more Spark support along with semantic changes below will be incorporated into Kudu 0.9.1.
>>>
>>> As a rule we do not release new features in patch releases, but the good news is that we are releasing regularly, and our next scheduled release is for the August timeframe (see JD's roadmap <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email about what we are aiming to include). Also, Cloudera does publish snapshot versions of the Spark connector here <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>, so the jars are available if you don't mind using snapshots.
>>>
>>> Anyone know of a better way to make unique primary keys other than using UUID to make every row unique if there is no unique column (or combination thereof) to use.
>>>
>>> Not that I know of. In general it's pretty rare to have a dataset without a natural primary key (even if it's just all of the columns), but in those cases UUID is a good solution.
>>>
>>> This is what I am using. I know auto incrementing is coming down the line (don’t know when), but is there a way to simulate this in Kudu using Spark out of curiosity?
>>>
>>> To my knowledge there is no plan to have auto increment in Kudu. Distributed, consistent, auto incrementing counters is a difficult problem, and I don't think there are any known solutions that would be fast enough for Kudu (happy to be proven wrong, though!).
>>>
>>> - Dan
>>>
>>>
>>> Thanks,
>>> Ben
>>>
>>>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>
>>>> I'm not sure exactly what the semantics will be, but at least one of them will be upsert. These modes come from spark, and they were really designed for file-backed storage and not table storage. We may want to do append = upsert, and overwrite = truncate + insert. I think that may match the normal spark semantics more closely.
>>>>
>>>> - Dan
>>>>
>>>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Dan,
>>>>
>>>> Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>
>>>>> Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>
>>>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> Now, I’m getting this error when trying to write to the table.
>>>>>>
>>>>>> import scala.collection.JavaConverters._
>>>>>> val key_seq = Seq(“my_id")
>>>>>> val key_list = List(“my_id”).asJava
>>>>>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>>>>
>>>>>> df.write
>>>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>>>>> .mode("overwrite")
>>>>>> .kudu
>>>>>>
>>>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>>>>>>
>>>>>> Does the key field need to be first in the DataFrame?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Dan,
>>>>>>>
>>>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>>>
>>>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>>>
>>>>>>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>>>>>>
>>>>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>>>>>>
>>>>>>> - Dan
>>>>>>>
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>>>>
>>>>>>>> Looks like we're missing an import statement in that example. Could you try:
>>>>>>>>
>>>>>>>> import org.kududb.client._
>>>>>>>> and try again?
>>>>>>>>
>>>>>>>> - Dan
>>>>>>>>
>>>>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>>>>>>
>>>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>>>
>>>>>>>> Is there something I’m missing?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> Hi J-D,
>>>>>>>>>
>>>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>
>>>>>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> It will be in 0.9.0.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>> Hi Chris,
>>>>>>>>>>>
>>>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>>>>>>
>>>>>>>>>>>> Example usages will look something like:
>>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>>>>>>
>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>>>>
>>>>>>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>>>>>>> -Chris
>>>>>>>>>>>>>
>>>>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>>>>>>
>>>>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>>wrote:
>>>>>>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>>wrote:
>>>>>>>>>>>>>> Mark,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra<mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>>wrote:
>>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>>
>>
>> --
>> Todd Lipcon
>> Software Engineer, Cloudera
>
Re: Spark on Kudu
Posted by Jordan Birdsell <jo...@gmail.com>.
http://kudu.apache.org/docs/developing.html#_kudu_integration_with_spark
On Tue, Sep 20, 2016 at 5:00 PM Benjamin Kim <bb...@gmail.com> wrote:
> I see that the API has changed a bit so my old code doesn’t work anymore.
> Can someone direct me to some code samples?
>
> Thanks,
> Ben
>
>
> On Sep 20, 2016, at 1:44 PM, Todd Lipcon <to...@cloudera.com> wrote:
>
> On Tue, Sep 20, 2016 at 1:18 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Now that Kudu 1.0.0 is officially out and ready for production use, where
>> do we find the spark connector jar for this release?
>>
>>
> It's available in the official ASF maven repository:
> https://repository.apache.org/#nexus-search;quick~kudu-spark
>
> <dependency>
> <groupId>org.apache.kudu</groupId>
> <artifactId>kudu-spark_2.10</artifactId>
> <version>1.0.0</version>
> </dependency>
>
>
> -Todd
>
>
>
>> On Jun 17, 2016, at 11:08 AM, Dan Burkert <da...@cloudera.com> wrote:
>>
>> Hi Ben,
>>
>> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I
>> do not think we support that at this point. I haven't looked deeply into
>> it, but we may hit issues specifying Kudu-specific options (partitioning,
>> column encoding, etc.). Probably issues that can be worked through
>> eventually, though. If you are interested in contributing to Kudu, this is
>> an area that could obviously use improvement! Most or all of our Spark
>> features have been completely community driven to date.
>>
>>
>>> I am assuming that more Spark support along with semantic changes below
>>> will be incorporated into Kudu 0.9.1.
>>>
>>
>> As a rule we do not release new features in patch releases, but the good
>> news is that we are releasing regularly, and our next scheduled release is
>> for the August timeframe (see JD's roadmap
>> <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email
>> about what we are aiming to include). Also, Cloudera does publish snapshot
>> versions of the Spark connector here
>> <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>,
>> so the jars are available if you don't mind using snapshots.
>>
>>
>>> Anyone know of a better way to make unique primary keys other than using
>>> UUID to make every row unique if there is no unique column (or combination
>>> thereof) to use.
>>>
>>
>> Not that I know of. In general it's pretty rare to have a dataset
>> without a natural primary key (even if it's just all of the columns), but
>> in those cases UUID is a good solution.
>>
>>
>>> This is what I am using. I know auto incrementing is coming down the
>>> line (don’t know when), but is there a way to simulate this in Kudu using
>>> Spark out of curiosity?
>>>
>>
>> To my knowledge there is no plan to have auto increment in Kudu.
>> Distributed, consistent, auto incrementing counters is a difficult problem,
>> and I don't think there are any known solutions that would be fast enough
>> for Kudu (happy to be proven wrong, though!).
>>
>> - Dan
>>
>>
>>>
>>> Thanks,
>>> Ben
>>>
>>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>
>>> I'm not sure exactly what the semantics will be, but at least one of
>>> them will be upsert. These modes come from spark, and they were really
>>> designed for file-backed storage and not table storage. We may want to do
>>> append = upsert, and overwrite = truncate + insert. I think that may match
>>> the normal spark semantics more closely.
>>>
>>> - Dan
>>>
>>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> Dan,
>>>>
>>>> Thanks for the information. That would mean both “append” and
>>>> “overwrite” modes would be combined or not needed in the future.
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>
>>>> Right now append uses an update Kudu operation, which requires the row
>>>> already be present in the table. Overwrite maps to insert. Kudu very
>>>> recently got upsert support baked in, but it hasn't yet been integrated
>>>> into the Spark connector. So pretty soon these sharp edges will get a lot
>>>> better, since upsert is the way to go for most spark workloads.
>>>>
>>>> - Dan
>>>>
>>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows
>>>>> in 64s. I would assume that now I can use the “overwrite” mode on existing
>>>>> data. Now, I have to find answers to these questions. What would happen if
>>>>> I “append” to the data in the Kudu table if the data already exists? What
>>>>> would happen if I “overwrite” existing data when the DataFrame has data in
>>>>> it that does not exist in the Kudu table? I need to evaluate the best way
>>>>> to simulate the UPSERT behavior in HBase because this is what our use case
>>>>> is.
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>
>>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>>>>
>>>>> Hi,
>>>>>
>>>>> Now, I’m getting this error when trying to write to the table.
>>>>>
>>>>> import scala.collection.JavaConverters._
>>>>> val key_seq = Seq(“my_id")
>>>>> val key_list = List(“my_id”).asJava
>>>>> kuduContext.createTable(tableName, df.schema, key_seq, new
>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>>>
>>>>> df.write
>>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" ->
>>>>> tableName))
>>>>> .mode("overwrite")
>>>>> .kudu
>>>>>
>>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame
>>>>> to Kudu; sample errors: Not found: key not found (error 0)Not found: key
>>>>> not found (error 0)Not found: key not found (error 0)Not found: key not
>>>>> found (error 0)Not found: key not found (error 0)
>>>>>
>>>>> Does the key field need to be first in the DataFrame?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>
>>>>>
>>>>>
>>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Dan,
>>>>>>
>>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a
>>>>>> column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>>
>>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
>>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>>
>>>>>> java.lang.IllegalArgumentException: Table partitioning must be
>>>>>> specified using setRangePartitionColumns or addHashPartitions
>>>>>>
>>>>>
>>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of
>>>>> primary key columns, so in this case you have specified the single PK
>>>>> column "my_id". The `addHashPartitions` call adds hash partitioning to the
>>>>> table, in this case over the column "my_id" (which is good, it must be over
>>>>> one or more PK columns, so in this case "my_id" is the one and only valid
>>>>> combination). However, the call to `addHashPartition` also takes the
>>>>> number of buckets as the second param. You shouldn't get the
>>>>> IllegalArgumentException as long as you are specifying either
>>>>> `addHashPartitions` or `setRangePartitionColumns`.
>>>>>
>>>>> - Dan
>>>>>
>>>>>
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>>
>>>>>> Looks like we're missing an import statement in that example. Could
>>>>>> you try:
>>>>>>
>>>>>> import org.kududb.client._
>>>>>>
>>>>>> and try again?
>>>>>>
>>>>>> - Dan
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> I encountered an error trying to create a table based on the
>>>>>>> documentation from a DataFrame.
>>>>>>>
>>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>>> kuduContext.createTable(tableName, df.schema,
>>>>>>> Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>>
>>>>>>> Is there something I’m missing?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>> wrote:
>>>>>>>
>>>>>>> It's only in Cloudera's maven repo:
>>>>>>> https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi J-D,
>>>>>>>>
>>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark
>>>>>>>> jar for spark-shell to use. Can you show me where to find it?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>> What's in this doc is what's gonna get released:
>>>>>>>> https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>> It will be in 0.9.0.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi Chris,
>>>>>>>>>>
>>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <
>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>
>>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource
>>>>>>>>>> api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>>>>>>
>>>>>>>>>> Example usages will look something like:
>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>>>>>>
>>>>>>>>>> -Chris George
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>>
>>>>>>>>>> Also, does anyone have any sample code on how to update/insert
>>>>>>>>>> data in Kudu using DataFrames?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <
>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>
>>>>>>>>>> SparkSQL cannot support these type of statements but we may be
>>>>>>>>>> able to implement similar functionality through the api.
>>>>>>>>>> -Chris
>>>>>>>>>>
>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an
>>>>>>>>>> “upsert” if it were to be implemented.
>>>>>>>>>>
>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <
>>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>>
>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it
>>>>>>>>>> into gerrit if you want to take a look.
>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2754/
>>>>>>>>>> It does pushdown predicates which the existing input formatter
>>>>>>>>>> based rdd does not.
>>>>>>>>>>
>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource
>>>>>>>>>> for spark that will have pushdown predicates and insertion/update
>>>>>>>>>> functionality (need to look more at cassandra and the hbase datasource for
>>>>>>>>>> best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>> Having a datasource would give us useful data frames and also
>>>>>>>>>> make spark sql usable for kudu.
>>>>>>>>>>
>>>>>>>>>> My reasoning for having a spark datasource and not using Impala
>>>>>>>>>> is: 1. We have had trouble getting impala to run fast with high concurrency
>>>>>>>>>> when compared to spark 2. We interact with datasources which do not
>>>>>>>>>> integrate with impala. 3. We have custom sql query planners for extended
>>>>>>>>>> sql functionality.
>>>>>>>>>>
>>>>>>>>>> -Chris George
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>> You guys make a convincing point, although on the upsert side
>>>>>>>>>> we'll need more support from the servers. Right now all you can do is an
>>>>>>>>>> INSERT then, if you get a dup key, do an UPDATE. I guess we could at least
>>>>>>>>>> add an API on the client side that would manage it, but it wouldn't be
>>>>>>>>>> atomic.
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <
>>>>>>>>>> mark@clearstorydata.com>wrote:
>>>>>>>>>>
>>>>>>>>>>> It's pretty simple, actually. I need to support versioned
>>>>>>>>>>> datasets in a Spark SQL environment. Instead of a hack on top of a Parquet
>>>>>>>>>>> data store, I'm hoping (among other reasons) to be able to use Kudu's write
>>>>>>>>>>> and timestamp-based read operations to support not only appending data, but
>>>>>>>>>>> also updating existing data, and even some schema migration. The most
>>>>>>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>>>>>>> be added/appended.
>>>>>>>>>>>
>>>>>>>>>>> Using Kudu's Java API and developing additional functionality on
>>>>>>>>>>> top of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>>>>>>> decision.
>>>>>>>>>>>
>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>>>>>>> jdcryans@apache.org>wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Mark,
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it
>>>>>>>>>>>> caught the attention of other folks!
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra<
>>>>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>>>>>>> to whether we adopt Kudu.
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary
>>>>>>>>>>>> for you. Is it just that you currently do it that way into some database or
>>>>>>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>>>>>>> KuduSession be too much work?
>>>>>>>>>>>>
>>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS
>>>>>>>>>>>> your current solution? If it's not completely clear, I'd love to help you
>>>>>>>>>>>> think through it.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> What are your DS folks looking for in terms of functionality
>>>>>>>>>>>>>> related to Spark? A SparkSQL integration that's as fully featured as
>>>>>>>>>>>>>> Impala's? Do they care being able to insert into Kudu with SparkSQL or just
>>>>>>>>>>>>>> being able to query real fast? Anything more specific to Spark that I'm
>>>>>>>>>>>>>> missing?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>>>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>>>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>>>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>>>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <
>>>>>>>>>>>>>> bbuild11@gmail.com>wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions.
>>>>>>>>>>>>>>> But, since it’s not “production-ready”, upper management doesn’t want to
>>>>>>>>>>>>>>> fully deploy it yet. They just want to keep an eye on it though. Kudu was
>>>>>>>>>>>>>>> so much simpler and easier to use in every aspect compared to HBase. Impala
>>>>>>>>>>>>>>> was great for the report writers and analysts to experiment with for the
>>>>>>>>>>>>>>> short time it was up. But, once again, the only blocker was the lack of
>>>>>>>>>>>>>>> Spark support for our Data Developers/Scientists. So, production-level data
>>>>>>>>>>>>>>> population won’t happen until then.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> As for our company, we have been looking for an updatable
>>>>>>>>>>>>>>>> data store for a long time that can be quickly queried directly either
>>>>>>>>>>>>>>>> using Spark SQL or Impala or some other SQL engine and still handle TB or
>>>>>>>>>>>>>>>> PB of data without performance degradation and many configuration
>>>>>>>>>>>>>>>> headaches. For now, we are using HBase to take on this role with Phoenix as
>>>>>>>>>>>>>>>> a fast way to directly query the data. I can see Kudu as the best way to
>>>>>>>>>>>>>>>> fill this gap easily, especially being the closest thing to other
>>>>>>>>>>>>>>>> relational databases out there in familiarity for the many SQL analytics
>>>>>>>>>>>>>>>> people in our company. The other alternative would be to go with AWS
>>>>>>>>>>>>>>>> Redshift for the same reasons, but it would come at a cost, of course. If
>>>>>>>>>>>>>>>> we went with either solutions, Kudu or Redshift, it would get rid of the
>>>>>>>>>>>>>>>> need to extract from HBase to parquet tables or export to PostgreSQL to
>>>>>>>>>>>>>>>> support more of the SQL language using by analysts or the reporting
>>>>>>>>>>>>>>>> software we use..
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off
>>>>>>>>>>>>>>> with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we
>>>>>>>>>>>>>>>> like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>>>>>>> dying down.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> There's definitely an overlap in the use cases that
>>>>>>>>>>>>>>>> Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C*
>>>>>>>>>>>>>>>> is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month,
>>>>>>>>>>>>>>>> it's a storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to
>>>>>>>>>>>>>>>> learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>>>>>>> replacement.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose.
>>>>>>>>>>>>>>>>> They told me that everything was about Spark and there is a big buzz about
>>>>>>>>>>>>>>>>> the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any
>>>>>>>>>>>>>>>>> timeline. I know of one person on the Kudu Slack who's working on a better
>>>>>>>>>>>>>>>>> RDD, but that's about it.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <
>>>>>>>>>>>>>>>>> bkim@amobee.com> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to
>>>>>>>>>>>>>>>>>> target a version of Kudu to begin real testing of Spark against it for our
>>>>>>>>>>>>>>>>>> devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA
>>>>>>>>>>>>>>>>>> 90405 | www.amobee.com
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if
>>>>>>>>>>>>>>>>>> it's needed either.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format,
>>>>>>>>>>>>>>>>>> ideally we'd use scans directly.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> The goal was to provide something for others to
>>>>>>>>>>>>>>>>>> contribute to. We have some basic unit tests that others can easily extend.
>>>>>>>>>>>>>>>>>> None of us on the team are Spark experts, but we'd be really happy to
>>>>>>>>>>>>>>>>>> assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements
>>>>>>>>>>>>>>>>>>> (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more
>>>>>>>>>>>>>>>>>>> Spark SQL functionality (Dataframes) and doing the documentation, what more
>>>>>>>>>>>>>>>>>>> needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark
>>>>>>>>>>>>>>>>>>> with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get
>>>>>>>>>>>>>>>>>>> this in for 0.7.0:
>>>>>>>>>>>>>>>>>>> https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use
>>>>>>>>>>>>>>>>>>> SparkSQL on Kudu, but it will require a lot more work to make it
>>>>>>>>>>>>>>>>>>> fast/useful.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>>>>>>>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>>>>>>>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>>>>>>>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
> --
> Todd Lipcon
> Software Engineer, Cloudera
>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
I see that the API has changed a bit so my old code doesn’t work anymore. Can someone direct me to some code samples?
Thanks,
Ben
> On Sep 20, 2016, at 1:44 PM, Todd Lipcon <to...@cloudera.com> wrote:
>
> On Tue, Sep 20, 2016 at 1:18 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Now that Kudu 1.0.0 is officially out and ready for production use, where do we find the spark connector jar for this release?
>
>
> It's available in the official ASF maven repository: https://repository.apache.org/#nexus-search;quick~kudu-spark <https://repository.apache.org/#nexus-search;quick~kudu-spark>
>
> <dependency>
> <groupId>org.apache.kudu</groupId>
> <artifactId>kudu-spark_2.10</artifactId>
> <version>1.0.0</version>
> </dependency>
>
>
> -Todd
>
>
>
>> On Jun 17, 2016, at 11:08 AM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>
>> Hi Ben,
>>
>> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I do not think we support that at this point. I haven't looked deeply into it, but we may hit issues specifying Kudu-specific options (partitioning, column encoding, etc.). Probably issues that can be worked through eventually, though. If you are interested in contributing to Kudu, this is an area that could obviously use improvement! Most or all of our Spark features have been completely community driven to date.
>>
>> I am assuming that more Spark support along with semantic changes below will be incorporated into Kudu 0.9.1.
>>
>> As a rule we do not release new features in patch releases, but the good news is that we are releasing regularly, and our next scheduled release is for the August timeframe (see JD's roadmap <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email about what we are aiming to include). Also, Cloudera does publish snapshot versions of the Spark connector here <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>, so the jars are available if you don't mind using snapshots.
>>
>> Anyone know of a better way to make unique primary keys other than using UUID to make every row unique if there is no unique column (or combination thereof) to use.
>>
>> Not that I know of. In general it's pretty rare to have a dataset without a natural primary key (even if it's just all of the columns), but in those cases UUID is a good solution.
>>
>> This is what I am using. I know auto incrementing is coming down the line (don’t know when), but is there a way to simulate this in Kudu using Spark out of curiosity?
>>
>> To my knowledge there is no plan to have auto increment in Kudu. Distributed, consistent, auto incrementing counters is a difficult problem, and I don't think there are any known solutions that would be fast enough for Kudu (happy to be proven wrong, though!).
>>
>> - Dan
>>
>>
>> Thanks,
>> Ben
>>
>>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>
>>> I'm not sure exactly what the semantics will be, but at least one of them will be upsert. These modes come from spark, and they were really designed for file-backed storage and not table storage. We may want to do append = upsert, and overwrite = truncate + insert. I think that may match the normal spark semantics more closely.
>>>
>>> - Dan
>>>
>>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> Dan,
>>>
>>> Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.
>>>
>>> Cheers,
>>> Ben
>>>
>>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>
>>>> Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.
>>>>
>>>> - Dan
>>>>
>>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>
>>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>
>>>>> Hi,
>>>>>
>>>>> Now, I’m getting this error when trying to write to the table.
>>>>>
>>>>> import scala.collection.JavaConverters._
>>>>> val key_seq = Seq(“my_id")
>>>>> val key_list = List(“my_id”).asJava
>>>>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>>>
>>>>> df.write
>>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>>>> .mode("overwrite")
>>>>> .kudu
>>>>>
>>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>>>>>
>>>>> Does the key field need to be first in the DataFrame?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> Dan,
>>>>>>
>>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>>
>>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>>
>>>>>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>>>>>
>>>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>>>>>
>>>>>> - Dan
>>>>>>
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>>>
>>>>>>> Looks like we're missing an import statement in that example. Could you try:
>>>>>>>
>>>>>>> import org.kududb.client._
>>>>>>> and try again?
>>>>>>>
>>>>>>> - Dan
>>>>>>>
>>>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>>>>>
>>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>>
>>>>>>> Is there something I’m missing?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> Hi J-D,
>>>>>>>>
>>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>
>>>>>>>>>> It will be in 0.9.0.
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>> Hi Chris,
>>>>>>>>>>
>>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>>>>>
>>>>>>>>>>> Example usages will look something like:
>>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>>>>>
>>>>>>>>>>> -Chris George
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>>>
>>>>>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>>>>>> -Chris
>>>>>>>>>>>>
>>>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>>>>>
>>>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>>>>>
>>>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>>>>>
>>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>>wrote:
>>>>>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>>wrote:
>>>>>>>>>>>>> Mark,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra<mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>>
>>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>>>>>
>>>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>>
>>>>>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>>>>>
>>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>>wrote:
>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
>
>
> --
> Todd Lipcon
> Software Engineer, Cloudera
Re: Spark on Kudu
Posted by Todd Lipcon <to...@cloudera.com>.
On Tue, Sep 20, 2016 at 1:18 PM, Benjamin Kim <bb...@gmail.com> wrote:
> Now that Kudu 1.0.0 is officially out and ready for production use, where
> do we find the spark connector jar for this release?
>
>
It's available in the official ASF maven repository:
https://repository.apache.org/#nexus-search;quick~kudu-spark
<dependency>
<groupId>org.apache.kudu</groupId>
<artifactId>kudu-spark_2.10</artifactId>
<version>1.0.0</version>
</dependency>
-Todd
> On Jun 17, 2016, at 11:08 AM, Dan Burkert <da...@cloudera.com> wrote:
>
> Hi Ben,
>
> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I
> do not think we support that at this point. I haven't looked deeply into
> it, but we may hit issues specifying Kudu-specific options (partitioning,
> column encoding, etc.). Probably issues that can be worked through
> eventually, though. If you are interested in contributing to Kudu, this is
> an area that could obviously use improvement! Most or all of our Spark
> features have been completely community driven to date.
>
>
>> I am assuming that more Spark support along with semantic changes below
>> will be incorporated into Kudu 0.9.1.
>>
>
> As a rule we do not release new features in patch releases, but the good
> news is that we are releasing regularly, and our next scheduled release is
> for the August timeframe (see JD's roadmap
> <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email
> about what we are aiming to include). Also, Cloudera does publish snapshot
> versions of the Spark connector here
> <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>, so
> the jars are available if you don't mind using snapshots.
>
>
>> Anyone know of a better way to make unique primary keys other than using
>> UUID to make every row unique if there is no unique column (or combination
>> thereof) to use.
>>
>
> Not that I know of. In general it's pretty rare to have a dataset without
> a natural primary key (even if it's just all of the columns), but in those
> cases UUID is a good solution.
>
>
>> This is what I am using. I know auto incrementing is coming down the line
>> (don’t know when), but is there a way to simulate this in Kudu using Spark
>> out of curiosity?
>>
>
> To my knowledge there is no plan to have auto increment in Kudu.
> Distributed, consistent, auto incrementing counters is a difficult problem,
> and I don't think there are any known solutions that would be fast enough
> for Kudu (happy to be proven wrong, though!).
>
> - Dan
>
>
>>
>> Thanks,
>> Ben
>>
>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>>
>> I'm not sure exactly what the semantics will be, but at least one of them
>> will be upsert. These modes come from spark, and they were really designed
>> for file-backed storage and not table storage. We may want to do append =
>> upsert, and overwrite = truncate + insert. I think that may match the
>> normal spark semantics more closely.
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> Dan,
>>>
>>> Thanks for the information. That would mean both “append” and
>>> “overwrite” modes would be combined or not needed in the future.
>>>
>>> Cheers,
>>> Ben
>>>
>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>
>>> Right now append uses an update Kudu operation, which requires the row
>>> already be present in the table. Overwrite maps to insert. Kudu very
>>> recently got upsert support baked in, but it hasn't yet been integrated
>>> into the Spark connector. So pretty soon these sharp edges will get a lot
>>> better, since upsert is the way to go for most spark workloads.
>>>
>>> - Dan
>>>
>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows
>>>> in 64s. I would assume that now I can use the “overwrite” mode on existing
>>>> data. Now, I have to find answers to these questions. What would happen if
>>>> I “append” to the data in the Kudu table if the data already exists? What
>>>> would happen if I “overwrite” existing data when the DataFrame has data in
>>>> it that does not exist in the Kudu table? I need to evaluate the best way
>>>> to simulate the UPSERT behavior in HBase because this is what our use case
>>>> is.
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>
>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>>>
>>>> Hi,
>>>>
>>>> Now, I’m getting this error when trying to write to the table.
>>>>
>>>> import scala.collection.JavaConverters._
>>>> val key_seq = Seq(“my_id")
>>>> val key_list = List(“my_id”).asJava
>>>> kuduContext.createTable(tableName, df.schema, key_seq, new
>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list,
>>>> 100))
>>>>
>>>> df.write
>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>>> .mode("overwrite")
>>>> .kudu
>>>>
>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to
>>>> Kudu; sample errors: Not found: key not found (error 0)Not found: key not
>>>> found (error 0)Not found: key not found (error 0)Not found: key not found
>>>> (error 0)Not found: key not found (error 0)
>>>>
>>>> Does the key field need to be first in the DataFrame?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>
>>>>
>>>>
>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> Dan,
>>>>>
>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a
>>>>> column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>
>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>
>>>>> java.lang.IllegalArgumentException: Table partitioning must be
>>>>> specified using setRangePartitionColumns or addHashPartitions
>>>>>
>>>>
>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of
>>>> primary key columns, so in this case you have specified the single PK
>>>> column "my_id". The `addHashPartitions` call adds hash partitioning to the
>>>> table, in this case over the column "my_id" (which is good, it must be over
>>>> one or more PK columns, so in this case "my_id" is the one and only valid
>>>> combination). However, the call to `addHashPartition` also takes the
>>>> number of buckets as the second param. You shouldn't get the
>>>> IllegalArgumentException as long as you are specifying either
>>>> `addHashPartitions` or `setRangePartitionColumns`.
>>>>
>>>> - Dan
>>>>
>>>>
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>
>>>>> Looks like we're missing an import statement in that example. Could
>>>>> you try:
>>>>>
>>>>> import org.kududb.client._
>>>>>
>>>>> and try again?
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I encountered an error trying to create a table based on the
>>>>>> documentation from a DataFrame.
>>>>>>
>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>> kuduContext.createTable(tableName, df.schema,
>>>>>> Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>
>>>>>> Is there something I’m missing?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> It's only in Cloudera's maven repo: https://repository.
>>>>>> cloudera.com/cloudera/cloudera-repos/org/kududb/
>>>>>> kudu-spark_2.10/0.9.0/
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar
>>>>>>> for spark-shell to use. Can you show me where to find it?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>> wrote:
>>>>>>>
>>>>>>> What's in this doc is what's gonna get released: https://github.com/
>>>>>>> cloudera/kudu/blob/master/docs/developing.adoc#kudu-
>>>>>>> integration-with-spark
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <
>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>
>>>>>>>> It will be in 0.9.0.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi Chris,
>>>>>>>>>
>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <
>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>
>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource
>>>>>>>>> api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>>>>>
>>>>>>>>> Example usages will look something like:
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>>>>>
>>>>>>>>> -Chris George
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>
>>>>>>>>> Also, does anyone have any sample code on how to update/insert
>>>>>>>>> data in Kudu using DataFrames?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <
>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>
>>>>>>>>> SparkSQL cannot support these type of statements but we may be
>>>>>>>>> able to implement similar functionality through the api.
>>>>>>>>> -Chris
>>>>>>>>>
>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an
>>>>>>>>> “upsert” if it were to be implemented.
>>>>>>>>>
>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <
>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>
>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it
>>>>>>>>> into gerrit if you want to take a look. http://gerrit.cloudera.
>>>>>>>>> org:8080/#/c/2754/
>>>>>>>>> It does pushdown predicates which the existing input formatter
>>>>>>>>> based rdd does not.
>>>>>>>>>
>>>>>>>>> Within the next two weeks I’m planning to implement a datasource
>>>>>>>>> for spark that will have pushdown predicates and insertion/update
>>>>>>>>> functionality (need to look more at cassandra and the hbase datasource for
>>>>>>>>> best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>> Having a datasource would give us useful data frames and also make
>>>>>>>>> spark sql usable for kudu.
>>>>>>>>>
>>>>>>>>> My reasoning for having a spark datasource and not using Impala
>>>>>>>>> is: 1. We have had trouble getting impala to run fast with high concurrency
>>>>>>>>> when compared to spark 2. We interact with datasources which do not
>>>>>>>>> integrate with impala. 3. We have custom sql query planners for extended
>>>>>>>>> sql functionality.
>>>>>>>>>
>>>>>>>>> -Chris George
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>> You guys make a convincing point, although on the upsert side
>>>>>>>>> we'll need more support from the servers. Right now all you can do is an
>>>>>>>>> INSERT then, if you get a dup key, do an UPDATE. I guess we could at least
>>>>>>>>> add an API on the client side that would manage it, but it wouldn't be
>>>>>>>>> atomic.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <
>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>
>>>>>>>>>> It's pretty simple, actually. I need to support versioned
>>>>>>>>>> datasets in a Spark SQL environment. Instead of a hack on top of a Parquet
>>>>>>>>>> data store, I'm hoping (among other reasons) to be able to use Kudu's write
>>>>>>>>>> and timestamp-based read operations to support not only appending data, but
>>>>>>>>>> also updating existing data, and even some schema migration. The most
>>>>>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>>>>>> be added/appended.
>>>>>>>>>>
>>>>>>>>>> Using Kudu's Java API and developing additional functionality on
>>>>>>>>>> top of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>>>>>> decision.
>>>>>>>>>>
>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>>> Mark,
>>>>>>>>>>>
>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it
>>>>>>>>>>> caught the attention of other folks!
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <
>>>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>>>>>> to whether we adopt Kudu.
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary
>>>>>>>>>>> for you. Is it just that you currently do it that way into some database or
>>>>>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>>>>>> KuduSession be too much work?
>>>>>>>>>>>
>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your
>>>>>>>>>>> current solution? If it's not completely clear, I'd love to help you think
>>>>>>>>>>> through it.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>>
>>>>>>>>>>>>> What are your DS folks looking for in terms of functionality
>>>>>>>>>>>>> related to Spark? A SparkSQL integration that's as fully featured as
>>>>>>>>>>>>> Impala's? Do they care being able to insert into Kudu with SparkSQL or just
>>>>>>>>>>>>> being able to query real fast? Anything more specific to Spark that I'm
>>>>>>>>>>>>> missing?
>>>>>>>>>>>>>
>>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <
>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions.
>>>>>>>>>>>>>> But, since it’s not “production-ready”, upper management doesn’t want to
>>>>>>>>>>>>>> fully deploy it yet. They just want to keep an eye on it though. Kudu was
>>>>>>>>>>>>>> so much simpler and easier to use in every aspect compared to HBase. Impala
>>>>>>>>>>>>>> was great for the report writers and analysts to experiment with for the
>>>>>>>>>>>>>> short time it was up. But, once again, the only blocker was the lack of
>>>>>>>>>>>>>> Spark support for our Data Developers/Scientists. So, production-level data
>>>>>>>>>>>>>> population won’t happen until then.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> As for our company, we have been looking for an updatable
>>>>>>>>>>>>>>> data store for a long time that can be quickly queried directly either
>>>>>>>>>>>>>>> using Spark SQL or Impala or some other SQL engine and still handle TB or
>>>>>>>>>>>>>>> PB of data without performance degradation and many configuration
>>>>>>>>>>>>>>> headaches. For now, we are using HBase to take on this role with Phoenix as
>>>>>>>>>>>>>>> a fast way to directly query the data. I can see Kudu as the best way to
>>>>>>>>>>>>>>> fill this gap easily, especially being the closest thing to other
>>>>>>>>>>>>>>> relational databases out there in familiarity for the many SQL analytics
>>>>>>>>>>>>>>> people in our company. The other alternative would be to go with AWS
>>>>>>>>>>>>>>> Redshift for the same reasons, but it would come at a cost, of course. If
>>>>>>>>>>>>>>> we went with either solutions, Kudu or Redshift, it would get rid of the
>>>>>>>>>>>>>>> need to extract from HBase to parquet tables or export to PostgreSQL to
>>>>>>>>>>>>>>> support more of the SQL language using by analysts or the reporting
>>>>>>>>>>>>>>> software we use..
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off
>>>>>>>>>>>>>> with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we
>>>>>>>>>>>>>>> like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>>>>>> dying down.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> There's definitely an overlap in the use cases that
>>>>>>>>>>>>>>> Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C*
>>>>>>>>>>>>>>> is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month,
>>>>>>>>>>>>>>> it's a storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to
>>>>>>>>>>>>>>> learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>>>>>> replacement.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose.
>>>>>>>>>>>>>>>> They told me that everything was about Spark and there is a big buzz about
>>>>>>>>>>>>>>>> the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any
>>>>>>>>>>>>>>>> timeline. I know of one person on the Kudu Slack who's working on a better
>>>>>>>>>>>>>>>> RDD, but that's about it.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <
>>>>>>>>>>>>>>>> bkim@amobee.com> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to
>>>>>>>>>>>>>>>>> target a version of Kudu to begin real testing of Spark against it for our
>>>>>>>>>>>>>>>>> devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA
>>>>>>>>>>>>>>>>> 90405 | www.amobee.com
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's
>>>>>>>>>>>>>>>>> needed either.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format,
>>>>>>>>>>>>>>>>> ideally we'd use scans directly.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The goal was to provide something for others to contribute
>>>>>>>>>>>>>>>>> to. We have some basic unit tests that others can easily extend. None of us
>>>>>>>>>>>>>>>>> on the team are Spark experts, but we'd be really happy to assist one
>>>>>>>>>>>>>>>>> improve the kudu-spark code.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements
>>>>>>>>>>>>>>>>>> (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more
>>>>>>>>>>>>>>>>>> Spark SQL functionality (Dataframes) and doing the documentation, what more
>>>>>>>>>>>>>>>>>> needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark
>>>>>>>>>>>>>>>>>> with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get
>>>>>>>>>>>>>>>>>> this in for 0.7.0: https://issues.
>>>>>>>>>>>>>>>>>> cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use
>>>>>>>>>>>>>>>>>> SparkSQL on Kudu, but it will require a lot more work to make it
>>>>>>>>>>>>>>>>>> fast/useful.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>>>>>>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>>>>>>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>>>>>>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
--
Todd Lipcon
Software Engineer, Cloudera
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Now that Kudu 1.0.0 is officially out and ready for production use, where do we find the spark connector jar for this release?
Thanks,
Ben
> On Jun 17, 2016, at 11:08 AM, Dan Burkert <da...@cloudera.com> wrote:
>
> Hi Ben,
>
> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I do not think we support that at this point. I haven't looked deeply into it, but we may hit issues specifying Kudu-specific options (partitioning, column encoding, etc.). Probably issues that can be worked through eventually, though. If you are interested in contributing to Kudu, this is an area that could obviously use improvement! Most or all of our Spark features have been completely community driven to date.
>
> I am assuming that more Spark support along with semantic changes below will be incorporated into Kudu 0.9.1.
>
> As a rule we do not release new features in patch releases, but the good news is that we are releasing regularly, and our next scheduled release is for the August timeframe (see JD's roadmap <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email about what we are aiming to include). Also, Cloudera does publish snapshot versions of the Spark connector here <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>, so the jars are available if you don't mind using snapshots.
>
> Anyone know of a better way to make unique primary keys other than using UUID to make every row unique if there is no unique column (or combination thereof) to use.
>
> Not that I know of. In general it's pretty rare to have a dataset without a natural primary key (even if it's just all of the columns), but in those cases UUID is a good solution.
>
> This is what I am using. I know auto incrementing is coming down the line (don’t know when), but is there a way to simulate this in Kudu using Spark out of curiosity?
>
> To my knowledge there is no plan to have auto increment in Kudu. Distributed, consistent, auto incrementing counters is a difficult problem, and I don't think there are any known solutions that would be fast enough for Kudu (happy to be proven wrong, though!).
>
> - Dan
>
>
> Thanks,
> Ben
>
>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>
>> I'm not sure exactly what the semantics will be, but at least one of them will be upsert. These modes come from spark, and they were really designed for file-backed storage and not table storage. We may want to do append = upsert, and overwrite = truncate + insert. I think that may match the normal spark semantics more closely.
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Dan,
>>
>> Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.
>>
>> Cheers,
>> Ben
>>
>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>
>>> Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.
>>>
>>> - Dan
>>>
>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>
>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>
>>>> Hi,
>>>>
>>>> Now, I’m getting this error when trying to write to the table.
>>>>
>>>> import scala.collection.JavaConverters._
>>>> val key_seq = Seq(“my_id")
>>>> val key_list = List(“my_id”).asJava
>>>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>>
>>>> df.write
>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>>> .mode("overwrite")
>>>> .kudu
>>>>
>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>>>>
>>>> Does the key field need to be first in the DataFrame?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>
>>>>>
>>>>>
>>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> Dan,
>>>>>
>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>
>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>
>>>>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>>>>
>>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>>>>
>>>>> - Dan
>>>>>
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>>
>>>>>> Looks like we're missing an import statement in that example. Could you try:
>>>>>>
>>>>>> import org.kududb.client._
>>>>>> and try again?
>>>>>>
>>>>>> - Dan
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>>>>
>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>
>>>>>> Is there something I’m missing?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> It will be in 0.9.0.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> Hi Chris,
>>>>>>>>>
>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>>>>
>>>>>>>>>> Example usages will look something like:
>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>>>>
>>>>>>>>>> -Chris George
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>>
>>>>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>>>>> -Chris
>>>>>>>>>>>
>>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>>>>
>>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>>>>
>>>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>>>>
>>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>>>>
>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>>>>
>>>>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>> Mark,
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>
>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>>>>
>>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>>>>
>>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>
>>>>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>>>>
>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>>>>
>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>>>>
>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>
>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Dan,
The roadmap is very informative. I am looking forward to the official 1.0 release! It would be so much easier for us to use in every aspect compared to HBase.
Cheers,
Ben
> On Jun 17, 2016, at 11:08 AM, Dan Burkert <da...@cloudera.com> wrote:
>
> Hi Ben,
>
> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I do not think we support that at this point. I haven't looked deeply into it, but we may hit issues specifying Kudu-specific options (partitioning, column encoding, etc.). Probably issues that can be worked through eventually, though. If you are interested in contributing to Kudu, this is an area that could obviously use improvement! Most or all of our Spark features have been completely community driven to date.
>
> I am assuming that more Spark support along with semantic changes below will be incorporated into Kudu 0.9.1.
>
> As a rule we do not release new features in patch releases, but the good news is that we are releasing regularly, and our next scheduled release is for the August timeframe (see JD's roadmap <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email about what we are aiming to include). Also, Cloudera does publish snapshot versions of the Spark connector here <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>, so the jars are available if you don't mind using snapshots.
>
> Anyone know of a better way to make unique primary keys other than using UUID to make every row unique if there is no unique column (or combination thereof) to use.
>
> Not that I know of. In general it's pretty rare to have a dataset without a natural primary key (even if it's just all of the columns), but in those cases UUID is a good solution.
>
> This is what I am using. I know auto incrementing is coming down the line (don’t know when), but is there a way to simulate this in Kudu using Spark out of curiosity?
>
> To my knowledge there is no plan to have auto increment in Kudu. Distributed, consistent, auto incrementing counters is a difficult problem, and I don't think there are any known solutions that would be fast enough for Kudu (happy to be proven wrong, though!).
>
> - Dan
>
>
> Thanks,
> Ben
>
>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>
>> I'm not sure exactly what the semantics will be, but at least one of them will be upsert. These modes come from spark, and they were really designed for file-backed storage and not table storage. We may want to do append = upsert, and overwrite = truncate + insert. I think that may match the normal spark semantics more closely.
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Dan,
>>
>> Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.
>>
>> Cheers,
>> Ben
>>
>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>
>>> Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.
>>>
>>> - Dan
>>>
>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>
>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>
>>>> Hi,
>>>>
>>>> Now, I’m getting this error when trying to write to the table.
>>>>
>>>> import scala.collection.JavaConverters._
>>>> val key_seq = Seq(“my_id")
>>>> val key_list = List(“my_id”).asJava
>>>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>>
>>>> df.write
>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>>> .mode("overwrite")
>>>> .kudu
>>>>
>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>>>>
>>>> Does the key field need to be first in the DataFrame?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>
>>>>>
>>>>>
>>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> Dan,
>>>>>
>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>
>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>
>>>>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>>>>
>>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>>>>
>>>>> - Dan
>>>>>
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>>
>>>>>> Looks like we're missing an import statement in that example. Could you try:
>>>>>>
>>>>>> import org.kududb.client._
>>>>>> and try again?
>>>>>>
>>>>>> - Dan
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>>>>
>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>
>>>>>> Is there something I’m missing?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> It will be in 0.9.0.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> Hi Chris,
>>>>>>>>>
>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>>>>
>>>>>>>>>> Example usages will look something like:
>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>>>>
>>>>>>>>>> -Chris George
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>>
>>>>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>>>>> -Chris
>>>>>>>>>>>
>>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>>>>
>>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>>>>
>>>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>>>>
>>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>>>>
>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>>>>
>>>>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>> Mark,
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>
>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>>>>
>>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>>>>
>>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>
>>>>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>>>>
>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>>>>
>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>>>>
>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>
>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Dan Burkert <da...@cloudera.com>.
Hi Ben,
That seems like a good place to start. You could also look at the
Cassandra spark connector, or some of the more complex ones shipped with
Spark (JDBC in particular).
- Dan
On Mon, Jun 20, 2016 at 6:07 PM, Benjamin Kim <bb...@gmail.com> wrote:
> Dan,
>
> Out of curiosity, I was looking through the spark-csv code in Github and
> tried to see what makes it work for the “CREATE TABLE” statement, while it
> doesn’t for spark-kudu. There are differences in the way both are done,
> CsvRelation vs. KuduRelation. I’m still learning how this works though and
> what implications these differences are. In your opinion, is this the right
> place to start?
>
> Thanks,
> Ben
>
>
> On Jun 17, 2016, at 11:08 AM, Dan Burkert <da...@cloudera.com> wrote:
>
> Hi Ben,
>
> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I
> do not think we support that at this point. I haven't looked deeply into
> it, but we may hit issues specifying Kudu-specific options (partitioning,
> column encoding, etc.). Probably issues that can be worked through
> eventually, though. If you are interested in contributing to Kudu, this is
> an area that could obviously use improvement! Most or all of our Spark
> features have been completely community driven to date.
>
>
>> I am assuming that more Spark support along with semantic changes below
>> will be incorporated into Kudu 0.9.1.
>>
>
> As a rule we do not release new features in patch releases, but the good
> news is that we are releasing regularly, and our next scheduled release is
> for the August timeframe (see JD's roadmap
> <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email
> about what we are aiming to include). Also, Cloudera does publish snapshot
> versions of the Spark connector here
> <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>, so
> the jars are available if you don't mind using snapshots.
>
>
>> Anyone know of a better way to make unique primary keys other than using
>> UUID to make every row unique if there is no unique column (or combination
>> thereof) to use.
>>
>
> Not that I know of. In general it's pretty rare to have a dataset without
> a natural primary key (even if it's just all of the columns), but in those
> cases UUID is a good solution.
>
>
>> This is what I am using. I know auto incrementing is coming down the line
>> (don’t know when), but is there a way to simulate this in Kudu using Spark
>> out of curiosity?
>>
>
> To my knowledge there is no plan to have auto increment in Kudu.
> Distributed, consistent, auto incrementing counters is a difficult problem,
> and I don't think there are any known solutions that would be fast enough
> for Kudu (happy to be proven wrong, though!).
>
> - Dan
>
>
>>
>> Thanks,
>> Ben
>>
>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>>
>> I'm not sure exactly what the semantics will be, but at least one of them
>> will be upsert. These modes come from spark, and they were really designed
>> for file-backed storage and not table storage. We may want to do append =
>> upsert, and overwrite = truncate + insert. I think that may match the
>> normal spark semantics more closely.
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> Dan,
>>>
>>> Thanks for the information. That would mean both “append” and
>>> “overwrite” modes would be combined or not needed in the future.
>>>
>>> Cheers,
>>> Ben
>>>
>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>
>>> Right now append uses an update Kudu operation, which requires the row
>>> already be present in the table. Overwrite maps to insert. Kudu very
>>> recently got upsert support baked in, but it hasn't yet been integrated
>>> into the Spark connector. So pretty soon these sharp edges will get a lot
>>> better, since upsert is the way to go for most spark workloads.
>>>
>>> - Dan
>>>
>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows
>>>> in 64s. I would assume that now I can use the “overwrite” mode on existing
>>>> data. Now, I have to find answers to these questions. What would happen if
>>>> I “append” to the data in the Kudu table if the data already exists? What
>>>> would happen if I “overwrite” existing data when the DataFrame has data in
>>>> it that does not exist in the Kudu table? I need to evaluate the best way
>>>> to simulate the UPSERT behavior in HBase because this is what our use case
>>>> is.
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>
>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>>>
>>>> Hi,
>>>>
>>>> Now, I’m getting this error when trying to write to the table.
>>>>
>>>> import scala.collection.JavaConverters._
>>>> val key_seq = Seq(“my_id")
>>>> val key_list = List(“my_id”).asJava
>>>> kuduContext.createTable(tableName, df.schema, key_seq, new
>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>>
>>>> df.write
>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>>> .mode("overwrite")
>>>> .kudu
>>>>
>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to
>>>> Kudu; sample errors: Not found: key not found (error 0)Not found: key not
>>>> found (error 0)Not found: key not found (error 0)Not found: key not found
>>>> (error 0)Not found: key not found (error 0)
>>>>
>>>> Does the key field need to be first in the DataFrame?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>
>>>>
>>>>
>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> Dan,
>>>>>
>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a
>>>>> column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>
>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
>>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>
>>>>> java.lang.IllegalArgumentException: Table partitioning must be
>>>>> specified using setRangePartitionColumns or addHashPartitions
>>>>>
>>>>
>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of
>>>> primary key columns, so in this case you have specified the single PK
>>>> column "my_id". The `addHashPartitions` call adds hash partitioning to the
>>>> table, in this case over the column "my_id" (which is good, it must be over
>>>> one or more PK columns, so in this case "my_id" is the one and only valid
>>>> combination). However, the call to `addHashPartition` also takes the
>>>> number of buckets as the second param. You shouldn't get the
>>>> IllegalArgumentException as long as you are specifying either
>>>> `addHashPartitions` or `setRangePartitionColumns`.
>>>>
>>>> - Dan
>>>>
>>>>
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>>
>>>>> Looks like we're missing an import statement in that example. Could
>>>>> you try:
>>>>>
>>>>> import org.kududb.client._
>>>>>
>>>>> and try again?
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I encountered an error trying to create a table based on the
>>>>>> documentation from a DataFrame.
>>>>>>
>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>> kuduContext.createTable(tableName, df.schema,
>>>>>> Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>
>>>>>> Is there something I’m missing?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> It's only in Cloudera's maven repo:
>>>>>> https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar
>>>>>>> for spark-shell to use. Can you show me where to find it?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>> wrote:
>>>>>>>
>>>>>>> What's in this doc is what's gonna get released:
>>>>>>> https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <
>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>
>>>>>>>> It will be in 0.9.0.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi Chris,
>>>>>>>>>
>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <
>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>
>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource
>>>>>>>>> api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>>>>>
>>>>>>>>> Example usages will look something like:
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>>>>>
>>>>>>>>> -Chris George
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>
>>>>>>>>> Also, does anyone have any sample code on how to update/insert
>>>>>>>>> data in Kudu using DataFrames?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <
>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>
>>>>>>>>> SparkSQL cannot support these type of statements but we may be
>>>>>>>>> able to implement similar functionality through the api.
>>>>>>>>> -Chris
>>>>>>>>>
>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an
>>>>>>>>> “upsert” if it were to be implemented.
>>>>>>>>>
>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <
>>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>>
>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it
>>>>>>>>> into gerrit if you want to take a look.
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2754/
>>>>>>>>> It does pushdown predicates which the existing input formatter
>>>>>>>>> based rdd does not.
>>>>>>>>>
>>>>>>>>> Within the next two weeks I’m planning to implement a datasource
>>>>>>>>> for spark that will have pushdown predicates and insertion/update
>>>>>>>>> functionality (need to look more at cassandra and the hbase datasource for
>>>>>>>>> best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>> Having a datasource would give us useful data frames and also make
>>>>>>>>> spark sql usable for kudu.
>>>>>>>>>
>>>>>>>>> My reasoning for having a spark datasource and not using Impala
>>>>>>>>> is: 1. We have had trouble getting impala to run fast with high concurrency
>>>>>>>>> when compared to spark 2. We interact with datasources which do not
>>>>>>>>> integrate with impala. 3. We have custom sql query planners for extended
>>>>>>>>> sql functionality.
>>>>>>>>>
>>>>>>>>> -Chris George
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>> You guys make a convincing point, although on the upsert side
>>>>>>>>> we'll need more support from the servers. Right now all you can do is an
>>>>>>>>> INSERT then, if you get a dup key, do an UPDATE. I guess we could at least
>>>>>>>>> add an API on the client side that would manage it, but it wouldn't be
>>>>>>>>> atomic.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <
>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>
>>>>>>>>>> It's pretty simple, actually. I need to support versioned
>>>>>>>>>> datasets in a Spark SQL environment. Instead of a hack on top of a Parquet
>>>>>>>>>> data store, I'm hoping (among other reasons) to be able to use Kudu's write
>>>>>>>>>> and timestamp-based read operations to support not only appending data, but
>>>>>>>>>> also updating existing data, and even some schema migration. The most
>>>>>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>>>>>> be added/appended.
>>>>>>>>>>
>>>>>>>>>> Using Kudu's Java API and developing additional functionality on
>>>>>>>>>> top of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>>>>>> decision.
>>>>>>>>>>
>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>>> Mark,
>>>>>>>>>>>
>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it
>>>>>>>>>>> caught the attention of other folks!
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <
>>>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>>>>>> to whether we adopt Kudu.
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary
>>>>>>>>>>> for you. Is it just that you currently do it that way into some database or
>>>>>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>>>>>> KuduSession be too much work?
>>>>>>>>>>>
>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your
>>>>>>>>>>> current solution? If it's not completely clear, I'd love to help you think
>>>>>>>>>>> through it.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>>
>>>>>>>>>>>>> What are your DS folks looking for in terms of functionality
>>>>>>>>>>>>> related to Spark? A SparkSQL integration that's as fully featured as
>>>>>>>>>>>>> Impala's? Do they care being able to insert into Kudu with SparkSQL or just
>>>>>>>>>>>>> being able to query real fast? Anything more specific to Spark that I'm
>>>>>>>>>>>>> missing?
>>>>>>>>>>>>>
>>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <
>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions.
>>>>>>>>>>>>>> But, since it’s not “production-ready”, upper management doesn’t want to
>>>>>>>>>>>>>> fully deploy it yet. They just want to keep an eye on it though. Kudu was
>>>>>>>>>>>>>> so much simpler and easier to use in every aspect compared to HBase. Impala
>>>>>>>>>>>>>> was great for the report writers and analysts to experiment with for the
>>>>>>>>>>>>>> short time it was up. But, once again, the only blocker was the lack of
>>>>>>>>>>>>>> Spark support for our Data Developers/Scientists. So, production-level data
>>>>>>>>>>>>>> population won’t happen until then.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> As for our company, we have been looking for an updatable
>>>>>>>>>>>>>>> data store for a long time that can be quickly queried directly either
>>>>>>>>>>>>>>> using Spark SQL or Impala or some other SQL engine and still handle TB or
>>>>>>>>>>>>>>> PB of data without performance degradation and many configuration
>>>>>>>>>>>>>>> headaches. For now, we are using HBase to take on this role with Phoenix as
>>>>>>>>>>>>>>> a fast way to directly query the data. I can see Kudu as the best way to
>>>>>>>>>>>>>>> fill this gap easily, especially being the closest thing to other
>>>>>>>>>>>>>>> relational databases out there in familiarity for the many SQL analytics
>>>>>>>>>>>>>>> people in our company. The other alternative would be to go with AWS
>>>>>>>>>>>>>>> Redshift for the same reasons, but it would come at a cost, of course. If
>>>>>>>>>>>>>>> we went with either solutions, Kudu or Redshift, it would get rid of the
>>>>>>>>>>>>>>> need to extract from HBase to parquet tables or export to PostgreSQL to
>>>>>>>>>>>>>>> support more of the SQL language using by analysts or the reporting
>>>>>>>>>>>>>>> software we use..
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off
>>>>>>>>>>>>>> with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we
>>>>>>>>>>>>>>> like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>>>>>> dying down.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> There's definitely an overlap in the use cases that
>>>>>>>>>>>>>>> Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C*
>>>>>>>>>>>>>>> is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month,
>>>>>>>>>>>>>>> it's a storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to
>>>>>>>>>>>>>>> learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>>>>>> replacement.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose.
>>>>>>>>>>>>>>>> They told me that everything was about Spark and there is a big buzz about
>>>>>>>>>>>>>>>> the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any
>>>>>>>>>>>>>>>> timeline. I know of one person on the Kudu Slack who's working on a better
>>>>>>>>>>>>>>>> RDD, but that's about it.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <
>>>>>>>>>>>>>>>> bkim@amobee.com> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to
>>>>>>>>>>>>>>>>> target a version of Kudu to begin real testing of Spark against it for our
>>>>>>>>>>>>>>>>> devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA
>>>>>>>>>>>>>>>>> 90405 | www.amobee.com
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's
>>>>>>>>>>>>>>>>> needed either.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format,
>>>>>>>>>>>>>>>>> ideally we'd use scans directly.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> The goal was to provide something for others to contribute
>>>>>>>>>>>>>>>>> to. We have some basic unit tests that others can easily extend. None of us
>>>>>>>>>>>>>>>>> on the team are Spark experts, but we'd be really happy to assist one
>>>>>>>>>>>>>>>>> improve the kudu-spark code.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements
>>>>>>>>>>>>>>>>>> (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more
>>>>>>>>>>>>>>>>>> Spark SQL functionality (Dataframes) and doing the documentation, what more
>>>>>>>>>>>>>>>>>> needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark
>>>>>>>>>>>>>>>>>> with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get
>>>>>>>>>>>>>>>>>> this in for 0.7.0:
>>>>>>>>>>>>>>>>>> https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use
>>>>>>>>>>>>>>>>>> SparkSQL on Kudu, but it will require a lot more work to make it
>>>>>>>>>>>>>>>>>> fast/useful.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>>>>>>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>>>>>>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>>>>>>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Dan,
Out of curiosity, I was looking through the spark-csv code in Github and tried to see what makes it work for the “CREATE TABLE” statement, while it doesn’t for spark-kudu. There are differences in the way both are done, CsvRelation vs. KuduRelation. I’m still learning how this works though and what implications these differences are. In your opinion, is this the right place to start?
Thanks,
Ben
> On Jun 17, 2016, at 11:08 AM, Dan Burkert <da...@cloudera.com> wrote:
>
> Hi Ben,
>
> To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I do not think we support that at this point. I haven't looked deeply into it, but we may hit issues specifying Kudu-specific options (partitioning, column encoding, etc.). Probably issues that can be worked through eventually, though. If you are interested in contributing to Kudu, this is an area that could obviously use improvement! Most or all of our Spark features have been completely community driven to date.
>
> I am assuming that more Spark support along with semantic changes below will be incorporated into Kudu 0.9.1.
>
> As a rule we do not release new features in patch releases, but the good news is that we are releasing regularly, and our next scheduled release is for the August timeframe (see JD's roadmap <https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E> email about what we are aiming to include). Also, Cloudera does publish snapshot versions of the Spark connector here <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>, so the jars are available if you don't mind using snapshots.
>
> Anyone know of a better way to make unique primary keys other than using UUID to make every row unique if there is no unique column (or combination thereof) to use.
>
> Not that I know of. In general it's pretty rare to have a dataset without a natural primary key (even if it's just all of the columns), but in those cases UUID is a good solution.
>
> This is what I am using. I know auto incrementing is coming down the line (don’t know when), but is there a way to simulate this in Kudu using Spark out of curiosity?
>
> To my knowledge there is no plan to have auto increment in Kudu. Distributed, consistent, auto incrementing counters is a difficult problem, and I don't think there are any known solutions that would be fast enough for Kudu (happy to be proven wrong, though!).
>
> - Dan
>
>
> Thanks,
> Ben
>
>> On Jun 14, 2016, at 6:08 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>
>> I'm not sure exactly what the semantics will be, but at least one of them will be upsert. These modes come from spark, and they were really designed for file-backed storage and not table storage. We may want to do append = upsert, and overwrite = truncate + insert. I think that may match the normal spark semantics more closely.
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Dan,
>>
>> Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.
>>
>> Cheers,
>> Ben
>>
>>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>
>>> Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.
>>>
>>> - Dan
>>>
>>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>
>>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>
>>>> Hi,
>>>>
>>>> Now, I’m getting this error when trying to write to the table.
>>>>
>>>> import scala.collection.JavaConverters._
>>>> val key_seq = Seq(“my_id")
>>>> val key_list = List(“my_id”).asJava
>>>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>>
>>>> df.write
>>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>>> .mode("overwrite")
>>>> .kudu
>>>>
>>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>>>>
>>>> Does the key field need to be first in the DataFrame?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>
>>>>>
>>>>>
>>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> Dan,
>>>>>
>>>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>>
>>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>>
>>>>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>>>>
>>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>>>>
>>>>> - Dan
>>>>>
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>>
>>>>>> Looks like we're missing an import statement in that example. Could you try:
>>>>>>
>>>>>> import org.kududb.client._
>>>>>> and try again?
>>>>>>
>>>>>> - Dan
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>>>>
>>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>>
>>>>>> Is there something I’m missing?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> It will be in 0.9.0.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> Hi Chris,
>>>>>>>>>
>>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>>>>
>>>>>>>>>> Example usages will look something like:
>>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>>>>
>>>>>>>>>> -Chris George
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>>
>>>>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>>>>> -Chris
>>>>>>>>>>>
>>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>>>>
>>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>>>>
>>>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>>>>
>>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>>>>
>>>>>>>>>>>> -Chris George
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>>>>
>>>>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>> Mark,
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>>
>>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>>>>
>>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>>>>
>>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>
>>>>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>>>>
>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>>>>
>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>>>>
>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>
>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Dan Burkert <da...@cloudera.com>.
Hi Ben,
To your first question about `CREATE TABLE` syntax with Kudu/Spark SQL, I
do not think we support that at this point. I haven't looked deeply into
it, but we may hit issues specifying Kudu-specific options (partitioning,
column encoding, etc.). Probably issues that can be worked through
eventually, though. If you are interested in contributing to Kudu, this is
an area that could obviously use improvement! Most or all of our Spark
features have been completely community driven to date.
> I am assuming that more Spark support along with semantic changes below
> will be incorporated into Kudu 0.9.1.
>
As a rule we do not release new features in patch releases, but the good
news is that we are releasing regularly, and our next scheduled release is
for the August timeframe (see JD's roadmap
<https://lists.apache.org/thread.html/1a3b949e715a74d7f26bd9c102247441a06d16d077324ba39a662e2a@1455234076@%3Cdev.kudu.apache.org%3E>
email
about what we are aiming to include). Also, Cloudera does publish snapshot
versions of the Spark connector here
<https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/>, so
the jars are available if you don't mind using snapshots.
> Anyone know of a better way to make unique primary keys other than using
> UUID to make every row unique if there is no unique column (or combination
> thereof) to use.
>
Not that I know of. In general it's pretty rare to have a dataset without
a natural primary key (even if it's just all of the columns), but in those
cases UUID is a good solution.
> This is what I am using. I know auto incrementing is coming down the line
> (don’t know when), but is there a way to simulate this in Kudu using Spark
> out of curiosity?
>
To my knowledge there is no plan to have auto increment in Kudu.
Distributed, consistent, auto incrementing counters is a difficult problem,
and I don't think there are any known solutions that would be fast enough
for Kudu (happy to be proven wrong, though!).
- Dan
>
> Thanks,
> Ben
>
> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>
> I'm not sure exactly what the semantics will be, but at least one of them
> will be upsert. These modes come from spark, and they were really designed
> for file-backed storage and not table storage. We may want to do append =
> upsert, and overwrite = truncate + insert. I think that may match the
> normal spark semantics more closely.
>
> - Dan
>
> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Dan,
>>
>> Thanks for the information. That would mean both “append” and “overwrite”
>> modes would be combined or not needed in the future.
>>
>> Cheers,
>> Ben
>>
>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <da...@cloudera.com> wrote:
>>
>> Right now append uses an update Kudu operation, which requires the row
>> already be present in the table. Overwrite maps to insert. Kudu very
>> recently got upsert support baked in, but it hasn't yet been integrated
>> into the Spark connector. So pretty soon these sharp edges will get a lot
>> better, since upsert is the way to go for most spark workloads.
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> I tried to use the “append” mode, and it worked. Over 3.8 million rows
>>> in 64s. I would assume that now I can use the “overwrite” mode on existing
>>> data. Now, I have to find answers to these questions. What would happen if
>>> I “append” to the data in the Kudu table if the data already exists? What
>>> would happen if I “overwrite” existing data when the DataFrame has data in
>>> it that does not exist in the Kudu table? I need to evaluate the best way
>>> to simulate the UPSERT behavior in HBase because this is what our use case
>>> is.
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>
>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>>
>>> Hi,
>>>
>>> Now, I’m getting this error when trying to write to the table.
>>>
>>> import scala.collection.JavaConverters._
>>> val key_seq = Seq(“my_id")
>>> val key_list = List(“my_id”).asJava
>>> kuduContext.createTable(tableName, df.schema, key_seq, new
>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>
>>> df.write
>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>> .mode("overwrite")
>>> .kudu
>>>
>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to
>>> Kudu; sample errors: Not found: key not found (error 0)Not found: key not
>>> found (error 0)Not found: key not found (error 0)Not found: key not found
>>> (error 0)Not found: key not found (error 0)
>>>
>>> Does the key field need to be first in the DataFrame?
>>>
>>> Thanks,
>>> Ben
>>>
>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>
>>>
>>>
>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> Dan,
>>>>
>>>> Thanks! It got further. Now, how do I set the Primary Key to be a
>>>> column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>
>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
>>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>
>>>> java.lang.IllegalArgumentException: Table partitioning must be
>>>> specified using setRangePartitionColumns or addHashPartitions
>>>>
>>>
>>> Yep. The `Seq("my_id")` part of that call is specifying the set of
>>> primary key columns, so in this case you have specified the single PK
>>> column "my_id". The `addHashPartitions` call adds hash partitioning to the
>>> table, in this case over the column "my_id" (which is good, it must be over
>>> one or more PK columns, so in this case "my_id" is the one and only valid
>>> combination). However, the call to `addHashPartition` also takes the
>>> number of buckets as the second param. You shouldn't get the
>>> IllegalArgumentException as long as you are specifying either
>>> `addHashPartitions` or `setRangePartitionColumns`.
>>>
>>> - Dan
>>>
>>>
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>>
>>>> Looks like we're missing an import statement in that example. Could
>>>> you try:
>>>>
>>>> import org.kududb.client._
>>>>
>>>> and try again?
>>>>
>>>> - Dan
>>>>
>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> I encountered an error trying to create a table based on the
>>>>> documentation from a DataFrame.
>>>>>
>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>> kuduContext.createTable(tableName, df.schema,
>>>>> Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>
>>>>> Is there something I’m missing?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>> wrote:
>>>>>
>>>>> It's only in Cloudera's maven repo:
>>>>> https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
>>>>>
>>>>> J-D
>>>>>
>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi J-D,
>>>>>>
>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar
>>>>>> for spark-shell to use. Can you show me where to find it?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> What's in this doc is what's gonna get released:
>>>>>> https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>> wrote:
>>>>>>>
>>>>>>> It will be in 0.9.0.
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi Chris,
>>>>>>>>
>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <
>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>
>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>> It will allow upsert/insert from a dataframe using the datasource
>>>>>>>> api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>>>>
>>>>>>>> Example usages will look something like:
>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>>>>
>>>>>>>> -Chris George
>>>>>>>>
>>>>>>>>
>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>
>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>
>>>>>>>> Also, does anyone have any sample code on how to update/insert data
>>>>>>>> in Kudu using DataFrames?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <
>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>
>>>>>>>> SparkSQL cannot support these type of statements but we may be able
>>>>>>>> to implement similar functionality through the api.
>>>>>>>> -Chris
>>>>>>>>
>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>>
>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert”
>>>>>>>> if it were to be implemented.
>>>>>>>>
>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>> WHEN MATCHED THEN
>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <
>>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>>
>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into
>>>>>>>> gerrit if you want to take a look.
>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2754/
>>>>>>>> It does pushdown predicates which the existing input formatter
>>>>>>>> based rdd does not.
>>>>>>>>
>>>>>>>> Within the next two weeks I’m planning to implement a datasource
>>>>>>>> for spark that will have pushdown predicates and insertion/update
>>>>>>>> functionality (need to look more at cassandra and the hbase datasource for
>>>>>>>> best way to do this) I agree that server side upsert would be helpful.
>>>>>>>> Having a datasource would give us useful data frames and also make
>>>>>>>> spark sql usable for kudu.
>>>>>>>>
>>>>>>>> My reasoning for having a spark datasource and not using Impala is:
>>>>>>>> 1. We have had trouble getting impala to run fast with high concurrency
>>>>>>>> when compared to spark 2. We interact with datasources which do not
>>>>>>>> integrate with impala. 3. We have custom sql query planners for extended
>>>>>>>> sql functionality.
>>>>>>>>
>>>>>>>> -Chris George
>>>>>>>>
>>>>>>>>
>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>> You guys make a convincing point, although on the upsert side we'll
>>>>>>>> need more support from the servers. Right now all you can do is an INSERT
>>>>>>>> then, if you get a dup key, do an UPDATE. I guess we could at least add an
>>>>>>>> API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <
>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>
>>>>>>>>> It's pretty simple, actually. I need to support versioned
>>>>>>>>> datasets in a Spark SQL environment. Instead of a hack on top of a Parquet
>>>>>>>>> data store, I'm hoping (among other reasons) to be able to use Kudu's write
>>>>>>>>> and timestamp-based read operations to support not only appending data, but
>>>>>>>>> also updating existing data, and even some schema migration. The most
>>>>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>>>>> be added/appended.
>>>>>>>>>
>>>>>>>>> Using Kudu's Java API and developing additional functionality on
>>>>>>>>> top of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>>>>> decision.
>>>>>>>>>
>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>>> Mark,
>>>>>>>>>>
>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it
>>>>>>>>>> caught the attention of other folks!
>>>>>>>>>>
>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <
>>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>>
>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>>>>> to whether we adopt Kudu.
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for
>>>>>>>>>> you. Is it just that you currently do it that way into some database or
>>>>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>>>>> KuduSession be too much work?
>>>>>>>>>>
>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your
>>>>>>>>>> current solution? If it's not completely clear, I'd love to help you think
>>>>>>>>>> through it.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>>
>>>>>>>>>>>> What are your DS folks looking for in terms of functionality
>>>>>>>>>>>> related to Spark? A SparkSQL integration that's as fully featured as
>>>>>>>>>>>> Impala's? Do they care being able to insert into Kudu with SparkSQL or just
>>>>>>>>>>>> being able to query real fast? Anything more specific to Spark that I'm
>>>>>>>>>>>> missing?
>>>>>>>>>>>>
>>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <
>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions.
>>>>>>>>>>>>> But, since it’s not “production-ready”, upper management doesn’t want to
>>>>>>>>>>>>> fully deploy it yet. They just want to keep an eye on it though. Kudu was
>>>>>>>>>>>>> so much simpler and easier to use in every aspect compared to HBase. Impala
>>>>>>>>>>>>> was great for the report writers and analysts to experiment with for the
>>>>>>>>>>>>> short time it was up. But, once again, the only blocker was the lack of
>>>>>>>>>>>>> Spark support for our Data Developers/Scientists. So, production-level data
>>>>>>>>>>>>> population won’t happen until then.
>>>>>>>>>>>>>
>>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> As for our company, we have been looking for an updatable
>>>>>>>>>>>>>> data store for a long time that can be quickly queried directly either
>>>>>>>>>>>>>> using Spark SQL or Impala or some other SQL engine and still handle TB or
>>>>>>>>>>>>>> PB of data without performance degradation and many configuration
>>>>>>>>>>>>>> headaches. For now, we are using HBase to take on this role with Phoenix as
>>>>>>>>>>>>>> a fast way to directly query the data. I can see Kudu as the best way to
>>>>>>>>>>>>>> fill this gap easily, especially being the closest thing to other
>>>>>>>>>>>>>> relational databases out there in familiarity for the many SQL analytics
>>>>>>>>>>>>>> people in our company. The other alternative would be to go with AWS
>>>>>>>>>>>>>> Redshift for the same reasons, but it would come at a cost, of course. If
>>>>>>>>>>>>>> we went with either solutions, Kudu or Redshift, it would get rid of the
>>>>>>>>>>>>>> need to extract from HBase to parquet tables or export to PostgreSQL to
>>>>>>>>>>>>>> support more of the SQL language using by analysts or the reporting
>>>>>>>>>>>>>> software we use..
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off
>>>>>>>>>>>>> with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we
>>>>>>>>>>>>>> like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>>>>> dying down.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> There's definitely an overlap in the use cases that
>>>>>>>>>>>>>> Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C*
>>>>>>>>>>>>>> is just an interim solution for the use case you describe.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month,
>>>>>>>>>>>>>> it's a storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to
>>>>>>>>>>>>>> learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>>>>> replacement.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose.
>>>>>>>>>>>>>>> They told me that everything was about Spark and there is a big buzz about
>>>>>>>>>>>>>>> the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline.
>>>>>>>>>>>>>>> I know of one person on the Kudu Slack who's working on a better RDD, but
>>>>>>>>>>>>>>> that's about it.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <
>>>>>>>>>>>>>>> bkim@amobee.com> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to
>>>>>>>>>>>>>>>> target a version of Kudu to begin real testing of Spark against it for our
>>>>>>>>>>>>>>>> devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405
>>>>>>>>>>>>>>>> | www.amobee.com
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's
>>>>>>>>>>>>>>>> needed either.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally
>>>>>>>>>>>>>>>> we'd use scans directly.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The goal was to provide something for others to contribute
>>>>>>>>>>>>>>>> to. We have some basic unit tests that others can easily extend. None of us
>>>>>>>>>>>>>>>> on the team are Spark experts, but we'd be really happy to assist one
>>>>>>>>>>>>>>>> improve the kudu-spark code.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements
>>>>>>>>>>>>>>>>> (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more
>>>>>>>>>>>>>>>>> Spark SQL functionality (Dataframes) and doing the documentation, what more
>>>>>>>>>>>>>>>>> needs to be done? Optimizations?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with
>>>>>>>>>>>>>>>>> Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get
>>>>>>>>>>>>>>>>> this in for 0.7.0:
>>>>>>>>>>>>>>>>> https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL
>>>>>>>>>>>>>>>>> on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>>>>>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>>>>>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>>>>>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
I am assuming that more Spark support along with semantic changes below will be incorporated into Kudu 0.9.1.
Anyone know of a better way to make unique primary keys other than using UUID to make every row unique if there is no unique column (or combination thereof) to use.
import java.util.UUID
val generateUUID = udf(() => UUID.randomUUID().toString)
This is what I am using. I know auto incrementing is coming down the line (don’t know when), but is there a way to simulate this in Kudu using Spark out of curiosity?
Thanks,
Ben
> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>
> I'm not sure exactly what the semantics will be, but at least one of them will be upsert. These modes come from spark, and they were really designed for file-backed storage and not table storage. We may want to do append = upsert, and overwrite = truncate + insert. I think that may match the normal spark semantics more closely.
>
> - Dan
>
> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Dan,
>
> Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.
>
> Cheers,
> Ben
>
>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>
>> Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
>>
>> Thanks,
>> Ben
>>
>>
>>
>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>
>>> Hi,
>>>
>>> Now, I’m getting this error when trying to write to the table.
>>>
>>> import scala.collection.JavaConverters._
>>> val key_seq = Seq(“my_id")
>>> val key_list = List(“my_id”).asJava
>>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>
>>> df.write
>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>> .mode("overwrite")
>>> .kudu
>>>
>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>>>
>>> Does the key field need to be first in the DataFrame?
>>>
>>> Thanks,
>>> Ben
>>>
>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>
>>>>
>>>>
>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Dan,
>>>>
>>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>
>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>
>>>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>>>
>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>>>
>>>> - Dan
>>>>
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>
>>>>> Looks like we're missing an import statement in that example. Could you try:
>>>>>
>>>>> import org.kududb.client._
>>>>> and try again?
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>>>
>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>
>>>>> Is there something I’m missing?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> Hi J-D,
>>>>>>
>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> It will be in 0.9.0.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> Hi Chris,
>>>>>>>>
>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>
>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>>>
>>>>>>>>> Example usages will look something like:
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>>>
>>>>>>>>> -Chris George
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>
>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>
>>>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>>>> -Chris
>>>>>>>>>>
>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>>>
>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>>>
>>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>>>
>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>>>
>>>>>>>>>>> -Chris George
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>>>
>>>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>>>
>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>> Mark,
>>>>>>>>>>>
>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>
>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>>>
>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>>>
>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>
>>>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>>>
>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>>>
>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>> J-D,
>>>>>>>>>>>>
>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>
>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>>>
>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>
>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>>>
>>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Ah, that makes more sense when you put it that way.
Thanks,
Ben
> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>
> I'm not sure exactly what the semantics will be, but at least one of them will be upsert. These modes come from spark, and they were really designed for file-backed storage and not table storage. We may want to do append = upsert, and overwrite = truncate + insert. I think that may match the normal spark semantics more closely.
>
> - Dan
>
> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Dan,
>
> Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.
>
> Cheers,
> Ben
>
>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>
>> Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
>>
>> Thanks,
>> Ben
>>
>>
>>
>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>
>>> Hi,
>>>
>>> Now, I’m getting this error when trying to write to the table.
>>>
>>> import scala.collection.JavaConverters._
>>> val key_seq = Seq(“my_id")
>>> val key_list = List(“my_id”).asJava
>>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>
>>> df.write
>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>> .mode("overwrite")
>>> .kudu
>>>
>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>>>
>>> Does the key field need to be first in the DataFrame?
>>>
>>> Thanks,
>>> Ben
>>>
>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>
>>>>
>>>>
>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Dan,
>>>>
>>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>
>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>
>>>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>>>
>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>>>
>>>> - Dan
>>>>
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>
>>>>> Looks like we're missing an import statement in that example. Could you try:
>>>>>
>>>>> import org.kududb.client._
>>>>> and try again?
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>>>
>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>
>>>>> Is there something I’m missing?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> Hi J-D,
>>>>>>
>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> It will be in 0.9.0.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> Hi Chris,
>>>>>>>>
>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>
>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>>>
>>>>>>>>> Example usages will look something like:
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>>>
>>>>>>>>> -Chris George
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>
>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>
>>>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>>>> -Chris
>>>>>>>>>>
>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>>>
>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>>>
>>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>>>
>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>>>
>>>>>>>>>>> -Chris George
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>>>
>>>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>>>
>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>> Mark,
>>>>>>>>>>>
>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>
>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>>>
>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>>>
>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>
>>>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>>>
>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>>>
>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>> J-D,
>>>>>>>>>>>>
>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>
>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>>>
>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>
>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>>>
>>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Since I have created permanent tables using org.apache.spark.sql.jdbc and com.databricks.spark.csv with sqlContext, I was wondering if I can do the same with Kudu tables?
CREATE TABLE <tablename>
USING org.kududb.spark.kudu
OPTIONS ("kudu.master” "kudu_master","kudu.table” "kudu_tablename”)
Is this possible? By the way, the above didn’t work for me.
Thanks,
Ben
> On Jun 14, 2016, at 6:08 PM, Dan Burkert <da...@cloudera.com> wrote:
>
> I'm not sure exactly what the semantics will be, but at least one of them will be upsert. These modes come from spark, and they were really designed for file-backed storage and not table storage. We may want to do append = upsert, and overwrite = truncate + insert. I think that may match the normal spark semantics more closely.
>
> - Dan
>
> On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Dan,
>
> Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.
>
> Cheers,
> Ben
>
>> On Jun 14, 2016, at 5:57 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>
>> Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
>>
>> Thanks,
>> Ben
>>
>>
>>
>>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>
>>> Hi,
>>>
>>> Now, I’m getting this error when trying to write to the table.
>>>
>>> import scala.collection.JavaConverters._
>>> val key_seq = Seq(“my_id")
>>> val key_list = List(“my_id”).asJava
>>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>>
>>> df.write
>>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>>> .mode("overwrite")
>>> .kudu
>>>
>>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>>>
>>> Does the key field need to be first in the DataFrame?
>>>
>>> Thanks,
>>> Ben
>>>
>>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>
>>>>
>>>>
>>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Dan,
>>>>
>>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>>>
>>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>>
>>>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>>>
>>>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>>>
>>>> - Dan
>>>>
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>>
>>>>> Looks like we're missing an import statement in that example. Could you try:
>>>>>
>>>>> import org.kududb.client._
>>>>> and try again?
>>>>>
>>>>> - Dan
>>>>>
>>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>>>
>>>>> <console>:49: error: not found: type CreateTableOptions
>>>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>>
>>>>> Is there something I’m missing?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> Hi J-D,
>>>>>>
>>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> It will be in 0.9.0.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> Hi Chris,
>>>>>>>>
>>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>
>>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>>>
>>>>>>>>> Example usages will look something like:
>>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>>>
>>>>>>>>> -Chris George
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>
>>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>>
>>>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>>>> -Chris
>>>>>>>>>>
>>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>>>
>>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>>>
>>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>>>
>>>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>>>
>>>>>>>>>>> -Chris George
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>>>
>>>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>>>
>>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>> Mark,
>>>>>>>>>>>
>>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>>
>>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>>>
>>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>>>
>>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>
>>>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>>>
>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>>>
>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>> J-D,
>>>>>>>>>>>>
>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>
>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>>>
>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>
>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>>>
>>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Dan Burkert <da...@cloudera.com>.
I'm not sure exactly what the semantics will be, but at least one of them
will be upsert. These modes come from spark, and they were really designed
for file-backed storage and not table storage. We may want to do append =
upsert, and overwrite = truncate + insert. I think that may match the
normal spark semantics more closely.
- Dan
On Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <bb...@gmail.com> wrote:
> Dan,
>
> Thanks for the information. That would mean both “append” and “overwrite”
> modes would be combined or not needed in the future.
>
> Cheers,
> Ben
>
> On Jun 14, 2016, at 5:57 PM, Dan Burkert <da...@cloudera.com> wrote:
>
> Right now append uses an update Kudu operation, which requires the row
> already be present in the table. Overwrite maps to insert. Kudu very
> recently got upsert support baked in, but it hasn't yet been integrated
> into the Spark connector. So pretty soon these sharp edges will get a lot
> better, since upsert is the way to go for most spark workloads.
>
> - Dan
>
> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> I tried to use the “append” mode, and it worked. Over 3.8 million rows in
>> 64s. I would assume that now I can use the “overwrite” mode on existing
>> data. Now, I have to find answers to these questions. What would happen if
>> I “append” to the data in the Kudu table if the data already exists? What
>> would happen if I “overwrite” existing data when the DataFrame has data in
>> it that does not exist in the Kudu table? I need to evaluate the best way
>> to simulate the UPSERT behavior in HBase because this is what our use case
>> is.
>>
>> Thanks,
>> Ben
>>
>>
>>
>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>> Hi,
>>
>> Now, I’m getting this error when trying to write to the table.
>>
>> import scala.collection.JavaConverters._
>> val key_seq = Seq(“my_id")
>> val key_list = List(“my_id”).asJava
>> kuduContext.createTable(tableName, df.schema, key_seq, new
>> CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>
>> df.write
>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>> .mode("overwrite")
>> .kudu
>>
>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to
>> Kudu; sample errors: Not found: key not found (error 0)Not found: key not
>> found (error 0)Not found: key not found (error 0)Not found: key not found
>> (error 0)Not found: key not found (error 0)
>>
>> Does the key field need to be first in the DataFrame?
>>
>> Thanks,
>> Ben
>>
>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>>
>>
>>
>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> Dan,
>>>
>>> Thanks! It got further. Now, how do I set the Primary Key to be a
>>> column(s) in the DataFrame and set the partitioning? Is it like this?
>>>
>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
>>> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>
>>> java.lang.IllegalArgumentException: Table partitioning must be specified
>>> using setRangePartitionColumns or addHashPartitions
>>>
>>
>> Yep. The `Seq("my_id")` part of that call is specifying the set of
>> primary key columns, so in this case you have specified the single PK
>> column "my_id". The `addHashPartitions` call adds hash partitioning to the
>> table, in this case over the column "my_id" (which is good, it must be over
>> one or more PK columns, so in this case "my_id" is the one and only valid
>> combination). However, the call to `addHashPartition` also takes the
>> number of buckets as the second param. You shouldn't get the
>> IllegalArgumentException as long as you are specifying either
>> `addHashPartitions` or `setRangePartitionColumns`.
>>
>> - Dan
>>
>>
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>>>
>>> Looks like we're missing an import statement in that example. Could you
>>> try:
>>>
>>> import org.kududb.client._
>>>
>>> and try again?
>>>
>>> - Dan
>>>
>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> I encountered an error trying to create a table based on the
>>>> documentation from a DataFrame.
>>>>
>>>> <console>:49: error: not found: type CreateTableOptions
>>>> kuduContext.createTable(tableName, df.schema, Seq("key"),
>>>> new CreateTableOptions().setNumReplicas(1))
>>>>
>>>> Is there something I’m missing?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>> wrote:
>>>>
>>>> It's only in Cloudera's maven repo:
>>>> https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
>>>>
>>>> J-D
>>>>
>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi J-D,
>>>>>
>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar
>>>>> for spark-shell to use. Can you show me where to find it?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>> wrote:
>>>>>
>>>>> What's in this doc is what's gonna get released:
>>>>> https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark
>>>>>
>>>>> J-D
>>>>>
>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> It will be in 0.9.0.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi Chris,
>>>>>>>
>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <
>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>
>>>>>>> There is some code in review that needs some more refinement.
>>>>>>> It will allow upsert/insert from a dataframe using the datasource
>>>>>>> api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>>>
>>>>>>> Example usages will look something like:
>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>>>
>>>>>>> -Chris George
>>>>>>>
>>>>>>>
>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>
>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>
>>>>>>> Also, does anyone have any sample code on how to update/insert data
>>>>>>> in Kudu using DataFrames?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <
>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>
>>>>>>> SparkSQL cannot support these type of statements but we may be able
>>>>>>> to implement similar functionality through the api.
>>>>>>> -Chris
>>>>>>>
>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>>
>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert”
>>>>>>> if it were to be implemented.
>>>>>>>
>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>> WHEN MATCHED THEN
>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>> WHEN NOT MATCHED THEN
>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <
>>>>>>> Christopher.George@rms.com> wrote:
>>>>>>>
>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into
>>>>>>> gerrit if you want to take a look.
>>>>>>> http://gerrit.cloudera.org:8080/#/c/2754/
>>>>>>> It does pushdown predicates which the existing input formatter based
>>>>>>> rdd does not.
>>>>>>>
>>>>>>> Within the next two weeks I’m planning to implement a datasource for
>>>>>>> spark that will have pushdown predicates and insertion/update functionality
>>>>>>> (need to look more at cassandra and the hbase datasource for best way to do
>>>>>>> this) I agree that server side upsert would be helpful.
>>>>>>> Having a datasource would give us useful data frames and also make
>>>>>>> spark sql usable for kudu.
>>>>>>>
>>>>>>> My reasoning for having a spark datasource and not using Impala is:
>>>>>>> 1. We have had trouble getting impala to run fast with high concurrency
>>>>>>> when compared to spark 2. We interact with datasources which do not
>>>>>>> integrate with impala. 3. We have custom sql query planners for extended
>>>>>>> sql functionality.
>>>>>>>
>>>>>>> -Chris George
>>>>>>>
>>>>>>>
>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>>>> wrote:
>>>>>>>
>>>>>>> You guys make a convincing point, although on the upsert side we'll
>>>>>>> need more support from the servers. Right now all you can do is an INSERT
>>>>>>> then, if you get a dup key, do an UPDATE. I guess we could at least add an
>>>>>>> API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <
>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>
>>>>>>>> It's pretty simple, actually. I need to support versioned datasets
>>>>>>>> in a Spark SQL environment. Instead of a hack on top of a Parquet data
>>>>>>>> store, I'm hoping (among other reasons) to be able to use Kudu's write and
>>>>>>>> timestamp-based read operations to support not only appending data, but
>>>>>>>> also updating existing data, and even some schema migration. The most
>>>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>>>> be added/appended.
>>>>>>>>
>>>>>>>> Using Kudu's Java API and developing additional functionality on
>>>>>>>> top of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>>>> decision.
>>>>>>>>
>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>
>>>>>>>>> Mark,
>>>>>>>>>
>>>>>>>>> Thanks for taking some time to reply in this thread, glad it
>>>>>>>>> caught the attention of other folks!
>>>>>>>>>
>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <
>>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>>
>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>>>> to whether we adopt Kudu.
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for
>>>>>>>>> you. Is it just that you currently do it that way into some database or
>>>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>>>> KuduSession be too much work?
>>>>>>>>>
>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your
>>>>>>>>> current solution? If it's not completely clear, I'd love to help you think
>>>>>>>>> through it.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>>
>>>>>>>>>>> What are your DS folks looking for in terms of functionality
>>>>>>>>>>> related to Spark? A SparkSQL integration that's as fully featured as
>>>>>>>>>>> Impala's? Do they care being able to insert into Kudu with SparkSQL or just
>>>>>>>>>>> being able to query real fast? Anything more specific to Spark that I'm
>>>>>>>>>>> missing?
>>>>>>>>>>>
>>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <
>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions.
>>>>>>>>>>>> But, since it’s not “production-ready”, upper management doesn’t want to
>>>>>>>>>>>> fully deploy it yet. They just want to keep an eye on it though. Kudu was
>>>>>>>>>>>> so much simpler and easier to use in every aspect compared to HBase. Impala
>>>>>>>>>>>> was great for the report writers and analysts to experiment with for the
>>>>>>>>>>>> short time it was up. But, once again, the only blocker was the lack of
>>>>>>>>>>>> Spark support for our Data Developers/Scientists. So, production-level data
>>>>>>>>>>>> population won’t happen until then.
>>>>>>>>>>>>
>>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> As for our company, we have been looking for an updatable data
>>>>>>>>>>>>> store for a long time that can be quickly queried directly either using
>>>>>>>>>>>>> Spark SQL or Impala or some other SQL engine and still handle TB or PB of
>>>>>>>>>>>>> data without performance degradation and many configuration headaches. For
>>>>>>>>>>>>> now, we are using HBase to take on this role with Phoenix as a fast way to
>>>>>>>>>>>>> directly query the data. I can see Kudu as the best way to fill this gap
>>>>>>>>>>>>> easily, especially being the closest thing to other relational databases
>>>>>>>>>>>>> out there in familiarity for the many SQL analytics people in our company.
>>>>>>>>>>>>> The other alternative would be to go with AWS Redshift for the same
>>>>>>>>>>>>> reasons, but it would come at a cost, of course. If we went with either
>>>>>>>>>>>>> solutions, Kudu or Redshift, it would get rid of the need to extract from
>>>>>>>>>>>>> HBase to parquet tables or export to PostgreSQL to support more of the SQL
>>>>>>>>>>>>> language using by analysts or the reporting software we use..
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off
>>>>>>>>>>>> with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like
>>>>>>>>>>>>> to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>>>> dying down.
>>>>>>>>>>>>>
>>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra,
>>>>>>>>>>>>> HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>>>>>>>>>>>> interim solution for the use case you describe.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's
>>>>>>>>>>>>> a storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to
>>>>>>>>>>>>> learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>>>> replacement.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose.
>>>>>>>>>>>>>> They told me that everything was about Spark and there is a big buzz about
>>>>>>>>>>>>>> the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline.
>>>>>>>>>>>>>> I know of one person on the Kudu Slack who's working on a better RDD, but
>>>>>>>>>>>>>> that's about it.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <
>>>>>>>>>>>>>> bkim@amobee.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to
>>>>>>>>>>>>>>> target a version of Kudu to begin real testing of Spark against it for our
>>>>>>>>>>>>>>> devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405
>>>>>>>>>>>>>>> | www.amobee.com
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's
>>>>>>>>>>>>>>> needed either.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally
>>>>>>>>>>>>>>> we'd use scans directly.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> The goal was to provide something for others to contribute
>>>>>>>>>>>>>>> to. We have some basic unit tests that others can easily extend. None of us
>>>>>>>>>>>>>>> on the team are Spark experts, but we'd be really happy to assist one
>>>>>>>>>>>>>>> improve the kudu-spark code.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements
>>>>>>>>>>>>>>>> (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more
>>>>>>>>>>>>>>>> Spark SQL functionality (Dataframes) and doing the documentation, what more
>>>>>>>>>>>>>>>> needs to be done? Optimizations?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with
>>>>>>>>>>>>>>>> Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get
>>>>>>>>>>>>>>>> this in for 0.7.0:
>>>>>>>>>>>>>>>> https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL
>>>>>>>>>>>>>>>> on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>>>>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>>>>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>>>>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Dan,
Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.
Cheers,
Ben
> On Jun 14, 2016, at 5:57 PM, Dan Burkert <da...@cloudera.com> wrote:
>
> Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.
>
> - Dan
>
> On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
>
> Thanks,
> Ben
>
>
>
>> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>
>> Hi,
>>
>> Now, I’m getting this error when trying to write to the table.
>>
>> import scala.collection.JavaConverters._
>> val key_seq = Seq(“my_id")
>> val key_list = List(“my_id”).asJava
>> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>>
>> df.write
>> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
>> .mode("overwrite")
>> .kudu
>>
>> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>>
>> Does the key field need to be first in the DataFrame?
>>
>> Thanks,
>> Ben
>>
>>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>
>>>
>>>
>>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> Dan,
>>>
>>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>>
>>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>>
>>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>>
>>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>>
>>> - Dan
>>>
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>>
>>>> Looks like we're missing an import statement in that example. Could you try:
>>>>
>>>> import org.kududb.client._
>>>> and try again?
>>>>
>>>> - Dan
>>>>
>>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>>
>>>> <console>:49: error: not found: type CreateTableOptions
>>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>>
>>>> Is there something I’m missing?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>>
>>>>> J-D
>>>>>
>>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> Hi J-D,
>>>>>
>>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> It will be in 0.9.0.
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Hi Chris,
>>>>>>>
>>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>
>>>>>>>> There is some code in review that needs some more refinement.
>>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>>
>>>>>>>> Example usages will look something like:
>>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>>
>>>>>>>> -Chris George
>>>>>>>>
>>>>>>>>
>>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>
>>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>>
>>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>
>>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>>> -Chris
>>>>>>>>>
>>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>
>>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>>
>>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>>> WHEN MATCHED THEN
>>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>>
>>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>>
>>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>>
>>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>>
>>>>>>>>>> -Chris George
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>
>>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>>
>>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>>
>>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>> Mark,
>>>>>>>>>>
>>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>>
>>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>>
>>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>>
>>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>>
>>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>
>>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>>
>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>>
>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>> J-D,
>>>>>>>>>>>
>>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>
>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>>
>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>
>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>>
>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>>
>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>>
>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>
>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>
>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>>
>>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>
>
Re: Spark on Kudu
Posted by Dan Burkert <da...@cloudera.com>.
Right now append uses an update Kudu operation, which requires the row
already be present in the table. Overwrite maps to insert. Kudu very
recently got upsert support baked in, but it hasn't yet been integrated
into the Spark connector. So pretty soon these sharp edges will get a lot
better, since upsert is the way to go for most spark workloads.
- Dan
On Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <bb...@gmail.com> wrote:
> I tried to use the “append” mode, and it worked. Over 3.8 million rows in
> 64s. I would assume that now I can use the “overwrite” mode on existing
> data. Now, I have to find answers to these questions. What would happen if
> I “append” to the data in the Kudu table if the data already exists? What
> would happen if I “overwrite” existing data when the DataFrame has data in
> it that does not exist in the Kudu table? I need to evaluate the best way
> to simulate the UPSERT behavior in HBase because this is what our use case
> is.
>
> Thanks,
> Ben
>
>
>
> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
> Hi,
>
> Now, I’m getting this error when trying to write to the table.
>
> import scala.collection.JavaConverters._
> val key_seq = Seq(“my_id")
> val key_list = List(“my_id”).asJava
> kuduContext.createTable(tableName, df.schema, key_seq, new
> CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>
> df.write
> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
> .mode("overwrite")
> .kudu
>
> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to
> Kudu; sample errors: Not found: key not found (error 0)Not found: key not
> found (error 0)Not found: key not found (error 0)Not found: key not found
> (error 0)Not found: key not found (error 0)
>
> Does the key field need to be first in the DataFrame?
>
> Thanks,
> Ben
>
> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>
>
>
> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Dan,
>>
>> Thanks! It got further. Now, how do I set the Primary Key to be a
>> column(s) in the DataFrame and set the partitioning? Is it like this?
>>
>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
>> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>
>> java.lang.IllegalArgumentException: Table partitioning must be specified
>> using setRangePartitionColumns or addHashPartitions
>>
>
> Yep. The `Seq("my_id")` part of that call is specifying the set of
> primary key columns, so in this case you have specified the single PK
> column "my_id". The `addHashPartitions` call adds hash partitioning to the
> table, in this case over the column "my_id" (which is good, it must be over
> one or more PK columns, so in this case "my_id" is the one and only valid
> combination). However, the call to `addHashPartition` also takes the
> number of buckets as the second param. You shouldn't get the
> IllegalArgumentException as long as you are specifying either
> `addHashPartitions` or `setRangePartitionColumns`.
>
> - Dan
>
>
>>
>> Thanks,
>> Ben
>>
>>
>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>>
>> Looks like we're missing an import statement in that example. Could you
>> try:
>>
>> import org.kududb.client._
>>
>> and try again?
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> I encountered an error trying to create a table based on the
>>> documentation from a DataFrame.
>>>
>>> <console>:49: error: not found: type CreateTableOptions
>>> kuduContext.createTable(tableName, df.schema, Seq("key"),
>>> new CreateTableOptions().setNumReplicas(1))
>>>
>>> Is there something I’m missing?
>>>
>>> Thanks,
>>> Ben
>>>
>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>> wrote:
>>>
>>> It's only in Cloudera's maven repo:
>>> https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
>>>
>>> J-D
>>>
>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> Hi J-D,
>>>>
>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar
>>>> for spark-shell to use. Can you show me where to find it?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>> wrote:
>>>>
>>>> What's in this doc is what's gonna get released:
>>>> https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark
>>>>
>>>> J-D
>>>>
>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>> wrote:
>>>>>
>>>>> It will be in 0.9.0.
>>>>>
>>>>> J-D
>>>>>
>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi Chris,
>>>>>>
>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Ch...@rms.com>
>>>>>> wrote:
>>>>>>
>>>>>> There is some code in review that needs some more refinement.
>>>>>> It will allow upsert/insert from a dataframe using the datasource
>>>>>> api. It will also allow the creation and deletion of tables from a dataframe
>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>>
>>>>>> Example usages will look something like:
>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>>
>>>>>> -Chris George
>>>>>>
>>>>>>
>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>
>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>
>>>>>> Also, does anyone have any sample code on how to update/insert data
>>>>>> in Kudu using DataFrames?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Ch...@rms.com>
>>>>>> wrote:
>>>>>>
>>>>>> SparkSQL cannot support these type of statements but we may be able
>>>>>> to implement similar functionality through the api.
>>>>>> -Chris
>>>>>>
>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>>
>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert”
>>>>>> if it were to be implemented.
>>>>>>
>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>> WHEN MATCHED THEN
>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>> WHEN NOT MATCHED THEN
>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <
>>>>>> Christopher.George@rms.com> wrote:
>>>>>>
>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into
>>>>>> gerrit if you want to take a look.
>>>>>> http://gerrit.cloudera.org:8080/#/c/2754/
>>>>>> It does pushdown predicates which the existing input formatter based
>>>>>> rdd does not.
>>>>>>
>>>>>> Within the next two weeks I’m planning to implement a datasource for
>>>>>> spark that will have pushdown predicates and insertion/update functionality
>>>>>> (need to look more at cassandra and the hbase datasource for best way to do
>>>>>> this) I agree that server side upsert would be helpful.
>>>>>> Having a datasource would give us useful data frames and also make
>>>>>> spark sql usable for kudu.
>>>>>>
>>>>>> My reasoning for having a spark datasource and not using Impala is:
>>>>>> 1. We have had trouble getting impala to run fast with high concurrency
>>>>>> when compared to spark 2. We interact with datasources which do not
>>>>>> integrate with impala. 3. We have custom sql query planners for extended
>>>>>> sql functionality.
>>>>>>
>>>>>> -Chris George
>>>>>>
>>>>>>
>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> You guys make a convincing point, although on the upsert side we'll
>>>>>> need more support from the servers. Right now all you can do is an INSERT
>>>>>> then, if you get a dup key, do an UPDATE. I guess we could at least add an
>>>>>> API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <
>>>>>> mark@clearstorydata.com> wrote:
>>>>>>
>>>>>>> It's pretty simple, actually. I need to support versioned datasets
>>>>>>> in a Spark SQL environment. Instead of a hack on top of a Parquet data
>>>>>>> store, I'm hoping (among other reasons) to be able to use Kudu's write and
>>>>>>> timestamp-based read operations to support not only appending data, but
>>>>>>> also updating existing data, and even some schema migration. The most
>>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>>> be added/appended.
>>>>>>>
>>>>>>> Using Kudu's Java API and developing additional functionality on top
>>>>>>> of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>>> decision.
>>>>>>>
>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>
>>>>>>>> Mark,
>>>>>>>>
>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught
>>>>>>>> the attention of other folks!
>>>>>>>>
>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <
>>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>>
>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>>> to whether we adopt Kudu.
>>>>>>>>>
>>>>>>>>
>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for
>>>>>>>> you. Is it just that you currently do it that way into some database or
>>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>>> KuduSession be too much work?
>>>>>>>>
>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your
>>>>>>>> current solution? If it's not completely clear, I'd love to help you think
>>>>>>>> through it.
>>>>>>>>
>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>>
>>>>>>>>>> What are your DS folks looking for in terms of functionality
>>>>>>>>>> related to Spark? A SparkSQL integration that's as fully featured as
>>>>>>>>>> Impala's? Do they care being able to insert into Kudu with SparkSQL or just
>>>>>>>>>> being able to query real fast? Anything more specific to Spark that I'm
>>>>>>>>>> missing?
>>>>>>>>>>
>>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <
>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions.
>>>>>>>>>>> But, since it’s not “production-ready”, upper management doesn’t want to
>>>>>>>>>>> fully deploy it yet. They just want to keep an eye on it though. Kudu was
>>>>>>>>>>> so much simpler and easier to use in every aspect compared to HBase. Impala
>>>>>>>>>>> was great for the report writers and analysts to experiment with for the
>>>>>>>>>>> short time it was up. But, once again, the only blocker was the lack of
>>>>>>>>>>> Spark support for our Data Developers/Scientists. So, production-level data
>>>>>>>>>>> population won’t happen until then.
>>>>>>>>>>>
>>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>
>>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> J-D,
>>>>>>>>>>>>
>>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> As for our company, we have been looking for an updatable data
>>>>>>>>>>>> store for a long time that can be quickly queried directly either using
>>>>>>>>>>>> Spark SQL or Impala or some other SQL engine and still handle TB or PB of
>>>>>>>>>>>> data without performance degradation and many configuration headaches. For
>>>>>>>>>>>> now, we are using HBase to take on this role with Phoenix as a fast way to
>>>>>>>>>>>> directly query the data. I can see Kudu as the best way to fill this gap
>>>>>>>>>>>> easily, especially being the closest thing to other relational databases
>>>>>>>>>>>> out there in familiarity for the many SQL analytics people in our company.
>>>>>>>>>>>> The other alternative would be to go with AWS Redshift for the same
>>>>>>>>>>>> reasons, but it would come at a cost, of course. If we went with either
>>>>>>>>>>>> solutions, Kudu or Redshift, it would get rid of the need to extract from
>>>>>>>>>>>> HBase to parquet tables or export to PostgreSQL to support more of the SQL
>>>>>>>>>>>> language using by analysts or the reporting software we use..
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off
>>>>>>>>>>> with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like
>>>>>>>>>>>> to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>>> dying down.
>>>>>>>>>>>>
>>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra,
>>>>>>>>>>>> HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>>>>>>>>>>> interim solution for the use case you describe.
>>>>>>>>>>>>
>>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's
>>>>>>>>>>>> a storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>>
>>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to
>>>>>>>>>>>> learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>>> replacement.
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They
>>>>>>>>>>>>> told me that everything was about Spark and there is a big buzz about the
>>>>>>>>>>>>> SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>>
>>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I
>>>>>>>>>>>>> know of one person on the Kudu Slack who's working on a better RDD, but
>>>>>>>>>>>>> that's about it.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com
>>>>>>>>>>>>> > wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to
>>>>>>>>>>>>>> target a version of Kudu to begin real testing of Spark against it for our
>>>>>>>>>>>>>> devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405
>>>>>>>>>>>>>> | www.amobee.com
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's
>>>>>>>>>>>>>> needed either.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally
>>>>>>>>>>>>>> we'd use scans directly.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> The goal was to provide something for others to contribute
>>>>>>>>>>>>>> to. We have some basic unit tests that others can easily extend. None of us
>>>>>>>>>>>>>> on the team are Spark experts, but we'd be really happy to assist one
>>>>>>>>>>>>>> improve the kudu-spark code.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements
>>>>>>>>>>>>>>> (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more
>>>>>>>>>>>>>>> Spark SQL functionality (Dataframes) and doing the documentation, what more
>>>>>>>>>>>>>>> needs to be done? Optimizations?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with
>>>>>>>>>>>>>>> Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this
>>>>>>>>>>>>>>> in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL
>>>>>>>>>>>>>>> on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>>>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>>>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>>>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.
Thanks,
Ben
> On Jun 14, 2016, at 5:05 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
> Hi,
>
> Now, I’m getting this error when trying to write to the table.
>
> import scala.collection.JavaConverters._
> val key_seq = Seq(“my_id")
> val key_list = List(“my_id”).asJava
> kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
>
> df.write
> .options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
> .mode("overwrite")
> .kudu
>
> java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
>
> Does the key field need to be first in the DataFrame?
>
> Thanks,
> Ben
>
>> On Jun 14, 2016, at 4:28 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>
>>
>>
>> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Dan,
>>
>> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>>
>> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>>
>> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>>
>> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>>
>> - Dan
>>
>>
>> Thanks,
>> Ben
>>
>>
>>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>>
>>> Looks like we're missing an import statement in that example. Could you try:
>>>
>>> import org.kududb.client._
>>> and try again?
>>>
>>> - Dan
>>>
>>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>>
>>> <console>:49: error: not found: type CreateTableOptions
>>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>>
>>> Is there something I’m missing?
>>>
>>> Thanks,
>>> Ben
>>>
>>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>>
>>>> J-D
>>>>
>>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Hi J-D,
>>>>
>>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>>
>>>>> J-D
>>>>>
>>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> It will be in 0.9.0.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> Hi Chris,
>>>>>>
>>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>
>>>>>>> There is some code in review that needs some more refinement.
>>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>>
>>>>>>> Example usages will look something like:
>>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>>
>>>>>>> -Chris George
>>>>>>>
>>>>>>>
>>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>
>>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>>
>>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>
>>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>>> -Chris
>>>>>>>>
>>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>
>>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>>
>>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>>> WHEN MATCHED THEN
>>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>>> WHEN NOT MATCHED THEN
>>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>>
>>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>>
>>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>>
>>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>>
>>>>>>>>> -Chris George
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>>
>>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>>
>>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>> Mark,
>>>>>>>>>
>>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>>
>>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>>
>>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>>
>>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>>
>>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>
>>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>>
>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>>
>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>
>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>> J-D,
>>>>>>>>>>
>>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>
>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>>
>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> I hope this helps.
>>>>>>>>>>
>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>>
>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>>
>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>>
>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>
>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>
>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>>
>>>>>>>>>>>> Regards,
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>
>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>
>>>>>>>>>>>> Just curious,
>>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>>
>>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>>
>>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>>
>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>>
>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>>
>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>>
>>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>>
>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Hi,
Now, I’m getting this error when trying to write to the table.
import scala.collection.JavaConverters._
val key_seq = Seq(“my_id")
val key_list = List(“my_id”).asJava
kuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))
df.write
.options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName))
.mode("overwrite")
.kudu
java.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)
Does the key field need to be first in the DataFrame?
Thanks,
Ben
> On Jun 14, 2016, at 4:28 PM, Dan Burkert <da...@cloudera.com> wrote:
>
>
>
> On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Dan,
>
> Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
>
> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>
> java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
>
> Yep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.
>
> - Dan
>
>
> Thanks,
> Ben
>
>
>> On Jun 14, 2016, at 4:07 PM, Dan Burkert <dan@cloudera.com <ma...@cloudera.com>> wrote:
>>
>> Looks like we're missing an import statement in that example. Could you try:
>>
>> import org.kududb.client._
>> and try again?
>>
>> - Dan
>>
>> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> I encountered an error trying to create a table based on the documentation from a DataFrame.
>>
>> <console>:49: error: not found: type CreateTableOptions
>> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>>
>> Is there something I’m missing?
>>
>> Thanks,
>> Ben
>>
>>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>>
>>> J-D
>>>
>>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> Hi J-D,
>>>
>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>>
>>>> J-D
>>>>
>>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> It will be in 0.9.0.
>>>>>
>>>>> J-D
>>>>>
>>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> Hi Chris,
>>>>>
>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>
>>>>>> There is some code in review that needs some more refinement.
>>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>>
>>>>>> Example usages will look something like:
>>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>>
>>>>>> -Chris George
>>>>>>
>>>>>>
>>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>
>>>>>> Can someone tell me what the state is of this Spark work?
>>>>>>
>>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>
>>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>>> -Chris
>>>>>>>
>>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>
>>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>>
>>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>>> WHEN MATCHED THEN
>>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>>> WHEN NOT MATCHED THEN
>>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>>
>>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>>
>>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>>
>>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>>
>>>>>>>> -Chris George
>>>>>>>>
>>>>>>>>
>>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>>
>>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>>
>>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>> Mark,
>>>>>>>>
>>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>>
>>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>
>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>>
>>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>>
>>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>>
>>>>>>>>
>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>
>>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>>
>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>>
>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> J-D,
>>>>>>>>>
>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>
>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>>
>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> I hope this helps.
>>>>>>>>>
>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>
>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>>
>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>>
>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>>
>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>> Hi J-D,
>>>>>>>>>>
>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>
>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>>
>>>>>>>>>>> Regards,
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>
>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>
>>>>>>>>>>> Just curious,
>>>>>>>>>>> Benjamin Kim
>>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>>
>>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>>
>>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>>
>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>>
>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>>
>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>>
>>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>> J-D,
>>>>>>>>>>>>
>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>>
>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>>
>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>>
>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Dan Burkert <da...@cloudera.com>.
On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <bb...@gmail.com> wrote:
> Dan,
>
> Thanks! It got further. Now, how do I set the Primary Key to be a
> column(s) in the DataFrame and set the partitioning? Is it like this?
>
> kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new
> CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
>
> java.lang.IllegalArgumentException: Table partitioning must be specified
> using setRangePartitionColumns or addHashPartitions
>
Yep. The `Seq("my_id")` part of that call is specifying the set of primary
key columns, so in this case you have specified the single PK column
"my_id". The `addHashPartitions` call adds hash partitioning to the table,
in this case over the column "my_id" (which is good, it must be over one or
more PK columns, so in this case "my_id" is the one and only valid
combination). However, the call to `addHashPartition` also takes the
number of buckets as the second param. You shouldn't get the
IllegalArgumentException as long as you are specifying either
`addHashPartitions` or `setRangePartitionColumns`.
- Dan
>
> Thanks,
> Ben
>
>
> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>
> Looks like we're missing an import statement in that example. Could you
> try:
>
> import org.kududb.client._
>
> and try again?
>
> - Dan
>
> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> I encountered an error trying to create a table based on the
>> documentation from a DataFrame.
>>
>> <console>:49: error: not found: type CreateTableOptions
>> kuduContext.createTable(tableName, df.schema, Seq("key"),
>> new CreateTableOptions().setNumReplicas(1))
>>
>> Is there something I’m missing?
>>
>> Thanks,
>> Ben
>>
>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>> wrote:
>>
>> It's only in Cloudera's maven repo:
>> https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
>>
>> J-D
>>
>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> Hi J-D,
>>>
>>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for
>>> spark-shell to use. Can you show me where to find it?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org>
>>> wrote:
>>>
>>> What's in this doc is what's gonna get released:
>>> https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark
>>>
>>> J-D
>>>
>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>>
>>>> Will this be documented with examples once 0.9.0 comes out?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>> wrote:
>>>>
>>>> It will be in 0.9.0.
>>>>
>>>> J-D
>>>>
>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi Chris,
>>>>>
>>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>> On May 18, 2016, at 9:01 AM, Chris George <Ch...@rms.com>
>>>>> wrote:
>>>>>
>>>>> There is some code in review that needs some more refinement.
>>>>> It will allow upsert/insert from a dataframe using the datasource api.
>>>>> It will also allow the creation and deletion of tables from a dataframe
>>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>>
>>>>> Example usages will look something like:
>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>>
>>>>> -Chris George
>>>>>
>>>>>
>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>
>>>>> Can someone tell me what the state is of this Spark work?
>>>>>
>>>>> Also, does anyone have any sample code on how to update/insert data in
>>>>> Kudu using DataFrames?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Ch...@rms.com>
>>>>> wrote:
>>>>>
>>>>> SparkSQL cannot support these type of statements but we may be able to
>>>>> implement similar functionality through the api.
>>>>> -Chris
>>>>>
>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>>
>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if
>>>>> it were to be implemented.
>>>>>
>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>> WHEN MATCHED THEN
>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>> WHEN NOT MATCHED THEN
>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Ch...@rms.com>
>>>>> wrote:
>>>>>
>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into
>>>>> gerrit if you want to take a look.
>>>>> http://gerrit.cloudera.org:8080/#/c/2754/
>>>>> It does pushdown predicates which the existing input formatter based
>>>>> rdd does not.
>>>>>
>>>>> Within the next two weeks I’m planning to implement a datasource for
>>>>> spark that will have pushdown predicates and insertion/update functionality
>>>>> (need to look more at cassandra and the hbase datasource for best way to do
>>>>> this) I agree that server side upsert would be helpful.
>>>>> Having a datasource would give us useful data frames and also make
>>>>> spark sql usable for kudu.
>>>>>
>>>>> My reasoning for having a spark datasource and not using Impala is: 1.
>>>>> We have had trouble getting impala to run fast with high concurrency when
>>>>> compared to spark 2. We interact with datasources which do not integrate
>>>>> with impala. 3. We have custom sql query planners for extended sql
>>>>> functionality.
>>>>>
>>>>> -Chris George
>>>>>
>>>>>
>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>
>>>>> wrote:
>>>>>
>>>>> You guys make a convincing point, although on the upsert side we'll
>>>>> need more support from the servers. Right now all you can do is an INSERT
>>>>> then, if you get a dup key, do an UPDATE. I guess we could at least add an
>>>>> API on the client side that would manage it, but it wouldn't be atomic.
>>>>>
>>>>> J-D
>>>>>
>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com
>>>>> > wrote:
>>>>>
>>>>>> It's pretty simple, actually. I need to support versioned datasets
>>>>>> in a Spark SQL environment. Instead of a hack on top of a Parquet data
>>>>>> store, I'm hoping (among other reasons) to be able to use Kudu's write and
>>>>>> timestamp-based read operations to support not only appending data, but
>>>>>> also updating existing data, and even some schema migration. The most
>>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>>> month) is updated with values that are expected to remain unchanged from
>>>>>> then on, and a new set of preliminary values for the current window need to
>>>>>> be added/appended.
>>>>>>
>>>>>> Using Kudu's Java API and developing additional functionality on top
>>>>>> of what Kudu has to offer isn't too much to ask, but the ease of
>>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>>> decision.
>>>>>>
>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>>> jdcryans@apache.org> wrote:
>>>>>>
>>>>>>> Mark,
>>>>>>>
>>>>>>> Thanks for taking some time to reply in this thread, glad it caught
>>>>>>> the attention of other folks!
>>>>>>>
>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <
>>>>>>> mark@clearstorydata.com> wrote:
>>>>>>>
>>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>>
>>>>>>>>
>>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>>> to whether we adopt Kudu.
>>>>>>>>
>>>>>>>
>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for
>>>>>>> you. Is it just that you currently do it that way into some database or
>>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>>> KuduSession be too much work?
>>>>>>>
>>>>>>> Additionally, what do you expect to gain from using Kudu VS your
>>>>>>> current solution? If it's not completely clear, I'd love to help you think
>>>>>>> through it.
>>>>>>>
>>>>>>>
>>>>>>>>
>>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>
>>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>>
>>>>>>>>> What are your DS folks looking for in terms of functionality
>>>>>>>>> related to Spark? A SparkSQL integration that's as fully featured as
>>>>>>>>> Impala's? Do they care being able to insert into Kudu with SparkSQL or just
>>>>>>>>> being able to query real fast? Anything more specific to Spark that I'm
>>>>>>>>> missing?
>>>>>>>>>
>>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com
>>>>>>>>> > wrote:
>>>>>>>>>
>>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But,
>>>>>>>>>> since it’s not “production-ready”, upper management doesn’t want to fully
>>>>>>>>>> deploy it yet. They just want to keep an eye on it though. Kudu was so much
>>>>>>>>>> simpler and easier to use in every aspect compared to HBase. Impala was
>>>>>>>>>> great for the report writers and analysts to experiment with for the short
>>>>>>>>>> time it was up. But, once again, the only blocker was the lack of Spark
>>>>>>>>>> support for our Data Developers/Scientists. So, production-level data
>>>>>>>>>> population won’t happen until then.
>>>>>>>>>>
>>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <
>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>
>>>>>>>>>>> J-D,
>>>>>>>>>>>
>>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> As for our company, we have been looking for an updatable data
>>>>>>>>>>> store for a long time that can be quickly queried directly either using
>>>>>>>>>>> Spark SQL or Impala or some other SQL engine and still handle TB or PB of
>>>>>>>>>>> data without performance degradation and many configuration headaches. For
>>>>>>>>>>> now, we are using HBase to take on this role with Phoenix as a fast way to
>>>>>>>>>>> directly query the data. I can see Kudu as the best way to fill this gap
>>>>>>>>>>> easily, especially being the closest thing to other relational databases
>>>>>>>>>>> out there in familiarity for the many SQL analytics people in our company.
>>>>>>>>>>> The other alternative would be to go with AWS Redshift for the same
>>>>>>>>>>> reasons, but it would come at a cost, of course. If we went with either
>>>>>>>>>>> solutions, Kudu or Redshift, it would get rid of the need to extract from
>>>>>>>>>>> HBase to parquet tables or export to PostgreSQL to support more of the SQL
>>>>>>>>>>> language using by analysts or the reporting software we use..
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with
>>>>>>>>>> Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> I hope this helps.
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>
>>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like
>>>>>>>>>>> to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>>> dying down.
>>>>>>>>>>>
>>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra,
>>>>>>>>>>> HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>>>>>>>>>> interim solution for the use case you describe.
>>>>>>>>>>>
>>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a
>>>>>>>>>>> storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>>> folks will happily review it.
>>>>>>>>>>>
>>>>>>>>>>> Do you have relevant experiences you can share? I'd love to
>>>>>>>>>>> learn more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>>> replacement.
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <
>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>
>>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They
>>>>>>>>>>>> told me that everything was about Spark and there is a big buzz about the
>>>>>>>>>>>> SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>>
>>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I
>>>>>>>>>>>> know of one person on the Kudu Slack who's working on a better RDD, but
>>>>>>>>>>>> that's about it.
>>>>>>>>>>>>
>>>>>>>>>>>> Regards,
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to
>>>>>>>>>>>>> target a version of Kudu to begin real testing of Spark against it for our
>>>>>>>>>>>>> devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Just curious,
>>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>>
>>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>>>>
>>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>>>>>>>>>>> www.amobee.com
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's
>>>>>>>>>>>>> needed either.
>>>>>>>>>>>>>
>>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally
>>>>>>>>>>>>> we'd use scans directly.
>>>>>>>>>>>>>
>>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>>>>
>>>>>>>>>>>>> The goal was to provide something for others to contribute to.
>>>>>>>>>>>>> We have some basic unit tests that others can easily extend. None of us on
>>>>>>>>>>>>> the team are Spark experts, but we'd be really happy to assist one improve
>>>>>>>>>>>>> the kudu-spark code.
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements
>>>>>>>>>>>>>> (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more
>>>>>>>>>>>>>> Spark SQL functionality (Dataframes) and doing the documentation, what more
>>>>>>>>>>>>>> needs to be done? Optimizations?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with
>>>>>>>>>>>>>> Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this
>>>>>>>>>>>>>> in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on
>>>>>>>>>>>>>> Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted for
>>>>>>>>>>>>>>> 0.8.0, but I see no progress on it. When this is complete, will this mean
>>>>>>>>>>>>>>> that Spark will be able to work with Kudu both programmatically and as a
>>>>>>>>>>>>>>> client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Dan,
Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?
kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))
java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitions
Thanks,
Ben
> On Jun 14, 2016, at 4:07 PM, Dan Burkert <da...@cloudera.com> wrote:
>
> Looks like we're missing an import statement in that example. Could you try:
>
> import org.kududb.client._
> and try again?
>
> - Dan
>
> On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> I encountered an error trying to create a table based on the documentation from a DataFrame.
>
> <console>:49: error: not found: type CreateTableOptions
> kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
>
> Is there something I’m missing?
>
> Thanks,
> Ben
>
>> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>>
>> J-D
>>
>> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Hi J-D,
>>
>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>>
>> Thanks,
>> Ben
>>
>>
>>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>>
>>> J-D
>>>
>>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> Will this be documented with examples once 0.9.0 comes out?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> It will be in 0.9.0.
>>>>
>>>> J-D
>>>>
>>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Hi Chris,
>>>>
>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>
>>>>> There is some code in review that needs some more refinement.
>>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>>
>>>>> Example usages will look something like:
>>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>>
>>>>> -Chris George
>>>>>
>>>>>
>>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>
>>>>> Can someone tell me what the state is of this Spark work?
>>>>>
>>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>
>>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>>> -Chris
>>>>>>
>>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>
>>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>>
>>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>>> WHEN MATCHED THEN
>>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>>> WHEN NOT MATCHED THEN
>>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>>
>>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>>
>>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>>
>>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>>
>>>>>>> -Chris George
>>>>>>>
>>>>>>>
>>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>>
>>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>>
>>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>> Mark,
>>>>>>>
>>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>>
>>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>
>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>>
>>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>>
>>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>>
>>>>>>>
>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>> Yup, starting to get a good idea.
>>>>>>>
>>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>
>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>>
>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> J-D,
>>>>>>>>
>>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>>
>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>>
>>>>>>>>
>>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>>
>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>
>>>>>>>>
>>>>>>>> I hope this helps.
>>>>>>>>
>>>>>>>> It does, thanks for nice reply.
>>>>>>>>
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>>
>>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>>
>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>>
>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> Hi J-D,
>>>>>>>>>
>>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>
>>>>>>>>>> Hi Ben,
>>>>>>>>>>
>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>>
>>>>>>>>>> Regards,
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>>> Hi J-D,
>>>>>>>>>>
>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>
>>>>>>>>>> Just curious,
>>>>>>>>>> Benjamin Kim
>>>>>>>>>> Data Solutions Architect
>>>>>>>>>>
>>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>>
>>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>>
>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>>
>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>>
>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>>
>>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>> J-D,
>>>>>>>>>>>
>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>>
>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>>
>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>
>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>>
>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Dan Burkert <da...@cloudera.com>.
Looks like we're missing an import statement in that example. Could you
try:
import org.kududb.client._
and try again?
- Dan
On Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <bb...@gmail.com> wrote:
> I encountered an error trying to create a table based on the documentation
> from a DataFrame.
>
> <console>:49: error: not found: type CreateTableOptions
> kuduContext.createTable(tableName, df.schema, Seq("key"),
> new CreateTableOptions().setNumReplicas(1))
>
> Is there something I’m missing?
>
> Thanks,
> Ben
>
> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org>
> wrote:
>
> It's only in Cloudera's maven repo:
> https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
>
> J-D
>
> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Hi J-D,
>>
>> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for
>> spark-shell to use. Can you show me where to find it?
>>
>> Thanks,
>> Ben
>>
>>
>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org>
>> wrote:
>>
>> What's in this doc is what's gonna get released:
>> https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark
>>
>> J-D
>>
>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> Will this be documented with examples once 0.9.0 comes out?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jd...@apache.org>
>>> wrote:
>>>
>>> It will be in 0.9.0.
>>>
>>> J-D
>>>
>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> Hi Chris,
>>>>
>>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>> On May 18, 2016, at 9:01 AM, Chris George <Ch...@rms.com>
>>>> wrote:
>>>>
>>>> There is some code in review that needs some more refinement.
>>>> It will allow upsert/insert from a dataframe using the datasource api.
>>>> It will also allow the creation and deletion of tables from a dataframe
>>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>>
>>>> Example usages will look something like:
>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>>
>>>> -Chris George
>>>>
>>>>
>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>
>>>> Can someone tell me what the state is of this Spark work?
>>>>
>>>> Also, does anyone have any sample code on how to update/insert data in
>>>> Kudu using DataFrames?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Ch...@rms.com>
>>>> wrote:
>>>>
>>>> SparkSQL cannot support these type of statements but we may be able to
>>>> implement similar functionality through the api.
>>>> -Chris
>>>>
>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>>
>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if
>>>> it were to be implemented.
>>>>
>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>> WHEN MATCHED THEN
>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>> WHEN NOT MATCHED THEN
>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Ch...@rms.com>
>>>> wrote:
>>>>
>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into
>>>> gerrit if you want to take a look.
>>>> http://gerrit.cloudera.org:8080/#/c/2754/
>>>> It does pushdown predicates which the existing input formatter based
>>>> rdd does not.
>>>>
>>>> Within the next two weeks I’m planning to implement a datasource for
>>>> spark that will have pushdown predicates and insertion/update functionality
>>>> (need to look more at cassandra and the hbase datasource for best way to do
>>>> this) I agree that server side upsert would be helpful.
>>>> Having a datasource would give us useful data frames and also make
>>>> spark sql usable for kudu.
>>>>
>>>> My reasoning for having a spark datasource and not using Impala is: 1.
>>>> We have had trouble getting impala to run fast with high concurrency when
>>>> compared to spark 2. We interact with datasources which do not integrate
>>>> with impala. 3. We have custom sql query planners for extended sql
>>>> functionality.
>>>>
>>>> -Chris George
>>>>
>>>>
>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org> wrote:
>>>>
>>>> You guys make a convincing point, although on the upsert side we'll
>>>> need more support from the servers. Right now all you can do is an INSERT
>>>> then, if you get a dup key, do an UPDATE. I guess we could at least add an
>>>> API on the client side that would manage it, but it wouldn't be atomic.
>>>>
>>>> J-D
>>>>
>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <ma...@clearstorydata.com>
>>>> wrote:
>>>>
>>>>> It's pretty simple, actually. I need to support versioned datasets in
>>>>> a Spark SQL environment. Instead of a hack on top of a Parquet data store,
>>>>> I'm hoping (among other reasons) to be able to use Kudu's write and
>>>>> timestamp-based read operations to support not only appending data, but
>>>>> also updating existing data, and even some schema migration. The most
>>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>>> monthly) in which the the preliminary data in the previous window (week or
>>>>> month) is updated with values that are expected to remain unchanged from
>>>>> then on, and a new set of preliminary values for the current window need to
>>>>> be added/appended.
>>>>>
>>>>> Using Kudu's Java API and developing additional functionality on top
>>>>> of what Kudu has to offer isn't too much to ask, but the ease of
>>>>> integration with Spark SQL will gate how quickly we would move to using
>>>>> Kudu and how seriously we'd look at alternatives before making that
>>>>> decision.
>>>>>
>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>>> jdcryans@apache.org> wrote:
>>>>>
>>>>>> Mark,
>>>>>>
>>>>>> Thanks for taking some time to reply in this thread, glad it caught
>>>>>> the attention of other folks!
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <
>>>>>> mark@clearstorydata.com> wrote:
>>>>>>
>>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>>
>>>>>>>
>>>>>>> I care about insert into Kudu with Spark SQL. I'm currently
>>>>>>> delaying a refactoring of some Spark SQL-oriented insert functionality
>>>>>>> while trying to evaluate what to expect from Kudu. Whether Kudu does a
>>>>>>> good job supporting inserts with Spark SQL will be a key consideration as
>>>>>>> to whether we adopt Kudu.
>>>>>>>
>>>>>>
>>>>>> I'd like to know more about why SparkSQL inserts in necessary for
>>>>>> you. Is it just that you currently do it that way into some database or
>>>>>> parquet so with minimal refactoring you'd be able to use Kudu? Would
>>>>>> re-writing those SQL lines into Scala and directly use the Java API's
>>>>>> KuduSession be too much work?
>>>>>>
>>>>>> Additionally, what do you expect to gain from using Kudu VS your
>>>>>> current solution? If it's not completely clear, I'd love to help you think
>>>>>> through it.
>>>>>>
>>>>>>
>>>>>>>
>>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>
>>>>>>>> Yup, starting to get a good idea.
>>>>>>>>
>>>>>>>> What are your DS folks looking for in terms of functionality
>>>>>>>> related to Spark? A SparkSQL integration that's as fully featured as
>>>>>>>> Impala's? Do they care being able to insert into Kudu with SparkSQL or just
>>>>>>>> being able to query real fast? Anything more specific to Spark that I'm
>>>>>>>> missing?
>>>>>>>>
>>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But,
>>>>>>>>> since it’s not “production-ready”, upper management doesn’t want to fully
>>>>>>>>> deploy it yet. They just want to keep an eye on it though. Kudu was so much
>>>>>>>>> simpler and easier to use in every aspect compared to HBase. Impala was
>>>>>>>>> great for the report writers and analysts to experiment with for the short
>>>>>>>>> time it was up. But, once again, the only blocker was the lack of Spark
>>>>>>>>> support for our Data Developers/Scientists. So, production-level data
>>>>>>>>> population won’t happen until then.
>>>>>>>>>
>>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com
>>>>>>>>> > wrote:
>>>>>>>>>
>>>>>>>>>> J-D,
>>>>>>>>>>
>>>>>>>>>> The main thing I hear that Cassandra is being used as an
>>>>>>>>>> updatable hot data store to ensure that duplicates are taken care of and
>>>>>>>>>> idempotency is maintained. Whether data was directly retrieved from
>>>>>>>>>> Cassandra for analytics, reports, or searches, it was not clear as to what
>>>>>>>>>> was its main use. Some also just used it for a staging area to populate
>>>>>>>>>> downstream tables in parquet format. The last thing I heard was that CQL
>>>>>>>>>> was terrible, so that rules out much use of direct queries against it.
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> As for our company, we have been looking for an updatable data
>>>>>>>>>> store for a long time that can be quickly queried directly either using
>>>>>>>>>> Spark SQL or Impala or some other SQL engine and still handle TB or PB of
>>>>>>>>>> data without performance degradation and many configuration headaches. For
>>>>>>>>>> now, we are using HBase to take on this role with Phoenix as a fast way to
>>>>>>>>>> directly query the data. I can see Kudu as the best way to fill this gap
>>>>>>>>>> easily, especially being the closest thing to other relational databases
>>>>>>>>>> out there in familiarity for the many SQL analytics people in our company.
>>>>>>>>>> The other alternative would be to go with AWS Redshift for the same
>>>>>>>>>> reasons, but it would come at a cost, of course. If we went with either
>>>>>>>>>> solutions, Kudu or Redshift, it would get rid of the need to extract from
>>>>>>>>>> HBase to parquet tables or export to PostgreSQL to support more of the SQL
>>>>>>>>>> language using by analysts or the reporting software we use..
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with
>>>>>>>>> Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> I hope this helps.
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>> It does, thanks for nice reply.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to
>>>>>>>>>> refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>>> dying down.
>>>>>>>>>>
>>>>>>>>>> There's definitely an overlap in the use cases that Cassandra,
>>>>>>>>>> HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>>>>>>>>> interim solution for the use case you describe.
>>>>>>>>>>
>>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a
>>>>>>>>>> storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>>> folks will happily review it.
>>>>>>>>>>
>>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn
>>>>>>>>>> more about the use cases for which you envision using Kudu as a C*
>>>>>>>>>> replacement.
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com
>>>>>>>>>> > wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>
>>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They
>>>>>>>>>>> told me that everything was about Spark and there is a big buzz about the
>>>>>>>>>>> SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>
>>>>>>>>>>> Hi Ben,
>>>>>>>>>>>
>>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I
>>>>>>>>>>> know of one person on the Kudu Slack who's working on a better RDD, but
>>>>>>>>>>> that's about it.
>>>>>>>>>>>
>>>>>>>>>>> Regards,
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com>
>>>>>>>>>>> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>>
>>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target
>>>>>>>>>>>> a version of Kudu to begin real testing of Spark against it for our devs.
>>>>>>>>>>>> At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>>
>>>>>>>>>>>> Just curious,
>>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>>
>>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>>>
>>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>>>>>>>>>> www.amobee.com
>>>>>>>>>>>>
>>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's
>>>>>>>>>>>> needed either.
>>>>>>>>>>>>
>>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally
>>>>>>>>>>>> we'd use scans directly.
>>>>>>>>>>>>
>>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>>>
>>>>>>>>>>>> The goal was to provide something for others to contribute to.
>>>>>>>>>>>> We have some basic unit tests that others can easily extend. None of us on
>>>>>>>>>>>> the team are Spark experts, but we'd be really happy to assist one improve
>>>>>>>>>>>> the kudu-spark code.
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> J-D,
>>>>>>>>>>>>>
>>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu
>>>>>>>>>>>>> RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark
>>>>>>>>>>>>> SQL functionality (Dataframes) and doing the documentation, what more needs
>>>>>>>>>>>>> to be done? Optimizations?
>>>>>>>>>>>>>
>>>>>>>>>>>>> I believe that it’s a good place to start using Spark with
>>>>>>>>>>>>> Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this
>>>>>>>>>>>>> in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>>
>>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on
>>>>>>>>>>>>> Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>>
>>>>>>>>>>>>> J-D
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted for
>>>>>>>>>>>>>> 0.8.0, but I see no progress on it. When this is complete, will this mean
>>>>>>>>>>>>>> that Spark will be able to work with Kudu both programmatically and as a
>>>>>>>>>>>>>> client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
I encountered an error trying to create a table based on the documentation from a DataFrame.
<console>:49: error: not found: type CreateTableOptions
kuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))
Is there something I’m missing?
Thanks,
Ben
> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org> wrote:
>
> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>
> J-D
>
> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Hi J-D,
>
> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>
> Thanks,
> Ben
>
>
>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>
>> J-D
>>
>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Will this be documented with examples once 0.9.0 comes out?
>>
>> Thanks,
>> Ben
>>
>>
>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> It will be in 0.9.0.
>>>
>>> J-D
>>>
>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> Hi Chris,
>>>
>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>
>>>> There is some code in review that needs some more refinement.
>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>
>>>> Example usages will look something like:
>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>
>>>> -Chris George
>>>>
>>>>
>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>
>>>> Can someone tell me what the state is of this Spark work?
>>>>
>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>
>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>> -Chris
>>>>>
>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>
>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>
>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>> WHEN MATCHED THEN
>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>> WHEN NOT MATCHED THEN
>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>
>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>
>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>
>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>
>>>>>> -Chris George
>>>>>>
>>>>>>
>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>
>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>
>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>> Mark,
>>>>>>
>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>
>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>
>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>
>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>> Yup, starting to get a good idea.
>>>>>>
>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>
>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>
>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> J-D,
>>>>>>>
>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>
>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>
>>>>>>>
>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>
>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>
>>>>>>>
>>>>>>> I hope this helps.
>>>>>>>
>>>>>>> It does, thanks for nice reply.
>>>>>>>
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>
>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>
>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>
>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> Hi J-D,
>>>>>>>>
>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> Hi Ben,
>>>>>>>>>
>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>
>>>>>>>>> Regards,
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>> Hi J-D,
>>>>>>>>>
>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>
>>>>>>>>> Just curious,
>>>>>>>>> Benjamin Kim
>>>>>>>>> Data Solutions Architect
>>>>>>>>>
>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>
>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>
>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>
>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>
>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>
>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>
>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>> J-D,
>>>>>>>>>>
>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>
>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>
>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>
>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>
>>>>>>>>>>> Just curious.
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Thank you.
> On Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <jd...@apache.org> wrote:
>
> It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/ <https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/>
>
> J-D
>
> On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Hi J-D,
>
> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
>
> Thanks,
> Ben
>
>
>> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>>
>> J-D
>>
>> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Will this be documented with examples once 0.9.0 comes out?
>>
>> Thanks,
>> Ben
>>
>>
>>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> It will be in 0.9.0.
>>>
>>> J-D
>>>
>>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> Hi Chris,
>>>
>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>
>>>> There is some code in review that needs some more refinement.
>>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>>
>>>> Example usages will look something like:
>>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>>
>>>> -Chris George
>>>>
>>>>
>>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>
>>>> Can someone tell me what the state is of this Spark work?
>>>>
>>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>
>>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>>> -Chris
>>>>>
>>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>
>>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>>
>>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>>> WHEN MATCHED THEN
>>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>>> WHEN NOT MATCHED THEN
>>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>>
>>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>>
>>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>>
>>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>>
>>>>>> -Chris George
>>>>>>
>>>>>>
>>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>>
>>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>>
>>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>> Mark,
>>>>>>
>>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>
>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>>
>>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>>
>>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>>
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>> Yup, starting to get a good idea.
>>>>>>
>>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>
>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>>
>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> J-D,
>>>>>>>
>>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>>
>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>>
>>>>>>>
>>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>>
>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>
>>>>>>>
>>>>>>> I hope this helps.
>>>>>>>
>>>>>>> It does, thanks for nice reply.
>>>>>>>
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>>
>>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>>
>>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>>
>>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> Hi J-D,
>>>>>>>>
>>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> Hi Ben,
>>>>>>>>>
>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>>
>>>>>>>>> Regards,
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>>> Hi J-D,
>>>>>>>>>
>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>>
>>>>>>>>> Just curious,
>>>>>>>>> Benjamin Kim
>>>>>>>>> Data Solutions Architect
>>>>>>>>>
>>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>>
>>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>>
>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>
>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>>
>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>>
>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>>
>>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>> J-D,
>>>>>>>>>>
>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>>
>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>>
>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>>
>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>
>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>>
>>>>>>>>>>> Just curious.
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
It's only in Cloudera's maven repo:
https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/
J-D
On Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <bb...@gmail.com> wrote:
> Hi J-D,
>
> I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for
> spark-shell to use. Can you show me where to find it?
>
> Thanks,
> Ben
>
>
> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org>
> wrote:
>
> What's in this doc is what's gonna get released:
> https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark
>
> J-D
>
> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Will this be documented with examples once 0.9.0 comes out?
>>
>> Thanks,
>> Ben
>>
>>
>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jd...@apache.org>
>> wrote:
>>
>> It will be in 0.9.0.
>>
>> J-D
>>
>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> Hi Chris,
>>>
>>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>> On May 18, 2016, at 9:01 AM, Chris George <Ch...@rms.com>
>>> wrote:
>>>
>>> There is some code in review that needs some more refinement.
>>> It will allow upsert/insert from a dataframe using the datasource api.
>>> It will also allow the creation and deletion of tables from a dataframe
>>> http://gerrit.cloudera.org:8080/#/c/2992/
>>>
>>> Example usages will look something like:
>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>>
>>> -Chris George
>>>
>>>
>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>
>>> Can someone tell me what the state is of this Spark work?
>>>
>>> Also, does anyone have any sample code on how to update/insert data in
>>> Kudu using DataFrames?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>> On Apr 13, 2016, at 8:22 AM, Chris George <Ch...@rms.com>
>>> wrote:
>>>
>>> SparkSQL cannot support these type of statements but we may be able to
>>> implement similar functionality through the api.
>>> -Chris
>>>
>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>>
>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if
>>> it were to be implemented.
>>>
>>> MERGE INTO table_name USING table_reference ON (condition)
>>> WHEN MATCHED THEN
>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>> WHEN NOT MATCHED THEN
>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>
>>> Cheers,
>>> Ben
>>>
>>> On Apr 11, 2016, at 12:21 PM, Chris George <Ch...@rms.com>
>>> wrote:
>>>
>>> I have a wip kuduRDD that I made a few months ago. I pushed it into
>>> gerrit if you want to take a look.
>>> http://gerrit.cloudera.org:8080/#/c/2754/
>>> It does pushdown predicates which the existing input formatter based rdd
>>> does not.
>>>
>>> Within the next two weeks I’m planning to implement a datasource for
>>> spark that will have pushdown predicates and insertion/update functionality
>>> (need to look more at cassandra and the hbase datasource for best way to do
>>> this) I agree that server side upsert would be helpful.
>>> Having a datasource would give us useful data frames and also make spark
>>> sql usable for kudu.
>>>
>>> My reasoning for having a spark datasource and not using Impala is: 1.
>>> We have had trouble getting impala to run fast with high concurrency when
>>> compared to spark 2. We interact with datasources which do not integrate
>>> with impala. 3. We have custom sql query planners for extended sql
>>> functionality.
>>>
>>> -Chris George
>>>
>>>
>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org> wrote:
>>>
>>> You guys make a convincing point, although on the upsert side we'll need
>>> more support from the servers. Right now all you can do is an INSERT then,
>>> if you get a dup key, do an UPDATE. I guess we could at least add an API on
>>> the client side that would manage it, but it wouldn't be atomic.
>>>
>>> J-D
>>>
>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <ma...@clearstorydata.com>
>>> wrote:
>>>
>>>> It's pretty simple, actually. I need to support versioned datasets in
>>>> a Spark SQL environment. Instead of a hack on top of a Parquet data store,
>>>> I'm hoping (among other reasons) to be able to use Kudu's write and
>>>> timestamp-based read operations to support not only appending data, but
>>>> also updating existing data, and even some schema migration. The most
>>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>>> monthly) in which the the preliminary data in the previous window (week or
>>>> month) is updated with values that are expected to remain unchanged from
>>>> then on, and a new set of preliminary values for the current window need to
>>>> be added/appended.
>>>>
>>>> Using Kudu's Java API and developing additional functionality on top of
>>>> what Kudu has to offer isn't too much to ask, but the ease of integration
>>>> with Spark SQL will gate how quickly we would move to using Kudu and how
>>>> seriously we'd look at alternatives before making that decision.
>>>>
>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <
>>>> jdcryans@apache.org> wrote:
>>>>
>>>>> Mark,
>>>>>
>>>>> Thanks for taking some time to reply in this thread, glad it caught
>>>>> the attention of other folks!
>>>>>
>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <
>>>>> mark@clearstorydata.com> wrote:
>>>>>
>>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>>
>>>>>>
>>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying
>>>>>> a refactoring of some Spark SQL-oriented insert functionality while trying
>>>>>> to evaluate what to expect from Kudu. Whether Kudu does a good job
>>>>>> supporting inserts with Spark SQL will be a key consideration as to whether
>>>>>> we adopt Kudu.
>>>>>>
>>>>>
>>>>> I'd like to know more about why SparkSQL inserts in necessary for you.
>>>>> Is it just that you currently do it that way into some database or parquet
>>>>> so with minimal refactoring you'd be able to use Kudu? Would re-writing
>>>>> those SQL lines into Scala and directly use the Java API's KuduSession be
>>>>> too much work?
>>>>>
>>>>> Additionally, what do you expect to gain from using Kudu VS your
>>>>> current solution? If it's not completely clear, I'd love to help you think
>>>>> through it.
>>>>>
>>>>>
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>>> jdcryans@apache.org> wrote:
>>>>>>
>>>>>>> Yup, starting to get a good idea.
>>>>>>>
>>>>>>> What are your DS folks looking for in terms of functionality related
>>>>>>> to Spark? A SparkSQL integration that's as fully featured as Impala's? Do
>>>>>>> they care being able to insert into Kudu with SparkSQL or just being able
>>>>>>> to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>>
>>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At
>>>>>>> Cloudera all our resources are committed to making things happen in time,
>>>>>>> and a more fully featured Spark integration isn't in our plans during that
>>>>>>> period. I'm really hoping someone in the community will help with Spark,
>>>>>>> the same way we got a big contribution for the Flume sink.
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But,
>>>>>>>> since it’s not “production-ready”, upper management doesn’t want to fully
>>>>>>>> deploy it yet. They just want to keep an eye on it though. Kudu was so much
>>>>>>>> simpler and easier to use in every aspect compared to HBase. Impala was
>>>>>>>> great for the report writers and analysts to experiment with for the short
>>>>>>>> time it was up. But, once again, the only blocker was the lack of Spark
>>>>>>>> support for our Data Developers/Scientists. So, production-level data
>>>>>>>> population won’t happen until then.
>>>>>>>>
>>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>
>>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> J-D,
>>>>>>>>>
>>>>>>>>> The main thing I hear that Cassandra is being used as an updatable
>>>>>>>>> hot data store to ensure that duplicates are taken care of and idempotency
>>>>>>>>> is maintained. Whether data was directly retrieved from Cassandra for
>>>>>>>>> analytics, reports, or searches, it was not clear as to what was its main
>>>>>>>>> use. Some also just used it for a staging area to populate downstream
>>>>>>>>> tables in parquet format. The last thing I heard was that CQL was terrible,
>>>>>>>>> so that rules out much use of direct queries against it.
>>>>>>>>>
>>>>>>>>
>>>>>>>> I'm no C* expert, but I don't think CQL is meant for real
>>>>>>>> analytics, just ease of use instead of plainly using the APIs. Even then,
>>>>>>>> Kudu should beat it easily on big scans. Same for HBase. We've done
>>>>>>>> benchmarks against the latter, not the former.
>>>>>>>>
>>>>>>>>
>>>>>>>>>
>>>>>>>>> As for our company, we have been looking for an updatable data
>>>>>>>>> store for a long time that can be quickly queried directly either using
>>>>>>>>> Spark SQL or Impala or some other SQL engine and still handle TB or PB of
>>>>>>>>> data without performance degradation and many configuration headaches. For
>>>>>>>>> now, we are using HBase to take on this role with Phoenix as a fast way to
>>>>>>>>> directly query the data. I can see Kudu as the best way to fill this gap
>>>>>>>>> easily, especially being the closest thing to other relational databases
>>>>>>>>> out there in familiarity for the many SQL analytics people in our company.
>>>>>>>>> The other alternative would be to go with AWS Redshift for the same
>>>>>>>>> reasons, but it would come at a cost, of course. If we went with either
>>>>>>>>> solutions, Kudu or Redshift, it would get rid of the need to extract from
>>>>>>>>> HBase to parquet tables or export to PostgreSQL to support more of the SQL
>>>>>>>>> language using by analysts or the reporting software we use..
>>>>>>>>>
>>>>>>>>
>>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with
>>>>>>>> Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>>
>>>>>>>>
>>>>>>>>>
>>>>>>>>> I hope this helps.
>>>>>>>>>
>>>>>>>>
>>>>>>>> It does, thanks for nice reply.
>>>>>>>>
>>>>>>>>
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to
>>>>>>>>> refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>>> dying down.
>>>>>>>>>
>>>>>>>>> There's definitely an overlap in the use cases that Cassandra,
>>>>>>>>> HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>>>>>>>> interim solution for the use case you describe.
>>>>>>>>>
>>>>>>>>> Nothing significant happened in Kudu over the past month, it's a
>>>>>>>>> storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>>> folks will happily review it.
>>>>>>>>>
>>>>>>>>> Do you have relevant experiences you can share? I'd love to learn
>>>>>>>>> more about the use cases for which you envision using Kudu as a C*
>>>>>>>>> replacement.
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi J-D,
>>>>>>>>>>
>>>>>>>>>> My colleagues recently came back from Strata in San Jose. They
>>>>>>>>>> told me that everything was about Spark and there is a big buzz about the
>>>>>>>>>> SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>>> stack acronym to promote?
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>> Hi Ben,
>>>>>>>>>>
>>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I
>>>>>>>>>> know of one person on the Kudu Slack who's working on a better RDD, but
>>>>>>>>>> that's about it.
>>>>>>>>>>
>>>>>>>>>> Regards,
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi J-D,
>>>>>>>>>>>
>>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target
>>>>>>>>>>> a version of Kudu to begin real testing of Spark against it for our devs.
>>>>>>>>>>> At least, I can tell them what timeframe to anticipate.
>>>>>>>>>>>
>>>>>>>>>>> Just curious,
>>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>>
>>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>>
>>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>>>>>>>>> www.amobee.com
>>>>>>>>>>>
>>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>
>>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's
>>>>>>>>>>> needed either.
>>>>>>>>>>>
>>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd
>>>>>>>>>>> use scans directly.
>>>>>>>>>>>
>>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>>
>>>>>>>>>>> The goal was to provide something for others to contribute to.
>>>>>>>>>>> We have some basic unit tests that others can easily extend. None of us on
>>>>>>>>>>> the team are Spark experts, but we'd be really happy to assist one improve
>>>>>>>>>>> the kudu-spark code.
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <
>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> J-D,
>>>>>>>>>>>>
>>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu
>>>>>>>>>>>> RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark
>>>>>>>>>>>> SQL functionality (Dataframes) and doing the documentation, what more needs
>>>>>>>>>>>> to be done? Optimizations?
>>>>>>>>>>>>
>>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu
>>>>>>>>>>>> and compare it to HBase with Spark (not clean).
>>>>>>>>>>>>
>>>>>>>>>>>> Thanks,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in
>>>>>>>>>>>> for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>>>>
>>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on
>>>>>>>>>>>> Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>>
>>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>>
>>>>>>>>>>>> J-D
>>>>>>>>>>>>
>>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted for
>>>>>>>>>>>>> 0.8.0, but I see no progress on it. When this is complete, will this mean
>>>>>>>>>>>>> that Spark will be able to work with Kudu both programmatically and as a
>>>>>>>>>>>>> client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>>
>>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>>
>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>> Ben
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Hi J-D,
I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?
Thanks,
Ben
> On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <jd...@apache.org> wrote:
>
> What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark <https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark>
>
> J-D
>
> On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Will this be documented with examples once 0.9.0 comes out?
>
> Thanks,
> Ben
>
>
>> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> It will be in 0.9.0.
>>
>> J-D
>>
>> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Hi Chris,
>>
>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>
>> Thanks,
>> Ben
>>
>>
>>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>
>>> There is some code in review that needs some more refinement.
>>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>>
>>> Example usages will look something like:
>>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>>
>>> -Chris George
>>>
>>>
>>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>
>>> Can someone tell me what the state is of this Spark work?
>>>
>>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>
>>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>>> -Chris
>>>>
>>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>
>>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>>
>>>> MERGE INTO table_name USING table_reference ON (condition)
>>>> WHEN MATCHED THEN
>>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>>> WHEN NOT MATCHED THEN
>>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>>
>>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>>
>>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>>
>>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>>
>>>>> -Chris George
>>>>>
>>>>>
>>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>>
>>>>> J-D
>>>>>
>>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>>
>>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>>
>>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>> Mark,
>>>>>
>>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>>
>>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>
>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>>
>>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>>
>>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>>
>>>>>
>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>> Yup, starting to get a good idea.
>>>>>
>>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>>
>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>>
>>>>> J-D
>>>>>
>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>>
>>>>> I hope this helps you get an idea where I am coming from…
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> J-D,
>>>>>>
>>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>>
>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>>
>>>>>>
>>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>>
>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>
>>>>>>
>>>>>> I hope this helps.
>>>>>>
>>>>>> It does, thanks for nice reply.
>>>>>>
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>>
>>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>>
>>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>>
>>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>>
>>>>>>> Thanks,
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> Hi Ben,
>>>>>>>>
>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>>> Hi J-D,
>>>>>>>>
>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>>
>>>>>>>> Just curious,
>>>>>>>> Benjamin Kim
>>>>>>>> Data Solutions Architect
>>>>>>>>
>>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>>
>>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>>
>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>>
>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>>
>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>>
>>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> J-D,
>>>>>>>>>
>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>>
>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>>
>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>>
>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>
>>>>>>>>>> Hope this helps,
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>>
>>>>>>>>>> Just curious.
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
What's in this doc is what's gonna get released:
https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-spark
J-D
On Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <bb...@gmail.com> wrote:
> Will this be documented with examples once 0.9.0 comes out?
>
> Thanks,
> Ben
>
>
> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jd...@apache.org>
> wrote:
>
> It will be in 0.9.0.
>
> J-D
>
> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Hi Chris,
>>
>> Will all this effort be rolled into 0.9.0 and be ready for use?
>>
>> Thanks,
>> Ben
>>
>>
>> On May 18, 2016, at 9:01 AM, Chris George <Ch...@rms.com>
>> wrote:
>>
>> There is some code in review that needs some more refinement.
>> It will allow upsert/insert from a dataframe using the datasource api. It
>> will also allow the creation and deletion of tables from a dataframe
>> http://gerrit.cloudera.org:8080/#/c/2992/
>>
>> Example usages will look something like:
>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>>
>> -Chris George
>>
>>
>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>
>> Can someone tell me what the state is of this Spark work?
>>
>> Also, does anyone have any sample code on how to update/insert data in
>> Kudu using DataFrames?
>>
>> Thanks,
>> Ben
>>
>>
>> On Apr 13, 2016, at 8:22 AM, Chris George <Ch...@rms.com>
>> wrote:
>>
>> SparkSQL cannot support these type of statements but we may be able to
>> implement similar functionality through the api.
>> -Chris
>>
>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>>
>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it
>> were to be implemented.
>>
>> MERGE INTO table_name USING table_reference ON (condition)
>> WHEN MATCHED THEN
>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>> WHEN NOT MATCHED THEN
>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>
>> Cheers,
>> Ben
>>
>> On Apr 11, 2016, at 12:21 PM, Chris George <Ch...@rms.com>
>> wrote:
>>
>> I have a wip kuduRDD that I made a few months ago. I pushed it into
>> gerrit if you want to take a look.
>> http://gerrit.cloudera.org:8080/#/c/2754/
>> It does pushdown predicates which the existing input formatter based rdd
>> does not.
>>
>> Within the next two weeks I’m planning to implement a datasource for
>> spark that will have pushdown predicates and insertion/update functionality
>> (need to look more at cassandra and the hbase datasource for best way to do
>> this) I agree that server side upsert would be helpful.
>> Having a datasource would give us useful data frames and also make spark
>> sql usable for kudu.
>>
>> My reasoning for having a spark datasource and not using Impala is: 1. We
>> have had trouble getting impala to run fast with high concurrency when
>> compared to spark 2. We interact with datasources which do not integrate
>> with impala. 3. We have custom sql query planners for extended sql
>> functionality.
>>
>> -Chris George
>>
>>
>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org> wrote:
>>
>> You guys make a convincing point, although on the upsert side we'll need
>> more support from the servers. Right now all you can do is an INSERT then,
>> if you get a dup key, do an UPDATE. I guess we could at least add an API on
>> the client side that would manage it, but it wouldn't be atomic.
>>
>> J-D
>>
>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <ma...@clearstorydata.com>
>> wrote:
>>
>>> It's pretty simple, actually. I need to support versioned datasets in a
>>> Spark SQL environment. Instead of a hack on top of a Parquet data store,
>>> I'm hoping (among other reasons) to be able to use Kudu's write and
>>> timestamp-based read operations to support not only appending data, but
>>> also updating existing data, and even some schema migration. The most
>>> typical use case is a dataset that is updated periodically (e.g., weekly or
>>> monthly) in which the the preliminary data in the previous window (week or
>>> month) is updated with values that are expected to remain unchanged from
>>> then on, and a new set of preliminary values for the current window need to
>>> be added/appended.
>>>
>>> Using Kudu's Java API and developing additional functionality on top of
>>> what Kudu has to offer isn't too much to ask, but the ease of integration
>>> with Spark SQL will gate how quickly we would move to using Kudu and how
>>> seriously we'd look at alternatives before making that decision.
>>>
>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org
>>> > wrote:
>>>
>>>> Mark,
>>>>
>>>> Thanks for taking some time to reply in this thread, glad it caught the
>>>> attention of other folks!
>>>>
>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com
>>>> > wrote:
>>>>
>>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>>
>>>>>
>>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying
>>>>> a refactoring of some Spark SQL-oriented insert functionality while trying
>>>>> to evaluate what to expect from Kudu. Whether Kudu does a good job
>>>>> supporting inserts with Spark SQL will be a key consideration as to whether
>>>>> we adopt Kudu.
>>>>>
>>>>
>>>> I'd like to know more about why SparkSQL inserts in necessary for you.
>>>> Is it just that you currently do it that way into some database or parquet
>>>> so with minimal refactoring you'd be able to use Kudu? Would re-writing
>>>> those SQL lines into Scala and directly use the Java API's KuduSession be
>>>> too much work?
>>>>
>>>> Additionally, what do you expect to gain from using Kudu VS your
>>>> current solution? If it's not completely clear, I'd love to help you think
>>>> through it.
>>>>
>>>>
>>>>>
>>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>>> jdcryans@apache.org> wrote:
>>>>>
>>>>>> Yup, starting to get a good idea.
>>>>>>
>>>>>> What are your DS folks looking for in terms of functionality related
>>>>>> to Spark? A SparkSQL integration that's as fully featured as Impala's? Do
>>>>>> they care being able to insert into Kudu with SparkSQL or just being able
>>>>>> to query real fast? Anything more specific to Spark that I'm missing?
>>>>>>
>>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera
>>>>>> all our resources are committed to making things happen in time, and a more
>>>>>> fully featured Spark integration isn't in our plans during that period. I'm
>>>>>> really hoping someone in the community will help with Spark, the same way
>>>>>> we got a big contribution for the Flume sink.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But,
>>>>>>> since it’s not “production-ready”, upper management doesn’t want to fully
>>>>>>> deploy it yet. They just want to keep an eye on it though. Kudu was so much
>>>>>>> simpler and easier to use in every aspect compared to HBase. Impala was
>>>>>>> great for the report writers and analysts to experiment with for the short
>>>>>>> time it was up. But, once again, the only blocker was the lack of Spark
>>>>>>> support for our Data Developers/Scientists. So, production-level data
>>>>>>> population won’t happen until then.
>>>>>>>
>>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <
>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>
>>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> J-D,
>>>>>>>>
>>>>>>>> The main thing I hear that Cassandra is being used as an updatable
>>>>>>>> hot data store to ensure that duplicates are taken care of and idempotency
>>>>>>>> is maintained. Whether data was directly retrieved from Cassandra for
>>>>>>>> analytics, reports, or searches, it was not clear as to what was its main
>>>>>>>> use. Some also just used it for a staging area to populate downstream
>>>>>>>> tables in parquet format. The last thing I heard was that CQL was terrible,
>>>>>>>> so that rules out much use of direct queries against it.
>>>>>>>>
>>>>>>>
>>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics,
>>>>>>> just ease of use instead of plainly using the APIs. Even then, Kudu should
>>>>>>> beat it easily on big scans. Same for HBase. We've done benchmarks against
>>>>>>> the latter, not the former.
>>>>>>>
>>>>>>>
>>>>>>>>
>>>>>>>> As for our company, we have been looking for an updatable data
>>>>>>>> store for a long time that can be quickly queried directly either using
>>>>>>>> Spark SQL or Impala or some other SQL engine and still handle TB or PB of
>>>>>>>> data without performance degradation and many configuration headaches. For
>>>>>>>> now, we are using HBase to take on this role with Phoenix as a fast way to
>>>>>>>> directly query the data. I can see Kudu as the best way to fill this gap
>>>>>>>> easily, especially being the closest thing to other relational databases
>>>>>>>> out there in familiarity for the many SQL analytics people in our company.
>>>>>>>> The other alternative would be to go with AWS Redshift for the same
>>>>>>>> reasons, but it would come at a cost, of course. If we went with either
>>>>>>>> solutions, Kudu or Redshift, it would get rid of the need to extract from
>>>>>>>> HBase to parquet tables or export to PostgreSQL to support more of the SQL
>>>>>>>> language using by analysts or the reporting software we use..
>>>>>>>>
>>>>>>>
>>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with
>>>>>>> Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>>
>>>>>>>
>>>>>>>>
>>>>>>>> I hope this helps.
>>>>>>>>
>>>>>>>
>>>>>>> It does, thanks for nice reply.
>>>>>>>
>>>>>>>
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to
>>>>>>>> refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>>> dying down.
>>>>>>>>
>>>>>>>> There's definitely an overlap in the use cases that Cassandra,
>>>>>>>> HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>>>>>>> interim solution for the use case you describe.
>>>>>>>>
>>>>>>>> Nothing significant happened in Kudu over the past month, it's a
>>>>>>>> storage engine so things move slowly *smile*. I'd love to see more
>>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>>> folks will happily review it.
>>>>>>>>
>>>>>>>> Do you have relevant experiences you can share? I'd love to learn
>>>>>>>> more about the use cases for which you envision using Kudu as a C*
>>>>>>>> replacement.
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi J-D,
>>>>>>>>>
>>>>>>>>> My colleagues recently came back from Strata in San Jose. They
>>>>>>>>> told me that everything was about Spark and there is a big buzz about the
>>>>>>>>> SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that
>>>>>>>>> Cassandra is just an interim solution as a low-latency, easily queried data
>>>>>>>>> store. I was wondering if anything significant happened in regards to Kudu,
>>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>>> stack acronym to promote?
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>> Hi Ben,
>>>>>>>>>
>>>>>>>>> AFAIK no one in the dev community committed to any timeline. I
>>>>>>>>> know of one person on the Kudu Slack who's working on a better RDD, but
>>>>>>>>> that's about it.
>>>>>>>>>
>>>>>>>>> Regards,
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi J-D,
>>>>>>>>>>
>>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a
>>>>>>>>>> version of Kudu to begin real testing of Spark against it for our devs. At
>>>>>>>>>> least, I can tell them what timeframe to anticipate.
>>>>>>>>>>
>>>>>>>>>> Just curious,
>>>>>>>>>> *Benjamin Kim*
>>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>>
>>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>>
>>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>>>>>>>> www.amobee.com
>>>>>>>>>>
>>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed
>>>>>>>>>> either.
>>>>>>>>>>
>>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd
>>>>>>>>>> use scans directly.
>>>>>>>>>>
>>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>>
>>>>>>>>>> The goal was to provide something for others to contribute to. We
>>>>>>>>>> have some basic unit tests that others can easily extend. None of us on the
>>>>>>>>>> team are Spark experts, but we'd be really happy to assist one improve the
>>>>>>>>>> kudu-spark code.
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com
>>>>>>>>>> > wrote:
>>>>>>>>>>
>>>>>>>>>>> J-D,
>>>>>>>>>>>
>>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu
>>>>>>>>>>> RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark
>>>>>>>>>>> SQL functionality (Dataframes) and doing the documentation, what more needs
>>>>>>>>>>> to be done? Optimizations?
>>>>>>>>>>>
>>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu
>>>>>>>>>>> and compare it to HBase with Spark (not clean).
>>>>>>>>>>>
>>>>>>>>>>> Thanks,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>>
>>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in
>>>>>>>>>>> for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>>>
>>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on
>>>>>>>>>>> Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>>
>>>>>>>>>>> Hope this helps,
>>>>>>>>>>>
>>>>>>>>>>> J-D
>>>>>>>>>>>
>>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <
>>>>>>>>>>> bbuild11@gmail.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted for
>>>>>>>>>>>> 0.8.0, but I see no progress on it. When this is complete, will this mean
>>>>>>>>>>>> that Spark will be able to work with Kudu both programmatically and as a
>>>>>>>>>>>> client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>>
>>>>>>>>>>>> Just curious.
>>>>>>>>>>>>
>>>>>>>>>>>> Cheers,
>>>>>>>>>>>> Ben
>>>>>>>>>>>>
>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Will this be documented with examples once 0.9.0 comes out?
Thanks,
Ben
> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jd...@apache.org> wrote:
>
> It will be in 0.9.0.
>
> J-D
>
> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Hi Chris,
>
> Will all this effort be rolled into 0.9.0 and be ready for use?
>
> Thanks,
> Ben
>
>
>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>
>> There is some code in review that needs some more refinement.
>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>
>> Example usages will look something like:
>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>
>> -Chris George
>>
>>
>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>
>> Can someone tell me what the state is of this Spark work?
>>
>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>
>> Thanks,
>> Ben
>>
>>
>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>
>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>> -Chris
>>>
>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>
>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>
>>> MERGE INTO table_name USING table_reference ON (condition)
>>> WHEN MATCHED THEN
>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>> WHEN NOT MATCHED THEN
>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>
>>> Cheers,
>>> Ben
>>>
>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>
>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>
>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>
>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>
>>>> -Chris George
>>>>
>>>>
>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>
>>>> J-D
>>>>
>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>
>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>
>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>> Mark,
>>>>
>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>
>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>
>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>
>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>
>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>
>>>>
>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>> Yup, starting to get a good idea.
>>>>
>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>
>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>
>>>> J-D
>>>>
>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>
>>>> I hope this helps you get an idea where I am coming from…
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>
>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> J-D,
>>>>>
>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>
>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>
>>>>>
>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>
>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>
>>>>>
>>>>> I hope this helps.
>>>>>
>>>>> It does, thanks for nice reply.
>>>>>
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>
>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>
>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>
>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>
>>>>>> Thanks,
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> Hi J-D,
>>>>>>
>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> Hi Ben,
>>>>>>>
>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>
>>>>>>> Regards,
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>
>>>>>>> Just curious,
>>>>>>> Benjamin Kim
>>>>>>> Data Solutions Architect
>>>>>>>
>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>
>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>
>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>
>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>
>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>
>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> J-D,
>>>>>>>>
>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>
>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>
>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>
>>>>>>>>> Hope this helps,
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>
>>>>>>>>> Just curious.
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
JD,
That’s awesome! I can’t wait to start working with it.
Thanks,
Ben
> On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <jd...@apache.org> wrote:
>
> It will be in 0.9.0.
>
> J-D
>
> On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Hi Chris,
>
> Will all this effort be rolled into 0.9.0 and be ready for use?
>
> Thanks,
> Ben
>
>
>> On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>
>> There is some code in review that needs some more refinement.
>> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
>> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>>
>> Example usages will look something like:
>> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>>
>> -Chris George
>>
>>
>> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>
>> Can someone tell me what the state is of this Spark work?
>>
>> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>>
>> Thanks,
>> Ben
>>
>>
>>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>
>>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>>> -Chris
>>>
>>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>
>>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>>
>>> MERGE INTO table_name USING table_reference ON (condition)
>>> WHEN MATCHED THEN
>>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>>> WHEN NOT MATCHED THEN
>>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>>
>>> Cheers,
>>> Ben
>>>
>>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>>
>>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>>
>>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>>
>>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>>
>>>> -Chris George
>>>>
>>>>
>>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>>
>>>> J-D
>>>>
>>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>>
>>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>>
>>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>> Mark,
>>>>
>>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>>
>>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>
>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>>
>>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>>
>>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>>
>>>>
>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>> Yup, starting to get a good idea.
>>>>
>>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>>
>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>>
>>>> J-D
>>>>
>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>>
>>>> I hope this helps you get an idea where I am coming from…
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>
>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> J-D,
>>>>>
>>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>>
>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>>
>>>>>
>>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>>
>>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>
>>>>>
>>>>> I hope this helps.
>>>>>
>>>>> It does, thanks for nice reply.
>>>>>
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>>
>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>>
>>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>>
>>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>>
>>>>>> Thanks,
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> Hi J-D,
>>>>>>
>>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> Hi Ben,
>>>>>>>
>>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>>
>>>>>>> Regards,
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>>
>>>>>>> Just curious,
>>>>>>> Benjamin Kim
>>>>>>> Data Solutions Architect
>>>>>>>
>>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>>
>>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>>
>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>>
>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>>
>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>>
>>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> J-D,
>>>>>>>>
>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>>
>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>>
>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>>
>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>
>>>>>>>>> Hope this helps,
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>>
>>>>>>>>> Just curious.
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>
>
>
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
It will be in 0.9.0.
J-D
On Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <bb...@gmail.com> wrote:
> Hi Chris,
>
> Will all this effort be rolled into 0.9.0 and be ready for use?
>
> Thanks,
> Ben
>
>
> On May 18, 2016, at 9:01 AM, Chris George <Ch...@rms.com>
> wrote:
>
> There is some code in review that needs some more refinement.
> It will allow upsert/insert from a dataframe using the datasource api. It
> will also allow the creation and deletion of tables from a dataframe
> http://gerrit.cloudera.org:8080/#/c/2992/
>
> Example usages will look something like:
> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
>
> -Chris George
>
>
> On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com> wrote:
>
> Can someone tell me what the state is of this Spark work?
>
> Also, does anyone have any sample code on how to update/insert data in
> Kudu using DataFrames?
>
> Thanks,
> Ben
>
>
> On Apr 13, 2016, at 8:22 AM, Chris George <Ch...@rms.com>
> wrote:
>
> SparkSQL cannot support these type of statements but we may be able to
> implement similar functionality through the api.
> -Chris
>
> On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com> wrote:
>
> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it
> were to be implemented.
>
> MERGE INTO table_name USING table_reference ON (condition)
> WHEN MATCHED THEN
> UPDATE SET column1 = value1 [, column2 = value2 ...]
> WHEN NOT MATCHED THEN
> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>
> Cheers,
> Ben
>
> On Apr 11, 2016, at 12:21 PM, Chris George <Ch...@rms.com>
> wrote:
>
> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit
> if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/
> It does pushdown predicates which the existing input formatter based rdd
> does not.
>
> Within the next two weeks I’m planning to implement a datasource for spark
> that will have pushdown predicates and insertion/update functionality (need
> to look more at cassandra and the hbase datasource for best way to do this)
> I agree that server side upsert would be helpful.
> Having a datasource would give us useful data frames and also make spark
> sql usable for kudu.
>
> My reasoning for having a spark datasource and not using Impala is: 1. We
> have had trouble getting impala to run fast with high concurrency when
> compared to spark 2. We interact with datasources which do not integrate
> with impala. 3. We have custom sql query planners for extended sql
> functionality.
>
> -Chris George
>
>
> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org> wrote:
>
> You guys make a convincing point, although on the upsert side we'll need
> more support from the servers. Right now all you can do is an INSERT then,
> if you get a dup key, do an UPDATE. I guess we could at least add an API on
> the client side that would manage it, but it wouldn't be atomic.
>
> J-D
>
> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <ma...@clearstorydata.com>
> wrote:
>
>> It's pretty simple, actually. I need to support versioned datasets in a
>> Spark SQL environment. Instead of a hack on top of a Parquet data store,
>> I'm hoping (among other reasons) to be able to use Kudu's write and
>> timestamp-based read operations to support not only appending data, but
>> also updating existing data, and even some schema migration. The most
>> typical use case is a dataset that is updated periodically (e.g., weekly or
>> monthly) in which the the preliminary data in the previous window (week or
>> month) is updated with values that are expected to remain unchanged from
>> then on, and a new set of preliminary values for the current window need to
>> be added/appended.
>>
>> Using Kudu's Java API and developing additional functionality on top of
>> what Kudu has to offer isn't too much to ask, but the ease of integration
>> with Spark SQL will gate how quickly we would move to using Kudu and how
>> seriously we'd look at alternatives before making that decision.
>>
>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jd...@apache.org>
>> wrote:
>>
>>> Mark,
>>>
>>> Thanks for taking some time to reply in this thread, glad it caught the
>>> attention of other folks!
>>>
>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <ma...@clearstorydata.com>
>>> wrote:
>>>
>>>> Do they care being able to insert into Kudu with SparkSQL
>>>>
>>>>
>>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a
>>>> refactoring of some Spark SQL-oriented insert functionality while trying to
>>>> evaluate what to expect from Kudu. Whether Kudu does a good job supporting
>>>> inserts with Spark SQL will be a key consideration as to whether we adopt
>>>> Kudu.
>>>>
>>>
>>> I'd like to know more about why SparkSQL inserts in necessary for you.
>>> Is it just that you currently do it that way into some database or parquet
>>> so with minimal refactoring you'd be able to use Kudu? Would re-writing
>>> those SQL lines into Scala and directly use the Java API's KuduSession be
>>> too much work?
>>>
>>> Additionally, what do you expect to gain from using Kudu VS your current
>>> solution? If it's not completely clear, I'd love to help you think through
>>> it.
>>>
>>>
>>>>
>>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>>> jdcryans@apache.org> wrote:
>>>>
>>>>> Yup, starting to get a good idea.
>>>>>
>>>>> What are your DS folks looking for in terms of functionality related
>>>>> to Spark? A SparkSQL integration that's as fully featured as Impala's? Do
>>>>> they care being able to insert into Kudu with SparkSQL or just being able
>>>>> to query real fast? Anything more specific to Spark that I'm missing?
>>>>>
>>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera
>>>>> all our resources are committed to making things happen in time, and a more
>>>>> fully featured Spark integration isn't in our plans during that period. I'm
>>>>> really hoping someone in the community will help with Spark, the same way
>>>>> we got a big contribution for the Flume sink.
>>>>>
>>>>> J-D
>>>>>
>>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But,
>>>>>> since it’s not “production-ready”, upper management doesn’t want to fully
>>>>>> deploy it yet. They just want to keep an eye on it though. Kudu was so much
>>>>>> simpler and easier to use in every aspect compared to HBase. Impala was
>>>>>> great for the report writers and analysts to experiment with for the short
>>>>>> time it was up. But, once again, the only blocker was the lack of Spark
>>>>>> support for our Data Developers/Scientists. So, production-level data
>>>>>> population won’t happen until then.
>>>>>>
>>>>>> I hope this helps you get an idea where I am coming from…
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> J-D,
>>>>>>>
>>>>>>> The main thing I hear that Cassandra is being used as an updatable
>>>>>>> hot data store to ensure that duplicates are taken care of and idempotency
>>>>>>> is maintained. Whether data was directly retrieved from Cassandra for
>>>>>>> analytics, reports, or searches, it was not clear as to what was its main
>>>>>>> use. Some also just used it for a staging area to populate downstream
>>>>>>> tables in parquet format. The last thing I heard was that CQL was terrible,
>>>>>>> so that rules out much use of direct queries against it.
>>>>>>>
>>>>>>
>>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics,
>>>>>> just ease of use instead of plainly using the APIs. Even then, Kudu should
>>>>>> beat it easily on big scans. Same for HBase. We've done benchmarks against
>>>>>> the latter, not the former.
>>>>>>
>>>>>>
>>>>>>>
>>>>>>> As for our company, we have been looking for an updatable data store
>>>>>>> for a long time that can be quickly queried directly either using Spark SQL
>>>>>>> or Impala or some other SQL engine and still handle TB or PB of data
>>>>>>> without performance degradation and many configuration headaches. For now,
>>>>>>> we are using HBase to take on this role with Phoenix as a fast way to
>>>>>>> directly query the data. I can see Kudu as the best way to fill this gap
>>>>>>> easily, especially being the closest thing to other relational databases
>>>>>>> out there in familiarity for the many SQL analytics people in our company.
>>>>>>> The other alternative would be to go with AWS Redshift for the same
>>>>>>> reasons, but it would come at a cost, of course. If we went with either
>>>>>>> solutions, Kudu or Redshift, it would get rid of the need to extract from
>>>>>>> HBase to parquet tables or export to PostgreSQL to support more of the SQL
>>>>>>> language using by analysts or the reporting software we use..
>>>>>>>
>>>>>>
>>>>>> Ok, the usual then *smile*. Looks like we're not too far off with
>>>>>> Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>>
>>>>>>
>>>>>>>
>>>>>>> I hope this helps.
>>>>>>>
>>>>>>
>>>>>> It does, thanks for nice reply.
>>>>>>
>>>>>>
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>> wrote:
>>>>>>>
>>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to
>>>>>>> refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>>> dying down.
>>>>>>>
>>>>>>> There's definitely an overlap in the use cases that Cassandra,
>>>>>>> HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>>>>>> interim solution for the use case you describe.
>>>>>>>
>>>>>>> Nothing significant happened in Kudu over the past month, it's a
>>>>>>> storage engine so things move slowly *smile*. I'd love to see more
>>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>>> folks will happily review it.
>>>>>>>
>>>>>>> Do you have relevant experiences you can share? I'd love to learn
>>>>>>> more about the use cases for which you envision using Kudu as a C*
>>>>>>> replacement.
>>>>>>>
>>>>>>> Thanks,
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi J-D,
>>>>>>>>
>>>>>>>> My colleagues recently came back from Strata in San Jose. They told
>>>>>>>> me that everything was about Spark and there is a big buzz about the SMACK
>>>>>>>> stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra
>>>>>>>> is just an interim solution as a low-latency, easily queried data store. I
>>>>>>>> was wondering if anything significant happened in regards to Kudu,
>>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>>> stack acronym to promote?
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <
>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>
>>>>>>>> Hi Ben,
>>>>>>>>
>>>>>>>> AFAIK no one in the dev community committed to any timeline. I know
>>>>>>>> of one person on the Kudu Slack who's working on a better RDD, but that's
>>>>>>>> about it.
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi J-D,
>>>>>>>>>
>>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a
>>>>>>>>> version of Kudu to begin real testing of Spark against it for our devs. At
>>>>>>>>> least, I can tell them what timeframe to anticipate.
>>>>>>>>>
>>>>>>>>> Just curious,
>>>>>>>>> *Benjamin Kim*
>>>>>>>>> *Data Solutions Architect*
>>>>>>>>>
>>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>>
>>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>>>>>>> www.amobee.com
>>>>>>>>>
>>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed
>>>>>>>>> either.
>>>>>>>>>
>>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd
>>>>>>>>> use scans directly.
>>>>>>>>>
>>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of
>>>>>>>>> pushdown. It's really basic.
>>>>>>>>>
>>>>>>>>> The goal was to provide something for others to contribute to. We
>>>>>>>>> have some basic unit tests that others can easily extend. None of us on the
>>>>>>>>> team are Spark experts, but we'd be really happy to assist one improve the
>>>>>>>>> kudu-spark code.
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> J-D,
>>>>>>>>>>
>>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu
>>>>>>>>>> RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark
>>>>>>>>>> SQL functionality (Dataframes) and doing the documentation, what more needs
>>>>>>>>>> to be done? Optimizations?
>>>>>>>>>>
>>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu
>>>>>>>>>> and compare it to HBase with Spark (not clean).
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>>
>>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in
>>>>>>>>>> for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>>
>>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on
>>>>>>>>>> Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>>
>>>>>>>>>> Hope this helps,
>>>>>>>>>>
>>>>>>>>>> J-D
>>>>>>>>>>
>>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com
>>>>>>>>>> > wrote:
>>>>>>>>>>
>>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted for
>>>>>>>>>>> 0.8.0, but I see no progress on it. When this is complete, will this mean
>>>>>>>>>>> that Spark will be able to work with Kudu both programmatically and as a
>>>>>>>>>>> client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>>
>>>>>>>>>>> Just curious.
>>>>>>>>>>>
>>>>>>>>>>> Cheers,
>>>>>>>>>>> Ben
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Hi Chris,
Will all this effort be rolled into 0.9.0 and be ready for use?
Thanks,
Ben
> On May 18, 2016, at 9:01 AM, Chris George <Ch...@rms.com> wrote:
>
> There is some code in review that needs some more refinement.
> It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
> http://gerrit.cloudera.org:8080/#/c/2992/ <http://gerrit.cloudera.org:8080/#/c/2992/>
>
> Example usages will look something like:
> http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc <http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc>
>
> -Chris George
>
>
> On 5/18/16, 9:45 AM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>
> Can someone tell me what the state is of this Spark work?
>
> Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
>
> Thanks,
> Ben
>
>
>> On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>
>> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
>> -Chris
>>
>> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>
>> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>>
>> MERGE INTO table_name USING table_reference ON (condition)
>> WHEN MATCHED THEN
>> UPDATE SET column1 = value1 [, column2 = value2 ...]
>> WHEN NOT MATCHED THEN
>> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>>
>> Cheers,
>> Ben
>>
>>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>>
>>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>>> It does pushdown predicates which the existing input formatter based rdd does not.
>>>
>>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>>
>>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>>
>>> -Chris George
>>>
>>>
>>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>>
>>> J-D
>>>
>>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>>
>>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>>
>>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>> Mark,
>>>
>>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>>
>>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>>> Do they care being able to insert into Kudu with SparkSQL
>>>
>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>>
>>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>>
>>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>>
>>>
>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>> Yup, starting to get a good idea.
>>>
>>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>>
>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>>
>>> J-D
>>>
>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>>
>>> I hope this helps you get an idea where I am coming from…
>>>
>>> Cheers,
>>> Ben
>>>
>>>
>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> J-D,
>>>>
>>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>>
>>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>>
>>>>
>>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>>
>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>
>>>>
>>>> I hope this helps.
>>>>
>>>> It does, thanks for nice reply.
>>>>
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>>
>>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>>
>>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>>
>>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>>
>>>>> Thanks,
>>>>>
>>>>> J-D
>>>>>
>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> Hi J-D,
>>>>>
>>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> Hi Ben,
>>>>>>
>>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>>
>>>>>> Regards,
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>>> Hi J-D,
>>>>>>
>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>>
>>>>>> Just curious,
>>>>>> Benjamin Kim
>>>>>> Data Solutions Architect
>>>>>>
>>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>>
>>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>>
>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>>
>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>>
>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>>
>>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> J-D,
>>>>>>>
>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>>
>>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>>
>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>>
>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>
>>>>>>>> Hope this helps,
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>>
>>>>>>>> Just curious.
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>
>
Re: Spark on Kudu
Posted by Chris George <Ch...@rms.com>.
There is some code in review that needs some more refinement.
It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
http://gerrit.cloudera.org:8080/#/c/2992/
Example usages will look something like:
http://gerrit.cloudera.org:8080/#/c/2992/5/docs/developing.adoc
-Chris George
On 5/18/16, 9:45 AM, "Benjamin Kim" <bb...@gmail.com>> wrote:
Can someone tell me what the state is of this Spark work?
Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
Thanks,
Ben
On Apr 13, 2016, at 8:22 AM, Chris George <Ch...@rms.com>> wrote:
SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
-Chris
On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com>> wrote:
It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
MERGE INTO table_name USING table_reference ON (condition)
WHEN MATCHED THEN
UPDATE SET column1 = value1 [, column2 = value2 ...]
WHEN NOT MATCHED THEN
INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
Cheers,
Ben
On Apr 11, 2016, at 12:21 PM, Chris George <Ch...@rms.com>> wrote:
I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/
It does pushdown predicates which the existing input formatter based rdd does not.
Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
Having a datasource would give us useful data frames and also make spark sql usable for kudu.
My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
-Chris George
On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>> wrote:
You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
J-D
On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <ma...@clearstorydata.com>> wrote:
It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Mark,
Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <ma...@clearstorydata.com>> wrote:
Do they care being able to insert into Kudu with SparkSQL
I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Yup, starting to get a good idea.
What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
J-D
On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com>> wrote:
Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
I hope this helps you get an idea where I am coming from…
Cheers,
Ben
On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>> wrote:
J-D,
The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
I hope this helps.
It does, thanks for nice reply.
Cheers,
Ben
On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
Thanks,
J-D
On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>> wrote:
Hi J-D,
My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
Cheers,
Ben
On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Hi Ben,
AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
Regards,
J-D
On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com>> wrote:
Hi J-D,
Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
Just curious,
Benjamin Kim
Data Solutions Architect
[a•mo•bee] (n.) the company defining digital marketing.
Mobile: +1 818 635 2900<tel:%2B1%20818%20635%202900>
3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com<http://www.amobee.com/>
On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
The DStream stuff isn't there at all. I'm not sure if it's needed either.
The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
J-D
On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>> wrote:
J-D,
It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
Thanks,
Ben
On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
Hope this helps,
J-D
On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>> wrote:
I see this KUDU-1214<https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
Just curious.
Cheers,
Ben
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Can someone tell me what the state is of this Spark work?
Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
Thanks,
Ben
> On Apr 13, 2016, at 8:22 AM, Chris George <Ch...@rms.com> wrote:
>
> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
> -Chris
>
> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>
> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>
> MERGE INTO table_name USING table_reference ON (condition)
> WHEN MATCHED THEN
> UPDATE SET column1 = value1 [, column2 = value2 ...]
> WHEN NOT MATCHED THEN
> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>
> Cheers,
> Ben
>
>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>
>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>> It does pushdown predicates which the existing input formatter based rdd does not.
>>
>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>
>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>
>> -Chris George
>>
>>
>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>
>> J-D
>>
>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>
>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>
>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>> Mark,
>>
>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>
>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>> Do they care being able to insert into Kudu with SparkSQL
>>
>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>
>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>
>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>
>>
>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>> Yup, starting to get a good idea.
>>
>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>
>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>
>> J-D
>>
>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>
>> I hope this helps you get an idea where I am coming from…
>>
>> Cheers,
>> Ben
>>
>>
>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> J-D,
>>>
>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>
>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>
>>>
>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>
>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>
>>>
>>> I hope this helps.
>>>
>>> It does, thanks for nice reply.
>>>
>>>
>>> Cheers,
>>> Ben
>>>
>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>
>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>
>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>
>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>
>>>> Thanks,
>>>>
>>>> J-D
>>>>
>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Hi J-D,
>>>>
>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>
>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> Hi Ben,
>>>>>
>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>
>>>>> Regards,
>>>>>
>>>>> J-D
>>>>>
>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>> Hi J-D,
>>>>>
>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>
>>>>> Just curious,
>>>>> Benjamin Kim
>>>>> Data Solutions Architect
>>>>>
>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>
>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>
>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>
>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>
>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>
>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> J-D,
>>>>>>
>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>
>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>
>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>
>>>>>>> Hope this helps,
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>
>>>>>>> Just curious.
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>>
>>
>>
>>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Chris,
That would be great! And a first! I think everyone would take notice if KImpala had this.
Cheers,
Ben
> On Apr 13, 2016, at 8:22 AM, Chris George <Ch...@rms.com> wrote:
>
> SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
> -Chris
>
> On 4/12/16, 5:19 PM, "Benjamin Kim" <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>
> It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
>
> MERGE INTO table_name USING table_reference ON (condition)
> WHEN MATCHED THEN
> UPDATE SET column1 = value1 [, column2 = value2 ...]
> WHEN NOT MATCHED THEN
> INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
>
> Cheers,
> Ben
>
>> On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com <ma...@rms.com>> wrote:
>>
>> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
>> It does pushdown predicates which the existing input formatter based rdd does not.
>>
>> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
>> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>>
>> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>>
>> -Chris George
>>
>>
>> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>>
>> J-D
>>
>> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>>
>> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>>
>> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>> Mark,
>>
>> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>>
>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
>> Do they care being able to insert into Kudu with SparkSQL
>>
>> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>>
>> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>>
>> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>>
>>
>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>> Yup, starting to get a good idea.
>>
>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>
>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>
>> J-D
>>
>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>
>> I hope this helps you get an idea where I am coming from…
>>
>> Cheers,
>> Ben
>>
>>
>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> J-D,
>>>
>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>
>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>
>>>
>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>
>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>
>>>
>>> I hope this helps.
>>>
>>> It does, thanks for nice reply.
>>>
>>>
>>> Cheers,
>>> Ben
>>>
>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>
>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>
>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>
>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>
>>>> Thanks,
>>>>
>>>> J-D
>>>>
>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Hi J-D,
>>>>
>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>
>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> Hi Ben,
>>>>>
>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>
>>>>> Regards,
>>>>>
>>>>> J-D
>>>>>
>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>> Hi J-D,
>>>>>
>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>
>>>>> Just curious,
>>>>> Benjamin Kim
>>>>> Data Solutions Architect
>>>>>
>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>
>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>
>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>
>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>
>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>
>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> J-D,
>>>>>>
>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>
>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>
>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>
>>>>>>> Hope this helps,
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>
>>>>>>> Just curious.
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>>
>>
>>
>>
>
Re: Spark on Kudu
Posted by Chris George <Ch...@rms.com>.
SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.
-Chris
On 4/12/16, 5:19 PM, "Benjamin Kim" <bb...@gmail.com>> wrote:
It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
MERGE INTO table_name USING table_reference ON (condition)
WHEN MATCHED THEN
UPDATE SET column1 = value1 [, column2 = value2 ...]
WHEN NOT MATCHED THEN
INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
Cheers,
Ben
On Apr 11, 2016, at 12:21 PM, Chris George <Ch...@rms.com>> wrote:
I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/
It does pushdown predicates which the existing input formatter based rdd does not.
Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
Having a datasource would give us useful data frames and also make spark sql usable for kudu.
My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
-Chris George
On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>> wrote:
You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
J-D
On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <ma...@clearstorydata.com>> wrote:
It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Mark,
Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <ma...@clearstorydata.com>> wrote:
Do they care being able to insert into Kudu with SparkSQL
I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Yup, starting to get a good idea.
What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
J-D
On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com>> wrote:
Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
I hope this helps you get an idea where I am coming from…
Cheers,
Ben
On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>> wrote:
J-D,
The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
I hope this helps.
It does, thanks for nice reply.
Cheers,
Ben
On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
Thanks,
J-D
On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>> wrote:
Hi J-D,
My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
Cheers,
Ben
On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Hi Ben,
AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
Regards,
J-D
On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com>> wrote:
Hi J-D,
Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
Just curious,
Benjamin Kim
Data Solutions Architect
[a•mo•bee] (n.) the company defining digital marketing.
Mobile: +1 818 635 2900<tel:%2B1%20818%20635%202900>
3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com<http://www.amobee.com/>
On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
The DStream stuff isn't there at all. I'm not sure if it's needed either.
The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
J-D
On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>> wrote:
J-D,
It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
Thanks,
Ben
On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
Hope this helps,
J-D
On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>> wrote:
I see this KUDU-1214<https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
Just curious.
Cheers,
Ben
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
MERGE INTO table_name USING table_reference ON (condition)
WHEN MATCHED THEN
UPDATE SET column1 = value1 [, column2 = value2 ...]
WHEN NOT MATCHED THEN
INSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
Cheers,
Ben
> On Apr 11, 2016, at 12:21 PM, Chris George <Ch...@rms.com> wrote:
>
> I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/ <http://gerrit.cloudera.org:8080/#/c/2754/>
> It does pushdown predicates which the existing input formatter based rdd does not.
>
> Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
> Having a datasource would give us useful data frames and also make spark sql usable for kudu.
>
> My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
>
> -Chris George
>
>
> On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jdcryans@apache.org <ma...@apache.org>> wrote:
>
> You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
>
> J-D
>
> On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
> It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
>
> Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
>
> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
> Mark,
>
> Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
>
> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <mark@clearstorydata.com <ma...@clearstorydata.com>> wrote:
> Do they care being able to insert into Kudu with SparkSQL
>
> I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
>
> I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
>
> Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
>
>
> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
> Yup, starting to get a good idea.
>
> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>
> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>
> J-D
>
> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>
> I hope this helps you get an idea where I am coming from…
>
> Cheers,
> Ben
>
>
>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> J-D,
>>
>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>
>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>
>>
>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>
>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>
>>
>> I hope this helps.
>>
>> It does, thanks for nice reply.
>>
>>
>> Cheers,
>> Ben
>>
>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>
>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>
>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>
>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>
>>> Thanks,
>>>
>>> J-D
>>>
>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> Hi J-D,
>>>
>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>
>>> Cheers,
>>> Ben
>>>
>>>
>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> Hi Ben,
>>>>
>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>
>>>> Regards,
>>>>
>>>> J-D
>>>>
>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>> Hi J-D,
>>>>
>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>
>>>> Just curious,
>>>> Benjamin Kim
>>>> Data Solutions Architect
>>>>
>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>
>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>
>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>
>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>
>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>
>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>
>>>>> J-D
>>>>>
>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> J-D,
>>>>>
>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>
>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>
>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>
>>>>>> Hope this helps,
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>
>>>>>> Just curious.
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
>
>
>
>
Re: Spark on Kudu
Posted by Chris George <Ch...@rms.com>.
I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/
It does pushdown predicates which the existing input formatter based rdd does not.
Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.
Having a datasource would give us useful data frames and also make spark sql usable for kudu.
My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
-Chris George
On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <jd...@apache.org>> wrote:
You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
J-D
On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <ma...@clearstorydata.com>> wrote:
It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Mark,
Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <ma...@clearstorydata.com>> wrote:
Do they care being able to insert into Kudu with SparkSQL
I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Yup, starting to get a good idea.
What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
J-D
On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com>> wrote:
Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
I hope this helps you get an idea where I am coming from…
Cheers,
Ben
On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>> wrote:
J-D,
The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
I hope this helps.
It does, thanks for nice reply.
Cheers,
Ben
On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
Thanks,
J-D
On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>> wrote:
Hi J-D,
My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
Cheers,
Ben
On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
Hi Ben,
AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
Regards,
J-D
On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com>> wrote:
Hi J-D,
Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
Just curious,
Benjamin Kim
Data Solutions Architect
[a•mo•bee] (n.) the company defining digital marketing.
Mobile: +1 818 635 2900<tel:%2B1%20818%20635%202900>
3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com<http://www.amobee.com/>
On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
The DStream stuff isn't there at all. I'm not sure if it's needed either.
The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
J-D
On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>> wrote:
J-D,
It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
Thanks,
Ben
On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>> wrote:
AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
Hope this helps,
J-D
On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>> wrote:
I see this KUDU-1214<https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
Just curious.
Cheers,
Ben
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
You guys make a convincing point, although on the upsert side we'll need
more support from the servers. Right now all you can do is an INSERT then,
if you get a dup key, do an UPDATE. I guess we could at least add an API on
the client side that would manage it, but it wouldn't be atomic.
J-D
On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <ma...@clearstorydata.com>
wrote:
> It's pretty simple, actually. I need to support versioned datasets in a
> Spark SQL environment. Instead of a hack on top of a Parquet data store,
> I'm hoping (among other reasons) to be able to use Kudu's write and
> timestamp-based read operations to support not only appending data, but
> also updating existing data, and even some schema migration. The most
> typical use case is a dataset that is updated periodically (e.g., weekly or
> monthly) in which the the preliminary data in the previous window (week or
> month) is updated with values that are expected to remain unchanged from
> then on, and a new set of preliminary values for the current window need to
> be added/appended.
>
> Using Kudu's Java API and developing additional functionality on top of
> what Kudu has to offer isn't too much to ask, but the ease of integration
> with Spark SQL will gate how quickly we would move to using Kudu and how
> seriously we'd look at alternatives before making that decision.
>
> On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jd...@apache.org>
> wrote:
>
>> Mark,
>>
>> Thanks for taking some time to reply in this thread, glad it caught the
>> attention of other folks!
>>
>> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <ma...@clearstorydata.com>
>> wrote:
>>
>>> Do they care being able to insert into Kudu with SparkSQL
>>>
>>>
>>> I care about insert into Kudu with Spark SQL. I'm currently delaying a
>>> refactoring of some Spark SQL-oriented insert functionality while trying to
>>> evaluate what to expect from Kudu. Whether Kudu does a good job supporting
>>> inserts with Spark SQL will be a key consideration as to whether we adopt
>>> Kudu.
>>>
>>
>> I'd like to know more about why SparkSQL inserts in necessary for you. Is
>> it just that you currently do it that way into some database or parquet so
>> with minimal refactoring you'd be able to use Kudu? Would re-writing those
>> SQL lines into Scala and directly use the Java API's KuduSession be too
>> much work?
>>
>> Additionally, what do you expect to gain from using Kudu VS your current
>> solution? If it's not completely clear, I'd love to help you think through
>> it.
>>
>>
>>>
>>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <
>>> jdcryans@apache.org> wrote:
>>>
>>>> Yup, starting to get a good idea.
>>>>
>>>> What are your DS folks looking for in terms of functionality related to
>>>> Spark? A SparkSQL integration that's as fully featured as Impala's? Do they
>>>> care being able to insert into Kudu with SparkSQL or just being able to
>>>> query real fast? Anything more specific to Spark that I'm missing?
>>>>
>>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera
>>>> all our resources are committed to making things happen in time, and a more
>>>> fully featured Spark integration isn't in our plans during that period. I'm
>>>> really hoping someone in the community will help with Spark, the same way
>>>> we got a big contribution for the Flume sink.
>>>>
>>>> J-D
>>>>
>>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But,
>>>>> since it’s not “production-ready”, upper management doesn’t want to fully
>>>>> deploy it yet. They just want to keep an eye on it though. Kudu was so much
>>>>> simpler and easier to use in every aspect compared to HBase. Impala was
>>>>> great for the report writers and analysts to experiment with for the short
>>>>> time it was up. But, once again, the only blocker was the lack of Spark
>>>>> support for our Data Developers/Scientists. So, production-level data
>>>>> population won’t happen until then.
>>>>>
>>>>> I hope this helps you get an idea where I am coming from…
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>
>>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org>
>>>>> wrote:
>>>>>
>>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> J-D,
>>>>>>
>>>>>> The main thing I hear that Cassandra is being used as an updatable
>>>>>> hot data store to ensure that duplicates are taken care of and idempotency
>>>>>> is maintained. Whether data was directly retrieved from Cassandra for
>>>>>> analytics, reports, or searches, it was not clear as to what was its main
>>>>>> use. Some also just used it for a staging area to populate downstream
>>>>>> tables in parquet format. The last thing I heard was that CQL was terrible,
>>>>>> so that rules out much use of direct queries against it.
>>>>>>
>>>>>
>>>>> I'm no C* expert, but I don't think CQL is meant for real analytics,
>>>>> just ease of use instead of plainly using the APIs. Even then, Kudu should
>>>>> beat it easily on big scans. Same for HBase. We've done benchmarks against
>>>>> the latter, not the former.
>>>>>
>>>>>
>>>>>>
>>>>>> As for our company, we have been looking for an updatable data store
>>>>>> for a long time that can be quickly queried directly either using Spark SQL
>>>>>> or Impala or some other SQL engine and still handle TB or PB of data
>>>>>> without performance degradation and many configuration headaches. For now,
>>>>>> we are using HBase to take on this role with Phoenix as a fast way to
>>>>>> directly query the data. I can see Kudu as the best way to fill this gap
>>>>>> easily, especially being the closest thing to other relational databases
>>>>>> out there in familiarity for the many SQL analytics people in our company.
>>>>>> The other alternative would be to go with AWS Redshift for the same
>>>>>> reasons, but it would come at a cost, of course. If we went with either
>>>>>> solutions, Kudu or Redshift, it would get rid of the need to extract from
>>>>>> HBase to parquet tables or export to PostgreSQL to support more of the SQL
>>>>>> language using by analysts or the reporting software we use..
>>>>>>
>>>>>
>>>>> Ok, the usual then *smile*. Looks like we're not too far off with
>>>>> Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>>>
>>>>>
>>>>>>
>>>>>> I hope this helps.
>>>>>>
>>>>>
>>>>> It does, thanks for nice reply.
>>>>>
>>>>>
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to
>>>>>> refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>>> dying down.
>>>>>>
>>>>>> There's definitely an overlap in the use cases that Cassandra, HBase,
>>>>>> and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>>>>> interim solution for the use case you describe.
>>>>>>
>>>>>> Nothing significant happened in Kudu over the past month, it's a
>>>>>> storage engine so things move slowly *smile*. I'd love to see more
>>>>>> contributions on the Spark front. I know there's code out there that could
>>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>>> folks will happily review it.
>>>>>>
>>>>>> Do you have relevant experiences you can share? I'd love to learn
>>>>>> more about the use cases for which you envision using Kudu as a C*
>>>>>> replacement.
>>>>>>
>>>>>> Thanks,
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> My colleagues recently came back from Strata in San Jose. They told
>>>>>>> me that everything was about Spark and there is a big buzz about the SMACK
>>>>>>> stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra
>>>>>>> is just an interim solution as a low-latency, easily queried data store. I
>>>>>>> was wondering if anything significant happened in regards to Kudu,
>>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>>> stack acronym to promote?
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>> wrote:
>>>>>>>
>>>>>>> Hi Ben,
>>>>>>>
>>>>>>> AFAIK no one in the dev community committed to any timeline. I know
>>>>>>> of one person on the Kudu Slack who's working on a better RDD, but that's
>>>>>>> about it.
>>>>>>>
>>>>>>> Regards,
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi J-D,
>>>>>>>>
>>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a
>>>>>>>> version of Kudu to begin real testing of Spark against it for our devs. At
>>>>>>>> least, I can tell them what timeframe to anticipate.
>>>>>>>>
>>>>>>>> Just curious,
>>>>>>>> *Benjamin Kim*
>>>>>>>> *Data Solutions Architect*
>>>>>>>>
>>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>>
>>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>>>>>> www.amobee.com
>>>>>>>>
>>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <
>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>
>>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed
>>>>>>>> either.
>>>>>>>>
>>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd
>>>>>>>> use scans directly.
>>>>>>>>
>>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown.
>>>>>>>> It's really basic.
>>>>>>>>
>>>>>>>> The goal was to provide something for others to contribute to. We
>>>>>>>> have some basic unit tests that others can easily extend. None of us on the
>>>>>>>> team are Spark experts, but we'd be really happy to assist one improve the
>>>>>>>> kudu-spark code.
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> J-D,
>>>>>>>>>
>>>>>>>>> It looks like it fulfills most of the basic requirements (kudu
>>>>>>>>> RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark
>>>>>>>>> SQL functionality (Dataframes) and doing the documentation, what more needs
>>>>>>>>> to be done? Optimizations?
>>>>>>>>>
>>>>>>>>> I believe that it’s a good place to start using Spark with Kudu
>>>>>>>>> and compare it to HBase with Spark (not clean).
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>>
>>>>>>>>> AFAIK no one is working on it, but we did manage to get this in
>>>>>>>>> for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>>
>>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on
>>>>>>>>> Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>>>
>>>>>>>>> Hope this helps,
>>>>>>>>>
>>>>>>>>> J-D
>>>>>>>>>
>>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> I see this KUDU-1214
>>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted for
>>>>>>>>>> 0.8.0, but I see no progress on it. When this is complete, will this mean
>>>>>>>>>> that Spark will be able to work with Kudu both programmatically and as a
>>>>>>>>>> client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>>> Spark side for it to work?
>>>>>>>>>>
>>>>>>>>>> Just curious.
>>>>>>>>>>
>>>>>>>>>> Cheers,
>>>>>>>>>> Ben
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>
>>
>
Re: Spark on Kudu
Posted by Mark Hamstra <ma...@clearstorydata.com>.
It's pretty simple, actually. I need to support versioned datasets in a
Spark SQL environment. Instead of a hack on top of a Parquet data store,
I'm hoping (among other reasons) to be able to use Kudu's write and
timestamp-based read operations to support not only appending data, but
also updating existing data, and even some schema migration. The most
typical use case is a dataset that is updated periodically (e.g., weekly or
monthly) in which the the preliminary data in the previous window (week or
month) is updated with values that are expected to remain unchanged from
then on, and a new set of preliminary values for the current window need to
be added/appended.
Using Kudu's Java API and developing additional functionality on top of
what Kudu has to offer isn't too much to ask, but the ease of integration
with Spark SQL will gate how quickly we would move to using Kudu and how
seriously we'd look at alternatives before making that decision.
On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <jd...@apache.org>
wrote:
> Mark,
>
> Thanks for taking some time to reply in this thread, glad it caught the
> attention of other folks!
>
> On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <ma...@clearstorydata.com>
> wrote:
>
>> Do they care being able to insert into Kudu with SparkSQL
>>
>>
>> I care about insert into Kudu with Spark SQL. I'm currently delaying a
>> refactoring of some Spark SQL-oriented insert functionality while trying to
>> evaluate what to expect from Kudu. Whether Kudu does a good job supporting
>> inserts with Spark SQL will be a key consideration as to whether we adopt
>> Kudu.
>>
>
> I'd like to know more about why SparkSQL inserts in necessary for you. Is
> it just that you currently do it that way into some database or parquet so
> with minimal refactoring you'd be able to use Kudu? Would re-writing those
> SQL lines into Scala and directly use the Java API's KuduSession be too
> much work?
>
> Additionally, what do you expect to gain from using Kudu VS your current
> solution? If it's not completely clear, I'd love to help you think through
> it.
>
>
>>
>> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org
>> > wrote:
>>
>>> Yup, starting to get a good idea.
>>>
>>> What are your DS folks looking for in terms of functionality related to
>>> Spark? A SparkSQL integration that's as fully featured as Impala's? Do they
>>> care being able to insert into Kudu with SparkSQL or just being able to
>>> query real fast? Anything more specific to Spark that I'm missing?
>>>
>>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera
>>> all our resources are committed to making things happen in time, and a more
>>> fully featured Spark integration isn't in our plans during that period. I'm
>>> really hoping someone in the community will help with Spark, the same way
>>> we got a big contribution for the Flume sink.
>>>
>>> J-D
>>>
>>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since
>>>> it’s not “production-ready”, upper management doesn’t want to fully deploy
>>>> it yet. They just want to keep an eye on it though. Kudu was so much
>>>> simpler and easier to use in every aspect compared to HBase. Impala was
>>>> great for the report writers and analysts to experiment with for the short
>>>> time it was up. But, once again, the only blocker was the lack of Spark
>>>> support for our Data Developers/Scientists. So, production-level data
>>>> population won’t happen until then.
>>>>
>>>> I hope this helps you get an idea where I am coming from…
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>
>>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org>
>>>> wrote:
>>>>
>>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> J-D,
>>>>>
>>>>> The main thing I hear that Cassandra is being used as an updatable hot
>>>>> data store to ensure that duplicates are taken care of and idempotency is
>>>>> maintained. Whether data was directly retrieved from Cassandra for
>>>>> analytics, reports, or searches, it was not clear as to what was its main
>>>>> use. Some also just used it for a staging area to populate downstream
>>>>> tables in parquet format. The last thing I heard was that CQL was terrible,
>>>>> so that rules out much use of direct queries against it.
>>>>>
>>>>
>>>> I'm no C* expert, but I don't think CQL is meant for real analytics,
>>>> just ease of use instead of plainly using the APIs. Even then, Kudu should
>>>> beat it easily on big scans. Same for HBase. We've done benchmarks against
>>>> the latter, not the former.
>>>>
>>>>
>>>>>
>>>>> As for our company, we have been looking for an updatable data store
>>>>> for a long time that can be quickly queried directly either using Spark SQL
>>>>> or Impala or some other SQL engine and still handle TB or PB of data
>>>>> without performance degradation and many configuration headaches. For now,
>>>>> we are using HBase to take on this role with Phoenix as a fast way to
>>>>> directly query the data. I can see Kudu as the best way to fill this gap
>>>>> easily, especially being the closest thing to other relational databases
>>>>> out there in familiarity for the many SQL analytics people in our company.
>>>>> The other alternative would be to go with AWS Redshift for the same
>>>>> reasons, but it would come at a cost, of course. If we went with either
>>>>> solutions, Kudu or Redshift, it would get rid of the need to extract from
>>>>> HBase to parquet tables or export to PostgreSQL to support more of the SQL
>>>>> language using by analysts or the reporting software we use..
>>>>>
>>>>
>>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu.
>>>> Have you folks tried Kudu with Impala yet with those use cases?
>>>>
>>>>
>>>>>
>>>>> I hope this helps.
>>>>>
>>>>
>>>> It does, thanks for nice reply.
>>>>
>>>>
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>> wrote:
>>>>>
>>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to
>>>>> refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My
>>>>> colleagues who were also there did say that the hype around Spark isn't
>>>>> dying down.
>>>>>
>>>>> There's definitely an overlap in the use cases that Cassandra, HBase,
>>>>> and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>>>> interim solution for the use case you describe.
>>>>>
>>>>> Nothing significant happened in Kudu over the past month, it's a
>>>>> storage engine so things move slowly *smile*. I'd love to see more
>>>>> contributions on the Spark front. I know there's code out there that could
>>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>>> folks will happily review it.
>>>>>
>>>>> Do you have relevant experiences you can share? I'd love to learn more
>>>>> about the use cases for which you envision using Kudu as a C* replacement.
>>>>>
>>>>> Thanks,
>>>>>
>>>>> J-D
>>>>>
>>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi J-D,
>>>>>>
>>>>>> My colleagues recently came back from Strata in San Jose. They told
>>>>>> me that everything was about Spark and there is a big buzz about the SMACK
>>>>>> stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra
>>>>>> is just an interim solution as a low-latency, easily queried data store. I
>>>>>> was wondering if anything significant happened in regards to Kudu,
>>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>>> stack acronym to promote?
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> Hi Ben,
>>>>>>
>>>>>> AFAIK no one in the dev community committed to any timeline. I know
>>>>>> of one person on the Kudu Slack who's working on a better RDD, but that's
>>>>>> about it.
>>>>>>
>>>>>> Regards,
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi J-D,
>>>>>>>
>>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a
>>>>>>> version of Kudu to begin real testing of Spark against it for our devs. At
>>>>>>> least, I can tell them what timeframe to anticipate.
>>>>>>>
>>>>>>> Just curious,
>>>>>>> *Benjamin Kim*
>>>>>>> *Data Solutions Architect*
>>>>>>>
>>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>>
>>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>>>>> www.amobee.com
>>>>>>>
>>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>> wrote:
>>>>>>>
>>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed
>>>>>>> either.
>>>>>>>
>>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use
>>>>>>> scans directly.
>>>>>>>
>>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown.
>>>>>>> It's really basic.
>>>>>>>
>>>>>>> The goal was to provide something for others to contribute to. We
>>>>>>> have some basic unit tests that others can easily extend. None of us on the
>>>>>>> team are Spark experts, but we'd be really happy to assist one improve the
>>>>>>> kudu-spark code.
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> J-D,
>>>>>>>>
>>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD,
>>>>>>>> kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL
>>>>>>>> functionality (Dataframes) and doing the documentation, what more needs to
>>>>>>>> be done? Optimizations?
>>>>>>>>
>>>>>>>> I believe that it’s a good place to start using Spark with Kudu and
>>>>>>>> compare it to HBase with Spark (not clean).
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <
>>>>>>>> jdcryans@apache.org> wrote:
>>>>>>>>
>>>>>>>> AFAIK no one is working on it, but we did manage to get this in for
>>>>>>>> 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>>
>>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu,
>>>>>>>> but it will require a lot more work to make it fast/useful.
>>>>>>>>
>>>>>>>> Hope this helps,
>>>>>>>>
>>>>>>>> J-D
>>>>>>>>
>>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> I see this KUDU-1214
>>>>>>>>> <https://issues.cloudera.org/browse/KUDU-1214> targeted for
>>>>>>>>> 0.8.0, but I see no progress on it. When this is complete, will this mean
>>>>>>>>> that Spark will be able to work with Kudu both programmatically and as a
>>>>>>>>> client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>>> Spark side for it to work?
>>>>>>>>>
>>>>>>>>> Just curious.
>>>>>>>>>
>>>>>>>>> Cheers,
>>>>>>>>> Ben
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>
>
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
Mark,
Thanks for taking some time to reply in this thread, glad it caught the
attention of other folks!
On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <ma...@clearstorydata.com>
wrote:
> Do they care being able to insert into Kudu with SparkSQL
>
>
> I care about insert into Kudu with Spark SQL. I'm currently delaying a
> refactoring of some Spark SQL-oriented insert functionality while trying to
> evaluate what to expect from Kudu. Whether Kudu does a good job supporting
> inserts with Spark SQL will be a key consideration as to whether we adopt
> Kudu.
>
I'd like to know more about why SparkSQL inserts in necessary for you. Is
it just that you currently do it that way into some database or parquet so
with minimal refactoring you'd be able to use Kudu? Would re-writing those
SQL lines into Scala and directly use the Java API's KuduSession be too
much work?
Additionally, what do you expect to gain from using Kudu VS your current
solution? If it's not completely clear, I'd love to help you think through
it.
>
> On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jd...@apache.org>
> wrote:
>
>> Yup, starting to get a good idea.
>>
>> What are your DS folks looking for in terms of functionality related to
>> Spark? A SparkSQL integration that's as fully featured as Impala's? Do they
>> care being able to insert into Kudu with SparkSQL or just being able to
>> query real fast? Anything more specific to Spark that I'm missing?
>>
>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all
>> our resources are committed to making things happen in time, and a more
>> fully featured Spark integration isn't in our plans during that period. I'm
>> really hoping someone in the community will help with Spark, the same way
>> we got a big contribution for the Flume sink.
>>
>> J-D
>>
>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com>
>> wrote:
>>
>>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since
>>> it’s not “production-ready”, upper management doesn’t want to fully deploy
>>> it yet. They just want to keep an eye on it though. Kudu was so much
>>> simpler and easier to use in every aspect compared to HBase. Impala was
>>> great for the report writers and analysts to experiment with for the short
>>> time it was up. But, once again, the only blocker was the lack of Spark
>>> support for our Data Developers/Scientists. So, production-level data
>>> population won’t happen until then.
>>>
>>> I hope this helps you get an idea where I am coming from…
>>>
>>> Cheers,
>>> Ben
>>>
>>>
>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org>
>>> wrote:
>>>
>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> J-D,
>>>>
>>>> The main thing I hear that Cassandra is being used as an updatable hot
>>>> data store to ensure that duplicates are taken care of and idempotency is
>>>> maintained. Whether data was directly retrieved from Cassandra for
>>>> analytics, reports, or searches, it was not clear as to what was its main
>>>> use. Some also just used it for a staging area to populate downstream
>>>> tables in parquet format. The last thing I heard was that CQL was terrible,
>>>> so that rules out much use of direct queries against it.
>>>>
>>>
>>> I'm no C* expert, but I don't think CQL is meant for real analytics,
>>> just ease of use instead of plainly using the APIs. Even then, Kudu should
>>> beat it easily on big scans. Same for HBase. We've done benchmarks against
>>> the latter, not the former.
>>>
>>>
>>>>
>>>> As for our company, we have been looking for an updatable data store
>>>> for a long time that can be quickly queried directly either using Spark SQL
>>>> or Impala or some other SQL engine and still handle TB or PB of data
>>>> without performance degradation and many configuration headaches. For now,
>>>> we are using HBase to take on this role with Phoenix as a fast way to
>>>> directly query the data. I can see Kudu as the best way to fill this gap
>>>> easily, especially being the closest thing to other relational databases
>>>> out there in familiarity for the many SQL analytics people in our company.
>>>> The other alternative would be to go with AWS Redshift for the same
>>>> reasons, but it would come at a cost, of course. If we went with either
>>>> solutions, Kudu or Redshift, it would get rid of the need to extract from
>>>> HBase to parquet tables or export to PostgreSQL to support more of the SQL
>>>> language using by analysts or the reporting software we use..
>>>>
>>>
>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu.
>>> Have you folks tried Kudu with Impala yet with those use cases?
>>>
>>>
>>>>
>>>> I hope this helps.
>>>>
>>>
>>> It does, thanks for nice reply.
>>>
>>>
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>> wrote:
>>>>
>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer
>>>> to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who
>>>> were also there did say that the hype around Spark isn't dying down.
>>>>
>>>> There's definitely an overlap in the use cases that Cassandra, HBase,
>>>> and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>>> interim solution for the use case you describe.
>>>>
>>>> Nothing significant happened in Kudu over the past month, it's a
>>>> storage engine so things move slowly *smile*. I'd love to see more
>>>> contributions on the Spark front. I know there's code out there that could
>>>> be integrated in kudu-spark, it just needs to land in gerrit. I'm sure
>>>> folks will happily review it.
>>>>
>>>> Do you have relevant experiences you can share? I'd love to learn more
>>>> about the use cases for which you envision using Kudu as a C* replacement.
>>>>
>>>> Thanks,
>>>>
>>>> J-D
>>>>
>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi J-D,
>>>>>
>>>>> My colleagues recently came back from Strata in San Jose. They told me
>>>>> that everything was about Spark and there is a big buzz about the SMACK
>>>>> stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra
>>>>> is just an interim solution as a low-latency, easily queried data store. I
>>>>> was wondering if anything significant happened in regards to Kudu,
>>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>>> stack acronym to promote?
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>
>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>> wrote:
>>>>>
>>>>> Hi Ben,
>>>>>
>>>>> AFAIK no one in the dev community committed to any timeline. I know of
>>>>> one person on the Kudu Slack who's working on a better RDD, but that's
>>>>> about it.
>>>>>
>>>>> Regards,
>>>>>
>>>>> J-D
>>>>>
>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com> wrote:
>>>>>
>>>>>> Hi J-D,
>>>>>>
>>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a
>>>>>> version of Kudu to begin real testing of Spark against it for our devs. At
>>>>>> least, I can tell them what timeframe to anticipate.
>>>>>>
>>>>>> Just curious,
>>>>>> *Benjamin Kim*
>>>>>> *Data Solutions Architect*
>>>>>>
>>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>>
>>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>>>> www.amobee.com
>>>>>>
>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed
>>>>>> either.
>>>>>>
>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use
>>>>>> scans directly.
>>>>>>
>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown.
>>>>>> It's really basic.
>>>>>>
>>>>>> The goal was to provide something for others to contribute to. We
>>>>>> have some basic unit tests that others can easily extend. None of us on the
>>>>>> team are Spark experts, but we'd be really happy to assist one improve the
>>>>>> kudu-spark code.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> J-D,
>>>>>>>
>>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD,
>>>>>>> kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL
>>>>>>> functionality (Dataframes) and doing the documentation, what more needs to
>>>>>>> be done? Optimizations?
>>>>>>>
>>>>>>> I believe that it’s a good place to start using Spark with Kudu and
>>>>>>> compare it to HBase with Spark (not clean).
>>>>>>>
>>>>>>> Thanks,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>>> wrote:
>>>>>>>
>>>>>>> AFAIK no one is working on it, but we did manage to get this in for
>>>>>>> 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>>
>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu,
>>>>>>> but it will require a lot more work to make it fast/useful.
>>>>>>>
>>>>>>> Hope this helps,
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>>> Spark side for it to work?
>>>>>>>>
>>>>>>>> Just curious.
>>>>>>>>
>>>>>>>> Cheers,
>>>>>>>> Ben
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>
Re: Spark on Kudu
Posted by Mark Hamstra <ma...@clearstorydata.com>.
>
> Do they care being able to insert into Kudu with SparkSQL
I care about insert into Kudu with Spark SQL. I'm currently delaying a
refactoring of some Spark SQL-oriented insert functionality while trying to
evaluate what to expect from Kudu. Whether Kudu does a good job supporting
inserts with Spark SQL will be a key consideration as to whether we adopt
Kudu.
On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <jd...@apache.org>
wrote:
> Yup, starting to get a good idea.
>
> What are your DS folks looking for in terms of functionality related to
> Spark? A SparkSQL integration that's as fully featured as Impala's? Do they
> care being able to insert into Kudu with SparkSQL or just being able to
> query real fast? Anything more specific to Spark that I'm missing?
>
> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all
> our resources are committed to making things happen in time, and a more
> fully featured Spark integration isn't in our plans during that period. I'm
> really hoping someone in the community will help with Spark, the same way
> we got a big contribution for the Flume sink.
>
> J-D
>
> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since
>> it’s not “production-ready”, upper management doesn’t want to fully deploy
>> it yet. They just want to keep an eye on it though. Kudu was so much
>> simpler and easier to use in every aspect compared to HBase. Impala was
>> great for the report writers and analysts to experiment with for the short
>> time it was up. But, once again, the only blocker was the lack of Spark
>> support for our Data Developers/Scientists. So, production-level data
>> population won’t happen until then.
>>
>> I hope this helps you get an idea where I am coming from…
>>
>> Cheers,
>> Ben
>>
>>
>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org>
>> wrote:
>>
>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>
>> wrote:
>>
>>> J-D,
>>>
>>> The main thing I hear that Cassandra is being used as an updatable hot
>>> data store to ensure that duplicates are taken care of and idempotency is
>>> maintained. Whether data was directly retrieved from Cassandra for
>>> analytics, reports, or searches, it was not clear as to what was its main
>>> use. Some also just used it for a staging area to populate downstream
>>> tables in parquet format. The last thing I heard was that CQL was terrible,
>>> so that rules out much use of direct queries against it.
>>>
>>
>> I'm no C* expert, but I don't think CQL is meant for real analytics, just
>> ease of use instead of plainly using the APIs. Even then, Kudu should beat
>> it easily on big scans. Same for HBase. We've done benchmarks against the
>> latter, not the former.
>>
>>
>>>
>>> As for our company, we have been looking for an updatable data store for
>>> a long time that can be quickly queried directly either using Spark SQL or
>>> Impala or some other SQL engine and still handle TB or PB of data without
>>> performance degradation and many configuration headaches. For now, we are
>>> using HBase to take on this role with Phoenix as a fast way to directly
>>> query the data. I can see Kudu as the best way to fill this gap easily,
>>> especially being the closest thing to other relational databases out there
>>> in familiarity for the many SQL analytics people in our company. The other
>>> alternative would be to go with AWS Redshift for the same reasons, but it
>>> would come at a cost, of course. If we went with either solutions, Kudu or
>>> Redshift, it would get rid of the need to extract from HBase to parquet
>>> tables or export to PostgreSQL to support more of the SQL language using by
>>> analysts or the reporting software we use..
>>>
>>
>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu.
>> Have you folks tried Kudu with Impala yet with those use cases?
>>
>>
>>>
>>> I hope this helps.
>>>
>>
>> It does, thanks for nice reply.
>>
>>
>>>
>>> Cheers,
>>> Ben
>>>
>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>> wrote:
>>>
>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer
>>> to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who
>>> were also there did say that the hype around Spark isn't dying down.
>>>
>>> There's definitely an overlap in the use cases that Cassandra, HBase,
>>> and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>> interim solution for the use case you describe.
>>>
>>> Nothing significant happened in Kudu over the past month, it's a storage
>>> engine so things move slowly *smile*. I'd love to see more contributions on
>>> the Spark front. I know there's code out there that could be integrated in
>>> kudu-spark, it just needs to land in gerrit. I'm sure folks will happily
>>> review it.
>>>
>>> Do you have relevant experiences you can share? I'd love to learn more
>>> about the use cases for which you envision using Kudu as a C* replacement.
>>>
>>> Thanks,
>>>
>>> J-D
>>>
>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> Hi J-D,
>>>>
>>>> My colleagues recently came back from Strata in San Jose. They told me
>>>> that everything was about Spark and there is a big buzz about the SMACK
>>>> stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra
>>>> is just an interim solution as a low-latency, easily queried data store. I
>>>> was wondering if anything significant happened in regards to Kudu,
>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>> stack acronym to promote?
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>
>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>> wrote:
>>>>
>>>> Hi Ben,
>>>>
>>>> AFAIK no one in the dev community committed to any timeline. I know of
>>>> one person on the Kudu Slack who's working on a better RDD, but that's
>>>> about it.
>>>>
>>>> Regards,
>>>>
>>>> J-D
>>>>
>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com> wrote:
>>>>
>>>>> Hi J-D,
>>>>>
>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a
>>>>> version of Kudu to begin real testing of Spark against it for our devs. At
>>>>> least, I can tell them what timeframe to anticipate.
>>>>>
>>>>> Just curious,
>>>>> *Benjamin Kim*
>>>>> *Data Solutions Architect*
>>>>>
>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>
>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>>> www.amobee.com
>>>>>
>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>> wrote:
>>>>>
>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed
>>>>> either.
>>>>>
>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use
>>>>> scans directly.
>>>>>
>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown.
>>>>> It's really basic.
>>>>>
>>>>> The goal was to provide something for others to contribute to. We have
>>>>> some basic unit tests that others can easily extend. None of us on the team
>>>>> are Spark experts, but we'd be really happy to assist one improve the
>>>>> kudu-spark code.
>>>>>
>>>>> J-D
>>>>>
>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> J-D,
>>>>>>
>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD,
>>>>>> kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL
>>>>>> functionality (Dataframes) and doing the documentation, what more needs to
>>>>>> be done? Optimizations?
>>>>>>
>>>>>> I believe that it’s a good place to start using Spark with Kudu and
>>>>>> compare it to HBase with Spark (not clean).
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> AFAIK no one is working on it, but we did manage to get this in for
>>>>>> 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>
>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu,
>>>>>> but it will require a lot more work to make it fast/useful.
>>>>>>
>>>>>> Hope this helps,
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>> Spark side for it to work?
>>>>>>>
>>>>>>> Just curious.
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
J-D,
I would say that querying is secondary because there is Impala. Plus, Drill is another one. The developers don’t need to be involved there. But, the wow factor for most would be the ability to upsert data into Kudu programmatically using DataFrames (SQLContext). When this is available, we would move more earnestly to using Kudu as a viable solution and migrate from HBase. If you didn’t know, HBase is a pain to work with.
Cheers,Ben
> On Apr 11, 2016, at 8:22 AM, Jean-Daniel Cryans <jd...@apache.org> wrote:
>
> Ben,
>
> Thanks for the additional information. You know, I was expecting that querying would be the most important part and writing into Kudu was secondary since it can easily be done with the Java API, but you guys are proving me wrong.
>
> I'm starting to think we should host a Spark + Kudu hackathon here in the Bay Area. Bringing experts together from both sides might unlock some potential. We did that with Drill and it was successful: https://issues.apache.org/jira/browse/DRILL-4241 <https://issues.apache.org/jira/browse/DRILL-4241>
>
> J-D
>
> On Sun, Apr 10, 2016 at 1:03 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> J-D,
>
> Priority is data population of tables using DataFrames. That’s all I heard the most. It is the same with HBase. But, I bet once this is taken care of, the fast querying part would follow because the data is now in Kudu. If SparkSQL integration is there, that would simplify things even more. That wouldn’t be bad to have.
>
> Cheers,
> Ben
>
>
>> On Apr 10, 2016, at 12:23 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> Yup, starting to get a good idea.
>>
>> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>>
>> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>>
>> J-D
>>
>> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>>
>> I hope this helps you get an idea where I am coming from…
>>
>> Cheers,
>> Ben
>>
>>
>>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> J-D,
>>>
>>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>>
>>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>>
>>>
>>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>>
>>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>>
>>>
>>> I hope this helps.
>>>
>>> It does, thanks for nice reply.
>>>
>>>
>>> Cheers,
>>> Ben
>>>
>>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>>
>>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>>
>>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>>
>>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>>
>>>> Thanks,
>>>>
>>>> J-D
>>>>
>>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> Hi J-D,
>>>>
>>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>
>>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> Hi Ben,
>>>>>
>>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>>
>>>>> Regards,
>>>>>
>>>>> J-D
>>>>>
>>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>>> Hi J-D,
>>>>>
>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>>
>>>>> Just curious,
>>>>> Benjamin Kim
>>>>> Data Solutions Architect
>>>>>
>>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>>
>>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>>
>>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>>
>>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>>
>>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>>
>>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> J-D,
>>>>>>
>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>>
>>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>>
>>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>>
>>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>>
>>>>>>> Hope this helps,
>>>>>>>
>>>>>>> J-D
>>>>>>>
>>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>>
>>>>>>> Just curious.
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
Ben,
Thanks for the additional information. You know, I was expecting that
querying would be the most important part and writing into Kudu was
secondary since it can easily be done with the Java API, but you guys are
proving me wrong.
I'm starting to think we should host a Spark + Kudu hackathon here in the
Bay Area. Bringing experts together from both sides might unlock some
potential. We did that with Drill and it was successful:
https://issues.apache.org/jira/browse/DRILL-4241
J-D
On Sun, Apr 10, 2016 at 1:03 PM, Benjamin Kim <bb...@gmail.com> wrote:
> J-D,
>
> Priority is data population of tables using DataFrames. That’s all I heard
> the most. It is the same with HBase. But, I bet once this is taken care of,
> the fast querying part would follow because the data is now in Kudu. If
> SparkSQL integration is there, that would simplify things even more. That
> wouldn’t be bad to have.
>
> Cheers,
> Ben
>
>
> On Apr 10, 2016, at 12:23 PM, Jean-Daniel Cryans <jd...@apache.org>
> wrote:
>
> Yup, starting to get a good idea.
>
> What are your DS folks looking for in terms of functionality related to
> Spark? A SparkSQL integration that's as fully featured as Impala's? Do they
> care being able to insert into Kudu with SparkSQL or just being able to
> query real fast? Anything more specific to Spark that I'm missing?
>
> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all
> our resources are committed to making things happen in time, and a more
> fully featured Spark integration isn't in our plans during that period. I'm
> really hoping someone in the community will help with Spark, the same way
> we got a big contribution for the Flume sink.
>
> J-D
>
> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since
>> it’s not “production-ready”, upper management doesn’t want to fully deploy
>> it yet. They just want to keep an eye on it though. Kudu was so much
>> simpler and easier to use in every aspect compared to HBase. Impala was
>> great for the report writers and analysts to experiment with for the short
>> time it was up. But, once again, the only blocker was the lack of Spark
>> support for our Data Developers/Scientists. So, production-level data
>> population won’t happen until then.
>>
>> I hope this helps you get an idea where I am coming from…
>>
>> Cheers,
>> Ben
>>
>>
>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org>
>> wrote:
>>
>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com>
>> wrote:
>>
>>> J-D,
>>>
>>> The main thing I hear that Cassandra is being used as an updatable hot
>>> data store to ensure that duplicates are taken care of and idempotency is
>>> maintained. Whether data was directly retrieved from Cassandra for
>>> analytics, reports, or searches, it was not clear as to what was its main
>>> use. Some also just used it for a staging area to populate downstream
>>> tables in parquet format. The last thing I heard was that CQL was terrible,
>>> so that rules out much use of direct queries against it.
>>>
>>
>> I'm no C* expert, but I don't think CQL is meant for real analytics, just
>> ease of use instead of plainly using the APIs. Even then, Kudu should beat
>> it easily on big scans. Same for HBase. We've done benchmarks against the
>> latter, not the former.
>>
>>
>>>
>>> As for our company, we have been looking for an updatable data store for
>>> a long time that can be quickly queried directly either using Spark SQL or
>>> Impala or some other SQL engine and still handle TB or PB of data without
>>> performance degradation and many configuration headaches. For now, we are
>>> using HBase to take on this role with Phoenix as a fast way to directly
>>> query the data. I can see Kudu as the best way to fill this gap easily,
>>> especially being the closest thing to other relational databases out there
>>> in familiarity for the many SQL analytics people in our company. The other
>>> alternative would be to go with AWS Redshift for the same reasons, but it
>>> would come at a cost, of course. If we went with either solutions, Kudu or
>>> Redshift, it would get rid of the need to extract from HBase to parquet
>>> tables or export to PostgreSQL to support more of the SQL language using by
>>> analysts or the reporting software we use..
>>>
>>
>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu.
>> Have you folks tried Kudu with Impala yet with those use cases?
>>
>>
>>>
>>> I hope this helps.
>>>
>>
>> It does, thanks for nice reply.
>>
>>
>>>
>>> Cheers,
>>> Ben
>>>
>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>>> wrote:
>>>
>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer
>>> to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who
>>> were also there did say that the hype around Spark isn't dying down.
>>>
>>> There's definitely an overlap in the use cases that Cassandra, HBase,
>>> and Kudu cater to. I wouldn't go as far as saying that C* is just an
>>> interim solution for the use case you describe.
>>>
>>> Nothing significant happened in Kudu over the past month, it's a storage
>>> engine so things move slowly *smile*. I'd love to see more contributions on
>>> the Spark front. I know there's code out there that could be integrated in
>>> kudu-spark, it just needs to land in gerrit. I'm sure folks will happily
>>> review it.
>>>
>>> Do you have relevant experiences you can share? I'd love to learn more
>>> about the use cases for which you envision using Kudu as a C* replacement.
>>>
>>> Thanks,
>>>
>>> J-D
>>>
>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> Hi J-D,
>>>>
>>>> My colleagues recently came back from Strata in San Jose. They told me
>>>> that everything was about Spark and there is a big buzz about the SMACK
>>>> stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra
>>>> is just an interim solution as a low-latency, easily queried data store. I
>>>> was wondering if anything significant happened in regards to Kudu,
>>>> especially on the Spark front. Plus, can you come up with your own proposed
>>>> stack acronym to promote?
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>
>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>> wrote:
>>>>
>>>> Hi Ben,
>>>>
>>>> AFAIK no one in the dev community committed to any timeline. I know of
>>>> one person on the Kudu Slack who's working on a better RDD, but that's
>>>> about it.
>>>>
>>>> Regards,
>>>>
>>>> J-D
>>>>
>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com> wrote:
>>>>
>>>>> Hi J-D,
>>>>>
>>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a
>>>>> version of Kudu to begin real testing of Spark against it for our devs. At
>>>>> least, I can tell them what timeframe to anticipate.
>>>>>
>>>>> Just curious,
>>>>> *Benjamin Kim*
>>>>> *Data Solutions Architect*
>>>>>
>>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>>
>>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>>> www.amobee.com
>>>>>
>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>> wrote:
>>>>>
>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed
>>>>> either.
>>>>>
>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use
>>>>> scans directly.
>>>>>
>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown.
>>>>> It's really basic.
>>>>>
>>>>> The goal was to provide something for others to contribute to. We have
>>>>> some basic unit tests that others can easily extend. None of us on the team
>>>>> are Spark experts, but we'd be really happy to assist one improve the
>>>>> kudu-spark code.
>>>>>
>>>>> J-D
>>>>>
>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> J-D,
>>>>>>
>>>>>> It looks like it fulfills most of the basic requirements (kudu RDD,
>>>>>> kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL
>>>>>> functionality (Dataframes) and doing the documentation, what more needs to
>>>>>> be done? Optimizations?
>>>>>>
>>>>>> I believe that it’s a good place to start using Spark with Kudu and
>>>>>> compare it to HBase with Spark (not clean).
>>>>>>
>>>>>> Thanks,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>>> wrote:
>>>>>>
>>>>>> AFAIK no one is working on it, but we did manage to get this in for
>>>>>> 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>>
>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu,
>>>>>> but it will require a lot more work to make it fast/useful.
>>>>>>
>>>>>> Hope this helps,
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>>> Spark side for it to work?
>>>>>>>
>>>>>>> Just curious.
>>>>>>>
>>>>>>> Cheers,
>>>>>>> Ben
>>>>>>>
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
J-D,
Priority is data population of tables using DataFrames. That’s all I heard the most. It is the same with HBase. But, I bet once this is taken care of, the fast querying part would follow because the data is now in Kudu. If SparkSQL integration is there, that would simplify things even more. That wouldn’t be bad to have.
Cheers,
Ben
> On Apr 10, 2016, at 12:23 PM, Jean-Daniel Cryans <jd...@apache.org> wrote:
>
> Yup, starting to get a good idea.
>
> What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
>
> FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
>
> J-D
>
> On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
>
> I hope this helps you get an idea where I am coming from…
>
> Cheers,
> Ben
>
>
>> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> J-D,
>>
>> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>>
>> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>>
>>
>> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>>
>> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>>
>>
>> I hope this helps.
>>
>> It does, thanks for nice reply.
>>
>>
>> Cheers,
>> Ben
>>
>>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>>
>>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>>
>>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>>
>>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>>
>>> Thanks,
>>>
>>> J-D
>>>
>>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> Hi J-D,
>>>
>>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>>
>>> Cheers,
>>> Ben
>>>
>>>
>>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> Hi Ben,
>>>>
>>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>>
>>>> Regards,
>>>>
>>>> J-D
>>>>
>>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>>> Hi J-D,
>>>>
>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>>
>>>> Just curious,
>>>> Benjamin Kim
>>>> Data Solutions Architect
>>>>
>>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>>
>>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>>
>>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>>
>>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>>
>>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>>
>>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>>
>>>>> J-D
>>>>>
>>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> J-D,
>>>>>
>>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>>
>>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>>
>>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>>
>>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>>
>>>>>> Hope this helps,
>>>>>>
>>>>>> J-D
>>>>>>
>>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>>
>>>>>> Just curious.
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
Yup, starting to get a good idea.
What are your DS folks looking for in terms of functionality related to
Spark? A SparkSQL integration that's as fully featured as Impala's? Do they
care being able to insert into Kudu with SparkSQL or just being able to
query real fast? Anything more specific to Spark that I'm missing?
FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all
our resources are committed to making things happen in time, and a more
fully featured Spark integration isn't in our plans during that period. I'm
really hoping someone in the community will help with Spark, the same way
we got a big contribution for the Flume sink.
J-D
On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <bb...@gmail.com> wrote:
> Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since
> it’s not “production-ready”, upper management doesn’t want to fully deploy
> it yet. They just want to keep an eye on it though. Kudu was so much
> simpler and easier to use in every aspect compared to HBase. Impala was
> great for the report writers and analysts to experiment with for the short
> time it was up. But, once again, the only blocker was the lack of Spark
> support for our Data Developers/Scientists. So, production-level data
> population won’t happen until then.
>
> I hope this helps you get an idea where I am coming from…
>
> Cheers,
> Ben
>
>
> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org>
> wrote:
>
> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> J-D,
>>
>> The main thing I hear that Cassandra is being used as an updatable hot
>> data store to ensure that duplicates are taken care of and idempotency is
>> maintained. Whether data was directly retrieved from Cassandra for
>> analytics, reports, or searches, it was not clear as to what was its main
>> use. Some also just used it for a staging area to populate downstream
>> tables in parquet format. The last thing I heard was that CQL was terrible,
>> so that rules out much use of direct queries against it.
>>
>
> I'm no C* expert, but I don't think CQL is meant for real analytics, just
> ease of use instead of plainly using the APIs. Even then, Kudu should beat
> it easily on big scans. Same for HBase. We've done benchmarks against the
> latter, not the former.
>
>
>>
>> As for our company, we have been looking for an updatable data store for
>> a long time that can be quickly queried directly either using Spark SQL or
>> Impala or some other SQL engine and still handle TB or PB of data without
>> performance degradation and many configuration headaches. For now, we are
>> using HBase to take on this role with Phoenix as a fast way to directly
>> query the data. I can see Kudu as the best way to fill this gap easily,
>> especially being the closest thing to other relational databases out there
>> in familiarity for the many SQL analytics people in our company. The other
>> alternative would be to go with AWS Redshift for the same reasons, but it
>> would come at a cost, of course. If we went with either solutions, Kudu or
>> Redshift, it would get rid of the need to extract from HBase to parquet
>> tables or export to PostgreSQL to support more of the SQL language using by
>> analysts or the reporting software we use..
>>
>
> Ok, the usual then *smile*. Looks like we're not too far off with Kudu.
> Have you folks tried Kudu with Impala yet with those use cases?
>
>
>>
>> I hope this helps.
>>
>
> It does, thanks for nice reply.
>
>
>>
>> Cheers,
>> Ben
>>
>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>
>> wrote:
>>
>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer
>> to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who
>> were also there did say that the hype around Spark isn't dying down.
>>
>> There's definitely an overlap in the use cases that Cassandra, HBase, and
>> Kudu cater to. I wouldn't go as far as saying that C* is just an interim
>> solution for the use case you describe.
>>
>> Nothing significant happened in Kudu over the past month, it's a storage
>> engine so things move slowly *smile*. I'd love to see more contributions on
>> the Spark front. I know there's code out there that could be integrated in
>> kudu-spark, it just needs to land in gerrit. I'm sure folks will happily
>> review it.
>>
>> Do you have relevant experiences you can share? I'd love to learn more
>> about the use cases for which you envision using Kudu as a C* replacement.
>>
>> Thanks,
>>
>> J-D
>>
>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> Hi J-D,
>>>
>>> My colleagues recently came back from Strata in San Jose. They told me
>>> that everything was about Spark and there is a big buzz about the SMACK
>>> stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra
>>> is just an interim solution as a low-latency, easily queried data store. I
>>> was wondering if anything significant happened in regards to Kudu,
>>> especially on the Spark front. Plus, can you come up with your own proposed
>>> stack acronym to promote?
>>>
>>> Cheers,
>>> Ben
>>>
>>>
>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>
>>> wrote:
>>>
>>> Hi Ben,
>>>
>>> AFAIK no one in the dev community committed to any timeline. I know of
>>> one person on the Kudu Slack who's working on a better RDD, but that's
>>> about it.
>>>
>>> Regards,
>>>
>>> J-D
>>>
>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com> wrote:
>>>
>>>> Hi J-D,
>>>>
>>>> Quick question… Is there an ETA for KUDU-1214? I want to target a
>>>> version of Kudu to begin real testing of Spark against it for our devs. At
>>>> least, I can tell them what timeframe to anticipate.
>>>>
>>>> Just curious,
>>>> *Benjamin Kim*
>>>> *Data Solutions Architect*
>>>>
>>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>>
>>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>>> www.amobee.com
>>>>
>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>> wrote:
>>>>
>>>> The DStream stuff isn't there at all. I'm not sure if it's needed
>>>> either.
>>>>
>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use
>>>> scans directly.
>>>>
>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown.
>>>> It's really basic.
>>>>
>>>> The goal was to provide something for others to contribute to. We have
>>>> some basic unit tests that others can easily extend. None of us on the team
>>>> are Spark experts, but we'd be really happy to assist one improve the
>>>> kudu-spark code.
>>>>
>>>> J-D
>>>>
>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> J-D,
>>>>>
>>>>> It looks like it fulfills most of the basic requirements (kudu RDD,
>>>>> kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL
>>>>> functionality (Dataframes) and doing the documentation, what more needs to
>>>>> be done? Optimizations?
>>>>>
>>>>> I believe that it’s a good place to start using Spark with Kudu and
>>>>> compare it to HBase with Spark (not clean).
>>>>>
>>>>> Thanks,
>>>>> Ben
>>>>>
>>>>>
>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>>> wrote:
>>>>>
>>>>> AFAIK no one is working on it, but we did manage to get this in for
>>>>> 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>>
>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu,
>>>>> but it will require a lot more work to make it fast/useful.
>>>>>
>>>>> Hope this helps,
>>>>>
>>>>> J-D
>>>>>
>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>>> Spark side for it to work?
>>>>>>
>>>>>> Just curious.
>>>>>>
>>>>>> Cheers,
>>>>>> Ben
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
I hope this helps you get an idea where I am coming from…
Cheers,
Ben
> On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <jd...@apache.org> wrote:
>
> On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> J-D,
>
> The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
>
> I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
>
>
> As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
>
> Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
>
>
> I hope this helps.
>
> It does, thanks for nice reply.
>
>
> Cheers,
> Ben
>
>> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>>
>> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>>
>> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>>
>> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>>
>> Thanks,
>>
>> J-D
>>
>> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> Hi J-D,
>>
>> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>>
>> Cheers,
>> Ben
>>
>>
>>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> Hi Ben,
>>>
>>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>>
>>> Regards,
>>>
>>> J-D
>>>
>>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>>> Hi J-D,
>>>
>>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>>
>>> Just curious,
>>> Benjamin Kim
>>> Data Solutions Architect
>>>
>>> [a•mo•bee] (n.) the company defining digital marketing.
>>>
>>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>>
>>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>>
>>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>>
>>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>>
>>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>>
>>>> J-D
>>>>
>>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> J-D,
>>>>
>>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>>
>>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>>
>>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>>
>>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>>
>>>>> Hope this helps,
>>>>>
>>>>> J-D
>>>>>
>>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>>
>>>>> Just curious.
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <bb...@gmail.com> wrote:
> J-D,
>
> The main thing I hear that Cassandra is being used as an updatable hot
> data store to ensure that duplicates are taken care of and idempotency is
> maintained. Whether data was directly retrieved from Cassandra for
> analytics, reports, or searches, it was not clear as to what was its main
> use. Some also just used it for a staging area to populate downstream
> tables in parquet format. The last thing I heard was that CQL was terrible,
> so that rules out much use of direct queries against it.
>
I'm no C* expert, but I don't think CQL is meant for real analytics, just
ease of use instead of plainly using the APIs. Even then, Kudu should beat
it easily on big scans. Same for HBase. We've done benchmarks against the
latter, not the former.
>
> As for our company, we have been looking for an updatable data store for a
> long time that can be quickly queried directly either using Spark SQL or
> Impala or some other SQL engine and still handle TB or PB of data without
> performance degradation and many configuration headaches. For now, we are
> using HBase to take on this role with Phoenix as a fast way to directly
> query the data. I can see Kudu as the best way to fill this gap easily,
> especially being the closest thing to other relational databases out there
> in familiarity for the many SQL analytics people in our company. The other
> alternative would be to go with AWS Redshift for the same reasons, but it
> would come at a cost, of course. If we went with either solutions, Kudu or
> Redshift, it would get rid of the need to extract from HBase to parquet
> tables or export to PostgreSQL to support more of the SQL language using by
> analysts or the reporting software we use..
>
Ok, the usual then *smile*. Looks like we're not too far off with Kudu.
Have you folks tried Kudu with Impala yet with those use cases?
>
> I hope this helps.
>
It does, thanks for nice reply.
>
> Cheers,
> Ben
>
> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org>
> wrote:
>
> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to
> "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who
> were also there did say that the hype around Spark isn't dying down.
>
> There's definitely an overlap in the use cases that Cassandra, HBase, and
> Kudu cater to. I wouldn't go as far as saying that C* is just an interim
> solution for the use case you describe.
>
> Nothing significant happened in Kudu over the past month, it's a storage
> engine so things move slowly *smile*. I'd love to see more contributions on
> the Spark front. I know there's code out there that could be integrated in
> kudu-spark, it just needs to land in gerrit. I'm sure folks will happily
> review it.
>
> Do you have relevant experiences you can share? I'd love to learn more
> about the use cases for which you envision using Kudu as a C* replacement.
>
> Thanks,
>
> J-D
>
> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> Hi J-D,
>>
>> My colleagues recently came back from Strata in San Jose. They told me
>> that everything was about Spark and there is a big buzz about the SMACK
>> stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra
>> is just an interim solution as a low-latency, easily queried data store. I
>> was wondering if anything significant happened in regards to Kudu,
>> especially on the Spark front. Plus, can you come up with your own proposed
>> stack acronym to promote?
>>
>> Cheers,
>> Ben
>>
>>
>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>
>> wrote:
>>
>> Hi Ben,
>>
>> AFAIK no one in the dev community committed to any timeline. I know of
>> one person on the Kudu Slack who's working on a better RDD, but that's
>> about it.
>>
>> Regards,
>>
>> J-D
>>
>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com> wrote:
>>
>>> Hi J-D,
>>>
>>> Quick question… Is there an ETA for KUDU-1214? I want to target a
>>> version of Kudu to begin real testing of Spark against it for our devs. At
>>> least, I can tell them what timeframe to anticipate.
>>>
>>> Just curious,
>>> *Benjamin Kim*
>>> *Data Solutions Architect*
>>>
>>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>>
>>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>>> www.amobee.com
>>>
>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>
>>> wrote:
>>>
>>> The DStream stuff isn't there at all. I'm not sure if it's needed
>>> either.
>>>
>>> The kuduRDD is just leveraging the MR input format, ideally we'd use
>>> scans directly.
>>>
>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's
>>> really basic.
>>>
>>> The goal was to provide something for others to contribute to. We have
>>> some basic unit tests that others can easily extend. None of us on the team
>>> are Spark experts, but we'd be really happy to assist one improve the
>>> kudu-spark code.
>>>
>>> J-D
>>>
>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> J-D,
>>>>
>>>> It looks like it fulfills most of the basic requirements (kudu RDD,
>>>> kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL
>>>> functionality (Dataframes) and doing the documentation, what more needs to
>>>> be done? Optimizations?
>>>>
>>>> I believe that it’s a good place to start using Spark with Kudu and
>>>> compare it to HBase with Spark (not clean).
>>>>
>>>> Thanks,
>>>> Ben
>>>>
>>>>
>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>
>>>> wrote:
>>>>
>>>> AFAIK no one is working on it, but we did manage to get this in for
>>>> 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>>
>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but
>>>> it will require a lot more work to make it fast/useful.
>>>>
>>>> Hope this helps,
>>>>
>>>> J-D
>>>>
>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>
>>>> wrote:
>>>>
>>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>>> Spark side for it to work?
>>>>>
>>>>> Just curious.
>>>>>
>>>>> Cheers,
>>>>> Ben
>>>>>
>>>>>
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
J-D,
The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
I hope this helps.
Cheers,
Ben
> On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <jd...@apache.org> wrote:
>
> Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
>
> There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
>
> Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
>
> Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
>
> Thanks,
>
> J-D
>
> On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
> Hi J-D,
>
> My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
>
> Cheers,
> Ben
>
>
>> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> Hi Ben,
>>
>> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>>
>> Regards,
>>
>> J-D
>>
>> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
>> Hi J-D,
>>
>> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>>
>> Just curious,
>> Benjamin Kim
>> Data Solutions Architect
>>
>> [a•mo•bee] (n.) the company defining digital marketing.
>>
>> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>>
>>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>>
>>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>>
>>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>>
>>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>>
>>> J-D
>>>
>>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> J-D,
>>>
>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>>
>>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>>
>>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>>
>>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>>
>>>> Hope this helps,
>>>>
>>>> J-D
>>>>
>>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>>
>>>> Just curious.
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to
"Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who
were also there did say that the hype around Spark isn't dying down.
There's definitely an overlap in the use cases that Cassandra, HBase, and
Kudu cater to. I wouldn't go as far as saying that C* is just an interim
solution for the use case you describe.
Nothing significant happened in Kudu over the past month, it's a storage
engine so things move slowly *smile*. I'd love to see more contributions on
the Spark front. I know there's code out there that could be integrated in
kudu-spark, it just needs to land in gerrit. I'm sure folks will happily
review it.
Do you have relevant experiences you can share? I'd love to learn more
about the use cases for which you envision using Kudu as a C* replacement.
Thanks,
J-D
On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <bb...@gmail.com> wrote:
> Hi J-D,
>
> My colleagues recently came back from Strata in San Jose. They told me
> that everything was about Spark and there is a big buzz about the SMACK
> stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra
> is just an interim solution as a low-latency, easily queried data store. I
> was wondering if anything significant happened in regards to Kudu,
> especially on the Spark front. Plus, can you come up with your own proposed
> stack acronym to promote?
>
> Cheers,
> Ben
>
>
> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org>
> wrote:
>
> Hi Ben,
>
> AFAIK no one in the dev community committed to any timeline. I know of one
> person on the Kudu Slack who's working on a better RDD, but that's about it.
>
> Regards,
>
> J-D
>
> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com> wrote:
>
>> Hi J-D,
>>
>> Quick question… Is there an ETA for KUDU-1214? I want to target a version
>> of Kudu to begin real testing of Spark against it for our devs. At least, I
>> can tell them what timeframe to anticipate.
>>
>> Just curious,
>> *Benjamin Kim*
>> *Data Solutions Architect*
>>
>> [a•mo•bee] *(n.)* the company defining digital marketing.
>>
>> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
>> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
>> www.amobee.com
>>
>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>
>> wrote:
>>
>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>
>> The kuduRDD is just leveraging the MR input format, ideally we'd use
>> scans directly.
>>
>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's
>> really basic.
>>
>> The goal was to provide something for others to contribute to. We have
>> some basic unit tests that others can easily extend. None of us on the team
>> are Spark experts, but we'd be really happy to assist one improve the
>> kudu-spark code.
>>
>> J-D
>>
>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> J-D,
>>>
>>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu
>>> DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL
>>> functionality (Dataframes) and doing the documentation, what more needs to
>>> be done? Optimizations?
>>>
>>> I believe that it’s a good place to start using Spark with Kudu and
>>> compare it to HBase with Spark (not clean).
>>>
>>> Thanks,
>>> Ben
>>>
>>>
>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>
>>> wrote:
>>>
>>> AFAIK no one is working on it, but we did manage to get this in for
>>> 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>>
>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but
>>> it will require a lot more work to make it fast/useful.
>>>
>>> Hope this helps,
>>>
>>> J-D
>>>
>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com>
>>> wrote:
>>>
>>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>>> mean that Spark will be able to work with Kudu both programmatically and as
>>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>>> Spark side for it to work?
>>>>
>>>> Just curious.
>>>>
>>>> Cheers,
>>>> Ben
>>>>
>>>>
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Benjamin Kim <bb...@gmail.com>.
Hi J-D,
My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
Cheers,
Ben
> On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <jd...@apache.org> wrote:
>
> Hi Ben,
>
> AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
>
> Regards,
>
> J-D
>
> On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bkim@amobee.com <ma...@amobee.com>> wrote:
> Hi J-D,
>
> Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
>
> Just curious,
> Benjamin Kim
> Data Solutions Architect
>
> [a•mo•bee] (n.) the company defining digital marketing.
>
> Mobile: +1 818 635 2900 <tel:%2B1%20818%20635%202900>
> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 | www.amobee.com <http://www.amobee.com/>
>
>> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>
>> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>>
>> The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
>>
>> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
>>
>> The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
>>
>> J-D
>>
>> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>> J-D,
>>
>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
>>
>> I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
>>
>> Thanks,
>> Ben
>>
>>
>>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jdcryans@apache.org <ma...@apache.org>> wrote:
>>>
>>> AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321 <https://issues.cloudera.org/browse/KUDU-1321>
>>>
>>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
>>>
>>> Hope this helps,
>>>
>>> J-D
>>>
>>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bbuild11@gmail.com <ma...@gmail.com>> wrote:
>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?
>>>
>>> Just curious.
>>>
>>> Cheers,
>>> Ben
>>>
>>>
>>
>>
>
>
Re: Spark on Kudu
Posted by Jean-Daniel Cryans <jd...@apache.org>.
Hi Ben,
AFAIK no one in the dev community committed to any timeline. I know of one
person on the Kudu Slack who's working on a better RDD, but that's about it.
Regards,
J-D
On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <bk...@amobee.com> wrote:
> Hi J-D,
>
> Quick question… Is there an ETA for KUDU-1214? I want to target a version
> of Kudu to begin real testing of Spark against it for our devs. At least, I
> can tell them what timeframe to anticipate.
>
> Just curious,
> *Benjamin Kim*
> *Data Solutions Architect*
>
> [a•mo•bee] *(n.)* the company defining digital marketing.
>
> *Mobile: +1 818 635 2900 <%2B1%20818%20635%202900>*
> 3250 Ocean Park Blvd, Suite 200 | Santa Monica, CA 90405 |
> www.amobee.com
>
> On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <jd...@apache.org>
> wrote:
>
> The DStream stuff isn't there at all. I'm not sure if it's needed either.
>
> The kuduRDD is just leveraging the MR input format, ideally we'd use scans
> directly.
>
> The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's
> really basic.
>
> The goal was to provide something for others to contribute to. We have
> some basic unit tests that others can easily extend. None of us on the team
> are Spark experts, but we'd be really happy to assist one improve the
> kudu-spark code.
>
> J-D
>
> On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <bb...@gmail.com> wrote:
>
>> J-D,
>>
>> It looks like it fulfills most of the basic requirements (kudu RDD, kudu
>> DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL
>> functionality (Dataframes) and doing the documentation, what more needs to
>> be done? Optimizations?
>>
>> I believe that it’s a good place to start using Spark with Kudu and
>> compare it to HBase with Spark (not clean).
>>
>> Thanks,
>> Ben
>>
>>
>> On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <jd...@apache.org>
>> wrote:
>>
>> AFAIK no one is working on it, but we did manage to get this in for
>> 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
>>
>> It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but
>> it will require a lot more work to make it fast/useful.
>>
>> Hope this helps,
>>
>> J-D
>>
>> On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <bb...@gmail.com> wrote:
>>
>>> I see this KUDU-1214 <https://issues.cloudera.org/browse/KUDU-1214> targeted
>>> for 0.8.0, but I see no progress on it. When this is complete, will this
>>> mean that Spark will be able to work with Kudu both programmatically and as
>>> a client via Spark SQL? Or is there more work that needs to be done on the
>>> Spark side for it to work?
>>>
>>> Just curious.
>>>
>>> Cheers,
>>> Ben
>>>
>>>
>>
>>
>
>