Re: How to efficiently join HBase tables?

[Jason Rutherglen <ja...@gmail.com>]

> The Hive-HBase integration allows you to create Hive tables that are backed
> by HBase

In addition, HBase can be made to go faster for MapReduce jobs, if the
HFile's could be used directly in HDFS, rather than proxying through
the RegionServer.

I'd imagine that join operations do not require realtime-ness, and so
faster batch jobs using Hive -> frozen HBase files in HDFS could be
the optimal way to go?

On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <pa...@cloudera.com> wrote:
> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com> wrote:
>
>> For my need I don't really need the general case, but even if I did I think
>> it can probably be done simpler.
>> The main problem is getting the data from both tables into the same MR job,
>> without resorting to lookups. So without the theoretical
>> MutliTableInputFormat, I could just copy all the data from both tables into
>> a temp table, just append the source table name to the row keys to make
>> sure
>> there are no conflicts. When all the data from both tables is in the same
>> temp table, run a MR job. For each row the mapper should emit a key which
>> is
>> composed of all the values of the join fields in that row (the value can be
>> emitted as is). This will cause all the rows from both tables, with same
>> join field values to arrive at the reducer together. The reducer could then
>> iterate over them and produce the Cartesian product as needed.
>>
>> I still don't like having to copy all the data into a temp table just
>> because I can't feed two tables into the MR job.
>>
>
> Loading the smaller table in memory is called a map join, versus a
> reduce-side join (a.k.a. common join). One reason to prefer a map join is
> you avoid the shuffle phase which potentially involves several trips to disk
> for the intermediate records due to spills, and also once through the
> network to get each intermediate KV pair to the right reducer. With a map
> join, everything is local, except for the part where you load the small
> table.
>
>
>>
>> As Jason Rutherglen mentioned above, Hive can do joins. I don't know if it
>> can do them for HBase and it will not suit my needs, but it would be
>> interesting to know how is it doing them, if anyone knows.
>>
>
> The Hive-HBase integration allows you to create Hive tables that are backed
> by HBase. You can do joins on those tables (and also with standard Hive
> tables). It might be worth trying out in your case as it lets you easily see
> the load characteristics and the job runtime without much coding investment.
>
> There are probably some specific optimizations that can be applied to your
> situation, but it's hard to say without knowing your use-case.
>
> Regards,
>
> - Patrick
>
>
>> -eran
>>
>>
>>
>> On Tue, May 31, 2011 at 22:02, Ted Dunning <td...@maprtech.com> wrote:
>>
>> > The Cartesian product often makes an honest-to-god join not such a good
>> > idea
>> > on large data.  The common alternative is co-group
>> > which is basically like doing the hard work of the join, but involves
>> > stopping just before emitting the cartesian product.  This allows
>> > you to inject whatever cleverness you need at this point.
>> >
>> > Common kinds of cleverness include down-sampling of problematically large
>> > sets of candidates.
>> >
>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
>> > <mi...@hotmail.com>wrote:
>> >
>> > > So the underlying problem that the OP was trying to solve was how to
>> join
>> > > two tables from HBase.
>> > > Unfortunately I goofed.
>> > > I gave a quick and dirty solution that is a bit incomplete. They row
>> key
>> > in
>> > > the temp table has to be unique and I forgot about the Cartesian
>> > > product. So my solution wouldn't work in the general case.
>> > >
>> >
>>
>

Re: How to efficiently join HBase tables?

[Eran Kutner <er...@gigya.com>]

Thanks everyone for all the helpful insights!

-eran



On Wed, Jun 1, 2011 at 03:41, Jason Rutherglen
<ja...@gmail.com>wrote:

> > I'd imagine that join operations do not require realtime-ness, and so
> > faster batch jobs using Hive -> frozen HBase files in HDFS could be
> > the optimal way to go?
>
> In addition to lessening the load on the perhaps live RegionServer.
> There's no Jira for this, I'm tempted to open one.
>
> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> <ja...@gmail.com> wrote:
> >> The Hive-HBase integration allows you to create Hive tables that are
> backed
> >> by HBase
> >
> > In addition, HBase can be made to go faster for MapReduce jobs, if the
> > HFile's could be used directly in HDFS, rather than proxying through
> > the RegionServer.
> >
> > I'd imagine that join operations do not require realtime-ness, and so
> > faster batch jobs using Hive -> frozen HBase files in HDFS could be
> > the optimal way to go?
> >
> > On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <pa...@cloudera.com>
> wrote:
> >> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com> wrote:
> >>
> >>> For my need I don't really need the general case, but even if I did I
> think
> >>> it can probably be done simpler.
> >>> The main problem is getting the data from both tables into the same MR
> job,
> >>> without resorting to lookups. So without the theoretical
> >>> MutliTableInputFormat, I could just copy all the data from both tables
> into
> >>> a temp table, just append the source table name to the row keys to make
> >>> sure
> >>> there are no conflicts. When all the data from both tables is in the
> same
> >>> temp table, run a MR job. For each row the mapper should emit a key
> which
> >>> is
> >>> composed of all the values of the join fields in that row (the value
> can be
> >>> emitted as is). This will cause all the rows from both tables, with
> same
> >>> join field values to arrive at the reducer together. The reducer could
> then
> >>> iterate over them and produce the Cartesian product as needed.
> >>>
> >>> I still don't like having to copy all the data into a temp table just
> >>> because I can't feed two tables into the MR job.
> >>>
> >>
> >> Loading the smaller table in memory is called a map join, versus a
> >> reduce-side join (a.k.a. common join). One reason to prefer a map join
> is
> >> you avoid the shuffle phase which potentially involves several trips to
> disk
> >> for the intermediate records due to spills, and also once through the
> >> network to get each intermediate KV pair to the right reducer. With a
> map
> >> join, everything is local, except for the part where you load the small
> >> table.
> >>
> >>
> >>>
> >>> As Jason Rutherglen mentioned above, Hive can do joins. I don't know if
> it
> >>> can do them for HBase and it will not suit my needs, but it would be
> >>> interesting to know how is it doing them, if anyone knows.
> >>>
> >>
> >> The Hive-HBase integration allows you to create Hive tables that are
> backed
> >> by HBase. You can do joins on those tables (and also with standard Hive
> >> tables). It might be worth trying out in your case as it lets you easily
> see
> >> the load characteristics and the job runtime without much coding
> investment.
> >>
> >> There are probably some specific optimizations that can be applied to
> your
> >> situation, but it's hard to say without knowing your use-case.
> >>
> >> Regards,
> >>
> >> - Patrick
> >>
> >>
> >>> -eran
> >>>
> >>>
> >>>
> >>> On Tue, May 31, 2011 at 22:02, Ted Dunning <td...@maprtech.com>
> wrote:
> >>>
> >>> > The Cartesian product often makes an honest-to-god join not such a
> good
> >>> > idea
> >>> > on large data.  The common alternative is co-group
> >>> > which is basically like doing the hard work of the join, but involves
> >>> > stopping just before emitting the cartesian product.  This allows
> >>> > you to inject whatever cleverness you need at this point.
> >>> >
> >>> > Common kinds of cleverness include down-sampling of problematically
> large
> >>> > sets of candidates.
> >>> >
> >>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> >>> > <mi...@hotmail.com>wrote:
> >>> >
> >>> > > So the underlying problem that the OP was trying to solve was how
> to
> >>> join
> >>> > > two tables from HBase.
> >>> > > Unfortunately I goofed.
> >>> > > I gave a quick and dirty solution that is a bit incomplete. They
> row
> >>> key
> >>> > in
> >>> > > the temp table has to be unique and I forgot about the Cartesian
> >>> > > product. So my solution wouldn't work in the general case.
> >>> > >
> >>> >
> >>>
> >>
> >
>

Re: How to efficiently join HBase tables?

[Michel Segel <mi...@hotmail.com>]

>> This sounds effective to me, so long as you can perform any desired
>> operations on the all the rows matching a single join value via a single
>> iteration through the stream of reduce input values (for example, if the
>> set
>> of data for each join value fits in memory).  Otherwise you'd need to put
>> the list of matches from table A some place that you can iterate over it
>> again for each match in table B.


This is why I was suggesting using the temp tables and not trying to do it as a sing map reduce job.  When your data sets get very large you will have problems......... 

;-)

Sent from a remote device. Please excuse any typos...

Mike Segel

On Jun 9, 2011, at 4:35 AM, Eran Kutner <er...@gigya.com> wrote:

> Exactly!
> Thanks Dave for a much better explanation than mine!
> 
> -eran
> 
> 
> 
> On Thu, Jun 9, 2011 at 00:35, Dave Latham <la...@davelink.net> wrote:
> 
>> I believe this is what Eran is suggesting:
>> 
>> Table A
>> -------
>> Row1 (has joinVal_1)
>> Row2 (has joinVal_2)
>> Row3 (has joinVal_1)
>> 
>> Table B
>> -------
>> Row4 (has joinVal_1)
>> Row5 (has joinVal_3)
>> Row6 (has joinVal_2)
>> 
>> Mapper receives a list of input rows (union of both input tables in any
>> order), and produces (=>) intermediate key, value pairs, where the key is
>> the join field, and the value is whatever portion of the row you want
>> available in your output
>> 
>> Map
>> ----------
>> A, Row1 => (joinVal_1, [A,Row1])
>> A, Row2 => (joinVal_2, [A,Row2])
>> A, Row3 => (joinVal_1, [A,Row3])
>> B, Row4 => (joinVal_1, [B,Row4])
>> B, Row5 => (joinVal_3, [B,Row5])
>> B, Row6 => (joinVal_2, [B,Row6])
>> 
>> Shuffle phase partitions and sorts by the map output key (which is the join
>> value)
>> The Reduce phase then gets a key for the join value and a list of values
>> containing all of the input rows (from either table) with that join value.
>> It can then perform whatever operations you want (like enumerate the subset
>> of the Cartesian product for that join value)
>> 
>> Reduce
>> ------------
>> joinVal_1, {[A,Row1], [A,Row3], [B,Row4]} => Row1 x Row4, Row3 x Row4
>> joinVal_2, {[A,Row2], [B,Row6]} => Row2 x Row6
>> joinVal_3, {[A,Row3]} => {}
>> 
>>>> This sounds effective to me, so long as you can perform any desired
>>>> operations on the all the rows matching a single join value via a single
>>>> iteration through the stream of reduce input values (for example, if the
>>>> set
>>>> of data for each join value fits in memory).  Otherwise you'd need to put
>>>> the list of matches from table A some place that you can iterate over it
>>>> again for each match in table B.
>> 
>> Dave
>> 
>> On Wed, Jun 8, 2011 at 1:45 PM, Buttler, David <bu...@llnl.gov> wrote:
>> 
>>> Let's make a toy example to see if we can capture all of the edge
>>> conditions:
>>> Table A
>>> -------
>>> Key1 joinVal_1
>>> Key2 joinVal_2
>>> Key3 joinVal_1
>>> 
>>> Table B
>>> -------
>>> Key4 joinVal_1
>>> Key5 joinVal_3
>>> Key6 joinVal_2
>>> 
>>> Now, assume that we have a mapper that takes two values, one row from A,
>>> and one row from B.  Are you suggesting that we get the following map
>> calls:
>>> Key1 & key4
>>> Key2 & key5
>>> Key3 & key6
>>> 
>>> Or are you suggesting we get the following:
>>> Key1 & key4
>>> Key1 & key5
>>> Key1 & key6
>>> Key2 & key4
>>> Key2 & key5
>>> Key2 & key6
>>> Key3 & key4
>>> Key3 & key5
>>> Key3 & key6
>>> 
>>> Or are you suggesting something different?
>>> 
>>> Dave
>>> 
>>> -----Original Message-----
>>> From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran
>>> Kutner
>>> Sent: Wednesday, June 08, 2011 11:47 AM
>>> To: user@hbase.apache.org
>>> Subject: Re: How to efficiently join HBase tables?
>>> 
>>> I'd like to clarify, again what I'm trying to do and why I still think
>> it's
>>> the best way to do it.
>>> I want to join two large tables, I'm assuming, and this is the key to the
>>> efficiency of this method, that: 1) I'm getting a lot of data from table
>> A,
>>> something which is close enough top a full table scan, and 2) this
>> implies
>>> that I will need to join with most of table B as well.
>>> All the suggestions from the SQL world are doing lookups, one way or
>>> another
>>> in table B. My suggestion is to use the power of the shuffle phase to do
>>> the
>>> join. It is obviously doable, so I don't understand the statement that it
>>> can't be done.
>>> So to go over it again:
>>> 1. You feed all the rows from table A and B into the mapper.
>>> 2. For each row, the mapper should output a new row with a key
>> constructed
>>> from the join fields and a value which is the row itself (same as the
>> input
>>> value it got).
>>> 3. The shuffle phase will make sure all rows with the same values in the
>>> join fields will end up together.
>>> 4. The reducer will get all the rows for a single set of join field
>> values
>>> together and perform the actual join. The reducer can be programmed to do
>>> an
>>> inner or outer join at this point.
>>> 
>>> I can't prove it without actually writing and testing it but I have a
>>> strong
>>> feeling this will be much more efficient for large joins than any form of
>>> lookup.
>>> 
>>> -eran
>>> 
>>> 
>>> 
>>> On Wed, Jun 8, 2011 at 16:01, Doug Meil <doug.meil@explorysmedical.com
>>>> wrote:
>>> 
>>>> 
>>>> Re: " With respect to Doug's posts, you can't do a multi-get off the
>> bat"
>>>> 
>>>> That's an assumption, but you're entitled to your opinion.
>>>> 
>>>> -----Original Message-----
>>>> From: Michael Segel [mailto:michael_segel@hotmail.com]
>>>> Sent: Monday, June 06, 2011 10:08 PM
>>>> To: user@hbase.apache.org
>>>> Subject: RE: How to efficiently join HBase tables?
>>>> 
>>>> 
>>>> Well....
>>>> 
>>>> David, is correct.
>>>> 
>>>> Eran wanted to do a join which is a relational concept that isn't
>>> natively
>>>> supported by a NoSQL database. A better model would be a hierarchical
>>> model
>>>> like Dick Pick's Revelation. (Univers aka U2 from
>> Ardent/Informix/IBM/now
>>>> JRockit?).
>>>> And yes, we're looking back 40 some odd years in to either a merge/sort
>>>> solution or how databases do a relational join. :-)
>>>> 
>>>> Eran wants to do this in a single m/r job. The short answer is you
>> can't.
>>>> Longer answer is that if your main class implements Tool Runner, you
>> can
>>>> launch two jobs in parallel to get your subsets, and then when they
>> both
>>>> complete, you run the join job on them. So I guess its a single 'job'
>> or
>>>> rather app. :-)
>>>> 
>>>> With respect to Doug's posts, you can't do a multi-get off the bat
>>> because
>>>> in the general case you're not fetching based on the row key but a
>> column
>>>> which is not part of the row key. (It could be a foreign key which
>> would
>>>> mean that at least one of your table fetches will be off the row key
>> but
>>> you
>>>> can't guarantee it.)
>>>> 
>>>> So if you don't want to use temp tables, then you have to put your
>>> results
>>>> in a sorted order, and you still want to get the unique set of the
>>> join-keys
>>>> which means you have to run a reduce job. Then you can use the unique
>> key
>>>> set and then do the scans. (You can't do a multi-get because you're
>> doing
>>> a
>>>> scan with a start and stop row(s).)
>>>> 
>>>> The reason I suggest that if you're going to do a join operation, you
>>> want
>>>> to use temp tables because it makes your life easier and probably
>> faster
>>>> too.
>>>> 
>>>> Bottom line... I guess many data architects are going to need rethink
>>> their
>>>> data models when working on big data. :-)
>>>> 
>>>> -Mike
>>>> 
>>>> PS. If I get a spare moment, I may code this up...
>>>> 
>>>> 
>>>>> From: doug.meil@explorysmedical.com
>>>>> To: user@hbase.apache.org
>>>>> Date: Mon, 6 Jun 2011 17:19:44 -0400
>>>>> Subject: RE: How to efficiently join HBase tables?
>>>>> 
>>>>> Re:  " So, you all realize the joins have been talked about in the
>>>> database community for 40 years?"
>>>>> 
>>>>> Great point.  What's old is new!    :-)
>>>>> 
>>>>> My suggested from earlier in the thread was a variant of nested loops
>>> by
>>>> using multi-get in HTable, which would reduce the number of RPC calls.
>>> So
>>>> it's a "bulk-select nested loops" of sorts (i.e., as opposed to the
>>> 1-by-1
>>>> lookup of regular nested loops).
>>>>> 
>>>>> 
>>>>> -----Original Message-----
>>>>> From: Buttler, David [mailto:buttler1@llnl.gov]
>>>>> Sent: Monday, June 06, 2011 4:30 PM
>>>>> To: user@hbase.apache.org
>>>>> Subject: RE: How to efficiently join HBase tables?
>>>>> 
>>>>> So, you all realize the joins have been talked about in the database
>>>> community for 40 years?  There are two main types of joins:
>>>>> Nested loops
>>>>> Hash table
>>>>> 
>>>>> Mike, in his various emails seems to be trying to re-imagine how to
>>>> implement both types of joins in HBase (which seems like a reasonable
>>> goal).
>>>> I am not exactly sure what Eran is going for here, but it seems like
>> Eran
>>> is
>>>> glossing over a piece.  If you have two scanners for table A and B,
>> then
>>>> table B needs to be rescanned for every unique part of the join
>> condition
>>> in
>>>> table A.  There are certain ways of improving the efficiency of that:
>> the
>>>> two most obvious are pushing the selection criteria down to the scans,
>>> and
>>>> scanning all of the same join values from table B at the same time
>> (which
>>>> requires that Table B's key is the join, or a secondary structure that
>>>> stores the join values as the primary order).
>>>>> 
>>>>> Dave
>>>>> 
>>>>> -----Original Message-----
>>>>> From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of
>> Eran
>>>>> Kutner
>>>>> Sent: Friday, June 03, 2011 12:24 AM
>>>>> To: user@hbase.apache.org
>>>>> Subject: Re: How to efficiently join HBase tables?
>>>>> 
>>>>> Mike, this more or less what I tried to  describe in my initial post,
>>>> only you explained it much better.
>>>>> The problem is that I want to do all of this in one M/R run, not 3
>> and
>>>> without explicit temp tables. If there was only a way to feed both
>> table
>>> A
>>>> and table B into the M/R job then it could be done.
>>>>> 
>>>>> Let's take your query and assumptions, for example.
>>>>> So we configure scanner A to return rows where c=xxx and d=yyy We
>> then
>>>> configure scanner B to return rows where e=zzz Now we feed all those
>> rows
>>> to
>>>> the mapper.
>>>>> For each row the mapper gets it outputs a new key which is "a|b" and
>>> the
>>>> same value it received, if either one doesn't exist in the row the
>> mapper
>>>> doesn't output anything for that row.
>>>>> The is an implicit "temp table" created at this stage by hadoop.
>>>>> Now the reducer is run, for every key "a|b" generated by the mapper
>> it
>>>> would get one or more value sets, each one representing a row from the
>>>> original two tables. For simplicity lets assume we got two rows, one
>> from
>>>> table A the other from table B. Now the reducer can combine the two
>> rows
>>> and
>>>> output the combined row. This will work just the same if there were
>>> multiple
>>>> rows from each table with the same "a|b" key, in that case the reducer
>>> would
>>>> have to generate the Cartesian product of all the rows. Outer joins can
>>> also
>>>> be done this way, in an outer join you only get one row in the reducer
>>> for a
>>>> given "a|b" key but still generate an output.
>>>>> 
>>>>> -eran
>>>>> 
>>>>> 
>>>>> 
>>>>> On Fri, Jun 3, 2011 at 00:05, Michael Segel <
>> michael_segel@hotmail.com
>>>>> wrote:
>>>>> 
>>>>>> 
>>>>>> Not to beat a dead horse, but I thought a bit more about the
>> problem.
>>>>>> If you want to do this all in HBase using a M/R job...
>>>>>> 
>>>>>> Lets define the following:
>>>>>> SELECT *
>>>>>> FROM A, B
>>>>>> WHERE A.a = B.a
>>>>>> AND     A.b = B.b
>>>>>> AND     A.c = xxx
>>>>>> AND     A.d = yyy
>>>>>> AND     B.e = zzz
>>>>>> 
>>>>>> Is the sample query.
>>>>>> 
>>>>>> So our join key is "a|b" because we're matching on columns a and b.
>>>>>> (The pipe is to delimit the columns, assuming the columns
>> themselves
>>>>>> don't contain pipes...)
>>>>>> 
>>>>>> Our filters on A are c and d while e is the filter on B.
>>>>>> 
>>>>>> So we want to do the following:
>>>>>> 
>>>>>> M/R Map job 1 gets the subset from table A along with a set of
>> unique
>>>> keys.
>>>>>> M/R Map job 2 gets the subset from table B along with a set of
>> unique
>>>> keys.
>>>>>> M/R Map job 3 takes either set of unique keys as the input list and
>>>>>> you split it based on the number of parallel mappers you want to
>> use.
>>>>>> 
>>>>>> You have a couple of options on how you want to proceed.
>>>>>> In each Mapper.map() your input is a unique key.
>>>>>> I guess you could create two scanners, one for tempTableA, and one
>>>>>> for tempTableB.
>>>>>> It looks like you can get the iterator for each result set, and
>> then
>>>>>> for each row in temp table A, you iterate through the result set
>>>>>> from temp table B, writing out the joined set.
>>>>>> 
>>>>>> The only problem is that your result set file isn't in sort order.
>>>>>> So I guess you could take the output from this job and reduce it to
>>>>>> get it in to sort order.
>>>>>> 
>>>>>> Option B. Using HDFS files for temp 'tables'.
>>>>>> You can do this... but you would still have to track the unique
>> keys
>>>>>> and also sort both the keys and the files which will require a
>> reduce
>>>> job.
>>>>>> 
>>>>>> 
>>>>>> Now this is just my opinion, but if I use HBase, I don't have to
>>>>>> worry about using a reducer except to order the final output set.
>>>>>> So I can save the time it takes to do the reduce step. So I have to
>>>> ask...
>>>>>> how much time is spent by HBase in splitting and compacting the
>> temp
>>>> tables?
>>>>>> Also can't you pre-split the temp table before you use them?
>>>>>> 
>>>>>> Or am I still missing something?
>>>>>> 
>>>>>> Note: In this example, you'd have to write an input format that
>>>>>> takes a java list object (or something similar) as your input and
>>>>>> then you can split it to get it to run in parallel.
>>>>>> Or you could just write this on the client and split the list up
>> and
>>>>>> run the join in parallel threads on the client node. Or a single
>>>>>> thread which would mean that it would run and output in sort order.
>>>>>> 
>>>>>> HTH
>>>>>> 
>>>>>> -Mike
>>>>>> 
>>>>>>> Date: Wed, 1 Jun 2011 07:47:30 -0700
>>>>>>> Subject: Re: How to efficiently join HBase tables?
>>>>>>> From: jason.rutherglen@gmail.com
>>>>>>> To: user@hbase.apache.org
>>>>>>> 
>>>>>>>> you somehow need to flush all in-memory data *and* perform a
>>>>>>>> major compaction
>>>>>>> 
>>>>>>> This makes sense.  Without compaction the linear HDFS scan isn't
>>>>>>> possible.  I suppose one could compact 'offline' in a different
>>>>>>> Map Reduce job.  However that would have it's own issues.
>>>>>>> 
>>>>>>>> The files do have a flag if they were made by a major
>>>>>>>> compaction, so you scan only those and ignore the newer ones -
>>>>>>>> but then you are
>>>>>> trailing
>>>>>>> 
>>>>>>> This could be ok in many cases.  The key would be to create a
>>>>>>> sync'd cut off point enabling a frozen point-in-time 'view' of
>> the
>>>> data.
>>>>>>> I'm not sure how that would be implemented.
>>>>>>> 
>>>>>>> On Wed, Jun 1, 2011 at 6:54 AM, Lars George
>>>>>>> <la...@gmail.com>
>>>>>> wrote:
>>>>>>>> Hi Jason,
>>>>>>>> 
>>>>>>>> This was discussed in the past, using the HFileInputFormat. The
>>>>>>>> issue is that you somehow need to flush all in-memory data
>> *and*
>>>>>>>> perform a major compaction - or else you would need all the
>>>>>>>> logic of the ColumnTracker in the HFIF. Since that needs to
>> scan
>>>>>>>> all storage files in parallel to achieve its job, the MR task
>>>>>>>> would not really be able to use the same approach.
>>>>>>>> 
>>>>>>>> Running a major compaction creates a lot of churn, so it is
>>>>>>>> questionable what the outcome is. The files do have a flag if
>>>>>>>> they were made by a major compaction, so you scan only those
>> and
>>>>>>>> ignore the newer ones - but then you are trailing, and you
>> still
>>>>>>>> do not handle delete markers/updates in newer files. No easy
>>> feat.
>>>>>>>> 
>>>>>>>> Lars
>>>>>>>> 
>>>>>>>> On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
>>>>>>>> <ja...@gmail.com> wrote:
>>>>>>>>>> I'd imagine that join operations do not require
>> realtime-ness,
>>>>>>>>>> and so faster batch jobs using Hive -> frozen HBase files in
>>>>>>>>>> HDFS could be the optimal way to go?
>>>>>>>>> 
>>>>>>>>> In addition to lessening the load on the perhaps live
>>>> RegionServer.
>>>>>>>>> There's no Jira for this, I'm tempted to open one.
>>>>>>>>> 
>>>>>>>>> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
>>>>>>>>> <ja...@gmail.com> wrote:
>>>>>>>>>>> The Hive-HBase integration allows you to create Hive tables
>>>>>>>>>>> that are
>>>>>> backed
>>>>>>>>>>> by HBase
>>>>>>>>>> 
>>>>>>>>>> In addition, HBase can be made to go faster for MapReduce
>>>>>>>>>> jobs, if
>>>>>> the
>>>>>>>>>> HFile's could be used directly in HDFS, rather than proxying
>>>>>>>>>> through the RegionServer.
>>>>>>>>>> 
>>>>>>>>>> I'd imagine that join operations do not require
>> realtime-ness,
>>>>>>>>>> and so faster batch jobs using Hive -> frozen HBase files in
>>>>>>>>>> HDFS could be the optimal way to go?
>>>>>>>>>> 
>>>>>>>>>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
>>>>>> patrick@cloudera.com> wrote:
>>>>>>>>>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <
>> eran@gigya.com>
>>>>>> wrote:
>>>>>>>>>>> 
>>>>>>>>>>>> For my need I don't really need the general case, but even
>>>>>>>>>>>> if I did
>>>>>> I think
>>>>>>>>>>>> it can probably be done simpler.
>>>>>>>>>>>> The main problem is getting the data from both tables into
>>>>>>>>>>>> the same
>>>>>> MR job,
>>>>>>>>>>>> without resorting to lookups. So without the theoretical
>>>>>>>>>>>> MutliTableInputFormat, I could just copy all the data from
>>>>>>>>>>>> both
>>>>>> tables into
>>>>>>>>>>>> a temp table, just append the source table name to the row
>>>>>>>>>>>> keys to
>>>>>> make
>>>>>>>>>>>> sure
>>>>>>>>>>>> there are no conflicts. When all the data from both tables
>>>>>>>>>>>> is in
>>>>>> the same
>>>>>>>>>>>> temp table, run a MR job. For each row the mapper should
>>>>>>>>>>>> emit a key
>>>>>> which
>>>>>>>>>>>> is
>>>>>>>>>>>> composed of all the values of the join fields in that row
>>>>>>>>>>>> (the
>>>>>> value can be
>>>>>>>>>>>> emitted as is). This will cause all the rows from both
>>>>>>>>>>>> tables, with
>>>>>> same
>>>>>>>>>>>> join field values to arrive at the reducer together. The
>>>>>>>>>>>> reducer
>>>>>> could then
>>>>>>>>>>>> iterate over them and produce the Cartesian product as
>>> needed.
>>>>>>>>>>>> 
>>>>>>>>>>>> I still don't like having to copy all the data into a temp
>>>>>>>>>>>> table
>>>>>> just
>>>>>>>>>>>> because I can't feed two tables into the MR job.
>>>>>>>>>>>> 
>>>>>>>>>>> 
>>>>>>>>>>> Loading the smaller table in memory is called a map join,
>>>>>>>>>>> versus a reduce-side join (a.k.a. common join). One reason
>> to
>>>>>>>>>>> prefer a map
>>>>>> join is
>>>>>>>>>>> you avoid the shuffle phase which potentially involves
>>>>>>>>>>> several trips
>>>>>> to disk
>>>>>>>>>>> for the intermediate records due to spills, and also once
>>>>>>>>>>> through
>>>>>> the
>>>>>>>>>>> network to get each intermediate KV pair to the right
>> reducer.
>>>>>>>>>>> With
>>>>>> a map
>>>>>>>>>>> join, everything is local, except for the part where you
>> load
>>>>>>>>>>> the
>>>>>> small
>>>>>>>>>>> table.
>>>>>>>>>>> 
>>>>>>>>>>> 
>>>>>>>>>>>> 
>>>>>>>>>>>> As Jason Rutherglen mentioned above, Hive can do joins. I
>>>>>>>>>>>> don't
>>>>>> know if it
>>>>>>>>>>>> can do them for HBase and it will not suit my needs, but it
>>>>>>>>>>>> would
>>>>>> be
>>>>>>>>>>>> interesting to know how is it doing them, if anyone knows.
>>>>>>>>>>>> 
>>>>>>>>>>> 
>>>>>>>>>>> The Hive-HBase integration allows you to create Hive tables
>>>>>>>>>>> that are
>>>>>> backed
>>>>>>>>>>> by HBase. You can do joins on those tables (and also with
>>>>>>>>>>> standard
>>>>>> Hive
>>>>>>>>>>> tables). It might be worth trying out in your case as it
>> lets
>>>>>>>>>>> you
>>>>>> easily see
>>>>>>>>>>> the load characteristics and the job runtime without much
>>>>>>>>>>> coding
>>>>>> investment.
>>>>>>>>>>> 
>>>>>>>>>>> There are probably some specific optimizations that can be
>>>>>>>>>>> applied
>>>>>> to your
>>>>>>>>>>> situation, but it's hard to say without knowing your
>> use-case.
>>>>>>>>>>> 
>>>>>>>>>>> Regards,
>>>>>>>>>>> 
>>>>>>>>>>> - Patrick
>>>>>>>>>>> 
>>>>>>>>>>> 
>>>>>>>>>>>> -eran
>>>>>>>>>>>> 
>>>>>>>>>>>> 
>>>>>>>>>>>> 
>>>>>>>>>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning
>>>>>>>>>>>> <td...@maprtech.com>
>>>>>> wrote:
>>>>>>>>>>>> 
>>>>>>>>>>>>> The Cartesian product often makes an honest-to-god join
>>>>>>>>>>>>> not such
>>>>>> a good
>>>>>>>>>>>>> idea
>>>>>>>>>>>>> on large data.  The common alternative is co-group which
>>>>>>>>>>>>> is basically like doing the hard work of the join, but
>>>>>> involves
>>>>>>>>>>>>> stopping just before emitting the cartesian product.
>> This
>>>>>>>>>>>>> allows you to inject whatever cleverness you need at this
>>>> point.
>>>>>>>>>>>>> 
>>>>>>>>>>>>> Common kinds of cleverness include down-sampling of
>>>>>> problematically large
>>>>>>>>>>>>> sets of candidates.
>>>>>>>>>>>>> 
>>>>>>>>>>>>> On Tue, May 31, 2011 at 11:56 AM, Michael Segel
>>>>>>>>>>>>> <mi...@hotmail.com>wrote:
>>>>>>>>>>>>> 
>>>>>>>>>>>>>> So the underlying problem that the OP was trying to
>>>>>>>>>>>>>> solve was
>>>>>> how to
>>>>>>>>>>>> join
>>>>>>>>>>>>>> two tables from HBase.
>>>>>>>>>>>>>> Unfortunately I goofed.
>>>>>>>>>>>>>> I gave a quick and dirty solution that is a bit
>>> incomplete.
>>>>>> They row
>>>>>>>>>>>> key
>>>>>>>>>>>>> in
>>>>>>>>>>>>>> the temp table has to be unique and I forgot about the
>>>>>> Cartesian
>>>>>>>>>>>>>> product. So my solution wouldn't work in the general
>>> case.
>>>>>>>>>>>>>> 
>>>>>>>>>>>>> 
>>>>>>>>>>>> 
>>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>> 
>>>>>>>> 
>>>>>> 
>>>>>> 
>>>> 
>>>> 
>>> 
>> 

Re: How to efficiently join HBase tables?

[Eran Kutner <er...@gigya.com>]

Exactly!
Thanks Dave for a much better explanation than mine!

-eran



On Thu, Jun 9, 2011 at 00:35, Dave Latham <la...@davelink.net> wrote:

> I believe this is what Eran is suggesting:
>
> Table A
> -------
> Row1 (has joinVal_1)
> Row2 (has joinVal_2)
> Row3 (has joinVal_1)
>
> Table B
> -------
> Row4 (has joinVal_1)
> Row5 (has joinVal_3)
> Row6 (has joinVal_2)
>
> Mapper receives a list of input rows (union of both input tables in any
> order), and produces (=>) intermediate key, value pairs, where the key is
> the join field, and the value is whatever portion of the row you want
> available in your output
>
> Map
> ----------
> A, Row1 => (joinVal_1, [A,Row1])
> A, Row2 => (joinVal_2, [A,Row2])
> A, Row3 => (joinVal_1, [A,Row3])
> B, Row4 => (joinVal_1, [B,Row4])
> B, Row5 => (joinVal_3, [B,Row5])
> B, Row6 => (joinVal_2, [B,Row6])
>
> Shuffle phase partitions and sorts by the map output key (which is the join
> value)
> The Reduce phase then gets a key for the join value and a list of values
> containing all of the input rows (from either table) with that join value.
> It can then perform whatever operations you want (like enumerate the subset
> of the Cartesian product for that join value)
>
> Reduce
> ------------
> joinVal_1, {[A,Row1], [A,Row3], [B,Row4]} => Row1 x Row4, Row3 x Row4
> joinVal_2, {[A,Row2], [B,Row6]} => Row2 x Row6
> joinVal_3, {[A,Row3]} => {}
>
> This sounds effective to me, so long as you can perform any desired
> operations on the all the rows matching a single join value via a single
> iteration through the stream of reduce input values (for example, if the
> set
> of data for each join value fits in memory).  Otherwise you'd need to put
> the list of matches from table A some place that you can iterate over it
> again for each match in table B.
>
> Dave
>
> On Wed, Jun 8, 2011 at 1:45 PM, Buttler, David <bu...@llnl.gov> wrote:
>
> > Let's make a toy example to see if we can capture all of the edge
> > conditions:
> > Table A
> > -------
> > Key1 joinVal_1
> > Key2 joinVal_2
> > Key3 joinVal_1
> >
> > Table B
> > -------
> > Key4 joinVal_1
> > Key5 joinVal_3
> > Key6 joinVal_2
> >
> > Now, assume that we have a mapper that takes two values, one row from A,
> > and one row from B.  Are you suggesting that we get the following map
> calls:
> > Key1 & key4
> > Key2 & key5
> > Key3 & key6
> >
> > Or are you suggesting we get the following:
> > Key1 & key4
> > Key1 & key5
> > Key1 & key6
> > Key2 & key4
> > Key2 & key5
> > Key2 & key6
> > Key3 & key4
> > Key3 & key5
> > Key3 & key6
> >
> > Or are you suggesting something different?
> >
> > Dave
> >
> > -----Original Message-----
> > From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran
> > Kutner
> > Sent: Wednesday, June 08, 2011 11:47 AM
> > To: user@hbase.apache.org
> > Subject: Re: How to efficiently join HBase tables?
> >
> > I'd like to clarify, again what I'm trying to do and why I still think
> it's
> > the best way to do it.
> > I want to join two large tables, I'm assuming, and this is the key to the
> > efficiency of this method, that: 1) I'm getting a lot of data from table
> A,
> > something which is close enough top a full table scan, and 2) this
> implies
> > that I will need to join with most of table B as well.
> > All the suggestions from the SQL world are doing lookups, one way or
> > another
> > in table B. My suggestion is to use the power of the shuffle phase to do
> > the
> > join. It is obviously doable, so I don't understand the statement that it
> > can't be done.
> > So to go over it again:
> > 1. You feed all the rows from table A and B into the mapper.
> > 2. For each row, the mapper should output a new row with a key
> constructed
> > from the join fields and a value which is the row itself (same as the
> input
> > value it got).
> > 3. The shuffle phase will make sure all rows with the same values in the
> > join fields will end up together.
> > 4. The reducer will get all the rows for a single set of join field
> values
> > together and perform the actual join. The reducer can be programmed to do
> > an
> > inner or outer join at this point.
> >
> > I can't prove it without actually writing and testing it but I have a
> > strong
> > feeling this will be much more efficient for large joins than any form of
> > lookup.
> >
> > -eran
> >
> >
> >
> > On Wed, Jun 8, 2011 at 16:01, Doug Meil <doug.meil@explorysmedical.com
> > >wrote:
> >
> > >
> > > Re: " With respect to Doug's posts, you can't do a multi-get off the
> bat"
> > >
> > > That's an assumption, but you're entitled to your opinion.
> > >
> > > -----Original Message-----
> > > From: Michael Segel [mailto:michael_segel@hotmail.com]
> > > Sent: Monday, June 06, 2011 10:08 PM
> > > To: user@hbase.apache.org
> > > Subject: RE: How to efficiently join HBase tables?
> > >
> > >
> > > Well....
> > >
> > > David, is correct.
> > >
> > > Eran wanted to do a join which is a relational concept that isn't
> > natively
> > > supported by a NoSQL database. A better model would be a hierarchical
> > model
> > > like Dick Pick's Revelation. (Univers aka U2 from
> Ardent/Informix/IBM/now
> > > JRockit?).
> > > And yes, we're looking back 40 some odd years in to either a merge/sort
> > > solution or how databases do a relational join. :-)
> > >
> > > Eran wants to do this in a single m/r job. The short answer is you
> can't.
> > >  Longer answer is that if your main class implements Tool Runner, you
> can
> > > launch two jobs in parallel to get your subsets, and then when they
> both
> > > complete, you run the join job on them. So I guess its a single 'job'
> or
> > > rather app. :-)
> > >
> > > With respect to Doug's posts, you can't do a multi-get off the bat
> > because
> > > in the general case you're not fetching based on the row key but a
> column
> > > which is not part of the row key. (It could be a foreign key which
> would
> > > mean that at least one of your table fetches will be off the row key
> but
> > you
> > > can't guarantee it.)
> > >
> > > So if you don't want to use temp tables, then you have to put your
> > results
> > > in a sorted order, and you still want to get the unique set of the
> > join-keys
> > > which means you have to run a reduce job. Then you can use the unique
> key
> > > set and then do the scans. (You can't do a multi-get because you're
> doing
> > a
> > > scan with a start and stop row(s).)
> > >
> > > The reason I suggest that if you're going to do a join operation, you
> > want
> > > to use temp tables because it makes your life easier and probably
> faster
> > > too.
> > >
> > > Bottom line... I guess many data architects are going to need rethink
> > their
> > > data models when working on big data. :-)
> > >
> > > -Mike
> > >
> > > PS. If I get a spare moment, I may code this up...
> > >
> > >
> > > > From: doug.meil@explorysmedical.com
> > > > To: user@hbase.apache.org
> > > > Date: Mon, 6 Jun 2011 17:19:44 -0400
> > > > Subject: RE: How to efficiently join HBase tables?
> > > >
> > > > Re:  " So, you all realize the joins have been talked about in the
> > > database community for 40 years?"
> > > >
> > > > Great point.  What's old is new!    :-)
> > > >
> > > > My suggested from earlier in the thread was a variant of nested loops
> > by
> > > using multi-get in HTable, which would reduce the number of RPC calls.
> >  So
> > > it's a "bulk-select nested loops" of sorts (i.e., as opposed to the
> > 1-by-1
> > > lookup of regular nested loops).
> > > >
> > > >
> > > > -----Original Message-----
> > > > From: Buttler, David [mailto:buttler1@llnl.gov]
> > > > Sent: Monday, June 06, 2011 4:30 PM
> > > > To: user@hbase.apache.org
> > > > Subject: RE: How to efficiently join HBase tables?
> > > >
> > > > So, you all realize the joins have been talked about in the database
> > > community for 40 years?  There are two main types of joins:
> > > > Nested loops
> > > > Hash table
> > > >
> > > > Mike, in his various emails seems to be trying to re-imagine how to
> > > implement both types of joins in HBase (which seems like a reasonable
> > goal).
> > > I am not exactly sure what Eran is going for here, but it seems like
> Eran
> > is
> > > glossing over a piece.  If you have two scanners for table A and B,
> then
> > > table B needs to be rescanned for every unique part of the join
> condition
> > in
> > > table A.  There are certain ways of improving the efficiency of that:
> the
> > > two most obvious are pushing the selection criteria down to the scans,
> > and
> > > scanning all of the same join values from table B at the same time
> (which
> > > requires that Table B's key is the join, or a secondary structure that
> > > stores the join values as the primary order).
> > > >
> > > > Dave
> > > >
> > > > -----Original Message-----
> > > > From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of
> Eran
> > > > Kutner
> > > > Sent: Friday, June 03, 2011 12:24 AM
> > > > To: user@hbase.apache.org
> > > > Subject: Re: How to efficiently join HBase tables?
> > > >
> > > > Mike, this more or less what I tried to  describe in my initial post,
> > > only you explained it much better.
> > > > The problem is that I want to do all of this in one M/R run, not 3
> and
> > > without explicit temp tables. If there was only a way to feed both
> table
> > A
> > > and table B into the M/R job then it could be done.
> > > >
> > > > Let's take your query and assumptions, for example.
> > > > So we configure scanner A to return rows where c=xxx and d=yyy We
> then
> > > configure scanner B to return rows where e=zzz Now we feed all those
> rows
> > to
> > > the mapper.
> > > > For each row the mapper gets it outputs a new key which is "a|b" and
> > the
> > > same value it received, if either one doesn't exist in the row the
> mapper
> > > doesn't output anything for that row.
> > > > The is an implicit "temp table" created at this stage by hadoop.
> > > > Now the reducer is run, for every key "a|b" generated by the mapper
> it
> > > would get one or more value sets, each one representing a row from the
> > > original two tables. For simplicity lets assume we got two rows, one
> from
> > > table A the other from table B. Now the reducer can combine the two
> rows
> > and
> > > output the combined row. This will work just the same if there were
> > multiple
> > > rows from each table with the same "a|b" key, in that case the reducer
> > would
> > > have to generate the Cartesian product of all the rows. Outer joins can
> > also
> > > be done this way, in an outer join you only get one row in the reducer
> > for a
> > > given "a|b" key but still generate an output.
> > > >
> > > > -eran
> > > >
> > > >
> > > >
> > > > On Fri, Jun 3, 2011 at 00:05, Michael Segel <
> michael_segel@hotmail.com
> > > >wrote:
> > > >
> > > > >
> > > > > Not to beat a dead horse, but I thought a bit more about the
> problem.
> > > > > If you want to do this all in HBase using a M/R job...
> > > > >
> > > > > Lets define the following:
> > > > > SELECT *
> > > > > FROM A, B
> > > > > WHERE A.a = B.a
> > > > > AND     A.b = B.b
> > > > > AND     A.c = xxx
> > > > > AND     A.d = yyy
> > > > > AND     B.e = zzz
> > > > >
> > > > > Is the sample query.
> > > > >
> > > > > So our join key is "a|b" because we're matching on columns a and b.
> > > > > (The pipe is to delimit the columns, assuming the columns
> themselves
> > > > > don't contain pipes...)
> > > > >
> > > > > Our filters on A are c and d while e is the filter on B.
> > > > >
> > > > > So we want to do the following:
> > > > >
> > > > > M/R Map job 1 gets the subset from table A along with a set of
> unique
> > > keys.
> > > > > M/R Map job 2 gets the subset from table B along with a set of
> unique
> > > keys.
> > > > > M/R Map job 3 takes either set of unique keys as the input list and
> > > > > you split it based on the number of parallel mappers you want to
> use.
> > > > >
> > > > > You have a couple of options on how you want to proceed.
> > > > > In each Mapper.map() your input is a unique key.
> > > > > I guess you could create two scanners, one for tempTableA, and one
> > > > > for tempTableB.
> > > > > It looks like you can get the iterator for each result set, and
> then
> > > > > for each row in temp table A, you iterate through the result set
> > > > > from temp table B, writing out the joined set.
> > > > >
> > > > > The only problem is that your result set file isn't in sort order.
> > > > > So I guess you could take the output from this job and reduce it to
> > > > > get it in to sort order.
> > > > >
> > > > > Option B. Using HDFS files for temp 'tables'.
> > > > > You can do this... but you would still have to track the unique
> keys
> > > > > and also sort both the keys and the files which will require a
> reduce
> > > job.
> > > > >
> > > > >
> > > > > Now this is just my opinion, but if I use HBase, I don't have to
> > > > > worry about using a reducer except to order the final output set.
> > > > > So I can save the time it takes to do the reduce step. So I have to
> > > ask...
> > > > > how much time is spent by HBase in splitting and compacting the
> temp
> > > tables?
> > > > > Also can't you pre-split the temp table before you use them?
> > > > >
> > > > > Or am I still missing something?
> > > > >
> > > > > Note: In this example, you'd have to write an input format that
> > > > > takes a java list object (or something similar) as your input and
> > > > > then you can split it to get it to run in parallel.
> > > > > Or you could just write this on the client and split the list up
> and
> > > > > run the join in parallel threads on the client node. Or a single
> > > > > thread which would mean that it would run and output in sort order.
> > > > >
> > > > > HTH
> > > > >
> > > > > -Mike
> > > > >
> > > > > > Date: Wed, 1 Jun 2011 07:47:30 -0700
> > > > > > Subject: Re: How to efficiently join HBase tables?
> > > > > > From: jason.rutherglen@gmail.com
> > > > > > To: user@hbase.apache.org
> > > > > >
> > > > > > > you somehow need to flush all in-memory data *and* perform a
> > > > > > > major compaction
> > > > > >
> > > > > > This makes sense.  Without compaction the linear HDFS scan isn't
> > > > > > possible.  I suppose one could compact 'offline' in a different
> > > > > > Map Reduce job.  However that would have it's own issues.
> > > > > >
> > > > > > > The files do have a flag if they were made by a major
> > > > > > > compaction, so you scan only those and ignore the newer ones -
> > > > > > > but then you are
> > > > > trailing
> > > > > >
> > > > > > This could be ok in many cases.  The key would be to create a
> > > > > > sync'd cut off point enabling a frozen point-in-time 'view' of
> the
> > > data.
> > > > > > I'm not sure how that would be implemented.
> > > > > >
> > > > > > On Wed, Jun 1, 2011 at 6:54 AM, Lars George
> > > > > > <la...@gmail.com>
> > > > > wrote:
> > > > > > > Hi Jason,
> > > > > > >
> > > > > > > This was discussed in the past, using the HFileInputFormat. The
> > > > > > > issue is that you somehow need to flush all in-memory data
> *and*
> > > > > > > perform a major compaction - or else you would need all the
> > > > > > > logic of the ColumnTracker in the HFIF. Since that needs to
> scan
> > > > > > > all storage files in parallel to achieve its job, the MR task
> > > > > > > would not really be able to use the same approach.
> > > > > > >
> > > > > > > Running a major compaction creates a lot of churn, so it is
> > > > > > > questionable what the outcome is. The files do have a flag if
> > > > > > > they were made by a major compaction, so you scan only those
> and
> > > > > > > ignore the newer ones - but then you are trailing, and you
> still
> > > > > > > do not handle delete markers/updates in newer files. No easy
> > feat.
> > > > > > >
> > > > > > > Lars
> > > > > > >
> > > > > > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
> > > > > > > <ja...@gmail.com> wrote:
> > > > > > >>> I'd imagine that join operations do not require
> realtime-ness,
> > > > > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > > > > >>> HDFS could be the optimal way to go?
> > > > > > >>
> > > > > > >> In addition to lessening the load on the perhaps live
> > > RegionServer.
> > > > > > >> There's no Jira for this, I'm tempted to open one.
> > > > > > >>
> > > > > > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> > > > > > >> <ja...@gmail.com> wrote:
> > > > > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > > > > >>>> that are
> > > > > backed
> > > > > > >>>> by HBase
> > > > > > >>>
> > > > > > >>> In addition, HBase can be made to go faster for MapReduce
> > > > > > >>> jobs, if
> > > > > the
> > > > > > >>> HFile's could be used directly in HDFS, rather than proxying
> > > > > > >>> through the RegionServer.
> > > > > > >>>
> > > > > > >>> I'd imagine that join operations do not require
> realtime-ness,
> > > > > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > > > > >>> HDFS could be the optimal way to go?
> > > > > > >>>
> > > > > > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
> > > > > patrick@cloudera.com> wrote:
> > > > > > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <
> eran@gigya.com>
> > > > > wrote:
> > > > > > >>>>
> > > > > > >>>>> For my need I don't really need the general case, but even
> > > > > > >>>>> if I did
> > > > > I think
> > > > > > >>>>> it can probably be done simpler.
> > > > > > >>>>> The main problem is getting the data from both tables into
> > > > > > >>>>> the same
> > > > > MR job,
> > > > > > >>>>> without resorting to lookups. So without the theoretical
> > > > > > >>>>> MutliTableInputFormat, I could just copy all the data from
> > > > > > >>>>> both
> > > > > tables into
> > > > > > >>>>> a temp table, just append the source table name to the row
> > > > > > >>>>> keys to
> > > > > make
> > > > > > >>>>> sure
> > > > > > >>>>> there are no conflicts. When all the data from both tables
> > > > > > >>>>> is in
> > > > > the same
> > > > > > >>>>> temp table, run a MR job. For each row the mapper should
> > > > > > >>>>> emit a key
> > > > > which
> > > > > > >>>>> is
> > > > > > >>>>> composed of all the values of the join fields in that row
> > > > > > >>>>> (the
> > > > > value can be
> > > > > > >>>>> emitted as is). This will cause all the rows from both
> > > > > > >>>>> tables, with
> > > > > same
> > > > > > >>>>> join field values to arrive at the reducer together. The
> > > > > > >>>>> reducer
> > > > > could then
> > > > > > >>>>> iterate over them and produce the Cartesian product as
> > needed.
> > > > > > >>>>>
> > > > > > >>>>> I still don't like having to copy all the data into a temp
> > > > > > >>>>> table
> > > > > just
> > > > > > >>>>> because I can't feed two tables into the MR job.
> > > > > > >>>>>
> > > > > > >>>>
> > > > > > >>>> Loading the smaller table in memory is called a map join,
> > > > > > >>>> versus a reduce-side join (a.k.a. common join). One reason
> to
> > > > > > >>>> prefer a map
> > > > > join is
> > > > > > >>>> you avoid the shuffle phase which potentially involves
> > > > > > >>>> several trips
> > > > > to disk
> > > > > > >>>> for the intermediate records due to spills, and also once
> > > > > > >>>> through
> > > > > the
> > > > > > >>>> network to get each intermediate KV pair to the right
> reducer.
> > > > > > >>>> With
> > > > > a map
> > > > > > >>>> join, everything is local, except for the part where you
> load
> > > > > > >>>> the
> > > > > small
> > > > > > >>>> table.
> > > > > > >>>>
> > > > > > >>>>
> > > > > > >>>>>
> > > > > > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I
> > > > > > >>>>> don't
> > > > > know if it
> > > > > > >>>>> can do them for HBase and it will not suit my needs, but it
> > > > > > >>>>> would
> > > > > be
> > > > > > >>>>> interesting to know how is it doing them, if anyone knows.
> > > > > > >>>>>
> > > > > > >>>>
> > > > > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > > > > >>>> that are
> > > > > backed
> > > > > > >>>> by HBase. You can do joins on those tables (and also with
> > > > > > >>>> standard
> > > > > Hive
> > > > > > >>>> tables). It might be worth trying out in your case as it
> lets
> > > > > > >>>> you
> > > > > easily see
> > > > > > >>>> the load characteristics and the job runtime without much
> > > > > > >>>> coding
> > > > > investment.
> > > > > > >>>>
> > > > > > >>>> There are probably some specific optimizations that can be
> > > > > > >>>> applied
> > > > > to your
> > > > > > >>>> situation, but it's hard to say without knowing your
> use-case.
> > > > > > >>>>
> > > > > > >>>> Regards,
> > > > > > >>>>
> > > > > > >>>> - Patrick
> > > > > > >>>>
> > > > > > >>>>
> > > > > > >>>>> -eran
> > > > > > >>>>>
> > > > > > >>>>>
> > > > > > >>>>>
> > > > > > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning
> > > > > > >>>>> <td...@maprtech.com>
> > > > > wrote:
> > > > > > >>>>>
> > > > > > >>>>> > The Cartesian product often makes an honest-to-god join
> > > > > > >>>>> > not such
> > > > > a good
> > > > > > >>>>> > idea
> > > > > > >>>>> > on large data.  The common alternative is co-group which
> > > > > > >>>>> > is basically like doing the hard work of the join, but
> > > > > involves
> > > > > > >>>>> > stopping just before emitting the cartesian product.
>  This
> > > > > > >>>>> > allows you to inject whatever cleverness you need at this
> > > point.
> > > > > > >>>>> >
> > > > > > >>>>> > Common kinds of cleverness include down-sampling of
> > > > > problematically large
> > > > > > >>>>> > sets of candidates.
> > > > > > >>>>> >
> > > > > > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> > > > > > >>>>> > <mi...@hotmail.com>wrote:
> > > > > > >>>>> >
> > > > > > >>>>> > > So the underlying problem that the OP was trying to
> > > > > > >>>>> > > solve was
> > > > > how to
> > > > > > >>>>> join
> > > > > > >>>>> > > two tables from HBase.
> > > > > > >>>>> > > Unfortunately I goofed.
> > > > > > >>>>> > > I gave a quick and dirty solution that is a bit
> > incomplete.
> > > > > They row
> > > > > > >>>>> key
> > > > > > >>>>> > in
> > > > > > >>>>> > > the temp table has to be unique and I forgot about the
> > > > > Cartesian
> > > > > > >>>>> > > product. So my solution wouldn't work in the general
> > case.
> > > > > > >>>>> > >
> > > > > > >>>>> >
> > > > > > >>>>>
> > > > > > >>>>
> > > > > > >>>
> > > > > > >>
> > > > > > >
> > > > >
> > > > >
> > >
> > >
> >
>

RE: How to efficiently join HBase tables?

["Buttler, David" <bu...@llnl.gov>]

Thank you for the explanation, I think I understand the suggestion now.  I completely agree with you that this would be effective for cases that you can do the join of the sorted values in memory.

A small tweak would make this more generic and effective for any size. If you had two separate Map/Reduce jobs for tables A and B, you could simple perform a merge join on the outputs that is very efficient (only touches data items once in each of the A and B output).  This, obviously, makes it larger than a single M/R job, but I don't think that is a major drawback.

Dave


-----Original Message-----
From: ddlatham@gmail.com [mailto:ddlatham@gmail.com] On Behalf Of Dave Latham
Sent: Wednesday, June 08, 2011 2:36 PM
To: user@hbase.apache.org
Subject: Re: How to efficiently join HBase tables?

I believe this is what Eran is suggesting:

Table A
-------
Row1 (has joinVal_1)
Row2 (has joinVal_2)
Row3 (has joinVal_1)

Table B
-------
Row4 (has joinVal_1)
Row5 (has joinVal_3)
Row6 (has joinVal_2)

Mapper receives a list of input rows (union of both input tables in any
order), and produces (=>) intermediate key, value pairs, where the key is
the join field, and the value is whatever portion of the row you want
available in your output

Map
----------
A, Row1 => (joinVal_1, [A,Row1])
A, Row2 => (joinVal_2, [A,Row2])
A, Row3 => (joinVal_1, [A,Row3])
B, Row4 => (joinVal_1, [B,Row4])
B, Row5 => (joinVal_3, [B,Row5])
B, Row6 => (joinVal_2, [B,Row6])

Shuffle phase partitions and sorts by the map output key (which is the join
value)
The Reduce phase then gets a key for the join value and a list of values
containing all of the input rows (from either table) with that join value.
It can then perform whatever operations you want (like enumerate the subset
of the Cartesian product for that join value)

Reduce
------------
joinVal_1, {[A,Row1], [A,Row3], [B,Row4]} => Row1 x Row4, Row3 x Row4
joinVal_2, {[A,Row2], [B,Row6]} => Row2 x Row6
joinVal_3, {[A,Row3]} => {}

This sounds effective to me, so long as you can perform any desired
operations on the all the rows matching a single join value via a single
iteration through the stream of reduce input values (for example, if the set
of data for each join value fits in memory).  Otherwise you'd need to put
the list of matches from table A some place that you can iterate over it
again for each match in table B.

Dave

On Wed, Jun 8, 2011 at 1:45 PM, Buttler, David <bu...@llnl.gov> wrote:

> Let's make a toy example to see if we can capture all of the edge
> conditions:
> Table A
> -------
> Key1 joinVal_1
> Key2 joinVal_2
> Key3 joinVal_1
>
> Table B
> -------
> Key4 joinVal_1
> Key5 joinVal_3
> Key6 joinVal_2
>
> Now, assume that we have a mapper that takes two values, one row from A,
> and one row from B.  Are you suggesting that we get the following map calls:
> Key1 & key4
> Key2 & key5
> Key3 & key6
>
> Or are you suggesting we get the following:
> Key1 & key4
> Key1 & key5
> Key1 & key6
> Key2 & key4
> Key2 & key5
> Key2 & key6
> Key3 & key4
> Key3 & key5
> Key3 & key6
>
> Or are you suggesting something different?
>
> Dave
>
> -----Original Message-----
> From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran
> Kutner
> Sent: Wednesday, June 08, 2011 11:47 AM
> To: user@hbase.apache.org
> Subject: Re: How to efficiently join HBase tables?
>
> I'd like to clarify, again what I'm trying to do and why I still think it's
> the best way to do it.
> I want to join two large tables, I'm assuming, and this is the key to the
> efficiency of this method, that: 1) I'm getting a lot of data from table A,
> something which is close enough top a full table scan, and 2) this implies
> that I will need to join with most of table B as well.
> All the suggestions from the SQL world are doing lookups, one way or
> another
> in table B. My suggestion is to use the power of the shuffle phase to do
> the
> join. It is obviously doable, so I don't understand the statement that it
> can't be done.
> So to go over it again:
> 1. You feed all the rows from table A and B into the mapper.
> 2. For each row, the mapper should output a new row with a key constructed
> from the join fields and a value which is the row itself (same as the input
> value it got).
> 3. The shuffle phase will make sure all rows with the same values in the
> join fields will end up together.
> 4. The reducer will get all the rows for a single set of join field values
> together and perform the actual join. The reducer can be programmed to do
> an
> inner or outer join at this point.
>
> I can't prove it without actually writing and testing it but I have a
> strong
> feeling this will be much more efficient for large joins than any form of
> lookup.
>
> -eran
>
>
>
> On Wed, Jun 8, 2011 at 16:01, Doug Meil <doug.meil@explorysmedical.com
> >wrote:
>
> >
> > Re: " With respect to Doug's posts, you can't do a multi-get off the bat"
> >
> > That's an assumption, but you're entitled to your opinion.
> >
> > -----Original Message-----
> > From: Michael Segel [mailto:michael_segel@hotmail.com]
> > Sent: Monday, June 06, 2011 10:08 PM
> > To: user@hbase.apache.org
> > Subject: RE: How to efficiently join HBase tables?
> >
> >
> > Well....
> >
> > David, is correct.
> >
> > Eran wanted to do a join which is a relational concept that isn't
> natively
> > supported by a NoSQL database. A better model would be a hierarchical
> model
> > like Dick Pick's Revelation. (Univers aka U2 from Ardent/Informix/IBM/now
> > JRockit?).
> > And yes, we're looking back 40 some odd years in to either a merge/sort
> > solution or how databases do a relational join. :-)
> >
> > Eran wants to do this in a single m/r job. The short answer is you can't.
> >  Longer answer is that if your main class implements Tool Runner, you can
> > launch two jobs in parallel to get your subsets, and then when they both
> > complete, you run the join job on them. So I guess its a single 'job' or
> > rather app. :-)
> >
> > With respect to Doug's posts, you can't do a multi-get off the bat
> because
> > in the general case you're not fetching based on the row key but a column
> > which is not part of the row key. (It could be a foreign key which would
> > mean that at least one of your table fetches will be off the row key but
> you
> > can't guarantee it.)
> >
> > So if you don't want to use temp tables, then you have to put your
> results
> > in a sorted order, and you still want to get the unique set of the
> join-keys
> > which means you have to run a reduce job. Then you can use the unique key
> > set and then do the scans. (You can't do a multi-get because you're doing
> a
> > scan with a start and stop row(s).)
> >
> > The reason I suggest that if you're going to do a join operation, you
> want
> > to use temp tables because it makes your life easier and probably faster
> > too.
> >
> > Bottom line... I guess many data architects are going to need rethink
> their
> > data models when working on big data. :-)
> >
> > -Mike
> >
> > PS. If I get a spare moment, I may code this up...
> >
> >
> > > From: doug.meil@explorysmedical.com
> > > To: user@hbase.apache.org
> > > Date: Mon, 6 Jun 2011 17:19:44 -0400
> > > Subject: RE: How to efficiently join HBase tables?
> > >
> > > Re:  " So, you all realize the joins have been talked about in the
> > database community for 40 years?"
> > >
> > > Great point.  What's old is new!    :-)
> > >
> > > My suggested from earlier in the thread was a variant of nested loops
> by
> > using multi-get in HTable, which would reduce the number of RPC calls.
>  So
> > it's a "bulk-select nested loops" of sorts (i.e., as opposed to the
> 1-by-1
> > lookup of regular nested loops).
> > >
> > >
> > > -----Original Message-----
> > > From: Buttler, David [mailto:buttler1@llnl.gov]
> > > Sent: Monday, June 06, 2011 4:30 PM
> > > To: user@hbase.apache.org
> > > Subject: RE: How to efficiently join HBase tables?
> > >
> > > So, you all realize the joins have been talked about in the database
> > community for 40 years?  There are two main types of joins:
> > > Nested loops
> > > Hash table
> > >
> > > Mike, in his various emails seems to be trying to re-imagine how to
> > implement both types of joins in HBase (which seems like a reasonable
> goal).
> > I am not exactly sure what Eran is going for here, but it seems like Eran
> is
> > glossing over a piece.  If you have two scanners for table A and B, then
> > table B needs to be rescanned for every unique part of the join condition
> in
> > table A.  There are certain ways of improving the efficiency of that: the
> > two most obvious are pushing the selection criteria down to the scans,
> and
> > scanning all of the same join values from table B at the same time (which
> > requires that Table B's key is the join, or a secondary structure that
> > stores the join values as the primary order).
> > >
> > > Dave
> > >
> > > -----Original Message-----
> > > From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran
> > > Kutner
> > > Sent: Friday, June 03, 2011 12:24 AM
> > > To: user@hbase.apache.org
> > > Subject: Re: How to efficiently join HBase tables?
> > >
> > > Mike, this more or less what I tried to  describe in my initial post,
> > only you explained it much better.
> > > The problem is that I want to do all of this in one M/R run, not 3 and
> > without explicit temp tables. If there was only a way to feed both table
> A
> > and table B into the M/R job then it could be done.
> > >
> > > Let's take your query and assumptions, for example.
> > > So we configure scanner A to return rows where c=xxx and d=yyy We then
> > configure scanner B to return rows where e=zzz Now we feed all those rows
> to
> > the mapper.
> > > For each row the mapper gets it outputs a new key which is "a|b" and
> the
> > same value it received, if either one doesn't exist in the row the mapper
> > doesn't output anything for that row.
> > > The is an implicit "temp table" created at this stage by hadoop.
> > > Now the reducer is run, for every key "a|b" generated by the mapper it
> > would get one or more value sets, each one representing a row from the
> > original two tables. For simplicity lets assume we got two rows, one from
> > table A the other from table B. Now the reducer can combine the two rows
> and
> > output the combined row. This will work just the same if there were
> multiple
> > rows from each table with the same "a|b" key, in that case the reducer
> would
> > have to generate the Cartesian product of all the rows. Outer joins can
> also
> > be done this way, in an outer join you only get one row in the reducer
> for a
> > given "a|b" key but still generate an output.
> > >
> > > -eran
> > >
> > >
> > >
> > > On Fri, Jun 3, 2011 at 00:05, Michael Segel <michael_segel@hotmail.com
> > >wrote:
> > >
> > > >
> > > > Not to beat a dead horse, but I thought a bit more about the problem.
> > > > If you want to do this all in HBase using a M/R job...
> > > >
> > > > Lets define the following:
> > > > SELECT *
> > > > FROM A, B
> > > > WHERE A.a = B.a
> > > > AND     A.b = B.b
> > > > AND     A.c = xxx
> > > > AND     A.d = yyy
> > > > AND     B.e = zzz
> > > >
> > > > Is the sample query.
> > > >
> > > > So our join key is "a|b" because we're matching on columns a and b.
> > > > (The pipe is to delimit the columns, assuming the columns themselves
> > > > don't contain pipes...)
> > > >
> > > > Our filters on A are c and d while e is the filter on B.
> > > >
> > > > So we want to do the following:
> > > >
> > > > M/R Map job 1 gets the subset from table A along with a set of unique
> > keys.
> > > > M/R Map job 2 gets the subset from table B along with a set of unique
> > keys.
> > > > M/R Map job 3 takes either set of unique keys as the input list and
> > > > you split it based on the number of parallel mappers you want to use.
> > > >
> > > > You have a couple of options on how you want to proceed.
> > > > In each Mapper.map() your input is a unique key.
> > > > I guess you could create two scanners, one for tempTableA, and one
> > > > for tempTableB.
> > > > It looks like you can get the iterator for each result set, and then
> > > > for each row in temp table A, you iterate through the result set
> > > > from temp table B, writing out the joined set.
> > > >
> > > > The only problem is that your result set file isn't in sort order.
> > > > So I guess you could take the output from this job and reduce it to
> > > > get it in to sort order.
> > > >
> > > > Option B. Using HDFS files for temp 'tables'.
> > > > You can do this... but you would still have to track the unique keys
> > > > and also sort both the keys and the files which will require a reduce
> > job.
> > > >
> > > >
> > > > Now this is just my opinion, but if I use HBase, I don't have to
> > > > worry about using a reducer except to order the final output set.
> > > > So I can save the time it takes to do the reduce step. So I have to
> > ask...
> > > > how much time is spent by HBase in splitting and compacting the temp
> > tables?
> > > > Also can't you pre-split the temp table before you use them?
> > > >
> > > > Or am I still missing something?
> > > >
> > > > Note: In this example, you'd have to write an input format that
> > > > takes a java list object (or something similar) as your input and
> > > > then you can split it to get it to run in parallel.
> > > > Or you could just write this on the client and split the list up and
> > > > run the join in parallel threads on the client node. Or a single
> > > > thread which would mean that it would run and output in sort order.
> > > >
> > > > HTH
> > > >
> > > > -Mike
> > > >
> > > > > Date: Wed, 1 Jun 2011 07:47:30 -0700
> > > > > Subject: Re: How to efficiently join HBase tables?
> > > > > From: jason.rutherglen@gmail.com
> > > > > To: user@hbase.apache.org
> > > > >
> > > > > > you somehow need to flush all in-memory data *and* perform a
> > > > > > major compaction
> > > > >
> > > > > This makes sense.  Without compaction the linear HDFS scan isn't
> > > > > possible.  I suppose one could compact 'offline' in a different
> > > > > Map Reduce job.  However that would have it's own issues.
> > > > >
> > > > > > The files do have a flag if they were made by a major
> > > > > > compaction, so you scan only those and ignore the newer ones -
> > > > > > but then you are
> > > > trailing
> > > > >
> > > > > This could be ok in many cases.  The key would be to create a
> > > > > sync'd cut off point enabling a frozen point-in-time 'view' of the
> > data.
> > > > > I'm not sure how that would be implemented.
> > > > >
> > > > > On Wed, Jun 1, 2011 at 6:54 AM, Lars George
> > > > > <la...@gmail.com>
> > > > wrote:
> > > > > > Hi Jason,
> > > > > >
> > > > > > This was discussed in the past, using the HFileInputFormat. The
> > > > > > issue is that you somehow need to flush all in-memory data *and*
> > > > > > perform a major compaction - or else you would need all the
> > > > > > logic of the ColumnTracker in the HFIF. Since that needs to scan
> > > > > > all storage files in parallel to achieve its job, the MR task
> > > > > > would not really be able to use the same approach.
> > > > > >
> > > > > > Running a major compaction creates a lot of churn, so it is
> > > > > > questionable what the outcome is. The files do have a flag if
> > > > > > they were made by a major compaction, so you scan only those and
> > > > > > ignore the newer ones - but then you are trailing, and you still
> > > > > > do not handle delete markers/updates in newer files. No easy
> feat.
> > > > > >
> > > > > > Lars
> > > > > >
> > > > > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
> > > > > > <ja...@gmail.com> wrote:
> > > > > >>> I'd imagine that join operations do not require realtime-ness,
> > > > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > > > >>> HDFS could be the optimal way to go?
> > > > > >>
> > > > > >> In addition to lessening the load on the perhaps live
> > RegionServer.
> > > > > >> There's no Jira for this, I'm tempted to open one.
> > > > > >>
> > > > > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> > > > > >> <ja...@gmail.com> wrote:
> > > > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > > > >>>> that are
> > > > backed
> > > > > >>>> by HBase
> > > > > >>>
> > > > > >>> In addition, HBase can be made to go faster for MapReduce
> > > > > >>> jobs, if
> > > > the
> > > > > >>> HFile's could be used directly in HDFS, rather than proxying
> > > > > >>> through the RegionServer.
> > > > > >>>
> > > > > >>> I'd imagine that join operations do not require realtime-ness,
> > > > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > > > >>> HDFS could be the optimal way to go?
> > > > > >>>
> > > > > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
> > > > patrick@cloudera.com> wrote:
> > > > > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
> > > > wrote:
> > > > > >>>>
> > > > > >>>>> For my need I don't really need the general case, but even
> > > > > >>>>> if I did
> > > > I think
> > > > > >>>>> it can probably be done simpler.
> > > > > >>>>> The main problem is getting the data from both tables into
> > > > > >>>>> the same
> > > > MR job,
> > > > > >>>>> without resorting to lookups. So without the theoretical
> > > > > >>>>> MutliTableInputFormat, I could just copy all the data from
> > > > > >>>>> both
> > > > tables into
> > > > > >>>>> a temp table, just append the source table name to the row
> > > > > >>>>> keys to
> > > > make
> > > > > >>>>> sure
> > > > > >>>>> there are no conflicts. When all the data from both tables
> > > > > >>>>> is in
> > > > the same
> > > > > >>>>> temp table, run a MR job. For each row the mapper should
> > > > > >>>>> emit a key
> > > > which
> > > > > >>>>> is
> > > > > >>>>> composed of all the values of the join fields in that row
> > > > > >>>>> (the
> > > > value can be
> > > > > >>>>> emitted as is). This will cause all the rows from both
> > > > > >>>>> tables, with
> > > > same
> > > > > >>>>> join field values to arrive at the reducer together. The
> > > > > >>>>> reducer
> > > > could then
> > > > > >>>>> iterate over them and produce the Cartesian product as
> needed.
> > > > > >>>>>
> > > > > >>>>> I still don't like having to copy all the data into a temp
> > > > > >>>>> table
> > > > just
> > > > > >>>>> because I can't feed two tables into the MR job.
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>> Loading the smaller table in memory is called a map join,
> > > > > >>>> versus a reduce-side join (a.k.a. common join). One reason to
> > > > > >>>> prefer a map
> > > > join is
> > > > > >>>> you avoid the shuffle phase which potentially involves
> > > > > >>>> several trips
> > > > to disk
> > > > > >>>> for the intermediate records due to spills, and also once
> > > > > >>>> through
> > > > the
> > > > > >>>> network to get each intermediate KV pair to the right reducer.
> > > > > >>>> With
> > > > a map
> > > > > >>>> join, everything is local, except for the part where you load
> > > > > >>>> the
> > > > small
> > > > > >>>> table.
> > > > > >>>>
> > > > > >>>>
> > > > > >>>>>
> > > > > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I
> > > > > >>>>> don't
> > > > know if it
> > > > > >>>>> can do them for HBase and it will not suit my needs, but it
> > > > > >>>>> would
> > > > be
> > > > > >>>>> interesting to know how is it doing them, if anyone knows.
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > > > >>>> that are
> > > > backed
> > > > > >>>> by HBase. You can do joins on those tables (and also with
> > > > > >>>> standard
> > > > Hive
> > > > > >>>> tables). It might be worth trying out in your case as it lets
> > > > > >>>> you
> > > > easily see
> > > > > >>>> the load characteristics and the job runtime without much
> > > > > >>>> coding
> > > > investment.
> > > > > >>>>
> > > > > >>>> There are probably some specific optimizations that can be
> > > > > >>>> applied
> > > > to your
> > > > > >>>> situation, but it's hard to say without knowing your use-case.
> > > > > >>>>
> > > > > >>>> Regards,
> > > > > >>>>
> > > > > >>>> - Patrick
> > > > > >>>>
> > > > > >>>>
> > > > > >>>>> -eran
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning
> > > > > >>>>> <td...@maprtech.com>
> > > > wrote:
> > > > > >>>>>
> > > > > >>>>> > The Cartesian product often makes an honest-to-god join
> > > > > >>>>> > not such
> > > > a good
> > > > > >>>>> > idea
> > > > > >>>>> > on large data.  The common alternative is co-group which
> > > > > >>>>> > is basically like doing the hard work of the join, but
> > > > involves
> > > > > >>>>> > stopping just before emitting the cartesian product.  This
> > > > > >>>>> > allows you to inject whatever cleverness you need at this
> > point.
> > > > > >>>>> >
> > > > > >>>>> > Common kinds of cleverness include down-sampling of
> > > > problematically large
> > > > > >>>>> > sets of candidates.
> > > > > >>>>> >
> > > > > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> > > > > >>>>> > <mi...@hotmail.com>wrote:
> > > > > >>>>> >
> > > > > >>>>> > > So the underlying problem that the OP was trying to
> > > > > >>>>> > > solve was
> > > > how to
> > > > > >>>>> join
> > > > > >>>>> > > two tables from HBase.
> > > > > >>>>> > > Unfortunately I goofed.
> > > > > >>>>> > > I gave a quick and dirty solution that is a bit
> incomplete.
> > > > They row
> > > > > >>>>> key
> > > > > >>>>> > in
> > > > > >>>>> > > the temp table has to be unique and I forgot about the
> > > > Cartesian
> > > > > >>>>> > > product. So my solution wouldn't work in the general
> case.
> > > > > >>>>> > >
> > > > > >>>>> >
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>
> > > > > >>
> > > > > >
> > > >
> > > >
> >
> >
>

Re: How to efficiently join HBase tables?

[Dave Latham <la...@davelink.net>]

I believe this is what Eran is suggesting:

Table A
-------
Row1 (has joinVal_1)
Row2 (has joinVal_2)
Row3 (has joinVal_1)

Table B
-------
Row4 (has joinVal_1)
Row5 (has joinVal_3)
Row6 (has joinVal_2)

Mapper receives a list of input rows (union of both input tables in any
order), and produces (=>) intermediate key, value pairs, where the key is
the join field, and the value is whatever portion of the row you want
available in your output

Map
----------
A, Row1 => (joinVal_1, [A,Row1])
A, Row2 => (joinVal_2, [A,Row2])
A, Row3 => (joinVal_1, [A,Row3])
B, Row4 => (joinVal_1, [B,Row4])
B, Row5 => (joinVal_3, [B,Row5])
B, Row6 => (joinVal_2, [B,Row6])

Shuffle phase partitions and sorts by the map output key (which is the join
value)
The Reduce phase then gets a key for the join value and a list of values
containing all of the input rows (from either table) with that join value.
It can then perform whatever operations you want (like enumerate the subset
of the Cartesian product for that join value)

Reduce
------------
joinVal_1, {[A,Row1], [A,Row3], [B,Row4]} => Row1 x Row4, Row3 x Row4
joinVal_2, {[A,Row2], [B,Row6]} => Row2 x Row6
joinVal_3, {[A,Row3]} => {}

This sounds effective to me, so long as you can perform any desired
operations on the all the rows matching a single join value via a single
iteration through the stream of reduce input values (for example, if the set
of data for each join value fits in memory).  Otherwise you'd need to put
the list of matches from table A some place that you can iterate over it
again for each match in table B.

Dave

On Wed, Jun 8, 2011 at 1:45 PM, Buttler, David <bu...@llnl.gov> wrote:

> Let's make a toy example to see if we can capture all of the edge
> conditions:
> Table A
> -------
> Key1 joinVal_1
> Key2 joinVal_2
> Key3 joinVal_1
>
> Table B
> -------
> Key4 joinVal_1
> Key5 joinVal_3
> Key6 joinVal_2
>
> Now, assume that we have a mapper that takes two values, one row from A,
> and one row from B.  Are you suggesting that we get the following map calls:
> Key1 & key4
> Key2 & key5
> Key3 & key6
>
> Or are you suggesting we get the following:
> Key1 & key4
> Key1 & key5
> Key1 & key6
> Key2 & key4
> Key2 & key5
> Key2 & key6
> Key3 & key4
> Key3 & key5
> Key3 & key6
>
> Or are you suggesting something different?
>
> Dave
>
> -----Original Message-----
> From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran
> Kutner
> Sent: Wednesday, June 08, 2011 11:47 AM
> To: user@hbase.apache.org
> Subject: Re: How to efficiently join HBase tables?
>
> I'd like to clarify, again what I'm trying to do and why I still think it's
> the best way to do it.
> I want to join two large tables, I'm assuming, and this is the key to the
> efficiency of this method, that: 1) I'm getting a lot of data from table A,
> something which is close enough top a full table scan, and 2) this implies
> that I will need to join with most of table B as well.
> All the suggestions from the SQL world are doing lookups, one way or
> another
> in table B. My suggestion is to use the power of the shuffle phase to do
> the
> join. It is obviously doable, so I don't understand the statement that it
> can't be done.
> So to go over it again:
> 1. You feed all the rows from table A and B into the mapper.
> 2. For each row, the mapper should output a new row with a key constructed
> from the join fields and a value which is the row itself (same as the input
> value it got).
> 3. The shuffle phase will make sure all rows with the same values in the
> join fields will end up together.
> 4. The reducer will get all the rows for a single set of join field values
> together and perform the actual join. The reducer can be programmed to do
> an
> inner or outer join at this point.
>
> I can't prove it without actually writing and testing it but I have a
> strong
> feeling this will be much more efficient for large joins than any form of
> lookup.
>
> -eran
>
>
>
> On Wed, Jun 8, 2011 at 16:01, Doug Meil <doug.meil@explorysmedical.com
> >wrote:
>
> >
> > Re: " With respect to Doug's posts, you can't do a multi-get off the bat"
> >
> > That's an assumption, but you're entitled to your opinion.
> >
> > -----Original Message-----
> > From: Michael Segel [mailto:michael_segel@hotmail.com]
> > Sent: Monday, June 06, 2011 10:08 PM
> > To: user@hbase.apache.org
> > Subject: RE: How to efficiently join HBase tables?
> >
> >
> > Well....
> >
> > David, is correct.
> >
> > Eran wanted to do a join which is a relational concept that isn't
> natively
> > supported by a NoSQL database. A better model would be a hierarchical
> model
> > like Dick Pick's Revelation. (Univers aka U2 from Ardent/Informix/IBM/now
> > JRockit?).
> > And yes, we're looking back 40 some odd years in to either a merge/sort
> > solution or how databases do a relational join. :-)
> >
> > Eran wants to do this in a single m/r job. The short answer is you can't.
> >  Longer answer is that if your main class implements Tool Runner, you can
> > launch two jobs in parallel to get your subsets, and then when they both
> > complete, you run the join job on them. So I guess its a single 'job' or
> > rather app. :-)
> >
> > With respect to Doug's posts, you can't do a multi-get off the bat
> because
> > in the general case you're not fetching based on the row key but a column
> > which is not part of the row key. (It could be a foreign key which would
> > mean that at least one of your table fetches will be off the row key but
> you
> > can't guarantee it.)
> >
> > So if you don't want to use temp tables, then you have to put your
> results
> > in a sorted order, and you still want to get the unique set of the
> join-keys
> > which means you have to run a reduce job. Then you can use the unique key
> > set and then do the scans. (You can't do a multi-get because you're doing
> a
> > scan with a start and stop row(s).)
> >
> > The reason I suggest that if you're going to do a join operation, you
> want
> > to use temp tables because it makes your life easier and probably faster
> > too.
> >
> > Bottom line... I guess many data architects are going to need rethink
> their
> > data models when working on big data. :-)
> >
> > -Mike
> >
> > PS. If I get a spare moment, I may code this up...
> >
> >
> > > From: doug.meil@explorysmedical.com
> > > To: user@hbase.apache.org
> > > Date: Mon, 6 Jun 2011 17:19:44 -0400
> > > Subject: RE: How to efficiently join HBase tables?
> > >
> > > Re:  " So, you all realize the joins have been talked about in the
> > database community for 40 years?"
> > >
> > > Great point.  What's old is new!    :-)
> > >
> > > My suggested from earlier in the thread was a variant of nested loops
> by
> > using multi-get in HTable, which would reduce the number of RPC calls.
>  So
> > it's a "bulk-select nested loops" of sorts (i.e., as opposed to the
> 1-by-1
> > lookup of regular nested loops).
> > >
> > >
> > > -----Original Message-----
> > > From: Buttler, David [mailto:buttler1@llnl.gov]
> > > Sent: Monday, June 06, 2011 4:30 PM
> > > To: user@hbase.apache.org
> > > Subject: RE: How to efficiently join HBase tables?
> > >
> > > So, you all realize the joins have been talked about in the database
> > community for 40 years?  There are two main types of joins:
> > > Nested loops
> > > Hash table
> > >
> > > Mike, in his various emails seems to be trying to re-imagine how to
> > implement both types of joins in HBase (which seems like a reasonable
> goal).
> > I am not exactly sure what Eran is going for here, but it seems like Eran
> is
> > glossing over a piece.  If you have two scanners for table A and B, then
> > table B needs to be rescanned for every unique part of the join condition
> in
> > table A.  There are certain ways of improving the efficiency of that: the
> > two most obvious are pushing the selection criteria down to the scans,
> and
> > scanning all of the same join values from table B at the same time (which
> > requires that Table B's key is the join, or a secondary structure that
> > stores the join values as the primary order).
> > >
> > > Dave
> > >
> > > -----Original Message-----
> > > From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran
> > > Kutner
> > > Sent: Friday, June 03, 2011 12:24 AM
> > > To: user@hbase.apache.org
> > > Subject: Re: How to efficiently join HBase tables?
> > >
> > > Mike, this more or less what I tried to  describe in my initial post,
> > only you explained it much better.
> > > The problem is that I want to do all of this in one M/R run, not 3 and
> > without explicit temp tables. If there was only a way to feed both table
> A
> > and table B into the M/R job then it could be done.
> > >
> > > Let's take your query and assumptions, for example.
> > > So we configure scanner A to return rows where c=xxx and d=yyy We then
> > configure scanner B to return rows where e=zzz Now we feed all those rows
> to
> > the mapper.
> > > For each row the mapper gets it outputs a new key which is "a|b" and
> the
> > same value it received, if either one doesn't exist in the row the mapper
> > doesn't output anything for that row.
> > > The is an implicit "temp table" created at this stage by hadoop.
> > > Now the reducer is run, for every key "a|b" generated by the mapper it
> > would get one or more value sets, each one representing a row from the
> > original two tables. For simplicity lets assume we got two rows, one from
> > table A the other from table B. Now the reducer can combine the two rows
> and
> > output the combined row. This will work just the same if there were
> multiple
> > rows from each table with the same "a|b" key, in that case the reducer
> would
> > have to generate the Cartesian product of all the rows. Outer joins can
> also
> > be done this way, in an outer join you only get one row in the reducer
> for a
> > given "a|b" key but still generate an output.
> > >
> > > -eran
> > >
> > >
> > >
> > > On Fri, Jun 3, 2011 at 00:05, Michael Segel <michael_segel@hotmail.com
> > >wrote:
> > >
> > > >
> > > > Not to beat a dead horse, but I thought a bit more about the problem.
> > > > If you want to do this all in HBase using a M/R job...
> > > >
> > > > Lets define the following:
> > > > SELECT *
> > > > FROM A, B
> > > > WHERE A.a = B.a
> > > > AND     A.b = B.b
> > > > AND     A.c = xxx
> > > > AND     A.d = yyy
> > > > AND     B.e = zzz
> > > >
> > > > Is the sample query.
> > > >
> > > > So our join key is "a|b" because we're matching on columns a and b.
> > > > (The pipe is to delimit the columns, assuming the columns themselves
> > > > don't contain pipes...)
> > > >
> > > > Our filters on A are c and d while e is the filter on B.
> > > >
> > > > So we want to do the following:
> > > >
> > > > M/R Map job 1 gets the subset from table A along with a set of unique
> > keys.
> > > > M/R Map job 2 gets the subset from table B along with a set of unique
> > keys.
> > > > M/R Map job 3 takes either set of unique keys as the input list and
> > > > you split it based on the number of parallel mappers you want to use.
> > > >
> > > > You have a couple of options on how you want to proceed.
> > > > In each Mapper.map() your input is a unique key.
> > > > I guess you could create two scanners, one for tempTableA, and one
> > > > for tempTableB.
> > > > It looks like you can get the iterator for each result set, and then
> > > > for each row in temp table A, you iterate through the result set
> > > > from temp table B, writing out the joined set.
> > > >
> > > > The only problem is that your result set file isn't in sort order.
> > > > So I guess you could take the output from this job and reduce it to
> > > > get it in to sort order.
> > > >
> > > > Option B. Using HDFS files for temp 'tables'.
> > > > You can do this... but you would still have to track the unique keys
> > > > and also sort both the keys and the files which will require a reduce
> > job.
> > > >
> > > >
> > > > Now this is just my opinion, but if I use HBase, I don't have to
> > > > worry about using a reducer except to order the final output set.
> > > > So I can save the time it takes to do the reduce step. So I have to
> > ask...
> > > > how much time is spent by HBase in splitting and compacting the temp
> > tables?
> > > > Also can't you pre-split the temp table before you use them?
> > > >
> > > > Or am I still missing something?
> > > >
> > > > Note: In this example, you'd have to write an input format that
> > > > takes a java list object (or something similar) as your input and
> > > > then you can split it to get it to run in parallel.
> > > > Or you could just write this on the client and split the list up and
> > > > run the join in parallel threads on the client node. Or a single
> > > > thread which would mean that it would run and output in sort order.
> > > >
> > > > HTH
> > > >
> > > > -Mike
> > > >
> > > > > Date: Wed, 1 Jun 2011 07:47:30 -0700
> > > > > Subject: Re: How to efficiently join HBase tables?
> > > > > From: jason.rutherglen@gmail.com
> > > > > To: user@hbase.apache.org
> > > > >
> > > > > > you somehow need to flush all in-memory data *and* perform a
> > > > > > major compaction
> > > > >
> > > > > This makes sense.  Without compaction the linear HDFS scan isn't
> > > > > possible.  I suppose one could compact 'offline' in a different
> > > > > Map Reduce job.  However that would have it's own issues.
> > > > >
> > > > > > The files do have a flag if they were made by a major
> > > > > > compaction, so you scan only those and ignore the newer ones -
> > > > > > but then you are
> > > > trailing
> > > > >
> > > > > This could be ok in many cases.  The key would be to create a
> > > > > sync'd cut off point enabling a frozen point-in-time 'view' of the
> > data.
> > > > > I'm not sure how that would be implemented.
> > > > >
> > > > > On Wed, Jun 1, 2011 at 6:54 AM, Lars George
> > > > > <la...@gmail.com>
> > > > wrote:
> > > > > > Hi Jason,
> > > > > >
> > > > > > This was discussed in the past, using the HFileInputFormat. The
> > > > > > issue is that you somehow need to flush all in-memory data *and*
> > > > > > perform a major compaction - or else you would need all the
> > > > > > logic of the ColumnTracker in the HFIF. Since that needs to scan
> > > > > > all storage files in parallel to achieve its job, the MR task
> > > > > > would not really be able to use the same approach.
> > > > > >
> > > > > > Running a major compaction creates a lot of churn, so it is
> > > > > > questionable what the outcome is. The files do have a flag if
> > > > > > they were made by a major compaction, so you scan only those and
> > > > > > ignore the newer ones - but then you are trailing, and you still
> > > > > > do not handle delete markers/updates in newer files. No easy
> feat.
> > > > > >
> > > > > > Lars
> > > > > >
> > > > > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
> > > > > > <ja...@gmail.com> wrote:
> > > > > >>> I'd imagine that join operations do not require realtime-ness,
> > > > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > > > >>> HDFS could be the optimal way to go?
> > > > > >>
> > > > > >> In addition to lessening the load on the perhaps live
> > RegionServer.
> > > > > >> There's no Jira for this, I'm tempted to open one.
> > > > > >>
> > > > > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> > > > > >> <ja...@gmail.com> wrote:
> > > > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > > > >>>> that are
> > > > backed
> > > > > >>>> by HBase
> > > > > >>>
> > > > > >>> In addition, HBase can be made to go faster for MapReduce
> > > > > >>> jobs, if
> > > > the
> > > > > >>> HFile's could be used directly in HDFS, rather than proxying
> > > > > >>> through the RegionServer.
> > > > > >>>
> > > > > >>> I'd imagine that join operations do not require realtime-ness,
> > > > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > > > >>> HDFS could be the optimal way to go?
> > > > > >>>
> > > > > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
> > > > patrick@cloudera.com> wrote:
> > > > > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
> > > > wrote:
> > > > > >>>>
> > > > > >>>>> For my need I don't really need the general case, but even
> > > > > >>>>> if I did
> > > > I think
> > > > > >>>>> it can probably be done simpler.
> > > > > >>>>> The main problem is getting the data from both tables into
> > > > > >>>>> the same
> > > > MR job,
> > > > > >>>>> without resorting to lookups. So without the theoretical
> > > > > >>>>> MutliTableInputFormat, I could just copy all the data from
> > > > > >>>>> both
> > > > tables into
> > > > > >>>>> a temp table, just append the source table name to the row
> > > > > >>>>> keys to
> > > > make
> > > > > >>>>> sure
> > > > > >>>>> there are no conflicts. When all the data from both tables
> > > > > >>>>> is in
> > > > the same
> > > > > >>>>> temp table, run a MR job. For each row the mapper should
> > > > > >>>>> emit a key
> > > > which
> > > > > >>>>> is
> > > > > >>>>> composed of all the values of the join fields in that row
> > > > > >>>>> (the
> > > > value can be
> > > > > >>>>> emitted as is). This will cause all the rows from both
> > > > > >>>>> tables, with
> > > > same
> > > > > >>>>> join field values to arrive at the reducer together. The
> > > > > >>>>> reducer
> > > > could then
> > > > > >>>>> iterate over them and produce the Cartesian product as
> needed.
> > > > > >>>>>
> > > > > >>>>> I still don't like having to copy all the data into a temp
> > > > > >>>>> table
> > > > just
> > > > > >>>>> because I can't feed two tables into the MR job.
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>> Loading the smaller table in memory is called a map join,
> > > > > >>>> versus a reduce-side join (a.k.a. common join). One reason to
> > > > > >>>> prefer a map
> > > > join is
> > > > > >>>> you avoid the shuffle phase which potentially involves
> > > > > >>>> several trips
> > > > to disk
> > > > > >>>> for the intermediate records due to spills, and also once
> > > > > >>>> through
> > > > the
> > > > > >>>> network to get each intermediate KV pair to the right reducer.
> > > > > >>>> With
> > > > a map
> > > > > >>>> join, everything is local, except for the part where you load
> > > > > >>>> the
> > > > small
> > > > > >>>> table.
> > > > > >>>>
> > > > > >>>>
> > > > > >>>>>
> > > > > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I
> > > > > >>>>> don't
> > > > know if it
> > > > > >>>>> can do them for HBase and it will not suit my needs, but it
> > > > > >>>>> would
> > > > be
> > > > > >>>>> interesting to know how is it doing them, if anyone knows.
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > > > >>>> that are
> > > > backed
> > > > > >>>> by HBase. You can do joins on those tables (and also with
> > > > > >>>> standard
> > > > Hive
> > > > > >>>> tables). It might be worth trying out in your case as it lets
> > > > > >>>> you
> > > > easily see
> > > > > >>>> the load characteristics and the job runtime without much
> > > > > >>>> coding
> > > > investment.
> > > > > >>>>
> > > > > >>>> There are probably some specific optimizations that can be
> > > > > >>>> applied
> > > > to your
> > > > > >>>> situation, but it's hard to say without knowing your use-case.
> > > > > >>>>
> > > > > >>>> Regards,
> > > > > >>>>
> > > > > >>>> - Patrick
> > > > > >>>>
> > > > > >>>>
> > > > > >>>>> -eran
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>>
> > > > > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning
> > > > > >>>>> <td...@maprtech.com>
> > > > wrote:
> > > > > >>>>>
> > > > > >>>>> > The Cartesian product often makes an honest-to-god join
> > > > > >>>>> > not such
> > > > a good
> > > > > >>>>> > idea
> > > > > >>>>> > on large data.  The common alternative is co-group which
> > > > > >>>>> > is basically like doing the hard work of the join, but
> > > > involves
> > > > > >>>>> > stopping just before emitting the cartesian product.  This
> > > > > >>>>> > allows you to inject whatever cleverness you need at this
> > point.
> > > > > >>>>> >
> > > > > >>>>> > Common kinds of cleverness include down-sampling of
> > > > problematically large
> > > > > >>>>> > sets of candidates.
> > > > > >>>>> >
> > > > > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> > > > > >>>>> > <mi...@hotmail.com>wrote:
> > > > > >>>>> >
> > > > > >>>>> > > So the underlying problem that the OP was trying to
> > > > > >>>>> > > solve was
> > > > how to
> > > > > >>>>> join
> > > > > >>>>> > > two tables from HBase.
> > > > > >>>>> > > Unfortunately I goofed.
> > > > > >>>>> > > I gave a quick and dirty solution that is a bit
> incomplete.
> > > > They row
> > > > > >>>>> key
> > > > > >>>>> > in
> > > > > >>>>> > > the temp table has to be unique and I forgot about the
> > > > Cartesian
> > > > > >>>>> > > product. So my solution wouldn't work in the general
> case.
> > > > > >>>>> > >
> > > > > >>>>> >
> > > > > >>>>>
> > > > > >>>>
> > > > > >>>
> > > > > >>
> > > > > >
> > > >
> > > >
> >
> >
>

RE: How to efficiently join HBase tables?

["Buttler, David" <bu...@llnl.gov>]

Let's make a toy example to see if we can capture all of the edge conditions:
Table A
-------
Key1 joinVal_1
Key2 joinVal_2
Key3 joinVal_1

Table B
-------
Key4 joinVal_1
Key5 joinVal_3
Key6 joinVal_2

Now, assume that we have a mapper that takes two values, one row from A, and one row from B.  Are you suggesting that we get the following map calls:
Key1 & key4
Key2 & key5
Key3 & key6

Or are you suggesting we get the following:
Key1 & key4
Key1 & key5
Key1 & key6
Key2 & key4
Key2 & key5
Key2 & key6
Key3 & key4
Key3 & key5
Key3 & key6

Or are you suggesting something different?

Dave

-----Original Message-----
From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran Kutner
Sent: Wednesday, June 08, 2011 11:47 AM
To: user@hbase.apache.org
Subject: Re: How to efficiently join HBase tables?

I'd like to clarify, again what I'm trying to do and why I still think it's
the best way to do it.
I want to join two large tables, I'm assuming, and this is the key to the
efficiency of this method, that: 1) I'm getting a lot of data from table A,
something which is close enough top a full table scan, and 2) this implies
that I will need to join with most of table B as well.
All the suggestions from the SQL world are doing lookups, one way or another
in table B. My suggestion is to use the power of the shuffle phase to do the
join. It is obviously doable, so I don't understand the statement that it
can't be done.
So to go over it again:
1. You feed all the rows from table A and B into the mapper.
2. For each row, the mapper should output a new row with a key constructed
from the join fields and a value which is the row itself (same as the input
value it got).
3. The shuffle phase will make sure all rows with the same values in the
join fields will end up together.
4. The reducer will get all the rows for a single set of join field values
together and perform the actual join. The reducer can be programmed to do an
inner or outer join at this point.

I can't prove it without actually writing and testing it but I have a strong
feeling this will be much more efficient for large joins than any form of
lookup.

-eran



On Wed, Jun 8, 2011 at 16:01, Doug Meil <do...@explorysmedical.com>wrote:

>
> Re: " With respect to Doug's posts, you can't do a multi-get off the bat"
>
> That's an assumption, but you're entitled to your opinion.
>
> -----Original Message-----
> From: Michael Segel [mailto:michael_segel@hotmail.com]
> Sent: Monday, June 06, 2011 10:08 PM
> To: user@hbase.apache.org
> Subject: RE: How to efficiently join HBase tables?
>
>
> Well....
>
> David, is correct.
>
> Eran wanted to do a join which is a relational concept that isn't natively
> supported by a NoSQL database. A better model would be a hierarchical model
> like Dick Pick's Revelation. (Univers aka U2 from Ardent/Informix/IBM/now
> JRockit?).
> And yes, we're looking back 40 some odd years in to either a merge/sort
> solution or how databases do a relational join. :-)
>
> Eran wants to do this in a single m/r job. The short answer is you can't.
>  Longer answer is that if your main class implements Tool Runner, you can
> launch two jobs in parallel to get your subsets, and then when they both
> complete, you run the join job on them. So I guess its a single 'job' or
> rather app. :-)
>
> With respect to Doug's posts, you can't do a multi-get off the bat because
> in the general case you're not fetching based on the row key but a column
> which is not part of the row key. (It could be a foreign key which would
> mean that at least one of your table fetches will be off the row key but you
> can't guarantee it.)
>
> So if you don't want to use temp tables, then you have to put your results
> in a sorted order, and you still want to get the unique set of the join-keys
> which means you have to run a reduce job. Then you can use the unique key
> set and then do the scans. (You can't do a multi-get because you're doing a
> scan with a start and stop row(s).)
>
> The reason I suggest that if you're going to do a join operation, you want
> to use temp tables because it makes your life easier and probably faster
> too.
>
> Bottom line... I guess many data architects are going to need rethink their
> data models when working on big data. :-)
>
> -Mike
>
> PS. If I get a spare moment, I may code this up...
>
>
> > From: doug.meil@explorysmedical.com
> > To: user@hbase.apache.org
> > Date: Mon, 6 Jun 2011 17:19:44 -0400
> > Subject: RE: How to efficiently join HBase tables?
> >
> > Re:  " So, you all realize the joins have been talked about in the
> database community for 40 years?"
> >
> > Great point.  What's old is new!    :-)
> >
> > My suggested from earlier in the thread was a variant of nested loops by
> using multi-get in HTable, which would reduce the number of RPC calls.  So
> it's a "bulk-select nested loops" of sorts (i.e., as opposed to the 1-by-1
> lookup of regular nested loops).
> >
> >
> > -----Original Message-----
> > From: Buttler, David [mailto:buttler1@llnl.gov]
> > Sent: Monday, June 06, 2011 4:30 PM
> > To: user@hbase.apache.org
> > Subject: RE: How to efficiently join HBase tables?
> >
> > So, you all realize the joins have been talked about in the database
> community for 40 years?  There are two main types of joins:
> > Nested loops
> > Hash table
> >
> > Mike, in his various emails seems to be trying to re-imagine how to
> implement both types of joins in HBase (which seems like a reasonable goal).
> I am not exactly sure what Eran is going for here, but it seems like Eran is
> glossing over a piece.  If you have two scanners for table A and B, then
> table B needs to be rescanned for every unique part of the join condition in
> table A.  There are certain ways of improving the efficiency of that: the
> two most obvious are pushing the selection criteria down to the scans, and
> scanning all of the same join values from table B at the same time (which
> requires that Table B's key is the join, or a secondary structure that
> stores the join values as the primary order).
> >
> > Dave
> >
> > -----Original Message-----
> > From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran
> > Kutner
> > Sent: Friday, June 03, 2011 12:24 AM
> > To: user@hbase.apache.org
> > Subject: Re: How to efficiently join HBase tables?
> >
> > Mike, this more or less what I tried to  describe in my initial post,
> only you explained it much better.
> > The problem is that I want to do all of this in one M/R run, not 3 and
> without explicit temp tables. If there was only a way to feed both table A
> and table B into the M/R job then it could be done.
> >
> > Let's take your query and assumptions, for example.
> > So we configure scanner A to return rows where c=xxx and d=yyy We then
> configure scanner B to return rows where e=zzz Now we feed all those rows to
> the mapper.
> > For each row the mapper gets it outputs a new key which is "a|b" and the
> same value it received, if either one doesn't exist in the row the mapper
> doesn't output anything for that row.
> > The is an implicit "temp table" created at this stage by hadoop.
> > Now the reducer is run, for every key "a|b" generated by the mapper it
> would get one or more value sets, each one representing a row from the
> original two tables. For simplicity lets assume we got two rows, one from
> table A the other from table B. Now the reducer can combine the two rows and
> output the combined row. This will work just the same if there were multiple
> rows from each table with the same "a|b" key, in that case the reducer would
> have to generate the Cartesian product of all the rows. Outer joins can also
> be done this way, in an outer join you only get one row in the reducer for a
> given "a|b" key but still generate an output.
> >
> > -eran
> >
> >
> >
> > On Fri, Jun 3, 2011 at 00:05, Michael Segel <michael_segel@hotmail.com
> >wrote:
> >
> > >
> > > Not to beat a dead horse, but I thought a bit more about the problem.
> > > If you want to do this all in HBase using a M/R job...
> > >
> > > Lets define the following:
> > > SELECT *
> > > FROM A, B
> > > WHERE A.a = B.a
> > > AND     A.b = B.b
> > > AND     A.c = xxx
> > > AND     A.d = yyy
> > > AND     B.e = zzz
> > >
> > > Is the sample query.
> > >
> > > So our join key is "a|b" because we're matching on columns a and b.
> > > (The pipe is to delimit the columns, assuming the columns themselves
> > > don't contain pipes...)
> > >
> > > Our filters on A are c and d while e is the filter on B.
> > >
> > > So we want to do the following:
> > >
> > > M/R Map job 1 gets the subset from table A along with a set of unique
> keys.
> > > M/R Map job 2 gets the subset from table B along with a set of unique
> keys.
> > > M/R Map job 3 takes either set of unique keys as the input list and
> > > you split it based on the number of parallel mappers you want to use.
> > >
> > > You have a couple of options on how you want to proceed.
> > > In each Mapper.map() your input is a unique key.
> > > I guess you could create two scanners, one for tempTableA, and one
> > > for tempTableB.
> > > It looks like you can get the iterator for each result set, and then
> > > for each row in temp table A, you iterate through the result set
> > > from temp table B, writing out the joined set.
> > >
> > > The only problem is that your result set file isn't in sort order.
> > > So I guess you could take the output from this job and reduce it to
> > > get it in to sort order.
> > >
> > > Option B. Using HDFS files for temp 'tables'.
> > > You can do this... but you would still have to track the unique keys
> > > and also sort both the keys and the files which will require a reduce
> job.
> > >
> > >
> > > Now this is just my opinion, but if I use HBase, I don't have to
> > > worry about using a reducer except to order the final output set.
> > > So I can save the time it takes to do the reduce step. So I have to
> ask...
> > > how much time is spent by HBase in splitting and compacting the temp
> tables?
> > > Also can't you pre-split the temp table before you use them?
> > >
> > > Or am I still missing something?
> > >
> > > Note: In this example, you'd have to write an input format that
> > > takes a java list object (or something similar) as your input and
> > > then you can split it to get it to run in parallel.
> > > Or you could just write this on the client and split the list up and
> > > run the join in parallel threads on the client node. Or a single
> > > thread which would mean that it would run and output in sort order.
> > >
> > > HTH
> > >
> > > -Mike
> > >
> > > > Date: Wed, 1 Jun 2011 07:47:30 -0700
> > > > Subject: Re: How to efficiently join HBase tables?
> > > > From: jason.rutherglen@gmail.com
> > > > To: user@hbase.apache.org
> > > >
> > > > > you somehow need to flush all in-memory data *and* perform a
> > > > > major compaction
> > > >
> > > > This makes sense.  Without compaction the linear HDFS scan isn't
> > > > possible.  I suppose one could compact 'offline' in a different
> > > > Map Reduce job.  However that would have it's own issues.
> > > >
> > > > > The files do have a flag if they were made by a major
> > > > > compaction, so you scan only those and ignore the newer ones -
> > > > > but then you are
> > > trailing
> > > >
> > > > This could be ok in many cases.  The key would be to create a
> > > > sync'd cut off point enabling a frozen point-in-time 'view' of the
> data.
> > > > I'm not sure how that would be implemented.
> > > >
> > > > On Wed, Jun 1, 2011 at 6:54 AM, Lars George
> > > > <la...@gmail.com>
> > > wrote:
> > > > > Hi Jason,
> > > > >
> > > > > This was discussed in the past, using the HFileInputFormat. The
> > > > > issue is that you somehow need to flush all in-memory data *and*
> > > > > perform a major compaction - or else you would need all the
> > > > > logic of the ColumnTracker in the HFIF. Since that needs to scan
> > > > > all storage files in parallel to achieve its job, the MR task
> > > > > would not really be able to use the same approach.
> > > > >
> > > > > Running a major compaction creates a lot of churn, so it is
> > > > > questionable what the outcome is. The files do have a flag if
> > > > > they were made by a major compaction, so you scan only those and
> > > > > ignore the newer ones - but then you are trailing, and you still
> > > > > do not handle delete markers/updates in newer files. No easy feat.
> > > > >
> > > > > Lars
> > > > >
> > > > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
> > > > > <ja...@gmail.com> wrote:
> > > > >>> I'd imagine that join operations do not require realtime-ness,
> > > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > > >>> HDFS could be the optimal way to go?
> > > > >>
> > > > >> In addition to lessening the load on the perhaps live
> RegionServer.
> > > > >> There's no Jira for this, I'm tempted to open one.
> > > > >>
> > > > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> > > > >> <ja...@gmail.com> wrote:
> > > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > > >>>> that are
> > > backed
> > > > >>>> by HBase
> > > > >>>
> > > > >>> In addition, HBase can be made to go faster for MapReduce
> > > > >>> jobs, if
> > > the
> > > > >>> HFile's could be used directly in HDFS, rather than proxying
> > > > >>> through the RegionServer.
> > > > >>>
> > > > >>> I'd imagine that join operations do not require realtime-ness,
> > > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > > >>> HDFS could be the optimal way to go?
> > > > >>>
> > > > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
> > > patrick@cloudera.com> wrote:
> > > > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
> > > wrote:
> > > > >>>>
> > > > >>>>> For my need I don't really need the general case, but even
> > > > >>>>> if I did
> > > I think
> > > > >>>>> it can probably be done simpler.
> > > > >>>>> The main problem is getting the data from both tables into
> > > > >>>>> the same
> > > MR job,
> > > > >>>>> without resorting to lookups. So without the theoretical
> > > > >>>>> MutliTableInputFormat, I could just copy all the data from
> > > > >>>>> both
> > > tables into
> > > > >>>>> a temp table, just append the source table name to the row
> > > > >>>>> keys to
> > > make
> > > > >>>>> sure
> > > > >>>>> there are no conflicts. When all the data from both tables
> > > > >>>>> is in
> > > the same
> > > > >>>>> temp table, run a MR job. For each row the mapper should
> > > > >>>>> emit a key
> > > which
> > > > >>>>> is
> > > > >>>>> composed of all the values of the join fields in that row
> > > > >>>>> (the
> > > value can be
> > > > >>>>> emitted as is). This will cause all the rows from both
> > > > >>>>> tables, with
> > > same
> > > > >>>>> join field values to arrive at the reducer together. The
> > > > >>>>> reducer
> > > could then
> > > > >>>>> iterate over them and produce the Cartesian product as needed.
> > > > >>>>>
> > > > >>>>> I still don't like having to copy all the data into a temp
> > > > >>>>> table
> > > just
> > > > >>>>> because I can't feed two tables into the MR job.
> > > > >>>>>
> > > > >>>>
> > > > >>>> Loading the smaller table in memory is called a map join,
> > > > >>>> versus a reduce-side join (a.k.a. common join). One reason to
> > > > >>>> prefer a map
> > > join is
> > > > >>>> you avoid the shuffle phase which potentially involves
> > > > >>>> several trips
> > > to disk
> > > > >>>> for the intermediate records due to spills, and also once
> > > > >>>> through
> > > the
> > > > >>>> network to get each intermediate KV pair to the right reducer.
> > > > >>>> With
> > > a map
> > > > >>>> join, everything is local, except for the part where you load
> > > > >>>> the
> > > small
> > > > >>>> table.
> > > > >>>>
> > > > >>>>
> > > > >>>>>
> > > > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I
> > > > >>>>> don't
> > > know if it
> > > > >>>>> can do them for HBase and it will not suit my needs, but it
> > > > >>>>> would
> > > be
> > > > >>>>> interesting to know how is it doing them, if anyone knows.
> > > > >>>>>
> > > > >>>>
> > > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > > >>>> that are
> > > backed
> > > > >>>> by HBase. You can do joins on those tables (and also with
> > > > >>>> standard
> > > Hive
> > > > >>>> tables). It might be worth trying out in your case as it lets
> > > > >>>> you
> > > easily see
> > > > >>>> the load characteristics and the job runtime without much
> > > > >>>> coding
> > > investment.
> > > > >>>>
> > > > >>>> There are probably some specific optimizations that can be
> > > > >>>> applied
> > > to your
> > > > >>>> situation, but it's hard to say without knowing your use-case.
> > > > >>>>
> > > > >>>> Regards,
> > > > >>>>
> > > > >>>> - Patrick
> > > > >>>>
> > > > >>>>
> > > > >>>>> -eran
> > > > >>>>>
> > > > >>>>>
> > > > >>>>>
> > > > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning
> > > > >>>>> <td...@maprtech.com>
> > > wrote:
> > > > >>>>>
> > > > >>>>> > The Cartesian product often makes an honest-to-god join
> > > > >>>>> > not such
> > > a good
> > > > >>>>> > idea
> > > > >>>>> > on large data.  The common alternative is co-group which
> > > > >>>>> > is basically like doing the hard work of the join, but
> > > involves
> > > > >>>>> > stopping just before emitting the cartesian product.  This
> > > > >>>>> > allows you to inject whatever cleverness you need at this
> point.
> > > > >>>>> >
> > > > >>>>> > Common kinds of cleverness include down-sampling of
> > > problematically large
> > > > >>>>> > sets of candidates.
> > > > >>>>> >
> > > > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> > > > >>>>> > <mi...@hotmail.com>wrote:
> > > > >>>>> >
> > > > >>>>> > > So the underlying problem that the OP was trying to
> > > > >>>>> > > solve was
> > > how to
> > > > >>>>> join
> > > > >>>>> > > two tables from HBase.
> > > > >>>>> > > Unfortunately I goofed.
> > > > >>>>> > > I gave a quick and dirty solution that is a bit incomplete.
> > > They row
> > > > >>>>> key
> > > > >>>>> > in
> > > > >>>>> > > the temp table has to be unique and I forgot about the
> > > Cartesian
> > > > >>>>> > > product. So my solution wouldn't work in the general case.
> > > > >>>>> > >
> > > > >>>>> >
> > > > >>>>>
> > > > >>>>
> > > > >>>
> > > > >>
> > > > >
> > >
> > >
>
>

Re: How to efficiently join HBase tables?

[Eran Kutner <er...@gigya.com>]

I'd like to clarify, again what I'm trying to do and why I still think it's
the best way to do it.
I want to join two large tables, I'm assuming, and this is the key to the
efficiency of this method, that: 1) I'm getting a lot of data from table A,
something which is close enough top a full table scan, and 2) this implies
that I will need to join with most of table B as well.
All the suggestions from the SQL world are doing lookups, one way or another
in table B. My suggestion is to use the power of the shuffle phase to do the
join. It is obviously doable, so I don't understand the statement that it
can't be done.
So to go over it again:
1. You feed all the rows from table A and B into the mapper.
2. For each row, the mapper should output a new row with a key constructed
from the join fields and a value which is the row itself (same as the input
value it got).
3. The shuffle phase will make sure all rows with the same values in the
join fields will end up together.
4. The reducer will get all the rows for a single set of join field values
together and perform the actual join. The reducer can be programmed to do an
inner or outer join at this point.

I can't prove it without actually writing and testing it but I have a strong
feeling this will be much more efficient for large joins than any form of
lookup.

-eran



On Wed, Jun 8, 2011 at 16:01, Doug Meil <do...@explorysmedical.com>wrote:

>
> Re: " With respect to Doug's posts, you can't do a multi-get off the bat"
>
> That's an assumption, but you're entitled to your opinion.
>
> -----Original Message-----
> From: Michael Segel [mailto:michael_segel@hotmail.com]
> Sent: Monday, June 06, 2011 10:08 PM
> To: user@hbase.apache.org
> Subject: RE: How to efficiently join HBase tables?
>
>
> Well....
>
> David, is correct.
>
> Eran wanted to do a join which is a relational concept that isn't natively
> supported by a NoSQL database. A better model would be a hierarchical model
> like Dick Pick's Revelation. (Univers aka U2 from Ardent/Informix/IBM/now
> JRockit?).
> And yes, we're looking back 40 some odd years in to either a merge/sort
> solution or how databases do a relational join. :-)
>
> Eran wants to do this in a single m/r job. The short answer is you can't.
>  Longer answer is that if your main class implements Tool Runner, you can
> launch two jobs in parallel to get your subsets, and then when they both
> complete, you run the join job on them. So I guess its a single 'job' or
> rather app. :-)
>
> With respect to Doug's posts, you can't do a multi-get off the bat because
> in the general case you're not fetching based on the row key but a column
> which is not part of the row key. (It could be a foreign key which would
> mean that at least one of your table fetches will be off the row key but you
> can't guarantee it.)
>
> So if you don't want to use temp tables, then you have to put your results
> in a sorted order, and you still want to get the unique set of the join-keys
> which means you have to run a reduce job. Then you can use the unique key
> set and then do the scans. (You can't do a multi-get because you're doing a
> scan with a start and stop row(s).)
>
> The reason I suggest that if you're going to do a join operation, you want
> to use temp tables because it makes your life easier and probably faster
> too.
>
> Bottom line... I guess many data architects are going to need rethink their
> data models when working on big data. :-)
>
> -Mike
>
> PS. If I get a spare moment, I may code this up...
>
>
> > From: doug.meil@explorysmedical.com
> > To: user@hbase.apache.org
> > Date: Mon, 6 Jun 2011 17:19:44 -0400
> > Subject: RE: How to efficiently join HBase tables?
> >
> > Re:  " So, you all realize the joins have been talked about in the
> database community for 40 years?"
> >
> > Great point.  What's old is new!    :-)
> >
> > My suggested from earlier in the thread was a variant of nested loops by
> using multi-get in HTable, which would reduce the number of RPC calls.  So
> it's a "bulk-select nested loops" of sorts (i.e., as opposed to the 1-by-1
> lookup of regular nested loops).
> >
> >
> > -----Original Message-----
> > From: Buttler, David [mailto:buttler1@llnl.gov]
> > Sent: Monday, June 06, 2011 4:30 PM
> > To: user@hbase.apache.org
> > Subject: RE: How to efficiently join HBase tables?
> >
> > So, you all realize the joins have been talked about in the database
> community for 40 years?  There are two main types of joins:
> > Nested loops
> > Hash table
> >
> > Mike, in his various emails seems to be trying to re-imagine how to
> implement both types of joins in HBase (which seems like a reasonable goal).
> I am not exactly sure what Eran is going for here, but it seems like Eran is
> glossing over a piece.  If you have two scanners for table A and B, then
> table B needs to be rescanned for every unique part of the join condition in
> table A.  There are certain ways of improving the efficiency of that: the
> two most obvious are pushing the selection criteria down to the scans, and
> scanning all of the same join values from table B at the same time (which
> requires that Table B's key is the join, or a secondary structure that
> stores the join values as the primary order).
> >
> > Dave
> >
> > -----Original Message-----
> > From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran
> > Kutner
> > Sent: Friday, June 03, 2011 12:24 AM
> > To: user@hbase.apache.org
> > Subject: Re: How to efficiently join HBase tables?
> >
> > Mike, this more or less what I tried to  describe in my initial post,
> only you explained it much better.
> > The problem is that I want to do all of this in one M/R run, not 3 and
> without explicit temp tables. If there was only a way to feed both table A
> and table B into the M/R job then it could be done.
> >
> > Let's take your query and assumptions, for example.
> > So we configure scanner A to return rows where c=xxx and d=yyy We then
> configure scanner B to return rows where e=zzz Now we feed all those rows to
> the mapper.
> > For each row the mapper gets it outputs a new key which is "a|b" and the
> same value it received, if either one doesn't exist in the row the mapper
> doesn't output anything for that row.
> > The is an implicit "temp table" created at this stage by hadoop.
> > Now the reducer is run, for every key "a|b" generated by the mapper it
> would get one or more value sets, each one representing a row from the
> original two tables. For simplicity lets assume we got two rows, one from
> table A the other from table B. Now the reducer can combine the two rows and
> output the combined row. This will work just the same if there were multiple
> rows from each table with the same "a|b" key, in that case the reducer would
> have to generate the Cartesian product of all the rows. Outer joins can also
> be done this way, in an outer join you only get one row in the reducer for a
> given "a|b" key but still generate an output.
> >
> > -eran
> >
> >
> >
> > On Fri, Jun 3, 2011 at 00:05, Michael Segel <michael_segel@hotmail.com
> >wrote:
> >
> > >
> > > Not to beat a dead horse, but I thought a bit more about the problem.
> > > If you want to do this all in HBase using a M/R job...
> > >
> > > Lets define the following:
> > > SELECT *
> > > FROM A, B
> > > WHERE A.a = B.a
> > > AND     A.b = B.b
> > > AND     A.c = xxx
> > > AND     A.d = yyy
> > > AND     B.e = zzz
> > >
> > > Is the sample query.
> > >
> > > So our join key is "a|b" because we're matching on columns a and b.
> > > (The pipe is to delimit the columns, assuming the columns themselves
> > > don't contain pipes...)
> > >
> > > Our filters on A are c and d while e is the filter on B.
> > >
> > > So we want to do the following:
> > >
> > > M/R Map job 1 gets the subset from table A along with a set of unique
> keys.
> > > M/R Map job 2 gets the subset from table B along with a set of unique
> keys.
> > > M/R Map job 3 takes either set of unique keys as the input list and
> > > you split it based on the number of parallel mappers you want to use.
> > >
> > > You have a couple of options on how you want to proceed.
> > > In each Mapper.map() your input is a unique key.
> > > I guess you could create two scanners, one for tempTableA, and one
> > > for tempTableB.
> > > It looks like you can get the iterator for each result set, and then
> > > for each row in temp table A, you iterate through the result set
> > > from temp table B, writing out the joined set.
> > >
> > > The only problem is that your result set file isn't in sort order.
> > > So I guess you could take the output from this job and reduce it to
> > > get it in to sort order.
> > >
> > > Option B. Using HDFS files for temp 'tables'.
> > > You can do this... but you would still have to track the unique keys
> > > and also sort both the keys and the files which will require a reduce
> job.
> > >
> > >
> > > Now this is just my opinion, but if I use HBase, I don't have to
> > > worry about using a reducer except to order the final output set.
> > > So I can save the time it takes to do the reduce step. So I have to
> ask...
> > > how much time is spent by HBase in splitting and compacting the temp
> tables?
> > > Also can't you pre-split the temp table before you use them?
> > >
> > > Or am I still missing something?
> > >
> > > Note: In this example, you'd have to write an input format that
> > > takes a java list object (or something similar) as your input and
> > > then you can split it to get it to run in parallel.
> > > Or you could just write this on the client and split the list up and
> > > run the join in parallel threads on the client node. Or a single
> > > thread which would mean that it would run and output in sort order.
> > >
> > > HTH
> > >
> > > -Mike
> > >
> > > > Date: Wed, 1 Jun 2011 07:47:30 -0700
> > > > Subject: Re: How to efficiently join HBase tables?
> > > > From: jason.rutherglen@gmail.com
> > > > To: user@hbase.apache.org
> > > >
> > > > > you somehow need to flush all in-memory data *and* perform a
> > > > > major compaction
> > > >
> > > > This makes sense.  Without compaction the linear HDFS scan isn't
> > > > possible.  I suppose one could compact 'offline' in a different
> > > > Map Reduce job.  However that would have it's own issues.
> > > >
> > > > > The files do have a flag if they were made by a major
> > > > > compaction, so you scan only those and ignore the newer ones -
> > > > > but then you are
> > > trailing
> > > >
> > > > This could be ok in many cases.  The key would be to create a
> > > > sync'd cut off point enabling a frozen point-in-time 'view' of the
> data.
> > > > I'm not sure how that would be implemented.
> > > >
> > > > On Wed, Jun 1, 2011 at 6:54 AM, Lars George
> > > > <la...@gmail.com>
> > > wrote:
> > > > > Hi Jason,
> > > > >
> > > > > This was discussed in the past, using the HFileInputFormat. The
> > > > > issue is that you somehow need to flush all in-memory data *and*
> > > > > perform a major compaction - or else you would need all the
> > > > > logic of the ColumnTracker in the HFIF. Since that needs to scan
> > > > > all storage files in parallel to achieve its job, the MR task
> > > > > would not really be able to use the same approach.
> > > > >
> > > > > Running a major compaction creates a lot of churn, so it is
> > > > > questionable what the outcome is. The files do have a flag if
> > > > > they were made by a major compaction, so you scan only those and
> > > > > ignore the newer ones - but then you are trailing, and you still
> > > > > do not handle delete markers/updates in newer files. No easy feat.
> > > > >
> > > > > Lars
> > > > >
> > > > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
> > > > > <ja...@gmail.com> wrote:
> > > > >>> I'd imagine that join operations do not require realtime-ness,
> > > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > > >>> HDFS could be the optimal way to go?
> > > > >>
> > > > >> In addition to lessening the load on the perhaps live
> RegionServer.
> > > > >> There's no Jira for this, I'm tempted to open one.
> > > > >>
> > > > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> > > > >> <ja...@gmail.com> wrote:
> > > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > > >>>> that are
> > > backed
> > > > >>>> by HBase
> > > > >>>
> > > > >>> In addition, HBase can be made to go faster for MapReduce
> > > > >>> jobs, if
> > > the
> > > > >>> HFile's could be used directly in HDFS, rather than proxying
> > > > >>> through the RegionServer.
> > > > >>>
> > > > >>> I'd imagine that join operations do not require realtime-ness,
> > > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > > >>> HDFS could be the optimal way to go?
> > > > >>>
> > > > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
> > > patrick@cloudera.com> wrote:
> > > > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
> > > wrote:
> > > > >>>>
> > > > >>>>> For my need I don't really need the general case, but even
> > > > >>>>> if I did
> > > I think
> > > > >>>>> it can probably be done simpler.
> > > > >>>>> The main problem is getting the data from both tables into
> > > > >>>>> the same
> > > MR job,
> > > > >>>>> without resorting to lookups. So without the theoretical
> > > > >>>>> MutliTableInputFormat, I could just copy all the data from
> > > > >>>>> both
> > > tables into
> > > > >>>>> a temp table, just append the source table name to the row
> > > > >>>>> keys to
> > > make
> > > > >>>>> sure
> > > > >>>>> there are no conflicts. When all the data from both tables
> > > > >>>>> is in
> > > the same
> > > > >>>>> temp table, run a MR job. For each row the mapper should
> > > > >>>>> emit a key
> > > which
> > > > >>>>> is
> > > > >>>>> composed of all the values of the join fields in that row
> > > > >>>>> (the
> > > value can be
> > > > >>>>> emitted as is). This will cause all the rows from both
> > > > >>>>> tables, with
> > > same
> > > > >>>>> join field values to arrive at the reducer together. The
> > > > >>>>> reducer
> > > could then
> > > > >>>>> iterate over them and produce the Cartesian product as needed.
> > > > >>>>>
> > > > >>>>> I still don't like having to copy all the data into a temp
> > > > >>>>> table
> > > just
> > > > >>>>> because I can't feed two tables into the MR job.
> > > > >>>>>
> > > > >>>>
> > > > >>>> Loading the smaller table in memory is called a map join,
> > > > >>>> versus a reduce-side join (a.k.a. common join). One reason to
> > > > >>>> prefer a map
> > > join is
> > > > >>>> you avoid the shuffle phase which potentially involves
> > > > >>>> several trips
> > > to disk
> > > > >>>> for the intermediate records due to spills, and also once
> > > > >>>> through
> > > the
> > > > >>>> network to get each intermediate KV pair to the right reducer.
> > > > >>>> With
> > > a map
> > > > >>>> join, everything is local, except for the part where you load
> > > > >>>> the
> > > small
> > > > >>>> table.
> > > > >>>>
> > > > >>>>
> > > > >>>>>
> > > > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I
> > > > >>>>> don't
> > > know if it
> > > > >>>>> can do them for HBase and it will not suit my needs, but it
> > > > >>>>> would
> > > be
> > > > >>>>> interesting to know how is it doing them, if anyone knows.
> > > > >>>>>
> > > > >>>>
> > > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > > >>>> that are
> > > backed
> > > > >>>> by HBase. You can do joins on those tables (and also with
> > > > >>>> standard
> > > Hive
> > > > >>>> tables). It might be worth trying out in your case as it lets
> > > > >>>> you
> > > easily see
> > > > >>>> the load characteristics and the job runtime without much
> > > > >>>> coding
> > > investment.
> > > > >>>>
> > > > >>>> There are probably some specific optimizations that can be
> > > > >>>> applied
> > > to your
> > > > >>>> situation, but it's hard to say without knowing your use-case.
> > > > >>>>
> > > > >>>> Regards,
> > > > >>>>
> > > > >>>> - Patrick
> > > > >>>>
> > > > >>>>
> > > > >>>>> -eran
> > > > >>>>>
> > > > >>>>>
> > > > >>>>>
> > > > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning
> > > > >>>>> <td...@maprtech.com>
> > > wrote:
> > > > >>>>>
> > > > >>>>> > The Cartesian product often makes an honest-to-god join
> > > > >>>>> > not such
> > > a good
> > > > >>>>> > idea
> > > > >>>>> > on large data.  The common alternative is co-group which
> > > > >>>>> > is basically like doing the hard work of the join, but
> > > involves
> > > > >>>>> > stopping just before emitting the cartesian product.  This
> > > > >>>>> > allows you to inject whatever cleverness you need at this
> point.
> > > > >>>>> >
> > > > >>>>> > Common kinds of cleverness include down-sampling of
> > > problematically large
> > > > >>>>> > sets of candidates.
> > > > >>>>> >
> > > > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> > > > >>>>> > <mi...@hotmail.com>wrote:
> > > > >>>>> >
> > > > >>>>> > > So the underlying problem that the OP was trying to
> > > > >>>>> > > solve was
> > > how to
> > > > >>>>> join
> > > > >>>>> > > two tables from HBase.
> > > > >>>>> > > Unfortunately I goofed.
> > > > >>>>> > > I gave a quick and dirty solution that is a bit incomplete.
> > > They row
> > > > >>>>> key
> > > > >>>>> > in
> > > > >>>>> > > the temp table has to be unique and I forgot about the
> > > Cartesian
> > > > >>>>> > > product. So my solution wouldn't work in the general case.
> > > > >>>>> > >
> > > > >>>>> >
> > > > >>>>>
> > > > >>>>
> > > > >>>
> > > > >>
> > > > >
> > >
> > >
>
>

Re: How to efficiently join HBase tables?

[Michel Segel <mi...@hotmail.com>]

Doug,
I think I should clarify something...

Yes I am the only one who is saying get() won't work. 
The question was asked on how to do an efficient join where there were no specific parameters like joining on key values. It wasn't until yesterday that Eran gave an example of the specific problem...

So you have to make some assumptions that you are attempting to solve this problem in the general case. That is you cant assume that you are joining on the row keys. Since we are talking about a big data problem you can assume that your data sets are going to be huge. 
So you have to consider how many rows you can store in memory and that you may not have enough memory.

Because most who are moving to a NoSQL database only have been exposed to relational models, they will approach HBase from a relational schema design. So solving the question of how to join two tables efficiently in general terms has a lot of value to the community as a whole. 

David is right in that I'm looking back and taking the approach of how joining two data sets.
It's not rocket science and this problem has been solved under different paradigms.

Sent from a remote device. Please excuse any typos...

Mike Segel

On Jun 8, 2011, at 9:56 PM, Doug Meil <do...@explorysmedical.com> wrote:

> Hi there-
> 
> Summary comment:
> 
> 1)  Preference
> 
> Several people in this thread have suggested approaches (map-side memory join, multi-get, temp files), all of which have merit and have advantages in certain situations.  Kudos to the dist-list for chiming in.  The "right" approach depends on the specific problem you are trying to solve, and that's preference.
> 
> 2)   Possibility
> 
> Mike, you're the only one in this thread arguing that one of the approaches isn't possible.  And you seem to be arguing that it's never possible to look up a record in HBase with a Get, but others don't seem to have this problem.
> 
> I wish everybody success with their HBase join research, but I'm checking out of this thread (ka-ching).
> 
> 
> -----Original Message-----
> From: im_gumby@hotmail.com [mailto:im_gumby@hotmail.com] On Behalf Of Michel Segel
> Sent: Wednesday, June 08, 2011 10:14 AM
> To: user@hbase.apache.org
> Subject: Re: How to efficiently join HBase tables?
> 
> Unless I am mistaken... get() requires a row key, right?
> And you can join tables on column data which isn't in the row key, right?
> 
> So how do you do a get()? :-)
> 
> Sure there is more than one way to skin a cat. But if you want to be efficient... You will create a set of unique keys based on the columns that you want to join. Note that if you are going to use a temp table in hbase, you will want to store the unique key value A|B and when you write the row to the temp table, you will append an unique identifier like a uuid so that you don't lose the row.
> 
> Here your input list to the actual join is going to be the list of unique keys and then you do a scan to get the rows.
> 
> Again, I could be wrong but how can you perform a get() when you only know a portion of the row key?
> 
> 
> 
> Sent from a remote device. Please excuse any typos...
> 
> Mike Segel
> 
> On Jun 8, 2011, at 8:01 AM, Doug Meil <do...@explorysmedical.com> wrote:
> 
>> 
>> Re: " With respect to Doug's posts, you can't do a multi-get off the bat"
>> 
>> That's an assumption, but you're entitled to your opinion.
>> 
>> -----Original Message-----
>> From: Michael Segel [mailto:michael_segel@hotmail.com]
>> Sent: Monday, June 06, 2011 10:08 PM
>> To: user@hbase.apache.org
>> Subject: RE: How to efficiently join HBase tables?
>> 
>> 
>> Well....
>> 
>> David, is correct.
>> 
>> Eran wanted to do a join which is a relational concept that isn't natively supported by a NoSQL database. A better model would be a hierarchical model like Dick Pick's Revelation. (Univers aka U2 from Ardent/Informix/IBM/now JRockit?).
>> And yes, we're looking back 40 some odd years in to either a
>> merge/sort solution or how databases do a relational join. :-)
>> 
>> Eran wants to do this in a single m/r job. The short answer is you
>> can't.  Longer answer is that if your main class implements Tool
>> Runner, you can launch two jobs in parallel to get your subsets, and
>> then when they both complete, you run the join job on them. So I guess
>> its a single 'job' or rather app. :-)
>> 
>> With respect to Doug's posts, you can't do a multi-get off the bat
>> because in the general case you're not fetching based on the row key
>> but a column which is not part of the row key. (It could be a foreign
>> key which would mean that at least one of your table fetches will be
>> off the row key but you can't guarantee it.)
>> 
>> So if you don't want to use temp tables, then you have to put your
>> results in a sorted order, and you still want to get the unique set of
>> the join-keys which means you have to run a reduce job. Then you can
>> use the unique key set and then do the scans. (You can't do a
>> multi-get because you're doing a scan with a start and stop row(s).)
>> 
>> The reason I suggest that if you're going to do a join operation, you want to use temp tables because it makes your life easier and probably faster too.
>> 
>> Bottom line... I guess many data architects are going to need rethink
>> their data models when working on big data. :-)
>> 
>> -Mike
>> 
>> PS. If I get a spare moment, I may code this up...
>> 
>> 
>>> From: doug.meil@explorysmedical.com
>>> To: user@hbase.apache.org
>>> Date: Mon, 6 Jun 2011 17:19:44 -0400
>>> Subject: RE: How to efficiently join HBase tables?
>>> 
>>> Re:  " So, you all realize the joins have been talked about in the database community for 40 years?"
>>> 
>>> Great point.  What's old is new!    :-)
>>> 
>>> My suggested from earlier in the thread was a variant of nested loops by using multi-get in HTable, which would reduce the number of RPC calls.  So it's a "bulk-select nested loops" of sorts (i.e., as opposed to the 1-by-1 lookup of regular nested loops).
>>> 
>>> 
>>> -----Original Message-----
>>> From: Buttler, David [mailto:buttler1@llnl.gov]
>>> Sent: Monday, June 06, 2011 4:30 PM
>>> To: user@hbase.apache.org
>>> Subject: RE: How to efficiently join HBase tables?
>>> 
>>> So, you all realize the joins have been talked about in the database community for 40 years?  There are two main types of joins:
>>> Nested loops
>>> Hash table
>>> 
>>> Mike, in his various emails seems to be trying to re-imagine how to implement both types of joins in HBase (which seems like a reasonable goal). I am not exactly sure what Eran is going for here, but it seems like Eran is glossing over a piece.  If you have two scanners for table A and B, then table B needs to be rescanned for every unique part of the join condition in table A.  There are certain ways of improving the efficiency of that: the two most obvious are pushing the selection criteria down to the scans, and scanning all of the same join values from table B at the same time (which requires that Table B's key is the join, or a secondary structure that stores the join values as the primary order).
>>> 
>>> Dave
>>> 
>>> -----Original Message-----
>>> From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of
>>> Eran Kutner
>>> Sent: Friday, June 03, 2011 12:24 AM
>>> To: user@hbase.apache.org
>>> Subject: Re: How to efficiently join HBase tables?
>>> 
>>> Mike, this more or less what I tried to  describe in my initial post, only you explained it much better.
>>> The problem is that I want to do all of this in one M/R run, not 3 and without explicit temp tables. If there was only a way to feed both table A and table B into the M/R job then it could be done.
>>> 
>>> Let's take your query and assumptions, for example.
>>> So we configure scanner A to return rows where c=xxx and d=yyy We then configure scanner B to return rows where e=zzz Now we feed all those rows to the mapper.
>>> For each row the mapper gets it outputs a new key which is "a|b" and the same value it received, if either one doesn't exist in the row the mapper doesn't output anything for that row.
>>> The is an implicit "temp table" created at this stage by hadoop.
>>> Now the reducer is run, for every key "a|b" generated by the mapper it would get one or more value sets, each one representing a row from the original two tables. For simplicity lets assume we got two rows, one from table A the other from table B. Now the reducer can combine the two rows and output the combined row. This will work just the same if there were multiple rows from each table with the same "a|b" key, in that case the reducer would have to generate the Cartesian product of all the rows. Outer joins can also be done this way, in an outer join you only get one row in the reducer for a given "a|b" key but still generate an output.
>>> 
>>> -eran
>>> 
>>> 
>>> 
>>> On Fri, Jun 3, 2011 at 00:05, Michael Segel <mi...@hotmail.com>wrote:
>>> 
>>>> 
>>>> Not to beat a dead horse, but I thought a bit more about the problem.
>>>> If you want to do this all in HBase using a M/R job...
>>>> 
>>>> Lets define the following:
>>>> SELECT *
>>>> FROM A, B
>>>> WHERE A.a = B.a
>>>> AND     A.b = B.b
>>>> AND     A.c = xxx
>>>> AND     A.d = yyy
>>>> AND     B.e = zzz
>>>> 
>>>> Is the sample query.
>>>> 
>>>> So our join key is "a|b" because we're matching on columns a and b.
>>>> (The pipe is to delimit the columns, assuming the columns themselves
>>>> don't contain pipes...)
>>>> 
>>>> Our filters on A are c and d while e is the filter on B.
>>>> 
>>>> So we want to do the following:
>>>> 
>>>> M/R Map job 1 gets the subset from table A along with a set of unique keys.
>>>> M/R Map job 2 gets the subset from table B along with a set of unique keys.
>>>> M/R Map job 3 takes either set of unique keys as the input list and
>>>> you split it based on the number of parallel mappers you want to use.
>>>> 
>>>> You have a couple of options on how you want to proceed.
>>>> In each Mapper.map() your input is a unique key.
>>>> I guess you could create two scanners, one for tempTableA, and one
>>>> for tempTableB.
>>>> It looks like you can get the iterator for each result set, and then
>>>> for each row in temp table A, you iterate through the result set
>>>> from temp table B, writing out the joined set.
>>>> 
>>>> The only problem is that your result set file isn't in sort order.
>>>> So I guess you could take the output from this job and reduce it to
>>>> get it in to sort order.
>>>> 
>>>> Option B. Using HDFS files for temp 'tables'.
>>>> You can do this... but you would still have to track the unique keys
>>>> and also sort both the keys and the files which will require a reduce job.
>>>> 
>>>> 
>>>> Now this is just my opinion, but if I use HBase, I don't have to
>>>> worry about using a reducer except to order the final output set.
>>>> So I can save the time it takes to do the reduce step. So I have to ask...
>>>> how much time is spent by HBase in splitting and compacting the temp tables?
>>>> Also can't you pre-split the temp table before you use them?
>>>> 
>>>> Or am I still missing something?
>>>> 
>>>> Note: In this example, you'd have to write an input format that
>>>> takes a java list object (or something similar) as your input and
>>>> then you can split it to get it to run in parallel.
>>>> Or you could just write this on the client and split the list up and
>>>> run the join in parallel threads on the client node. Or a single
>>>> thread which would mean that it would run and output in sort order.
>>>> 
>>>> HTH
>>>> 
>>>> -Mike
>>>> 
>>>>> Date: Wed, 1 Jun 2011 07:47:30 -0700
>>>>> Subject: Re: How to efficiently join HBase tables?
>>>>> From: jason.rutherglen@gmail.com
>>>>> To: user@hbase.apache.org
>>>>> 
>>>>>> you somehow need to flush all in-memory data *and* perform a major
>>>>>> compaction
>>>>> 
>>>>> This makes sense.  Without compaction the linear HDFS scan isn't
>>>>> possible.  I suppose one could compact 'offline' in a different Map
>>>>> Reduce job.  However that would have it's own issues.
>>>>> 
>>>>>> The files do have a flag if they were made by a major compaction,
>>>>>> so you scan only those and ignore the newer ones - but then you
>>>>>> are
>>>> trailing
>>>>> 
>>>>> This could be ok in many cases.  The key would be to create a
>>>>> sync'd cut off point enabling a frozen point-in-time 'view' of the data.
>>>>> I'm not sure how that would be implemented.
>>>>> 
>>>>> On Wed, Jun 1, 2011 at 6:54 AM, Lars George <la...@gmail.com>
>>>> wrote:
>>>>>> Hi Jason,
>>>>>> 
>>>>>> This was discussed in the past, using the HFileInputFormat. The
>>>>>> issue is that you somehow need to flush all in-memory data *and*
>>>>>> perform a major compaction - or else you would need all the logic
>>>>>> of the ColumnTracker in the HFIF. Since that needs to scan all
>>>>>> storage files in parallel to achieve its job, the MR task would
>>>>>> not really be able to use the same approach.
>>>>>> 
>>>>>> Running a major compaction creates a lot of churn, so it is
>>>>>> questionable what the outcome is. The files do have a flag if they
>>>>>> were made by a major compaction, so you scan only those and ignore
>>>>>> the newer ones - but then you are trailing, and you still do not
>>>>>> handle delete markers/updates in newer files. No easy feat.
>>>>>> 
>>>>>> Lars
>>>>>> 
>>>>>> On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
>>>>>> <ja...@gmail.com> wrote:
>>>>>>>> I'd imagine that join operations do not require realtime-ness,
>>>>>>>> and so faster batch jobs using Hive -> frozen HBase files in
>>>>>>>> HDFS could be the optimal way to go?
>>>>>>> 
>>>>>>> In addition to lessening the load on the perhaps live RegionServer.
>>>>>>> There's no Jira for this, I'm tempted to open one.
>>>>>>> 
>>>>>>> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
>>>>>>> <ja...@gmail.com> wrote:
>>>>>>>>> The Hive-HBase integration allows you to create Hive tables
>>>>>>>>> that are
>>>> backed
>>>>>>>>> by HBase
>>>>>>>> 
>>>>>>>> In addition, HBase can be made to go faster for MapReduce jobs,
>>>>>>>> if
>>>> the
>>>>>>>> HFile's could be used directly in HDFS, rather than proxying
>>>>>>>> through the RegionServer.
>>>>>>>> 
>>>>>>>> I'd imagine that join operations do not require realtime-ness,
>>>>>>>> and so faster batch jobs using Hive -> frozen HBase files in
>>>>>>>> HDFS could be the optimal way to go?
>>>>>>>> 
>>>>>>>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
>>>> patrick@cloudera.com> wrote:
>>>>>>>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
>>>> wrote:
>>>>>>>>> 
>>>>>>>>>> For my need I don't really need the general case, but even if
>>>>>>>>>> I did
>>>> I think
>>>>>>>>>> it can probably be done simpler.
>>>>>>>>>> The main problem is getting the data from both tables into the
>>>>>>>>>> same
>>>> MR job,
>>>>>>>>>> without resorting to lookups. So without the theoretical
>>>>>>>>>> MutliTableInputFormat, I could just copy all the data from
>>>>>>>>>> both
>>>> tables into
>>>>>>>>>> a temp table, just append the source table name to the row
>>>>>>>>>> keys to
>>>> make
>>>>>>>>>> sure
>>>>>>>>>> there are no conflicts. When all the data from both tables is
>>>>>>>>>> in
>>>> the same
>>>>>>>>>> temp table, run a MR job. For each row the mapper should emit
>>>>>>>>>> a key
>>>> which
>>>>>>>>>> is
>>>>>>>>>> composed of all the values of the join fields in that row (the
>>>> value can be
>>>>>>>>>> emitted as is). This will cause all the rows from both tables,
>>>>>>>>>> with
>>>> same
>>>>>>>>>> join field values to arrive at the reducer together. The
>>>>>>>>>> reducer
>>>> could then
>>>>>>>>>> iterate over them and produce the Cartesian product as needed.
>>>>>>>>>> 
>>>>>>>>>> I still don't like having to copy all the data into a temp
>>>>>>>>>> table
>>>> just
>>>>>>>>>> because I can't feed two tables into the MR job.
>>>>>>>>>> 
>>>>>>>>> 
>>>>>>>>> Loading the smaller table in memory is called a map join,
>>>>>>>>> versus a reduce-side join (a.k.a. common join). One reason to
>>>>>>>>> prefer a map
>>>> join is
>>>>>>>>> you avoid the shuffle phase which potentially involves several
>>>>>>>>> trips
>>>> to disk
>>>>>>>>> for the intermediate records due to spills, and also once
>>>>>>>>> through
>>>> the
>>>>>>>>> network to get each intermediate KV pair to the right reducer.
>>>>>>>>> With
>>>> a map
>>>>>>>>> join, everything is local, except for the part where you load
>>>>>>>>> the
>>>> small
>>>>>>>>> table.
>>>>>>>>> 
>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> As Jason Rutherglen mentioned above, Hive can do joins. I
>>>>>>>>>> don't
>>>> know if it
>>>>>>>>>> can do them for HBase and it will not suit my needs, but it
>>>>>>>>>> would
>>>> be
>>>>>>>>>> interesting to know how is it doing them, if anyone knows.
>>>>>>>>>> 
>>>>>>>>> 
>>>>>>>>> The Hive-HBase integration allows you to create Hive tables
>>>>>>>>> that are
>>>> backed
>>>>>>>>> by HBase. You can do joins on those tables (and also with
>>>>>>>>> standard
>>>> Hive
>>>>>>>>> tables). It might be worth trying out in your case as it lets
>>>>>>>>> you
>>>> easily see
>>>>>>>>> the load characteristics and the job runtime without much
>>>>>>>>> coding
>>>> investment.
>>>>>>>>> 
>>>>>>>>> There are probably some specific optimizations that can be
>>>>>>>>> applied
>>>> to your
>>>>>>>>> situation, but it's hard to say without knowing your use-case.
>>>>>>>>> 
>>>>>>>>> Regards,
>>>>>>>>> 
>>>>>>>>> - Patrick
>>>>>>>>> 
>>>>>>>>> 
>>>>>>>>>> -eran
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning
>>>>>>>>>> <td...@maprtech.com>
>>>> wrote:
>>>>>>>>>> 
>>>>>>>>>>> The Cartesian product often makes an honest-to-god join not
>>>>>>>>>>> such
>>>> a good
>>>>>>>>>>> idea
>>>>>>>>>>> on large data.  The common alternative is co-group which is
>>>>>>>>>>> basically like doing the hard work of the join, but
>>>> involves
>>>>>>>>>>> stopping just before emitting the cartesian product.  This
>>>>>>>>>>> allows you to inject whatever cleverness you need at this point.
>>>>>>>>>>> 
>>>>>>>>>>> Common kinds of cleverness include down-sampling of
>>>> problematically large
>>>>>>>>>>> sets of candidates.
>>>>>>>>>>> 
>>>>>>>>>>> On Tue, May 31, 2011 at 11:56 AM, Michael Segel
>>>>>>>>>>> <mi...@hotmail.com>wrote:
>>>>>>>>>>> 
>>>>>>>>>>>> So the underlying problem that the OP was trying to solve
>>>>>>>>>>>> was
>>>> how to
>>>>>>>>>> join
>>>>>>>>>>>> two tables from HBase.
>>>>>>>>>>>> Unfortunately I goofed.
>>>>>>>>>>>> I gave a quick and dirty solution that is a bit incomplete.
>>>> They row
>>>>>>>>>> key
>>>>>>>>>>> in
>>>>>>>>>>>> the temp table has to be unique and I forgot about the
>>>> Cartesian
>>>>>>>>>>>> product. So my solution wouldn't work in the general case.
>>>>>>>>>>>> 
>>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>> 
>>>>>>>> 
>>>>>>> 
>>>>>> 
>>>> 
>>>> 
>> 
>> 
> 

RE: How to efficiently join HBase tables?

[Doug Meil <do...@explorysmedical.com>]

Hi there-

Summary comment:

1)  Preference

Several people in this thread have suggested approaches (map-side memory join, multi-get, temp files), all of which have merit and have advantages in certain situations.  Kudos to the dist-list for chiming in.  The "right" approach depends on the specific problem you are trying to solve, and that's preference.

2)   Possibility

Mike, you're the only one in this thread arguing that one of the approaches isn't possible.  And you seem to be arguing that it's never possible to look up a record in HBase with a Get, but others don't seem to have this problem.

I wish everybody success with their HBase join research, but I'm checking out of this thread (ka-ching).


-----Original Message-----
From: im_gumby@hotmail.com [mailto:im_gumby@hotmail.com] On Behalf Of Michel Segel
Sent: Wednesday, June 08, 2011 10:14 AM
To: user@hbase.apache.org
Subject: Re: How to efficiently join HBase tables?

Unless I am mistaken... get() requires a row key, right?
And you can join tables on column data which isn't in the row key, right?

So how do you do a get()? :-)

Sure there is more than one way to skin a cat. But if you want to be efficient... You will create a set of unique keys based on the columns that you want to join. Note that if you are going to use a temp table in hbase, you will want to store the unique key value A|B and when you write the row to the temp table, you will append an unique identifier like a uuid so that you don't lose the row.

Here your input list to the actual join is going to be the list of unique keys and then you do a scan to get the rows.

Again, I could be wrong but how can you perform a get() when you only know a portion of the row key?



Sent from a remote device. Please excuse any typos...

Mike Segel

On Jun 8, 2011, at 8:01 AM, Doug Meil <do...@explorysmedical.com> wrote:

>
> Re: " With respect to Doug's posts, you can't do a multi-get off the bat"
>
> That's an assumption, but you're entitled to your opinion.
>
> -----Original Message-----
> From: Michael Segel [mailto:michael_segel@hotmail.com]
> Sent: Monday, June 06, 2011 10:08 PM
> To: user@hbase.apache.org
> Subject: RE: How to efficiently join HBase tables?
>
>
> Well....
>
> David, is correct.
>
> Eran wanted to do a join which is a relational concept that isn't natively supported by a NoSQL database. A better model would be a hierarchical model like Dick Pick's Revelation. (Univers aka U2 from Ardent/Informix/IBM/now JRockit?).
> And yes, we're looking back 40 some odd years in to either a
> merge/sort solution or how databases do a relational join. :-)
>
> Eran wants to do this in a single m/r job. The short answer is you
> can't.  Longer answer is that if your main class implements Tool
> Runner, you can launch two jobs in parallel to get your subsets, and
> then when they both complete, you run the join job on them. So I guess
> its a single 'job' or rather app. :-)
>
> With respect to Doug's posts, you can't do a multi-get off the bat
> because in the general case you're not fetching based on the row key
> but a column which is not part of the row key. (It could be a foreign
> key which would mean that at least one of your table fetches will be
> off the row key but you can't guarantee it.)
>
> So if you don't want to use temp tables, then you have to put your
> results in a sorted order, and you still want to get the unique set of
> the join-keys which means you have to run a reduce job. Then you can
> use the unique key set and then do the scans. (You can't do a
> multi-get because you're doing a scan with a start and stop row(s).)
>
> The reason I suggest that if you're going to do a join operation, you want to use temp tables because it makes your life easier and probably faster too.
>
> Bottom line... I guess many data architects are going to need rethink
> their data models when working on big data. :-)
>
> -Mike
>
> PS. If I get a spare moment, I may code this up...
>
>
>> From: doug.meil@explorysmedical.com
>> To: user@hbase.apache.org
>> Date: Mon, 6 Jun 2011 17:19:44 -0400
>> Subject: RE: How to efficiently join HBase tables?
>>
>> Re:  " So, you all realize the joins have been talked about in the database community for 40 years?"
>>
>> Great point.  What's old is new!    :-)
>>
>> My suggested from earlier in the thread was a variant of nested loops by using multi-get in HTable, which would reduce the number of RPC calls.  So it's a "bulk-select nested loops" of sorts (i.e., as opposed to the 1-by-1 lookup of regular nested loops).
>>
>>
>> -----Original Message-----
>> From: Buttler, David [mailto:buttler1@llnl.gov]
>> Sent: Monday, June 06, 2011 4:30 PM
>> To: user@hbase.apache.org
>> Subject: RE: How to efficiently join HBase tables?
>>
>> So, you all realize the joins have been talked about in the database community for 40 years?  There are two main types of joins:
>> Nested loops
>> Hash table
>>
>> Mike, in his various emails seems to be trying to re-imagine how to implement both types of joins in HBase (which seems like a reasonable goal). I am not exactly sure what Eran is going for here, but it seems like Eran is glossing over a piece.  If you have two scanners for table A and B, then table B needs to be rescanned for every unique part of the join condition in table A.  There are certain ways of improving the efficiency of that: the two most obvious are pushing the selection criteria down to the scans, and scanning all of the same join values from table B at the same time (which requires that Table B's key is the join, or a secondary structure that stores the join values as the primary order).
>>
>> Dave
>>
>> -----Original Message-----
>> From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of
>> Eran Kutner
>> Sent: Friday, June 03, 2011 12:24 AM
>> To: user@hbase.apache.org
>> Subject: Re: How to efficiently join HBase tables?
>>
>> Mike, this more or less what I tried to  describe in my initial post, only you explained it much better.
>> The problem is that I want to do all of this in one M/R run, not 3 and without explicit temp tables. If there was only a way to feed both table A and table B into the M/R job then it could be done.
>>
>> Let's take your query and assumptions, for example.
>> So we configure scanner A to return rows where c=xxx and d=yyy We then configure scanner B to return rows where e=zzz Now we feed all those rows to the mapper.
>> For each row the mapper gets it outputs a new key which is "a|b" and the same value it received, if either one doesn't exist in the row the mapper doesn't output anything for that row.
>> The is an implicit "temp table" created at this stage by hadoop.
>> Now the reducer is run, for every key "a|b" generated by the mapper it would get one or more value sets, each one representing a row from the original two tables. For simplicity lets assume we got two rows, one from table A the other from table B. Now the reducer can combine the two rows and output the combined row. This will work just the same if there were multiple rows from each table with the same "a|b" key, in that case the reducer would have to generate the Cartesian product of all the rows. Outer joins can also be done this way, in an outer join you only get one row in the reducer for a given "a|b" key but still generate an output.
>>
>> -eran
>>
>>
>>
>> On Fri, Jun 3, 2011 at 00:05, Michael Segel <mi...@hotmail.com>wrote:
>>
>>>
>>> Not to beat a dead horse, but I thought a bit more about the problem.
>>> If you want to do this all in HBase using a M/R job...
>>>
>>> Lets define the following:
>>> SELECT *
>>> FROM A, B
>>> WHERE A.a = B.a
>>> AND     A.b = B.b
>>> AND     A.c = xxx
>>> AND     A.d = yyy
>>> AND     B.e = zzz
>>>
>>> Is the sample query.
>>>
>>> So our join key is "a|b" because we're matching on columns a and b.
>>> (The pipe is to delimit the columns, assuming the columns themselves
>>> don't contain pipes...)
>>>
>>> Our filters on A are c and d while e is the filter on B.
>>>
>>> So we want to do the following:
>>>
>>> M/R Map job 1 gets the subset from table A along with a set of unique keys.
>>> M/R Map job 2 gets the subset from table B along with a set of unique keys.
>>> M/R Map job 3 takes either set of unique keys as the input list and
>>> you split it based on the number of parallel mappers you want to use.
>>>
>>> You have a couple of options on how you want to proceed.
>>> In each Mapper.map() your input is a unique key.
>>> I guess you could create two scanners, one for tempTableA, and one
>>> for tempTableB.
>>> It looks like you can get the iterator for each result set, and then
>>> for each row in temp table A, you iterate through the result set
>>> from temp table B, writing out the joined set.
>>>
>>> The only problem is that your result set file isn't in sort order.
>>> So I guess you could take the output from this job and reduce it to
>>> get it in to sort order.
>>>
>>> Option B. Using HDFS files for temp 'tables'.
>>> You can do this... but you would still have to track the unique keys
>>> and also sort both the keys and the files which will require a reduce job.
>>>
>>>
>>> Now this is just my opinion, but if I use HBase, I don't have to
>>> worry about using a reducer except to order the final output set.
>>> So I can save the time it takes to do the reduce step. So I have to ask...
>>> how much time is spent by HBase in splitting and compacting the temp tables?
>>> Also can't you pre-split the temp table before you use them?
>>>
>>> Or am I still missing something?
>>>
>>> Note: In this example, you'd have to write an input format that
>>> takes a java list object (or something similar) as your input and
>>> then you can split it to get it to run in parallel.
>>> Or you could just write this on the client and split the list up and
>>> run the join in parallel threads on the client node. Or a single
>>> thread which would mean that it would run and output in sort order.
>>>
>>> HTH
>>>
>>> -Mike
>>>
>>>> Date: Wed, 1 Jun 2011 07:47:30 -0700
>>>> Subject: Re: How to efficiently join HBase tables?
>>>> From: jason.rutherglen@gmail.com
>>>> To: user@hbase.apache.org
>>>>
>>>>> you somehow need to flush all in-memory data *and* perform a major
>>>>> compaction
>>>>
>>>> This makes sense.  Without compaction the linear HDFS scan isn't
>>>> possible.  I suppose one could compact 'offline' in a different Map
>>>> Reduce job.  However that would have it's own issues.
>>>>
>>>>> The files do have a flag if they were made by a major compaction,
>>>>> so you scan only those and ignore the newer ones - but then you
>>>>> are
>>> trailing
>>>>
>>>> This could be ok in many cases.  The key would be to create a
>>>> sync'd cut off point enabling a frozen point-in-time 'view' of the data.
>>>> I'm not sure how that would be implemented.
>>>>
>>>> On Wed, Jun 1, 2011 at 6:54 AM, Lars George <la...@gmail.com>
>>> wrote:
>>>>> Hi Jason,
>>>>>
>>>>> This was discussed in the past, using the HFileInputFormat. The
>>>>> issue is that you somehow need to flush all in-memory data *and*
>>>>> perform a major compaction - or else you would need all the logic
>>>>> of the ColumnTracker in the HFIF. Since that needs to scan all
>>>>> storage files in parallel to achieve its job, the MR task would
>>>>> not really be able to use the same approach.
>>>>>
>>>>> Running a major compaction creates a lot of churn, so it is
>>>>> questionable what the outcome is. The files do have a flag if they
>>>>> were made by a major compaction, so you scan only those and ignore
>>>>> the newer ones - but then you are trailing, and you still do not
>>>>> handle delete markers/updates in newer files. No easy feat.
>>>>>
>>>>> Lars
>>>>>
>>>>> On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
>>>>> <ja...@gmail.com> wrote:
>>>>>>> I'd imagine that join operations do not require realtime-ness,
>>>>>>> and so faster batch jobs using Hive -> frozen HBase files in
>>>>>>> HDFS could be the optimal way to go?
>>>>>>
>>>>>> In addition to lessening the load on the perhaps live RegionServer.
>>>>>> There's no Jira for this, I'm tempted to open one.
>>>>>>
>>>>>> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
>>>>>> <ja...@gmail.com> wrote:
>>>>>>>> The Hive-HBase integration allows you to create Hive tables
>>>>>>>> that are
>>> backed
>>>>>>>> by HBase
>>>>>>>
>>>>>>> In addition, HBase can be made to go faster for MapReduce jobs,
>>>>>>> if
>>> the
>>>>>>> HFile's could be used directly in HDFS, rather than proxying
>>>>>>> through the RegionServer.
>>>>>>>
>>>>>>> I'd imagine that join operations do not require realtime-ness,
>>>>>>> and so faster batch jobs using Hive -> frozen HBase files in
>>>>>>> HDFS could be the optimal way to go?
>>>>>>>
>>>>>>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
>>> patrick@cloudera.com> wrote:
>>>>>>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
>>> wrote:
>>>>>>>>
>>>>>>>>> For my need I don't really need the general case, but even if
>>>>>>>>> I did
>>> I think
>>>>>>>>> it can probably be done simpler.
>>>>>>>>> The main problem is getting the data from both tables into the
>>>>>>>>> same
>>> MR job,
>>>>>>>>> without resorting to lookups. So without the theoretical
>>>>>>>>> MutliTableInputFormat, I could just copy all the data from
>>>>>>>>> both
>>> tables into
>>>>>>>>> a temp table, just append the source table name to the row
>>>>>>>>> keys to
>>> make
>>>>>>>>> sure
>>>>>>>>> there are no conflicts. When all the data from both tables is
>>>>>>>>> in
>>> the same
>>>>>>>>> temp table, run a MR job. For each row the mapper should emit
>>>>>>>>> a key
>>> which
>>>>>>>>> is
>>>>>>>>> composed of all the values of the join fields in that row (the
>>> value can be
>>>>>>>>> emitted as is). This will cause all the rows from both tables,
>>>>>>>>> with
>>> same
>>>>>>>>> join field values to arrive at the reducer together. The
>>>>>>>>> reducer
>>> could then
>>>>>>>>> iterate over them and produce the Cartesian product as needed.
>>>>>>>>>
>>>>>>>>> I still don't like having to copy all the data into a temp
>>>>>>>>> table
>>> just
>>>>>>>>> because I can't feed two tables into the MR job.
>>>>>>>>>
>>>>>>>>
>>>>>>>> Loading the smaller table in memory is called a map join,
>>>>>>>> versus a reduce-side join (a.k.a. common join). One reason to
>>>>>>>> prefer a map
>>> join is
>>>>>>>> you avoid the shuffle phase which potentially involves several
>>>>>>>> trips
>>> to disk
>>>>>>>> for the intermediate records due to spills, and also once
>>>>>>>> through
>>> the
>>>>>>>> network to get each intermediate KV pair to the right reducer.
>>>>>>>> With
>>> a map
>>>>>>>> join, everything is local, except for the part where you load
>>>>>>>> the
>>> small
>>>>>>>> table.
>>>>>>>>
>>>>>>>>
>>>>>>>>>
>>>>>>>>> As Jason Rutherglen mentioned above, Hive can do joins. I
>>>>>>>>> don't
>>> know if it
>>>>>>>>> can do them for HBase and it will not suit my needs, but it
>>>>>>>>> would
>>> be
>>>>>>>>> interesting to know how is it doing them, if anyone knows.
>>>>>>>>>
>>>>>>>>
>>>>>>>> The Hive-HBase integration allows you to create Hive tables
>>>>>>>> that are
>>> backed
>>>>>>>> by HBase. You can do joins on those tables (and also with
>>>>>>>> standard
>>> Hive
>>>>>>>> tables). It might be worth trying out in your case as it lets
>>>>>>>> you
>>> easily see
>>>>>>>> the load characteristics and the job runtime without much
>>>>>>>> coding
>>> investment.
>>>>>>>>
>>>>>>>> There are probably some specific optimizations that can be
>>>>>>>> applied
>>> to your
>>>>>>>> situation, but it's hard to say without knowing your use-case.
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>>
>>>>>>>> - Patrick
>>>>>>>>
>>>>>>>>
>>>>>>>>> -eran
>>>>>>>>>
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning
>>>>>>>>> <td...@maprtech.com>
>>> wrote:
>>>>>>>>>
>>>>>>>>>> The Cartesian product often makes an honest-to-god join not
>>>>>>>>>> such
>>> a good
>>>>>>>>>> idea
>>>>>>>>>> on large data.  The common alternative is co-group which is
>>>>>>>>>> basically like doing the hard work of the join, but
>>> involves
>>>>>>>>>> stopping just before emitting the cartesian product.  This
>>>>>>>>>> allows you to inject whatever cleverness you need at this point.
>>>>>>>>>>
>>>>>>>>>> Common kinds of cleverness include down-sampling of
>>> problematically large
>>>>>>>>>> sets of candidates.
>>>>>>>>>>
>>>>>>>>>> On Tue, May 31, 2011 at 11:56 AM, Michael Segel
>>>>>>>>>> <mi...@hotmail.com>wrote:
>>>>>>>>>>
>>>>>>>>>>> So the underlying problem that the OP was trying to solve
>>>>>>>>>>> was
>>> how to
>>>>>>>>> join
>>>>>>>>>>> two tables from HBase.
>>>>>>>>>>> Unfortunately I goofed.
>>>>>>>>>>> I gave a quick and dirty solution that is a bit incomplete.
>>> They row
>>>>>>>>> key
>>>>>>>>>> in
>>>>>>>>>>> the temp table has to be unique and I forgot about the
>>> Cartesian
>>>>>>>>>>> product. So my solution wouldn't work in the general case.
>>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>
>>>
>
>

Re: How to efficiently join HBase tables?

[Michel Segel <mi...@hotmail.com>]

Unless I am mistaken... get() requires a row key, right?
And you can join tables on column data which isn't in the row key, right?

So how do you do a get()? :-)

Sure there is more than one way to skin a cat. But if you want to be efficient... You will create a set of unique keys based on the columns that you want to join. Note that if you are going to use a temp table in hbase, you will want to store the unique key value A|B and when you write the row to the temp table, you will append an unique identifier like a uuid so that you don't lose the row.

Here your input list to the actual join is going to be the list of unique keys and then you do a scan to get the rows. 

Again, I could be wrong but how can you perform a get() when you only know a portion of the row key?



Sent from a remote device. Please excuse any typos...

Mike Segel

On Jun 8, 2011, at 8:01 AM, Doug Meil <do...@explorysmedical.com> wrote:

> 
> Re: " With respect to Doug's posts, you can't do a multi-get off the bat"
> 
> That's an assumption, but you're entitled to your opinion.
> 
> -----Original Message-----
> From: Michael Segel [mailto:michael_segel@hotmail.com]
> Sent: Monday, June 06, 2011 10:08 PM
> To: user@hbase.apache.org
> Subject: RE: How to efficiently join HBase tables?
> 
> 
> Well....
> 
> David, is correct.
> 
> Eran wanted to do a join which is a relational concept that isn't natively supported by a NoSQL database. A better model would be a hierarchical model like Dick Pick's Revelation. (Univers aka U2 from Ardent/Informix/IBM/now JRockit?).
> And yes, we're looking back 40 some odd years in to either a merge/sort solution or how databases do a relational join. :-)
> 
> Eran wants to do this in a single m/r job. The short answer is you can't.  Longer answer is that if your main class implements Tool Runner, you can launch two jobs in parallel to get your subsets, and then when they both complete, you run the join job on them. So I guess its a single 'job' or rather app. :-)
> 
> With respect to Doug's posts, you can't do a multi-get off the bat because in the general case you're not fetching based on the row key but a column which is not part of the row key. (It could be a foreign key which would mean that at least one of your table fetches will be off the row key but you can't guarantee it.)
> 
> So if you don't want to use temp tables, then you have to put your results in a sorted order, and you still want to get the unique set of the join-keys which means you have to run a reduce job. Then you can use the unique key set and then do the scans. (You can't do a multi-get because you're doing a scan with a start and stop row(s).)
> 
> The reason I suggest that if you're going to do a join operation, you want to use temp tables because it makes your life easier and probably faster too.
> 
> Bottom line... I guess many data architects are going to need rethink their data models when working on big data. :-)
> 
> -Mike
> 
> PS. If I get a spare moment, I may code this up...
> 
> 
>> From: doug.meil@explorysmedical.com
>> To: user@hbase.apache.org
>> Date: Mon, 6 Jun 2011 17:19:44 -0400
>> Subject: RE: How to efficiently join HBase tables?
>> 
>> Re:  " So, you all realize the joins have been talked about in the database community for 40 years?"
>> 
>> Great point.  What's old is new!    :-)
>> 
>> My suggested from earlier in the thread was a variant of nested loops by using multi-get in HTable, which would reduce the number of RPC calls.  So it's a "bulk-select nested loops" of sorts (i.e., as opposed to the 1-by-1 lookup of regular nested loops).
>> 
>> 
>> -----Original Message-----
>> From: Buttler, David [mailto:buttler1@llnl.gov]
>> Sent: Monday, June 06, 2011 4:30 PM
>> To: user@hbase.apache.org
>> Subject: RE: How to efficiently join HBase tables?
>> 
>> So, you all realize the joins have been talked about in the database community for 40 years?  There are two main types of joins:
>> Nested loops
>> Hash table
>> 
>> Mike, in his various emails seems to be trying to re-imagine how to implement both types of joins in HBase (which seems like a reasonable goal). I am not exactly sure what Eran is going for here, but it seems like Eran is glossing over a piece.  If you have two scanners for table A and B, then table B needs to be rescanned for every unique part of the join condition in table A.  There are certain ways of improving the efficiency of that: the two most obvious are pushing the selection criteria down to the scans, and scanning all of the same join values from table B at the same time (which requires that Table B's key is the join, or a secondary structure that stores the join values as the primary order).
>> 
>> Dave
>> 
>> -----Original Message-----
>> From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran
>> Kutner
>> Sent: Friday, June 03, 2011 12:24 AM
>> To: user@hbase.apache.org
>> Subject: Re: How to efficiently join HBase tables?
>> 
>> Mike, this more or less what I tried to  describe in my initial post, only you explained it much better.
>> The problem is that I want to do all of this in one M/R run, not 3 and without explicit temp tables. If there was only a way to feed both table A and table B into the M/R job then it could be done.
>> 
>> Let's take your query and assumptions, for example.
>> So we configure scanner A to return rows where c=xxx and d=yyy We then configure scanner B to return rows where e=zzz Now we feed all those rows to the mapper.
>> For each row the mapper gets it outputs a new key which is "a|b" and the same value it received, if either one doesn't exist in the row the mapper doesn't output anything for that row.
>> The is an implicit "temp table" created at this stage by hadoop.
>> Now the reducer is run, for every key "a|b" generated by the mapper it would get one or more value sets, each one representing a row from the original two tables. For simplicity lets assume we got two rows, one from table A the other from table B. Now the reducer can combine the two rows and output the combined row. This will work just the same if there were multiple rows from each table with the same "a|b" key, in that case the reducer would have to generate the Cartesian product of all the rows. Outer joins can also be done this way, in an outer join you only get one row in the reducer for a given "a|b" key but still generate an output.
>> 
>> -eran
>> 
>> 
>> 
>> On Fri, Jun 3, 2011 at 00:05, Michael Segel <mi...@hotmail.com>wrote:
>> 
>>> 
>>> Not to beat a dead horse, but I thought a bit more about the problem.
>>> If you want to do this all in HBase using a M/R job...
>>> 
>>> Lets define the following:
>>> SELECT *
>>> FROM A, B
>>> WHERE A.a = B.a
>>> AND     A.b = B.b
>>> AND     A.c = xxx
>>> AND     A.d = yyy
>>> AND     B.e = zzz
>>> 
>>> Is the sample query.
>>> 
>>> So our join key is "a|b" because we're matching on columns a and b.
>>> (The pipe is to delimit the columns, assuming the columns themselves
>>> don't contain pipes...)
>>> 
>>> Our filters on A are c and d while e is the filter on B.
>>> 
>>> So we want to do the following:
>>> 
>>> M/R Map job 1 gets the subset from table A along with a set of unique keys.
>>> M/R Map job 2 gets the subset from table B along with a set of unique keys.
>>> M/R Map job 3 takes either set of unique keys as the input list and
>>> you split it based on the number of parallel mappers you want to use.
>>> 
>>> You have a couple of options on how you want to proceed.
>>> In each Mapper.map() your input is a unique key.
>>> I guess you could create two scanners, one for tempTableA, and one
>>> for tempTableB.
>>> It looks like you can get the iterator for each result set, and then
>>> for each row in temp table A, you iterate through the result set
>>> from temp table B, writing out the joined set.
>>> 
>>> The only problem is that your result set file isn't in sort order.
>>> So I guess you could take the output from this job and reduce it to
>>> get it in to sort order.
>>> 
>>> Option B. Using HDFS files for temp 'tables'.
>>> You can do this... but you would still have to track the unique keys
>>> and also sort both the keys and the files which will require a reduce job.
>>> 
>>> 
>>> Now this is just my opinion, but if I use HBase, I don't have to
>>> worry about using a reducer except to order the final output set.
>>> So I can save the time it takes to do the reduce step. So I have to ask...
>>> how much time is spent by HBase in splitting and compacting the temp tables?
>>> Also can't you pre-split the temp table before you use them?
>>> 
>>> Or am I still missing something?
>>> 
>>> Note: In this example, you'd have to write an input format that
>>> takes a java list object (or something similar) as your input and
>>> then you can split it to get it to run in parallel.
>>> Or you could just write this on the client and split the list up and
>>> run the join in parallel threads on the client node. Or a single
>>> thread which would mean that it would run and output in sort order.
>>> 
>>> HTH
>>> 
>>> -Mike
>>> 
>>>> Date: Wed, 1 Jun 2011 07:47:30 -0700
>>>> Subject: Re: How to efficiently join HBase tables?
>>>> From: jason.rutherglen@gmail.com
>>>> To: user@hbase.apache.org
>>>> 
>>>>> you somehow need to flush all in-memory data *and* perform a
>>>>> major compaction
>>>> 
>>>> This makes sense.  Without compaction the linear HDFS scan isn't
>>>> possible.  I suppose one could compact 'offline' in a different
>>>> Map Reduce job.  However that would have it's own issues.
>>>> 
>>>>> The files do have a flag if they were made by a major
>>>>> compaction, so you scan only those and ignore the newer ones -
>>>>> but then you are
>>> trailing
>>>> 
>>>> This could be ok in many cases.  The key would be to create a
>>>> sync'd cut off point enabling a frozen point-in-time 'view' of the data.
>>>> I'm not sure how that would be implemented.
>>>> 
>>>> On Wed, Jun 1, 2011 at 6:54 AM, Lars George
>>>> <la...@gmail.com>
>>> wrote:
>>>>> Hi Jason,
>>>>> 
>>>>> This was discussed in the past, using the HFileInputFormat. The
>>>>> issue is that you somehow need to flush all in-memory data *and*
>>>>> perform a major compaction - or else you would need all the
>>>>> logic of the ColumnTracker in the HFIF. Since that needs to scan
>>>>> all storage files in parallel to achieve its job, the MR task
>>>>> would not really be able to use the same approach.
>>>>> 
>>>>> Running a major compaction creates a lot of churn, so it is
>>>>> questionable what the outcome is. The files do have a flag if
>>>>> they were made by a major compaction, so you scan only those and
>>>>> ignore the newer ones - but then you are trailing, and you still
>>>>> do not handle delete markers/updates in newer files. No easy feat.
>>>>> 
>>>>> Lars
>>>>> 
>>>>> On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
>>>>> <ja...@gmail.com> wrote:
>>>>>>> I'd imagine that join operations do not require realtime-ness,
>>>>>>> and so faster batch jobs using Hive -> frozen HBase files in
>>>>>>> HDFS could be the optimal way to go?
>>>>>> 
>>>>>> In addition to lessening the load on the perhaps live RegionServer.
>>>>>> There's no Jira for this, I'm tempted to open one.
>>>>>> 
>>>>>> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
>>>>>> <ja...@gmail.com> wrote:
>>>>>>>> The Hive-HBase integration allows you to create Hive tables
>>>>>>>> that are
>>> backed
>>>>>>>> by HBase
>>>>>>> 
>>>>>>> In addition, HBase can be made to go faster for MapReduce
>>>>>>> jobs, if
>>> the
>>>>>>> HFile's could be used directly in HDFS, rather than proxying
>>>>>>> through the RegionServer.
>>>>>>> 
>>>>>>> I'd imagine that join operations do not require realtime-ness,
>>>>>>> and so faster batch jobs using Hive -> frozen HBase files in
>>>>>>> HDFS could be the optimal way to go?
>>>>>>> 
>>>>>>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
>>> patrick@cloudera.com> wrote:
>>>>>>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
>>> wrote:
>>>>>>>> 
>>>>>>>>> For my need I don't really need the general case, but even
>>>>>>>>> if I did
>>> I think
>>>>>>>>> it can probably be done simpler.
>>>>>>>>> The main problem is getting the data from both tables into
>>>>>>>>> the same
>>> MR job,
>>>>>>>>> without resorting to lookups. So without the theoretical
>>>>>>>>> MutliTableInputFormat, I could just copy all the data from
>>>>>>>>> both
>>> tables into
>>>>>>>>> a temp table, just append the source table name to the row
>>>>>>>>> keys to
>>> make
>>>>>>>>> sure
>>>>>>>>> there are no conflicts. When all the data from both tables
>>>>>>>>> is in
>>> the same
>>>>>>>>> temp table, run a MR job. For each row the mapper should
>>>>>>>>> emit a key
>>> which
>>>>>>>>> is
>>>>>>>>> composed of all the values of the join fields in that row
>>>>>>>>> (the
>>> value can be
>>>>>>>>> emitted as is). This will cause all the rows from both
>>>>>>>>> tables, with
>>> same
>>>>>>>>> join field values to arrive at the reducer together. The
>>>>>>>>> reducer
>>> could then
>>>>>>>>> iterate over them and produce the Cartesian product as needed.
>>>>>>>>> 
>>>>>>>>> I still don't like having to copy all the data into a temp
>>>>>>>>> table
>>> just
>>>>>>>>> because I can't feed two tables into the MR job.
>>>>>>>>> 
>>>>>>>> 
>>>>>>>> Loading the smaller table in memory is called a map join,
>>>>>>>> versus a reduce-side join (a.k.a. common join). One reason to
>>>>>>>> prefer a map
>>> join is
>>>>>>>> you avoid the shuffle phase which potentially involves
>>>>>>>> several trips
>>> to disk
>>>>>>>> for the intermediate records due to spills, and also once
>>>>>>>> through
>>> the
>>>>>>>> network to get each intermediate KV pair to the right reducer.
>>>>>>>> With
>>> a map
>>>>>>>> join, everything is local, except for the part where you load
>>>>>>>> the
>>> small
>>>>>>>> table.
>>>>>>>> 
>>>>>>>> 
>>>>>>>>> 
>>>>>>>>> As Jason Rutherglen mentioned above, Hive can do joins. I
>>>>>>>>> don't
>>> know if it
>>>>>>>>> can do them for HBase and it will not suit my needs, but it
>>>>>>>>> would
>>> be
>>>>>>>>> interesting to know how is it doing them, if anyone knows.
>>>>>>>>> 
>>>>>>>> 
>>>>>>>> The Hive-HBase integration allows you to create Hive tables
>>>>>>>> that are
>>> backed
>>>>>>>> by HBase. You can do joins on those tables (and also with
>>>>>>>> standard
>>> Hive
>>>>>>>> tables). It might be worth trying out in your case as it lets
>>>>>>>> you
>>> easily see
>>>>>>>> the load characteristics and the job runtime without much
>>>>>>>> coding
>>> investment.
>>>>>>>> 
>>>>>>>> There are probably some specific optimizations that can be
>>>>>>>> applied
>>> to your
>>>>>>>> situation, but it's hard to say without knowing your use-case.
>>>>>>>> 
>>>>>>>> Regards,
>>>>>>>> 
>>>>>>>> - Patrick
>>>>>>>> 
>>>>>>>> 
>>>>>>>>> -eran
>>>>>>>>> 
>>>>>>>>> 
>>>>>>>>> 
>>>>>>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning
>>>>>>>>> <td...@maprtech.com>
>>> wrote:
>>>>>>>>> 
>>>>>>>>>> The Cartesian product often makes an honest-to-god join
>>>>>>>>>> not such
>>> a good
>>>>>>>>>> idea
>>>>>>>>>> on large data.  The common alternative is co-group which
>>>>>>>>>> is basically like doing the hard work of the join, but
>>> involves
>>>>>>>>>> stopping just before emitting the cartesian product.  This
>>>>>>>>>> allows you to inject whatever cleverness you need at this point.
>>>>>>>>>> 
>>>>>>>>>> Common kinds of cleverness include down-sampling of
>>> problematically large
>>>>>>>>>> sets of candidates.
>>>>>>>>>> 
>>>>>>>>>> On Tue, May 31, 2011 at 11:56 AM, Michael Segel
>>>>>>>>>> <mi...@hotmail.com>wrote:
>>>>>>>>>> 
>>>>>>>>>>> So the underlying problem that the OP was trying to
>>>>>>>>>>> solve was
>>> how to
>>>>>>>>> join
>>>>>>>>>>> two tables from HBase.
>>>>>>>>>>> Unfortunately I goofed.
>>>>>>>>>>> I gave a quick and dirty solution that is a bit incomplete.
>>> They row
>>>>>>>>> key
>>>>>>>>>> in
>>>>>>>>>>> the temp table has to be unique and I forgot about the
>>> Cartesian
>>>>>>>>>>> product. So my solution wouldn't work in the general case.
>>>>>>>>>>> 
>>>>>>>>>> 
>>>>>>>>> 
>>>>>>>> 
>>>>>>> 
>>>>>> 
>>>>> 
>>> 
>>> 
> 
> 

RE: How to efficiently join HBase tables?

[Doug Meil <do...@explorysmedical.com>]

Re: " With respect to Doug's posts, you can't do a multi-get off the bat"

That's an assumption, but you're entitled to your opinion.

-----Original Message-----
From: Michael Segel [mailto:michael_segel@hotmail.com]
Sent: Monday, June 06, 2011 10:08 PM
To: user@hbase.apache.org
Subject: RE: How to efficiently join HBase tables?


Well....

David, is correct.

Eran wanted to do a join which is a relational concept that isn't natively supported by a NoSQL database. A better model would be a hierarchical model like Dick Pick's Revelation. (Univers aka U2 from Ardent/Informix/IBM/now JRockit?).
And yes, we're looking back 40 some odd years in to either a merge/sort solution or how databases do a relational join. :-)

Eran wants to do this in a single m/r job. The short answer is you can't.  Longer answer is that if your main class implements Tool Runner, you can launch two jobs in parallel to get your subsets, and then when they both complete, you run the join job on them. So I guess its a single 'job' or rather app. :-)

With respect to Doug's posts, you can't do a multi-get off the bat because in the general case you're not fetching based on the row key but a column which is not part of the row key. (It could be a foreign key which would mean that at least one of your table fetches will be off the row key but you can't guarantee it.)

So if you don't want to use temp tables, then you have to put your results in a sorted order, and you still want to get the unique set of the join-keys which means you have to run a reduce job. Then you can use the unique key set and then do the scans. (You can't do a multi-get because you're doing a scan with a start and stop row(s).)

The reason I suggest that if you're going to do a join operation, you want to use temp tables because it makes your life easier and probably faster too.

Bottom line... I guess many data architects are going to need rethink their data models when working on big data. :-)

-Mike

PS. If I get a spare moment, I may code this up...


> From: doug.meil@explorysmedical.com
> To: user@hbase.apache.org
> Date: Mon, 6 Jun 2011 17:19:44 -0400
> Subject: RE: How to efficiently join HBase tables?
>
> Re:  " So, you all realize the joins have been talked about in the database community for 40 years?"
>
> Great point.  What's old is new!    :-)
>
> My suggested from earlier in the thread was a variant of nested loops by using multi-get in HTable, which would reduce the number of RPC calls.  So it's a "bulk-select nested loops" of sorts (i.e., as opposed to the 1-by-1 lookup of regular nested loops).
>
>
> -----Original Message-----
> From: Buttler, David [mailto:buttler1@llnl.gov]
> Sent: Monday, June 06, 2011 4:30 PM
> To: user@hbase.apache.org
> Subject: RE: How to efficiently join HBase tables?
>
> So, you all realize the joins have been talked about in the database community for 40 years?  There are two main types of joins:
> Nested loops
> Hash table
>
> Mike, in his various emails seems to be trying to re-imagine how to implement both types of joins in HBase (which seems like a reasonable goal). I am not exactly sure what Eran is going for here, but it seems like Eran is glossing over a piece.  If you have two scanners for table A and B, then table B needs to be rescanned for every unique part of the join condition in table A.  There are certain ways of improving the efficiency of that: the two most obvious are pushing the selection criteria down to the scans, and scanning all of the same join values from table B at the same time (which requires that Table B's key is the join, or a secondary structure that stores the join values as the primary order).
>
> Dave
>
> -----Original Message-----
> From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran
> Kutner
> Sent: Friday, June 03, 2011 12:24 AM
> To: user@hbase.apache.org
> Subject: Re: How to efficiently join HBase tables?
>
> Mike, this more or less what I tried to  describe in my initial post, only you explained it much better.
> The problem is that I want to do all of this in one M/R run, not 3 and without explicit temp tables. If there was only a way to feed both table A and table B into the M/R job then it could be done.
>
> Let's take your query and assumptions, for example.
> So we configure scanner A to return rows where c=xxx and d=yyy We then configure scanner B to return rows where e=zzz Now we feed all those rows to the mapper.
> For each row the mapper gets it outputs a new key which is "a|b" and the same value it received, if either one doesn't exist in the row the mapper doesn't output anything for that row.
> The is an implicit "temp table" created at this stage by hadoop.
> Now the reducer is run, for every key "a|b" generated by the mapper it would get one or more value sets, each one representing a row from the original two tables. For simplicity lets assume we got two rows, one from table A the other from table B. Now the reducer can combine the two rows and output the combined row. This will work just the same if there were multiple rows from each table with the same "a|b" key, in that case the reducer would have to generate the Cartesian product of all the rows. Outer joins can also be done this way, in an outer join you only get one row in the reducer for a given "a|b" key but still generate an output.
>
> -eran
>
>
>
> On Fri, Jun 3, 2011 at 00:05, Michael Segel <mi...@hotmail.com>wrote:
>
> >
> > Not to beat a dead horse, but I thought a bit more about the problem.
> > If you want to do this all in HBase using a M/R job...
> >
> > Lets define the following:
> > SELECT *
> > FROM A, B
> > WHERE A.a = B.a
> > AND     A.b = B.b
> > AND     A.c = xxx
> > AND     A.d = yyy
> > AND     B.e = zzz
> >
> > Is the sample query.
> >
> > So our join key is "a|b" because we're matching on columns a and b.
> > (The pipe is to delimit the columns, assuming the columns themselves
> > don't contain pipes...)
> >
> > Our filters on A are c and d while e is the filter on B.
> >
> > So we want to do the following:
> >
> > M/R Map job 1 gets the subset from table A along with a set of unique keys.
> > M/R Map job 2 gets the subset from table B along with a set of unique keys.
> > M/R Map job 3 takes either set of unique keys as the input list and
> > you split it based on the number of parallel mappers you want to use.
> >
> > You have a couple of options on how you want to proceed.
> > In each Mapper.map() your input is a unique key.
> > I guess you could create two scanners, one for tempTableA, and one
> > for tempTableB.
> > It looks like you can get the iterator for each result set, and then
> > for each row in temp table A, you iterate through the result set
> > from temp table B, writing out the joined set.
> >
> > The only problem is that your result set file isn't in sort order.
> > So I guess you could take the output from this job and reduce it to
> > get it in to sort order.
> >
> > Option B. Using HDFS files for temp 'tables'.
> > You can do this... but you would still have to track the unique keys
> > and also sort both the keys and the files which will require a reduce job.
> >
> >
> > Now this is just my opinion, but if I use HBase, I don't have to
> > worry about using a reducer except to order the final output set.
> > So I can save the time it takes to do the reduce step. So I have to ask...
> > how much time is spent by HBase in splitting and compacting the temp tables?
> > Also can't you pre-split the temp table before you use them?
> >
> > Or am I still missing something?
> >
> > Note: In this example, you'd have to write an input format that
> > takes a java list object (or something similar) as your input and
> > then you can split it to get it to run in parallel.
> > Or you could just write this on the client and split the list up and
> > run the join in parallel threads on the client node. Or a single
> > thread which would mean that it would run and output in sort order.
> >
> > HTH
> >
> > -Mike
> >
> > > Date: Wed, 1 Jun 2011 07:47:30 -0700
> > > Subject: Re: How to efficiently join HBase tables?
> > > From: jason.rutherglen@gmail.com
> > > To: user@hbase.apache.org
> > >
> > > > you somehow need to flush all in-memory data *and* perform a
> > > > major compaction
> > >
> > > This makes sense.  Without compaction the linear HDFS scan isn't
> > > possible.  I suppose one could compact 'offline' in a different
> > > Map Reduce job.  However that would have it's own issues.
> > >
> > > > The files do have a flag if they were made by a major
> > > > compaction, so you scan only those and ignore the newer ones -
> > > > but then you are
> > trailing
> > >
> > > This could be ok in many cases.  The key would be to create a
> > > sync'd cut off point enabling a frozen point-in-time 'view' of the data.
> > > I'm not sure how that would be implemented.
> > >
> > > On Wed, Jun 1, 2011 at 6:54 AM, Lars George
> > > <la...@gmail.com>
> > wrote:
> > > > Hi Jason,
> > > >
> > > > This was discussed in the past, using the HFileInputFormat. The
> > > > issue is that you somehow need to flush all in-memory data *and*
> > > > perform a major compaction - or else you would need all the
> > > > logic of the ColumnTracker in the HFIF. Since that needs to scan
> > > > all storage files in parallel to achieve its job, the MR task
> > > > would not really be able to use the same approach.
> > > >
> > > > Running a major compaction creates a lot of churn, so it is
> > > > questionable what the outcome is. The files do have a flag if
> > > > they were made by a major compaction, so you scan only those and
> > > > ignore the newer ones - but then you are trailing, and you still
> > > > do not handle delete markers/updates in newer files. No easy feat.
> > > >
> > > > Lars
> > > >
> > > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
> > > > <ja...@gmail.com> wrote:
> > > >>> I'd imagine that join operations do not require realtime-ness,
> > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > >>> HDFS could be the optimal way to go?
> > > >>
> > > >> In addition to lessening the load on the perhaps live RegionServer.
> > > >> There's no Jira for this, I'm tempted to open one.
> > > >>
> > > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> > > >> <ja...@gmail.com> wrote:
> > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > >>>> that are
> > backed
> > > >>>> by HBase
> > > >>>
> > > >>> In addition, HBase can be made to go faster for MapReduce
> > > >>> jobs, if
> > the
> > > >>> HFile's could be used directly in HDFS, rather than proxying
> > > >>> through the RegionServer.
> > > >>>
> > > >>> I'd imagine that join operations do not require realtime-ness,
> > > >>> and so faster batch jobs using Hive -> frozen HBase files in
> > > >>> HDFS could be the optimal way to go?
> > > >>>
> > > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
> > patrick@cloudera.com> wrote:
> > > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
> > wrote:
> > > >>>>
> > > >>>>> For my need I don't really need the general case, but even
> > > >>>>> if I did
> > I think
> > > >>>>> it can probably be done simpler.
> > > >>>>> The main problem is getting the data from both tables into
> > > >>>>> the same
> > MR job,
> > > >>>>> without resorting to lookups. So without the theoretical
> > > >>>>> MutliTableInputFormat, I could just copy all the data from
> > > >>>>> both
> > tables into
> > > >>>>> a temp table, just append the source table name to the row
> > > >>>>> keys to
> > make
> > > >>>>> sure
> > > >>>>> there are no conflicts. When all the data from both tables
> > > >>>>> is in
> > the same
> > > >>>>> temp table, run a MR job. For each row the mapper should
> > > >>>>> emit a key
> > which
> > > >>>>> is
> > > >>>>> composed of all the values of the join fields in that row
> > > >>>>> (the
> > value can be
> > > >>>>> emitted as is). This will cause all the rows from both
> > > >>>>> tables, with
> > same
> > > >>>>> join field values to arrive at the reducer together. The
> > > >>>>> reducer
> > could then
> > > >>>>> iterate over them and produce the Cartesian product as needed.
> > > >>>>>
> > > >>>>> I still don't like having to copy all the data into a temp
> > > >>>>> table
> > just
> > > >>>>> because I can't feed two tables into the MR job.
> > > >>>>>
> > > >>>>
> > > >>>> Loading the smaller table in memory is called a map join,
> > > >>>> versus a reduce-side join (a.k.a. common join). One reason to
> > > >>>> prefer a map
> > join is
> > > >>>> you avoid the shuffle phase which potentially involves
> > > >>>> several trips
> > to disk
> > > >>>> for the intermediate records due to spills, and also once
> > > >>>> through
> > the
> > > >>>> network to get each intermediate KV pair to the right reducer.
> > > >>>> With
> > a map
> > > >>>> join, everything is local, except for the part where you load
> > > >>>> the
> > small
> > > >>>> table.
> > > >>>>
> > > >>>>
> > > >>>>>
> > > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I
> > > >>>>> don't
> > know if it
> > > >>>>> can do them for HBase and it will not suit my needs, but it
> > > >>>>> would
> > be
> > > >>>>> interesting to know how is it doing them, if anyone knows.
> > > >>>>>
> > > >>>>
> > > >>>> The Hive-HBase integration allows you to create Hive tables
> > > >>>> that are
> > backed
> > > >>>> by HBase. You can do joins on those tables (and also with
> > > >>>> standard
> > Hive
> > > >>>> tables). It might be worth trying out in your case as it lets
> > > >>>> you
> > easily see
> > > >>>> the load characteristics and the job runtime without much
> > > >>>> coding
> > investment.
> > > >>>>
> > > >>>> There are probably some specific optimizations that can be
> > > >>>> applied
> > to your
> > > >>>> situation, but it's hard to say without knowing your use-case.
> > > >>>>
> > > >>>> Regards,
> > > >>>>
> > > >>>> - Patrick
> > > >>>>
> > > >>>>
> > > >>>>> -eran
> > > >>>>>
> > > >>>>>
> > > >>>>>
> > > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning
> > > >>>>> <td...@maprtech.com>
> > wrote:
> > > >>>>>
> > > >>>>> > The Cartesian product often makes an honest-to-god join
> > > >>>>> > not such
> > a good
> > > >>>>> > idea
> > > >>>>> > on large data.  The common alternative is co-group which
> > > >>>>> > is basically like doing the hard work of the join, but
> > involves
> > > >>>>> > stopping just before emitting the cartesian product.  This
> > > >>>>> > allows you to inject whatever cleverness you need at this point.
> > > >>>>> >
> > > >>>>> > Common kinds of cleverness include down-sampling of
> > problematically large
> > > >>>>> > sets of candidates.
> > > >>>>> >
> > > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> > > >>>>> > <mi...@hotmail.com>wrote:
> > > >>>>> >
> > > >>>>> > > So the underlying problem that the OP was trying to
> > > >>>>> > > solve was
> > how to
> > > >>>>> join
> > > >>>>> > > two tables from HBase.
> > > >>>>> > > Unfortunately I goofed.
> > > >>>>> > > I gave a quick and dirty solution that is a bit incomplete.
> > They row
> > > >>>>> key
> > > >>>>> > in
> > > >>>>> > > the temp table has to be unique and I forgot about the
> > Cartesian
> > > >>>>> > > product. So my solution wouldn't work in the general case.
> > > >>>>> > >
> > > >>>>> >
> > > >>>>>
> > > >>>>
> > > >>>
> > > >>
> > > >
> >
> >

Re: What the optimization method of when to delete Zk connection?

[Stack <st...@duboce.net>]

On Wed, Jun 8, 2011 at 1:44 AM, bijieshan <bi...@huawei.com> wrote:
> Thanks Suraj.
> Yes, It's a better method. For I haven't test on that.
> So use HTablePool, it seems we haven't need to delete Zk connections manually? Is that correct?
>



Yes.
St.Ack

Re: What the optimization method of when to delete Zk connection?

[bijieshan <bi...@huawei.com>]

Thanks Suraj.
Yes, It's a better method. For I haven't test on that.
So use HTablePool, it seems we haven't need to delete Zk connections manually? Is that correct?

Thanks!

Jieshan Bean

------------------

How about using HTablePool - doesn't that work for you?
http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/HTablePool.html

--Suraj

On Tue, Jun 7, 2011 at 2:23 AM, bijieshan <bi...@huawei.com> wrote:
> Hi,
>
> As we know , the zk connection could be created by the following method:
>
> Configuration newConfig = new Configuration(originalConf);
> HConnection connection = HConnectionManager.getConnection(newConfig);
>
> One HConnection instance proxy to one Configuration instance. Under some scenarios, we can share Configuration instance, and also we can share the HConnection instance.
>
> Consider the following scenario:
>
> While the program running, there's so many scan operations. Each operation will be executed at a random time. There's two schemes:
> (Suppose the program shared one Configuation instance.)
>
> 1. Create one HTable at a time, after the scanning, delete the connection and close the HTable.
> 2. Create one HTable at a time, after the scanning ,only close the HTable. So remain the zk connection.
>
> (Maybe I can share the HTable instance, but it wasn't what I want to discuss here)
>
> Which is the best way? Or what the optimization method of when to delete Zk connection?
>
>
> Thanks!
>
> Jieshan Bean
>
>
>

Re: What the optimization method of when to delete Zk connection?

[Suraj Varma <sv...@gmail.com>]

How about using HTablePool - doesn't that work for you?
http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/client/HTablePool.html

--Suraj

On Tue, Jun 7, 2011 at 2:23 AM, bijieshan <bi...@huawei.com> wrote:
> Hi,
>
> As we know , the zk connection could be created by the following method:
>
> Configuration newConfig = new Configuration(originalConf);
> HConnection connection = HConnectionManager.getConnection(newConfig);
>
> One HConnection instance proxy to one Configuration instance. Under some scenarios, we can share Configuration instance, and also we can share the HConnection instance.
>
> Consider the following scenario:
>
> While the program running, there's so many scan operations. Each operation will be executed at a random time. There's two schemes:
> (Suppose the program shared one Configuation instance.)
>
> 1. Create one HTable at a time, after the scanning, delete the connection and close the HTable.
> 2. Create one HTable at a time, after the scanning ,only close the HTable. So remain the zk connection.
>
> (Maybe I can share the HTable instance, but it wasn't what I want to discuss here)
>
> Which is the best way? Or what the optimization method of when to delete Zk connection?
>
>
> Thanks!
>
> Jieshan Bean
>
>
>

What the optimization method of when to delete Zk connection?

[bijieshan <bi...@huawei.com>]

Hi,

As we know , the zk connection could be created by the following method:

Configuration newConfig = new Configuration(originalConf);
HConnection connection = HConnectionManager.getConnection(newConfig); 

One HConnection instance proxy to one Configuration instance. Under some scenarios, we can share Configuration instance, and also we can share the HConnection instance. 

Consider the following scenario:

While the program running, there's so many scan operations. Each operation will be executed at a random time. There's two schemes:
(Suppose the program shared one Configuation instance.)

1. Create one HTable at a time, after the scanning, delete the connection and close the HTable.
2. Create one HTable at a time, after the scanning ,only close the HTable. So remain the zk connection.

(Maybe I can share the HTable instance, but it wasn't what I want to discuss here)

Which is the best way? Or what the optimization method of when to delete Zk connection?


Thanks!

Jieshan Bean

RE: How to efficiently join HBase tables?

[Michael Segel <mi...@hotmail.com>]

Well....

David, is correct.

Eran wanted to do a join which is a relational concept that isn't natively supported by a NoSQL database. A better model would be a hierarchical model like Dick Pick's Revelation. (Univers aka U2 from Ardent/Informix/IBM/now JRockit?).
And yes, we're looking back 40 some odd years in to either a merge/sort solution or how databases do a relational join. :-)

Eran wants to do this in a single m/r job. The short answer is you can't.  Longer answer is that if your main class implements Tool Runner, you can launch two jobs in parallel to get your subsets, and then when they both complete, you run the join job on them. So I guess its a single 'job' or rather app. :-)

With respect to Doug's posts, you can't do a multi-get off the bat because in the general case you're not fetching based on the row key but a column which is not part of the row key. (It could be a foreign key which would mean that at least one of your table fetches will be off the row key but you can't guarantee it.)

So if you don't want to use temp tables, then you have to put your results in a sorted order, and you still want to get the unique set of the join-keys which means you have to run a reduce job. Then you can use the unique key set and then do the scans. (You can't do a multi-get because you're doing a scan with a start and stop row(s).)

The reason I suggest that if you're going to do a join operation, you want to use temp tables because it makes your life easier and probably faster too.

Bottom line... I guess many data architects are going to need rethink their data models when working on big data. :-)

-Mike

PS. If I get a spare moment, I may code this up...


> From: doug.meil@explorysmedical.com
> To: user@hbase.apache.org
> Date: Mon, 6 Jun 2011 17:19:44 -0400
> Subject: RE: How to efficiently join HBase tables?
> 
> Re:  " So, you all realize the joins have been talked about in the database community for 40 years?"
> 
> Great point.  What's old is new!    :-)
> 
> My suggested from earlier in the thread was a variant of nested loops by using multi-get in HTable, which would reduce the number of RPC calls.  So it's a "bulk-select nested loops" of sorts (i.e., as opposed to the 1-by-1 lookup of regular nested loops).
> 
> 
> -----Original Message-----
> From: Buttler, David [mailto:buttler1@llnl.gov] 
> Sent: Monday, June 06, 2011 4:30 PM
> To: user@hbase.apache.org
> Subject: RE: How to efficiently join HBase tables?
> 
> So, you all realize the joins have been talked about in the database community for 40 years?  There are two main types of joins:
> Nested loops
> Hash table
> 
> Mike, in his various emails seems to be trying to re-imagine how to implement both types of joins in HBase (which seems like a reasonable goal). I am not exactly sure what Eran is going for here, but it seems like Eran is glossing over a piece.  If you have two scanners for table A and B, then table B needs to be rescanned for every unique part of the join condition in table A.  There are certain ways of improving the efficiency of that: the two most obvious are pushing the selection criteria down to the scans, and scanning all of the same join values from table B at the same time (which requires that Table B's key is the join, or a secondary structure that stores the join values as the primary order).
> 
> Dave
> 
> -----Original Message-----
> From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran Kutner
> Sent: Friday, June 03, 2011 12:24 AM
> To: user@hbase.apache.org
> Subject: Re: How to efficiently join HBase tables?
> 
> Mike, this more or less what I tried to  describe in my initial post, only you explained it much better.
> The problem is that I want to do all of this in one M/R run, not 3 and without explicit temp tables. If there was only a way to feed both table A and table B into the M/R job then it could be done.
> 
> Let's take your query and assumptions, for example.
> So we configure scanner A to return rows where c=xxx and d=yyy We then configure scanner B to return rows where e=zzz Now we feed all those rows to the mapper.
> For each row the mapper gets it outputs a new key which is "a|b" and the same value it received, if either one doesn't exist in the row the mapper doesn't output anything for that row.
> The is an implicit "temp table" created at this stage by hadoop.
> Now the reducer is run, for every key "a|b" generated by the mapper it would get one or more value sets, each one representing a row from the original two tables. For simplicity lets assume we got two rows, one from table A the other from table B. Now the reducer can combine the two rows and output the combined row. This will work just the same if there were multiple rows from each table with the same "a|b" key, in that case the reducer would have to generate the Cartesian product of all the rows. Outer joins can also be done this way, in an outer join you only get one row in the reducer for a given "a|b" key but still generate an output.
> 
> -eran
> 
> 
> 
> On Fri, Jun 3, 2011 at 00:05, Michael Segel <mi...@hotmail.com>wrote:
> 
> >
> > Not to beat a dead horse, but I thought a bit more about the problem.
> > If you want to do this all in HBase using a M/R job...
> >
> > Lets define the following:
> > SELECT *
> > FROM A, B
> > WHERE A.a = B.a
> > AND     A.b = B.b
> > AND     A.c = xxx
> > AND     A.d = yyy
> > AND     B.e = zzz
> >
> > Is the sample query.
> >
> > So our join key is "a|b" because we're matching on columns a and b. 
> > (The pipe is to delimit the columns, assuming the columns themselves 
> > don't contain pipes...)
> >
> > Our filters on A are c and d while e is the filter on B.
> >
> > So we want to do the following:
> >
> > M/R Map job 1 gets the subset from table A along with a set of unique keys.
> > M/R Map job 2 gets the subset from table B along with a set of unique keys.
> > M/R Map job 3 takes either set of unique keys as the input list and 
> > you split it based on the number of parallel mappers you want to use.
> >
> > You have a couple of options on how you want to proceed.
> > In each Mapper.map() your input is a unique key.
> > I guess you could create two scanners, one for tempTableA, and one for 
> > tempTableB.
> > It looks like you can get the iterator for each result set, and then 
> > for each row in temp table A, you iterate through the result set from 
> > temp table B, writing out the joined set.
> >
> > The only problem is that your result set file isn't in sort order. So 
> > I guess you could take the output from this job and reduce it to get 
> > it in to sort order.
> >
> > Option B. Using HDFS files for temp 'tables'.
> > You can do this... but you would still have to track the unique keys 
> > and also sort both the keys and the files which will require a reduce job.
> >
> >
> > Now this is just my opinion, but if I use HBase, I don't have to worry 
> > about using a reducer except to order the final output set.
> > So I can save the time it takes to do the reduce step. So I have to ask...
> > how much time is spent by HBase in splitting and compacting the temp tables?
> > Also can't you pre-split the temp table before you use them?
> >
> > Or am I still missing something?
> >
> > Note: In this example, you'd have to write an input format that takes 
> > a java list object (or something similar) as your input and then you 
> > can split it to get it to run in parallel.
> > Or you could just write this on the client and split the list up and 
> > run the join in parallel threads on the client node. Or a single 
> > thread which would mean that it would run and output in sort order.
> >
> > HTH
> >
> > -Mike
> >
> > > Date: Wed, 1 Jun 2011 07:47:30 -0700
> > > Subject: Re: How to efficiently join HBase tables?
> > > From: jason.rutherglen@gmail.com
> > > To: user@hbase.apache.org
> > >
> > > > you somehow need to flush all in-memory data *and* perform a major 
> > > > compaction
> > >
> > > This makes sense.  Without compaction the linear HDFS scan isn't 
> > > possible.  I suppose one could compact 'offline' in a different Map 
> > > Reduce job.  However that would have it's own issues.
> > >
> > > > The files do have a flag if they were made by a major compaction, 
> > > > so you scan only those and ignore the newer ones - but then you 
> > > > are
> > trailing
> > >
> > > This could be ok in many cases.  The key would be to create a sync'd 
> > > cut off point enabling a frozen point-in-time 'view' of the data.  
> > > I'm not sure how that would be implemented.
> > >
> > > On Wed, Jun 1, 2011 at 6:54 AM, Lars George <la...@gmail.com>
> > wrote:
> > > > Hi Jason,
> > > >
> > > > This was discussed in the past, using the HFileInputFormat. The 
> > > > issue is that you somehow need to flush all in-memory data *and* 
> > > > perform a major compaction - or else you would need all the logic 
> > > > of the ColumnTracker in the HFIF. Since that needs to scan all 
> > > > storage files in parallel to achieve its job, the MR task would 
> > > > not really be able to use the same approach.
> > > >
> > > > Running a major compaction creates a lot of churn, so it is 
> > > > questionable what the outcome is. The files do have a flag if they 
> > > > were made by a major compaction, so you scan only those and ignore 
> > > > the newer ones - but then you are trailing, and you still do not 
> > > > handle delete markers/updates in newer files. No easy feat.
> > > >
> > > > Lars
> > > >
> > > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen 
> > > > <ja...@gmail.com> wrote:
> > > >>> I'd imagine that join operations do not require realtime-ness, 
> > > >>> and so faster batch jobs using Hive -> frozen HBase files in 
> > > >>> HDFS could be the optimal way to go?
> > > >>
> > > >> In addition to lessening the load on the perhaps live RegionServer.
> > > >> There's no Jira for this, I'm tempted to open one.
> > > >>
> > > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen 
> > > >> <ja...@gmail.com> wrote:
> > > >>>> The Hive-HBase integration allows you to create Hive tables 
> > > >>>> that are
> > backed
> > > >>>> by HBase
> > > >>>
> > > >>> In addition, HBase can be made to go faster for MapReduce jobs, 
> > > >>> if
> > the
> > > >>> HFile's could be used directly in HDFS, rather than proxying 
> > > >>> through the RegionServer.
> > > >>>
> > > >>> I'd imagine that join operations do not require realtime-ness, 
> > > >>> and so faster batch jobs using Hive -> frozen HBase files in 
> > > >>> HDFS could be the optimal way to go?
> > > >>>
> > > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
> > patrick@cloudera.com> wrote:
> > > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
> > wrote:
> > > >>>>
> > > >>>>> For my need I don't really need the general case, but even if 
> > > >>>>> I did
> > I think
> > > >>>>> it can probably be done simpler.
> > > >>>>> The main problem is getting the data from both tables into the 
> > > >>>>> same
> > MR job,
> > > >>>>> without resorting to lookups. So without the theoretical 
> > > >>>>> MutliTableInputFormat, I could just copy all the data from 
> > > >>>>> both
> > tables into
> > > >>>>> a temp table, just append the source table name to the row 
> > > >>>>> keys to
> > make
> > > >>>>> sure
> > > >>>>> there are no conflicts. When all the data from both tables is 
> > > >>>>> in
> > the same
> > > >>>>> temp table, run a MR job. For each row the mapper should emit 
> > > >>>>> a key
> > which
> > > >>>>> is
> > > >>>>> composed of all the values of the join fields in that row (the
> > value can be
> > > >>>>> emitted as is). This will cause all the rows from both tables, 
> > > >>>>> with
> > same
> > > >>>>> join field values to arrive at the reducer together. The 
> > > >>>>> reducer
> > could then
> > > >>>>> iterate over them and produce the Cartesian product as needed.
> > > >>>>>
> > > >>>>> I still don't like having to copy all the data into a temp 
> > > >>>>> table
> > just
> > > >>>>> because I can't feed two tables into the MR job.
> > > >>>>>
> > > >>>>
> > > >>>> Loading the smaller table in memory is called a map join, 
> > > >>>> versus a reduce-side join (a.k.a. common join). One reason to 
> > > >>>> prefer a map
> > join is
> > > >>>> you avoid the shuffle phase which potentially involves several 
> > > >>>> trips
> > to disk
> > > >>>> for the intermediate records due to spills, and also once 
> > > >>>> through
> > the
> > > >>>> network to get each intermediate KV pair to the right reducer. 
> > > >>>> With
> > a map
> > > >>>> join, everything is local, except for the part where you load 
> > > >>>> the
> > small
> > > >>>> table.
> > > >>>>
> > > >>>>
> > > >>>>>
> > > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I 
> > > >>>>> don't
> > know if it
> > > >>>>> can do them for HBase and it will not suit my needs, but it 
> > > >>>>> would
> > be
> > > >>>>> interesting to know how is it doing them, if anyone knows.
> > > >>>>>
> > > >>>>
> > > >>>> The Hive-HBase integration allows you to create Hive tables 
> > > >>>> that are
> > backed
> > > >>>> by HBase. You can do joins on those tables (and also with 
> > > >>>> standard
> > Hive
> > > >>>> tables). It might be worth trying out in your case as it lets 
> > > >>>> you
> > easily see
> > > >>>> the load characteristics and the job runtime without much 
> > > >>>> coding
> > investment.
> > > >>>>
> > > >>>> There are probably some specific optimizations that can be 
> > > >>>> applied
> > to your
> > > >>>> situation, but it's hard to say without knowing your use-case.
> > > >>>>
> > > >>>> Regards,
> > > >>>>
> > > >>>> - Patrick
> > > >>>>
> > > >>>>
> > > >>>>> -eran
> > > >>>>>
> > > >>>>>
> > > >>>>>
> > > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning 
> > > >>>>> <td...@maprtech.com>
> > wrote:
> > > >>>>>
> > > >>>>> > The Cartesian product often makes an honest-to-god join not 
> > > >>>>> > such
> > a good
> > > >>>>> > idea
> > > >>>>> > on large data.  The common alternative is co-group which is 
> > > >>>>> > basically like doing the hard work of the join, but
> > involves
> > > >>>>> > stopping just before emitting the cartesian product.  This 
> > > >>>>> > allows you to inject whatever cleverness you need at this point.
> > > >>>>> >
> > > >>>>> > Common kinds of cleverness include down-sampling of
> > problematically large
> > > >>>>> > sets of candidates.
> > > >>>>> >
> > > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> > > >>>>> > <mi...@hotmail.com>wrote:
> > > >>>>> >
> > > >>>>> > > So the underlying problem that the OP was trying to solve 
> > > >>>>> > > was
> > how to
> > > >>>>> join
> > > >>>>> > > two tables from HBase.
> > > >>>>> > > Unfortunately I goofed.
> > > >>>>> > > I gave a quick and dirty solution that is a bit incomplete.
> > They row
> > > >>>>> key
> > > >>>>> > in
> > > >>>>> > > the temp table has to be unique and I forgot about the
> > Cartesian
> > > >>>>> > > product. So my solution wouldn't work in the general case.
> > > >>>>> > >
> > > >>>>> >
> > > >>>>>
> > > >>>>
> > > >>>
> > > >>
> > > >
> >
> >
 		 	   		  

RE: How to efficiently join HBase tables?

[Doug Meil <do...@explorysmedical.com>]

Re:  " So, you all realize the joins have been talked about in the database community for 40 years?"

Great point.  What's old is new!    :-)

My suggested from earlier in the thread was a variant of nested loops by using multi-get in HTable, which would reduce the number of RPC calls.  So it's a "bulk-select nested loops" of sorts (i.e., as opposed to the 1-by-1 lookup of regular nested loops).


-----Original Message-----
From: Buttler, David [mailto:buttler1@llnl.gov] 
Sent: Monday, June 06, 2011 4:30 PM
To: user@hbase.apache.org
Subject: RE: How to efficiently join HBase tables?

So, you all realize the joins have been talked about in the database community for 40 years?  There are two main types of joins:
Nested loops
Hash table

Mike, in his various emails seems to be trying to re-imagine how to implement both types of joins in HBase (which seems like a reasonable goal). I am not exactly sure what Eran is going for here, but it seems like Eran is glossing over a piece.  If you have two scanners for table A and B, then table B needs to be rescanned for every unique part of the join condition in table A.  There are certain ways of improving the efficiency of that: the two most obvious are pushing the selection criteria down to the scans, and scanning all of the same join values from table B at the same time (which requires that Table B's key is the join, or a secondary structure that stores the join values as the primary order).

Dave

-----Original Message-----
From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran Kutner
Sent: Friday, June 03, 2011 12:24 AM
To: user@hbase.apache.org
Subject: Re: How to efficiently join HBase tables?

Mike, this more or less what I tried to  describe in my initial post, only you explained it much better.
The problem is that I want to do all of this in one M/R run, not 3 and without explicit temp tables. If there was only a way to feed both table A and table B into the M/R job then it could be done.

Let's take your query and assumptions, for example.
So we configure scanner A to return rows where c=xxx and d=yyy We then configure scanner B to return rows where e=zzz Now we feed all those rows to the mapper.
For each row the mapper gets it outputs a new key which is "a|b" and the same value it received, if either one doesn't exist in the row the mapper doesn't output anything for that row.
The is an implicit "temp table" created at this stage by hadoop.
Now the reducer is run, for every key "a|b" generated by the mapper it would get one or more value sets, each one representing a row from the original two tables. For simplicity lets assume we got two rows, one from table A the other from table B. Now the reducer can combine the two rows and output the combined row. This will work just the same if there were multiple rows from each table with the same "a|b" key, in that case the reducer would have to generate the Cartesian product of all the rows. Outer joins can also be done this way, in an outer join you only get one row in the reducer for a given "a|b" key but still generate an output.

-eran



On Fri, Jun 3, 2011 at 00:05, Michael Segel <mi...@hotmail.com>wrote:

>
> Not to beat a dead horse, but I thought a bit more about the problem.
> If you want to do this all in HBase using a M/R job...
>
> Lets define the following:
> SELECT *
> FROM A, B
> WHERE A.a = B.a
> AND     A.b = B.b
> AND     A.c = xxx
> AND     A.d = yyy
> AND     B.e = zzz
>
> Is the sample query.
>
> So our join key is "a|b" because we're matching on columns a and b. 
> (The pipe is to delimit the columns, assuming the columns themselves 
> don't contain pipes...)
>
> Our filters on A are c and d while e is the filter on B.
>
> So we want to do the following:
>
> M/R Map job 1 gets the subset from table A along with a set of unique keys.
> M/R Map job 2 gets the subset from table B along with a set of unique keys.
> M/R Map job 3 takes either set of unique keys as the input list and 
> you split it based on the number of parallel mappers you want to use.
>
> You have a couple of options on how you want to proceed.
> In each Mapper.map() your input is a unique key.
> I guess you could create two scanners, one for tempTableA, and one for 
> tempTableB.
> It looks like you can get the iterator for each result set, and then 
> for each row in temp table A, you iterate through the result set from 
> temp table B, writing out the joined set.
>
> The only problem is that your result set file isn't in sort order. So 
> I guess you could take the output from this job and reduce it to get 
> it in to sort order.
>
> Option B. Using HDFS files for temp 'tables'.
> You can do this... but you would still have to track the unique keys 
> and also sort both the keys and the files which will require a reduce job.
>
>
> Now this is just my opinion, but if I use HBase, I don't have to worry 
> about using a reducer except to order the final output set.
> So I can save the time it takes to do the reduce step. So I have to ask...
> how much time is spent by HBase in splitting and compacting the temp tables?
> Also can't you pre-split the temp table before you use them?
>
> Or am I still missing something?
>
> Note: In this example, you'd have to write an input format that takes 
> a java list object (or something similar) as your input and then you 
> can split it to get it to run in parallel.
> Or you could just write this on the client and split the list up and 
> run the join in parallel threads on the client node. Or a single 
> thread which would mean that it would run and output in sort order.
>
> HTH
>
> -Mike
>
> > Date: Wed, 1 Jun 2011 07:47:30 -0700
> > Subject: Re: How to efficiently join HBase tables?
> > From: jason.rutherglen@gmail.com
> > To: user@hbase.apache.org
> >
> > > you somehow need to flush all in-memory data *and* perform a major 
> > > compaction
> >
> > This makes sense.  Without compaction the linear HDFS scan isn't 
> > possible.  I suppose one could compact 'offline' in a different Map 
> > Reduce job.  However that would have it's own issues.
> >
> > > The files do have a flag if they were made by a major compaction, 
> > > so you scan only those and ignore the newer ones - but then you 
> > > are
> trailing
> >
> > This could be ok in many cases.  The key would be to create a sync'd 
> > cut off point enabling a frozen point-in-time 'view' of the data.  
> > I'm not sure how that would be implemented.
> >
> > On Wed, Jun 1, 2011 at 6:54 AM, Lars George <la...@gmail.com>
> wrote:
> > > Hi Jason,
> > >
> > > This was discussed in the past, using the HFileInputFormat. The 
> > > issue is that you somehow need to flush all in-memory data *and* 
> > > perform a major compaction - or else you would need all the logic 
> > > of the ColumnTracker in the HFIF. Since that needs to scan all 
> > > storage files in parallel to achieve its job, the MR task would 
> > > not really be able to use the same approach.
> > >
> > > Running a major compaction creates a lot of churn, so it is 
> > > questionable what the outcome is. The files do have a flag if they 
> > > were made by a major compaction, so you scan only those and ignore 
> > > the newer ones - but then you are trailing, and you still do not 
> > > handle delete markers/updates in newer files. No easy feat.
> > >
> > > Lars
> > >
> > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen 
> > > <ja...@gmail.com> wrote:
> > >>> I'd imagine that join operations do not require realtime-ness, 
> > >>> and so faster batch jobs using Hive -> frozen HBase files in 
> > >>> HDFS could be the optimal way to go?
> > >>
> > >> In addition to lessening the load on the perhaps live RegionServer.
> > >> There's no Jira for this, I'm tempted to open one.
> > >>
> > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen 
> > >> <ja...@gmail.com> wrote:
> > >>>> The Hive-HBase integration allows you to create Hive tables 
> > >>>> that are
> backed
> > >>>> by HBase
> > >>>
> > >>> In addition, HBase can be made to go faster for MapReduce jobs, 
> > >>> if
> the
> > >>> HFile's could be used directly in HDFS, rather than proxying 
> > >>> through the RegionServer.
> > >>>
> > >>> I'd imagine that join operations do not require realtime-ness, 
> > >>> and so faster batch jobs using Hive -> frozen HBase files in 
> > >>> HDFS could be the optimal way to go?
> > >>>
> > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
> patrick@cloudera.com> wrote:
> > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
> wrote:
> > >>>>
> > >>>>> For my need I don't really need the general case, but even if 
> > >>>>> I did
> I think
> > >>>>> it can probably be done simpler.
> > >>>>> The main problem is getting the data from both tables into the 
> > >>>>> same
> MR job,
> > >>>>> without resorting to lookups. So without the theoretical 
> > >>>>> MutliTableInputFormat, I could just copy all the data from 
> > >>>>> both
> tables into
> > >>>>> a temp table, just append the source table name to the row 
> > >>>>> keys to
> make
> > >>>>> sure
> > >>>>> there are no conflicts. When all the data from both tables is 
> > >>>>> in
> the same
> > >>>>> temp table, run a MR job. For each row the mapper should emit 
> > >>>>> a key
> which
> > >>>>> is
> > >>>>> composed of all the values of the join fields in that row (the
> value can be
> > >>>>> emitted as is). This will cause all the rows from both tables, 
> > >>>>> with
> same
> > >>>>> join field values to arrive at the reducer together. The 
> > >>>>> reducer
> could then
> > >>>>> iterate over them and produce the Cartesian product as needed.
> > >>>>>
> > >>>>> I still don't like having to copy all the data into a temp 
> > >>>>> table
> just
> > >>>>> because I can't feed two tables into the MR job.
> > >>>>>
> > >>>>
> > >>>> Loading the smaller table in memory is called a map join, 
> > >>>> versus a reduce-side join (a.k.a. common join). One reason to 
> > >>>> prefer a map
> join is
> > >>>> you avoid the shuffle phase which potentially involves several 
> > >>>> trips
> to disk
> > >>>> for the intermediate records due to spills, and also once 
> > >>>> through
> the
> > >>>> network to get each intermediate KV pair to the right reducer. 
> > >>>> With
> a map
> > >>>> join, everything is local, except for the part where you load 
> > >>>> the
> small
> > >>>> table.
> > >>>>
> > >>>>
> > >>>>>
> > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I 
> > >>>>> don't
> know if it
> > >>>>> can do them for HBase and it will not suit my needs, but it 
> > >>>>> would
> be
> > >>>>> interesting to know how is it doing them, if anyone knows.
> > >>>>>
> > >>>>
> > >>>> The Hive-HBase integration allows you to create Hive tables 
> > >>>> that are
> backed
> > >>>> by HBase. You can do joins on those tables (and also with 
> > >>>> standard
> Hive
> > >>>> tables). It might be worth trying out in your case as it lets 
> > >>>> you
> easily see
> > >>>> the load characteristics and the job runtime without much 
> > >>>> coding
> investment.
> > >>>>
> > >>>> There are probably some specific optimizations that can be 
> > >>>> applied
> to your
> > >>>> situation, but it's hard to say without knowing your use-case.
> > >>>>
> > >>>> Regards,
> > >>>>
> > >>>> - Patrick
> > >>>>
> > >>>>
> > >>>>> -eran
> > >>>>>
> > >>>>>
> > >>>>>
> > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning 
> > >>>>> <td...@maprtech.com>
> wrote:
> > >>>>>
> > >>>>> > The Cartesian product often makes an honest-to-god join not 
> > >>>>> > such
> a good
> > >>>>> > idea
> > >>>>> > on large data.  The common alternative is co-group which is 
> > >>>>> > basically like doing the hard work of the join, but
> involves
> > >>>>> > stopping just before emitting the cartesian product.  This 
> > >>>>> > allows you to inject whatever cleverness you need at this point.
> > >>>>> >
> > >>>>> > Common kinds of cleverness include down-sampling of
> problematically large
> > >>>>> > sets of candidates.
> > >>>>> >
> > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> > >>>>> > <mi...@hotmail.com>wrote:
> > >>>>> >
> > >>>>> > > So the underlying problem that the OP was trying to solve 
> > >>>>> > > was
> how to
> > >>>>> join
> > >>>>> > > two tables from HBase.
> > >>>>> > > Unfortunately I goofed.
> > >>>>> > > I gave a quick and dirty solution that is a bit incomplete.
> They row
> > >>>>> key
> > >>>>> > in
> > >>>>> > > the temp table has to be unique and I forgot about the
> Cartesian
> > >>>>> > > product. So my solution wouldn't work in the general case.
> > >>>>> > >
> > >>>>> >
> > >>>>>
> > >>>>
> > >>>
> > >>
> > >
>
>

RE: How to efficiently join HBase tables?

["Buttler, David" <bu...@llnl.gov>]

So, you all realize the joins have been talked about in the database community for 40 years?  There are two main types of joins:
Nested loops
Hash table

Mike, in his various emails seems to be trying to re-imagine how to implement both types of joins in HBase (which seems like a reasonable goal). I am not exactly sure what Eran is going for here, but it seems like Eran is glossing over a piece.  If you have two scanners for table A and B, then table B needs to be rescanned for every unique part of the join condition in table A.  There are certain ways of improving the efficiency of that: the two most obvious are pushing the selection criteria down to the scans, and scanning all of the same join values from table B at the same time (which requires that Table B's key is the join, or a secondary structure that stores the join values as the primary order).

Dave

-----Original Message-----
From: eran@gigya-inc.com [mailto:eran@gigya-inc.com] On Behalf Of Eran Kutner
Sent: Friday, June 03, 2011 12:24 AM
To: user@hbase.apache.org
Subject: Re: How to efficiently join HBase tables?

Mike, this more or less what I tried to  describe in my initial post, only
you explained it much better.
The problem is that I want to do all of this in one M/R run, not 3 and
without explicit temp tables. If there was only a way to feed both table A
and table B into the M/R job then it could be done.

Let's take your query and assumptions, for example.
So we configure scanner A to return rows where c=xxx and d=yyy
We then configure scanner B to return rows where e=zzz
Now we feed all those rows to the mapper.
For each row the mapper gets it outputs a new key which is "a|b" and the
same value it received, if either one doesn't exist in the row the mapper
doesn't output anything for that row.
The is an implicit "temp table" created at this stage by hadoop.
Now the reducer is run, for every key "a|b" generated by the mapper it would
get one or more value sets, each one representing a row from the original
two tables. For simplicity lets assume we got two rows, one from table A the
other from table B. Now the reducer can combine the two rows and output the
combined row. This will work just the same if there were multiple rows from
each table with the same "a|b" key, in that case the reducer would have to
generate the Cartesian product of all the rows. Outer joins can also be done
this way, in an outer join you only get one row in the reducer for a given
"a|b" key but still generate an output.

-eran



On Fri, Jun 3, 2011 at 00:05, Michael Segel <mi...@hotmail.com>wrote:

>
> Not to beat a dead horse, but I thought a bit more about the problem.
> If you want to do this all in HBase using a M/R job...
>
> Lets define the following:
> SELECT *
> FROM A, B
> WHERE A.a = B.a
> AND     A.b = B.b
> AND     A.c = xxx
> AND     A.d = yyy
> AND     B.e = zzz
>
> Is the sample query.
>
> So our join key is "a|b" because we're matching on columns a and b. (The
> pipe is to delimit the columns, assuming the columns themselves don't
> contain pipes...)
>
> Our filters on A are c and d while e is the filter on B.
>
> So we want to do the following:
>
> M/R Map job 1 gets the subset from table A along with a set of unique keys.
> M/R Map job 2 gets the subset from table B along with a set of unique keys.
> M/R Map job 3 takes either set of unique keys as the input list and you
> split it based on the number of parallel mappers you want to use.
>
> You have a couple of options on how you want to proceed.
> In each Mapper.map() your input is a unique key.
> I guess you could create two scanners, one for tempTableA, and one for
> tempTableB.
> It looks like you can get the iterator for each result set, and then for
> each row in temp table A, you iterate through the result set from temp table
> B, writing out the joined set.
>
> The only problem is that your result set file isn't in sort order. So I
> guess you could take the output from this job and reduce it to get it in to
> sort order.
>
> Option B. Using HDFS files for temp 'tables'.
> You can do this... but you would still have to track the unique keys and
> also sort both the keys and the files which will require a reduce job.
>
>
> Now this is just my opinion, but if I use HBase, I don't have to worry
> about using a reducer except to order the final output set.
> So I can save the time it takes to do the reduce step. So I have to ask...
> how much time is spent by HBase in splitting and compacting the temp tables?
> Also can't you pre-split the temp table before you use them?
>
> Or am I still missing something?
>
> Note: In this example, you'd have to write an input format that takes a
> java list object (or something similar) as your input and then you can split
> it to get it to run in parallel.
> Or you could just write this on the client and split the list up and run
> the join in parallel threads on the client node. Or a single thread which
> would mean that it would run and output in sort order.
>
> HTH
>
> -Mike
>
> > Date: Wed, 1 Jun 2011 07:47:30 -0700
> > Subject: Re: How to efficiently join HBase tables?
> > From: jason.rutherglen@gmail.com
> > To: user@hbase.apache.org
> >
> > > you somehow need to flush all in-memory data *and* perform a
> > > major compaction
> >
> > This makes sense.  Without compaction the linear HDFS scan isn't
> > possible.  I suppose one could compact 'offline' in a different Map
> > Reduce job.  However that would have it's own issues.
> >
> > > The files do have a flag if they were made by a major compaction,
> > > so you scan only those and ignore the newer ones - but then you are
> trailing
> >
> > This could be ok in many cases.  The key would be to create a sync'd
> > cut off point enabling a frozen point-in-time 'view' of the data.  I'm
> > not sure how that would be implemented.
> >
> > On Wed, Jun 1, 2011 at 6:54 AM, Lars George <la...@gmail.com>
> wrote:
> > > Hi Jason,
> > >
> > > This was discussed in the past, using the HFileInputFormat. The issue
> > > is that you somehow need to flush all in-memory data *and* perform a
> > > major compaction - or else you would need all the logic of the
> > > ColumnTracker in the HFIF. Since that needs to scan all storage files
> > > in parallel to achieve its job, the MR task would not really be able
> > > to use the same approach.
> > >
> > > Running a major compaction creates a lot of churn, so it is
> > > questionable what the outcome is. The files do have a flag if they
> > > were made by a major compaction, so you scan only those and ignore the
> > > newer ones - but then you are trailing, and you still do not handle
> > > delete markers/updates in newer files. No easy feat.
> > >
> > > Lars
> > >
> > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
> > > <ja...@gmail.com> wrote:
> > >>> I'd imagine that join operations do not require realtime-ness, and so
> > >>> faster batch jobs using Hive -> frozen HBase files in HDFS could be
> > >>> the optimal way to go?
> > >>
> > >> In addition to lessening the load on the perhaps live RegionServer.
> > >> There's no Jira for this, I'm tempted to open one.
> > >>
> > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> > >> <ja...@gmail.com> wrote:
> > >>>> The Hive-HBase integration allows you to create Hive tables that are
> backed
> > >>>> by HBase
> > >>>
> > >>> In addition, HBase can be made to go faster for MapReduce jobs, if
> the
> > >>> HFile's could be used directly in HDFS, rather than proxying through
> > >>> the RegionServer.
> > >>>
> > >>> I'd imagine that join operations do not require realtime-ness, and so
> > >>> faster batch jobs using Hive -> frozen HBase files in HDFS could be
> > >>> the optimal way to go?
> > >>>
> > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
> patrick@cloudera.com> wrote:
> > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
> wrote:
> > >>>>
> > >>>>> For my need I don't really need the general case, but even if I did
> I think
> > >>>>> it can probably be done simpler.
> > >>>>> The main problem is getting the data from both tables into the same
> MR job,
> > >>>>> without resorting to lookups. So without the theoretical
> > >>>>> MutliTableInputFormat, I could just copy all the data from both
> tables into
> > >>>>> a temp table, just append the source table name to the row keys to
> make
> > >>>>> sure
> > >>>>> there are no conflicts. When all the data from both tables is in
> the same
> > >>>>> temp table, run a MR job. For each row the mapper should emit a key
> which
> > >>>>> is
> > >>>>> composed of all the values of the join fields in that row (the
> value can be
> > >>>>> emitted as is). This will cause all the rows from both tables, with
> same
> > >>>>> join field values to arrive at the reducer together. The reducer
> could then
> > >>>>> iterate over them and produce the Cartesian product as needed.
> > >>>>>
> > >>>>> I still don't like having to copy all the data into a temp table
> just
> > >>>>> because I can't feed two tables into the MR job.
> > >>>>>
> > >>>>
> > >>>> Loading the smaller table in memory is called a map join, versus a
> > >>>> reduce-side join (a.k.a. common join). One reason to prefer a map
> join is
> > >>>> you avoid the shuffle phase which potentially involves several trips
> to disk
> > >>>> for the intermediate records due to spills, and also once through
> the
> > >>>> network to get each intermediate KV pair to the right reducer. With
> a map
> > >>>> join, everything is local, except for the part where you load the
> small
> > >>>> table.
> > >>>>
> > >>>>
> > >>>>>
> > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I don't
> know if it
> > >>>>> can do them for HBase and it will not suit my needs, but it would
> be
> > >>>>> interesting to know how is it doing them, if anyone knows.
> > >>>>>
> > >>>>
> > >>>> The Hive-HBase integration allows you to create Hive tables that are
> backed
> > >>>> by HBase. You can do joins on those tables (and also with standard
> Hive
> > >>>> tables). It might be worth trying out in your case as it lets you
> easily see
> > >>>> the load characteristics and the job runtime without much coding
> investment.
> > >>>>
> > >>>> There are probably some specific optimizations that can be applied
> to your
> > >>>> situation, but it's hard to say without knowing your use-case.
> > >>>>
> > >>>> Regards,
> > >>>>
> > >>>> - Patrick
> > >>>>
> > >>>>
> > >>>>> -eran
> > >>>>>
> > >>>>>
> > >>>>>
> > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning <td...@maprtech.com>
> wrote:
> > >>>>>
> > >>>>> > The Cartesian product often makes an honest-to-god join not such
> a good
> > >>>>> > idea
> > >>>>> > on large data.  The common alternative is co-group
> > >>>>> > which is basically like doing the hard work of the join, but
> involves
> > >>>>> > stopping just before emitting the cartesian product.  This allows
> > >>>>> > you to inject whatever cleverness you need at this point.
> > >>>>> >
> > >>>>> > Common kinds of cleverness include down-sampling of
> problematically large
> > >>>>> > sets of candidates.
> > >>>>> >
> > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> > >>>>> > <mi...@hotmail.com>wrote:
> > >>>>> >
> > >>>>> > > So the underlying problem that the OP was trying to solve was
> how to
> > >>>>> join
> > >>>>> > > two tables from HBase.
> > >>>>> > > Unfortunately I goofed.
> > >>>>> > > I gave a quick and dirty solution that is a bit incomplete.
> They row
> > >>>>> key
> > >>>>> > in
> > >>>>> > > the temp table has to be unique and I forgot about the
> Cartesian
> > >>>>> > > product. So my solution wouldn't work in the general case.
> > >>>>> > >
> > >>>>> >
> > >>>>>
> > >>>>
> > >>>
> > >>
> > >
>
>

Re: How to efficiently join HBase tables?

[Eran Kutner <er...@gigya.com>]

Mike, this more or less what I tried to  describe in my initial post, only
you explained it much better.
The problem is that I want to do all of this in one M/R run, not 3 and
without explicit temp tables. If there was only a way to feed both table A
and table B into the M/R job then it could be done.

Let's take your query and assumptions, for example.
So we configure scanner A to return rows where c=xxx and d=yyy
We then configure scanner B to return rows where e=zzz
Now we feed all those rows to the mapper.
For each row the mapper gets it outputs a new key which is "a|b" and the
same value it received, if either one doesn't exist in the row the mapper
doesn't output anything for that row.
The is an implicit "temp table" created at this stage by hadoop.
Now the reducer is run, for every key "a|b" generated by the mapper it would
get one or more value sets, each one representing a row from the original
two tables. For simplicity lets assume we got two rows, one from table A the
other from table B. Now the reducer can combine the two rows and output the
combined row. This will work just the same if there were multiple rows from
each table with the same "a|b" key, in that case the reducer would have to
generate the Cartesian product of all the rows. Outer joins can also be done
this way, in an outer join you only get one row in the reducer for a given
"a|b" key but still generate an output.

-eran



On Fri, Jun 3, 2011 at 00:05, Michael Segel <mi...@hotmail.com>wrote:

>
> Not to beat a dead horse, but I thought a bit more about the problem.
> If you want to do this all in HBase using a M/R job...
>
> Lets define the following:
> SELECT *
> FROM A, B
> WHERE A.a = B.a
> AND     A.b = B.b
> AND     A.c = xxx
> AND     A.d = yyy
> AND     B.e = zzz
>
> Is the sample query.
>
> So our join key is "a|b" because we're matching on columns a and b. (The
> pipe is to delimit the columns, assuming the columns themselves don't
> contain pipes...)
>
> Our filters on A are c and d while e is the filter on B.
>
> So we want to do the following:
>
> M/R Map job 1 gets the subset from table A along with a set of unique keys.
> M/R Map job 2 gets the subset from table B along with a set of unique keys.
> M/R Map job 3 takes either set of unique keys as the input list and you
> split it based on the number of parallel mappers you want to use.
>
> You have a couple of options on how you want to proceed.
> In each Mapper.map() your input is a unique key.
> I guess you could create two scanners, one for tempTableA, and one for
> tempTableB.
> It looks like you can get the iterator for each result set, and then for
> each row in temp table A, you iterate through the result set from temp table
> B, writing out the joined set.
>
> The only problem is that your result set file isn't in sort order. So I
> guess you could take the output from this job and reduce it to get it in to
> sort order.
>
> Option B. Using HDFS files for temp 'tables'.
> You can do this... but you would still have to track the unique keys and
> also sort both the keys and the files which will require a reduce job.
>
>
> Now this is just my opinion, but if I use HBase, I don't have to worry
> about using a reducer except to order the final output set.
> So I can save the time it takes to do the reduce step. So I have to ask...
> how much time is spent by HBase in splitting and compacting the temp tables?
> Also can't you pre-split the temp table before you use them?
>
> Or am I still missing something?
>
> Note: In this example, you'd have to write an input format that takes a
> java list object (or something similar) as your input and then you can split
> it to get it to run in parallel.
> Or you could just write this on the client and split the list up and run
> the join in parallel threads on the client node. Or a single thread which
> would mean that it would run and output in sort order.
>
> HTH
>
> -Mike
>
> > Date: Wed, 1 Jun 2011 07:47:30 -0700
> > Subject: Re: How to efficiently join HBase tables?
> > From: jason.rutherglen@gmail.com
> > To: user@hbase.apache.org
> >
> > > you somehow need to flush all in-memory data *and* perform a
> > > major compaction
> >
> > This makes sense.  Without compaction the linear HDFS scan isn't
> > possible.  I suppose one could compact 'offline' in a different Map
> > Reduce job.  However that would have it's own issues.
> >
> > > The files do have a flag if they were made by a major compaction,
> > > so you scan only those and ignore the newer ones - but then you are
> trailing
> >
> > This could be ok in many cases.  The key would be to create a sync'd
> > cut off point enabling a frozen point-in-time 'view' of the data.  I'm
> > not sure how that would be implemented.
> >
> > On Wed, Jun 1, 2011 at 6:54 AM, Lars George <la...@gmail.com>
> wrote:
> > > Hi Jason,
> > >
> > > This was discussed in the past, using the HFileInputFormat. The issue
> > > is that you somehow need to flush all in-memory data *and* perform a
> > > major compaction - or else you would need all the logic of the
> > > ColumnTracker in the HFIF. Since that needs to scan all storage files
> > > in parallel to achieve its job, the MR task would not really be able
> > > to use the same approach.
> > >
> > > Running a major compaction creates a lot of churn, so it is
> > > questionable what the outcome is. The files do have a flag if they
> > > were made by a major compaction, so you scan only those and ignore the
> > > newer ones - but then you are trailing, and you still do not handle
> > > delete markers/updates in newer files. No easy feat.
> > >
> > > Lars
> > >
> > > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
> > > <ja...@gmail.com> wrote:
> > >>> I'd imagine that join operations do not require realtime-ness, and so
> > >>> faster batch jobs using Hive -> frozen HBase files in HDFS could be
> > >>> the optimal way to go?
> > >>
> > >> In addition to lessening the load on the perhaps live RegionServer.
> > >> There's no Jira for this, I'm tempted to open one.
> > >>
> > >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> > >> <ja...@gmail.com> wrote:
> > >>>> The Hive-HBase integration allows you to create Hive tables that are
> backed
> > >>>> by HBase
> > >>>
> > >>> In addition, HBase can be made to go faster for MapReduce jobs, if
> the
> > >>> HFile's could be used directly in HDFS, rather than proxying through
> > >>> the RegionServer.
> > >>>
> > >>> I'd imagine that join operations do not require realtime-ness, and so
> > >>> faster batch jobs using Hive -> frozen HBase files in HDFS could be
> > >>> the optimal way to go?
> > >>>
> > >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <
> patrick@cloudera.com> wrote:
> > >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com>
> wrote:
> > >>>>
> > >>>>> For my need I don't really need the general case, but even if I did
> I think
> > >>>>> it can probably be done simpler.
> > >>>>> The main problem is getting the data from both tables into the same
> MR job,
> > >>>>> without resorting to lookups. So without the theoretical
> > >>>>> MutliTableInputFormat, I could just copy all the data from both
> tables into
> > >>>>> a temp table, just append the source table name to the row keys to
> make
> > >>>>> sure
> > >>>>> there are no conflicts. When all the data from both tables is in
> the same
> > >>>>> temp table, run a MR job. For each row the mapper should emit a key
> which
> > >>>>> is
> > >>>>> composed of all the values of the join fields in that row (the
> value can be
> > >>>>> emitted as is). This will cause all the rows from both tables, with
> same
> > >>>>> join field values to arrive at the reducer together. The reducer
> could then
> > >>>>> iterate over them and produce the Cartesian product as needed.
> > >>>>>
> > >>>>> I still don't like having to copy all the data into a temp table
> just
> > >>>>> because I can't feed two tables into the MR job.
> > >>>>>
> > >>>>
> > >>>> Loading the smaller table in memory is called a map join, versus a
> > >>>> reduce-side join (a.k.a. common join). One reason to prefer a map
> join is
> > >>>> you avoid the shuffle phase which potentially involves several trips
> to disk
> > >>>> for the intermediate records due to spills, and also once through
> the
> > >>>> network to get each intermediate KV pair to the right reducer. With
> a map
> > >>>> join, everything is local, except for the part where you load the
> small
> > >>>> table.
> > >>>>
> > >>>>
> > >>>>>
> > >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I don't
> know if it
> > >>>>> can do them for HBase and it will not suit my needs, but it would
> be
> > >>>>> interesting to know how is it doing them, if anyone knows.
> > >>>>>
> > >>>>
> > >>>> The Hive-HBase integration allows you to create Hive tables that are
> backed
> > >>>> by HBase. You can do joins on those tables (and also with standard
> Hive
> > >>>> tables). It might be worth trying out in your case as it lets you
> easily see
> > >>>> the load characteristics and the job runtime without much coding
> investment.
> > >>>>
> > >>>> There are probably some specific optimizations that can be applied
> to your
> > >>>> situation, but it's hard to say without knowing your use-case.
> > >>>>
> > >>>> Regards,
> > >>>>
> > >>>> - Patrick
> > >>>>
> > >>>>
> > >>>>> -eran
> > >>>>>
> > >>>>>
> > >>>>>
> > >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning <td...@maprtech.com>
> wrote:
> > >>>>>
> > >>>>> > The Cartesian product often makes an honest-to-god join not such
> a good
> > >>>>> > idea
> > >>>>> > on large data.  The common alternative is co-group
> > >>>>> > which is basically like doing the hard work of the join, but
> involves
> > >>>>> > stopping just before emitting the cartesian product.  This allows
> > >>>>> > you to inject whatever cleverness you need at this point.
> > >>>>> >
> > >>>>> > Common kinds of cleverness include down-sampling of
> problematically large
> > >>>>> > sets of candidates.
> > >>>>> >
> > >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> > >>>>> > <mi...@hotmail.com>wrote:
> > >>>>> >
> > >>>>> > > So the underlying problem that the OP was trying to solve was
> how to
> > >>>>> join
> > >>>>> > > two tables from HBase.
> > >>>>> > > Unfortunately I goofed.
> > >>>>> > > I gave a quick and dirty solution that is a bit incomplete.
> They row
> > >>>>> key
> > >>>>> > in
> > >>>>> > > the temp table has to be unique and I forgot about the
> Cartesian
> > >>>>> > > product. So my solution wouldn't work in the general case.
> > >>>>> > >
> > >>>>> >
> > >>>>>
> > >>>>
> > >>>
> > >>
> > >
>
>

RE: How to efficiently join HBase tables?

[Michael Segel <mi...@hotmail.com>]

Not to beat a dead horse, but I thought a bit more about the problem. 
If you want to do this all in HBase using a M/R job...

Lets define the following:
SELECT *
FROM A, B
WHERE A.a = B.a
AND     A.b = B.b
AND     A.c = xxx
AND     A.d = yyy
AND     B.e = zzz

Is the sample query.

So our join key is "a|b" because we're matching on columns a and b. (The pipe is to delimit the columns, assuming the columns themselves don't contain pipes...)

Our filters on A are c and d while e is the filter on B. 

So we want to do the following:

M/R Map job 1 gets the subset from table A along with a set of unique keys. 
M/R Map job 2 gets the subset from table B along with a set of unique keys.
M/R Map job 3 takes either set of unique keys as the input list and you split it based on the number of parallel mappers you want to use.

You have a couple of options on how you want to proceed. 
In each Mapper.map() your input is a unique key.
I guess you could create two scanners, one for tempTableA, and one for tempTableB.
It looks like you can get the iterator for each result set, and then for each row in temp table A, you iterate through the result set from temp table B, writing out the joined set.

The only problem is that your result set file isn't in sort order. So I guess you could take the output from this job and reduce it to get it in to sort order.

Option B. Using HDFS files for temp 'tables'.
You can do this... but you would still have to track the unique keys and also sort both the keys and the files which will require a reduce job.


Now this is just my opinion, but if I use HBase, I don't have to worry about using a reducer except to order the final output set. 
So I can save the time it takes to do the reduce step. So I have to ask... how much time is spent by HBase in splitting and compacting the temp tables?
Also can't you pre-split the temp table before you use them?

Or am I still missing something?

Note: In this example, you'd have to write an input format that takes a java list object (or something similar) as your input and then you can split it to get it to run in parallel. 
Or you could just write this on the client and split the list up and run the join in parallel threads on the client node. Or a single thread which would mean that it would run and output in sort order. 

HTH

-Mike

> Date: Wed, 1 Jun 2011 07:47:30 -0700
> Subject: Re: How to efficiently join HBase tables?
> From: jason.rutherglen@gmail.com
> To: user@hbase.apache.org
> 
> > you somehow need to flush all in-memory data *and* perform a
> > major compaction
> 
> This makes sense.  Without compaction the linear HDFS scan isn't
> possible.  I suppose one could compact 'offline' in a different Map
> Reduce job.  However that would have it's own issues.
> 
> > The files do have a flag if they were made by a major compaction,
> > so you scan only those and ignore the newer ones - but then you are trailing
> 
> This could be ok in many cases.  The key would be to create a sync'd
> cut off point enabling a frozen point-in-time 'view' of the data.  I'm
> not sure how that would be implemented.
> 
> On Wed, Jun 1, 2011 at 6:54 AM, Lars George <la...@gmail.com> wrote:
> > Hi Jason,
> >
> > This was discussed in the past, using the HFileInputFormat. The issue
> > is that you somehow need to flush all in-memory data *and* perform a
> > major compaction - or else you would need all the logic of the
> > ColumnTracker in the HFIF. Since that needs to scan all storage files
> > in parallel to achieve its job, the MR task would not really be able
> > to use the same approach.
> >
> > Running a major compaction creates a lot of churn, so it is
> > questionable what the outcome is. The files do have a flag if they
> > were made by a major compaction, so you scan only those and ignore the
> > newer ones - but then you are trailing, and you still do not handle
> > delete markers/updates in newer files. No easy feat.
> >
> > Lars
> >
> > On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
> > <ja...@gmail.com> wrote:
> >>> I'd imagine that join operations do not require realtime-ness, and so
> >>> faster batch jobs using Hive -> frozen HBase files in HDFS could be
> >>> the optimal way to go?
> >>
> >> In addition to lessening the load on the perhaps live RegionServer.
> >> There's no Jira for this, I'm tempted to open one.
> >>
> >> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> >> <ja...@gmail.com> wrote:
> >>>> The Hive-HBase integration allows you to create Hive tables that are backed
> >>>> by HBase
> >>>
> >>> In addition, HBase can be made to go faster for MapReduce jobs, if the
> >>> HFile's could be used directly in HDFS, rather than proxying through
> >>> the RegionServer.
> >>>
> >>> I'd imagine that join operations do not require realtime-ness, and so
> >>> faster batch jobs using Hive -> frozen HBase files in HDFS could be
> >>> the optimal way to go?
> >>>
> >>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <pa...@cloudera.com> wrote:
> >>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com> wrote:
> >>>>
> >>>>> For my need I don't really need the general case, but even if I did I think
> >>>>> it can probably be done simpler.
> >>>>> The main problem is getting the data from both tables into the same MR job,
> >>>>> without resorting to lookups. So without the theoretical
> >>>>> MutliTableInputFormat, I could just copy all the data from both tables into
> >>>>> a temp table, just append the source table name to the row keys to make
> >>>>> sure
> >>>>> there are no conflicts. When all the data from both tables is in the same
> >>>>> temp table, run a MR job. For each row the mapper should emit a key which
> >>>>> is
> >>>>> composed of all the values of the join fields in that row (the value can be
> >>>>> emitted as is). This will cause all the rows from both tables, with same
> >>>>> join field values to arrive at the reducer together. The reducer could then
> >>>>> iterate over them and produce the Cartesian product as needed.
> >>>>>
> >>>>> I still don't like having to copy all the data into a temp table just
> >>>>> because I can't feed two tables into the MR job.
> >>>>>
> >>>>
> >>>> Loading the smaller table in memory is called a map join, versus a
> >>>> reduce-side join (a.k.a. common join). One reason to prefer a map join is
> >>>> you avoid the shuffle phase which potentially involves several trips to disk
> >>>> for the intermediate records due to spills, and also once through the
> >>>> network to get each intermediate KV pair to the right reducer. With a map
> >>>> join, everything is local, except for the part where you load the small
> >>>> table.
> >>>>
> >>>>
> >>>>>
> >>>>> As Jason Rutherglen mentioned above, Hive can do joins. I don't know if it
> >>>>> can do them for HBase and it will not suit my needs, but it would be
> >>>>> interesting to know how is it doing them, if anyone knows.
> >>>>>
> >>>>
> >>>> The Hive-HBase integration allows you to create Hive tables that are backed
> >>>> by HBase. You can do joins on those tables (and also with standard Hive
> >>>> tables). It might be worth trying out in your case as it lets you easily see
> >>>> the load characteristics and the job runtime without much coding investment.
> >>>>
> >>>> There are probably some specific optimizations that can be applied to your
> >>>> situation, but it's hard to say without knowing your use-case.
> >>>>
> >>>> Regards,
> >>>>
> >>>> - Patrick
> >>>>
> >>>>
> >>>>> -eran
> >>>>>
> >>>>>
> >>>>>
> >>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning <td...@maprtech.com> wrote:
> >>>>>
> >>>>> > The Cartesian product often makes an honest-to-god join not such a good
> >>>>> > idea
> >>>>> > on large data.  The common alternative is co-group
> >>>>> > which is basically like doing the hard work of the join, but involves
> >>>>> > stopping just before emitting the cartesian product.  This allows
> >>>>> > you to inject whatever cleverness you need at this point.
> >>>>> >
> >>>>> > Common kinds of cleverness include down-sampling of problematically large
> >>>>> > sets of candidates.
> >>>>> >
> >>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> >>>>> > <mi...@hotmail.com>wrote:
> >>>>> >
> >>>>> > > So the underlying problem that the OP was trying to solve was how to
> >>>>> join
> >>>>> > > two tables from HBase.
> >>>>> > > Unfortunately I goofed.
> >>>>> > > I gave a quick and dirty solution that is a bit incomplete. They row
> >>>>> key
> >>>>> > in
> >>>>> > > the temp table has to be unique and I forgot about the Cartesian
> >>>>> > > product. So my solution wouldn't work in the general case.
> >>>>> > >
> >>>>> >
> >>>>>
> >>>>
> >>>
> >>
> >
 		 	   		  

Re: How to efficiently join HBase tables?

[Jason Rutherglen <ja...@gmail.com>]

> you somehow need to flush all in-memory data *and* perform a
> major compaction

This makes sense.  Without compaction the linear HDFS scan isn't
possible.  I suppose one could compact 'offline' in a different Map
Reduce job.  However that would have it's own issues.

> The files do have a flag if they were made by a major compaction,
> so you scan only those and ignore the newer ones - but then you are trailing

This could be ok in many cases.  The key would be to create a sync'd
cut off point enabling a frozen point-in-time 'view' of the data.  I'm
not sure how that would be implemented.

On Wed, Jun 1, 2011 at 6:54 AM, Lars George <la...@gmail.com> wrote:
> Hi Jason,
>
> This was discussed in the past, using the HFileInputFormat. The issue
> is that you somehow need to flush all in-memory data *and* perform a
> major compaction - or else you would need all the logic of the
> ColumnTracker in the HFIF. Since that needs to scan all storage files
> in parallel to achieve its job, the MR task would not really be able
> to use the same approach.
>
> Running a major compaction creates a lot of churn, so it is
> questionable what the outcome is. The files do have a flag if they
> were made by a major compaction, so you scan only those and ignore the
> newer ones - but then you are trailing, and you still do not handle
> delete markers/updates in newer files. No easy feat.
>
> Lars
>
> On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
> <ja...@gmail.com> wrote:
>>> I'd imagine that join operations do not require realtime-ness, and so
>>> faster batch jobs using Hive -> frozen HBase files in HDFS could be
>>> the optimal way to go?
>>
>> In addition to lessening the load on the perhaps live RegionServer.
>> There's no Jira for this, I'm tempted to open one.
>>
>> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
>> <ja...@gmail.com> wrote:
>>>> The Hive-HBase integration allows you to create Hive tables that are backed
>>>> by HBase
>>>
>>> In addition, HBase can be made to go faster for MapReduce jobs, if the
>>> HFile's could be used directly in HDFS, rather than proxying through
>>> the RegionServer.
>>>
>>> I'd imagine that join operations do not require realtime-ness, and so
>>> faster batch jobs using Hive -> frozen HBase files in HDFS could be
>>> the optimal way to go?
>>>
>>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <pa...@cloudera.com> wrote:
>>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com> wrote:
>>>>
>>>>> For my need I don't really need the general case, but even if I did I think
>>>>> it can probably be done simpler.
>>>>> The main problem is getting the data from both tables into the same MR job,
>>>>> without resorting to lookups. So without the theoretical
>>>>> MutliTableInputFormat, I could just copy all the data from both tables into
>>>>> a temp table, just append the source table name to the row keys to make
>>>>> sure
>>>>> there are no conflicts. When all the data from both tables is in the same
>>>>> temp table, run a MR job. For each row the mapper should emit a key which
>>>>> is
>>>>> composed of all the values of the join fields in that row (the value can be
>>>>> emitted as is). This will cause all the rows from both tables, with same
>>>>> join field values to arrive at the reducer together. The reducer could then
>>>>> iterate over them and produce the Cartesian product as needed.
>>>>>
>>>>> I still don't like having to copy all the data into a temp table just
>>>>> because I can't feed two tables into the MR job.
>>>>>
>>>>
>>>> Loading the smaller table in memory is called a map join, versus a
>>>> reduce-side join (a.k.a. common join). One reason to prefer a map join is
>>>> you avoid the shuffle phase which potentially involves several trips to disk
>>>> for the intermediate records due to spills, and also once through the
>>>> network to get each intermediate KV pair to the right reducer. With a map
>>>> join, everything is local, except for the part where you load the small
>>>> table.
>>>>
>>>>
>>>>>
>>>>> As Jason Rutherglen mentioned above, Hive can do joins. I don't know if it
>>>>> can do them for HBase and it will not suit my needs, but it would be
>>>>> interesting to know how is it doing them, if anyone knows.
>>>>>
>>>>
>>>> The Hive-HBase integration allows you to create Hive tables that are backed
>>>> by HBase. You can do joins on those tables (and also with standard Hive
>>>> tables). It might be worth trying out in your case as it lets you easily see
>>>> the load characteristics and the job runtime without much coding investment.
>>>>
>>>> There are probably some specific optimizations that can be applied to your
>>>> situation, but it's hard to say without knowing your use-case.
>>>>
>>>> Regards,
>>>>
>>>> - Patrick
>>>>
>>>>
>>>>> -eran
>>>>>
>>>>>
>>>>>
>>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning <td...@maprtech.com> wrote:
>>>>>
>>>>> > The Cartesian product often makes an honest-to-god join not such a good
>>>>> > idea
>>>>> > on large data.  The common alternative is co-group
>>>>> > which is basically like doing the hard work of the join, but involves
>>>>> > stopping just before emitting the cartesian product.  This allows
>>>>> > you to inject whatever cleverness you need at this point.
>>>>> >
>>>>> > Common kinds of cleverness include down-sampling of problematically large
>>>>> > sets of candidates.
>>>>> >
>>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
>>>>> > <mi...@hotmail.com>wrote:
>>>>> >
>>>>> > > So the underlying problem that the OP was trying to solve was how to
>>>>> join
>>>>> > > two tables from HBase.
>>>>> > > Unfortunately I goofed.
>>>>> > > I gave a quick and dirty solution that is a bit incomplete. They row
>>>>> key
>>>>> > in
>>>>> > > the temp table has to be unique and I forgot about the Cartesian
>>>>> > > product. So my solution wouldn't work in the general case.
>>>>> > >
>>>>> >
>>>>>
>>>>
>>>
>>
>

Re: How to efficiently join HBase tables?

[Lars George <la...@gmail.com>]

Hi Jason,

This was discussed in the past, using the HFileInputFormat. The issue
is that you somehow need to flush all in-memory data *and* perform a
major compaction - or else you would need all the logic of the
ColumnTracker in the HFIF. Since that needs to scan all storage files
in parallel to achieve its job, the MR task would not really be able
to use the same approach.

Running a major compaction creates a lot of churn, so it is
questionable what the outcome is. The files do have a flag if they
were made by a major compaction, so you scan only those and ignore the
newer ones - but then you are trailing, and you still do not handle
delete markers/updates in newer files. No easy feat.

Lars

On Wed, Jun 1, 2011 at 2:41 AM, Jason Rutherglen
<ja...@gmail.com> wrote:
>> I'd imagine that join operations do not require realtime-ness, and so
>> faster batch jobs using Hive -> frozen HBase files in HDFS could be
>> the optimal way to go?
>
> In addition to lessening the load on the perhaps live RegionServer.
> There's no Jira for this, I'm tempted to open one.
>
> On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
> <ja...@gmail.com> wrote:
>>> The Hive-HBase integration allows you to create Hive tables that are backed
>>> by HBase
>>
>> In addition, HBase can be made to go faster for MapReduce jobs, if the
>> HFile's could be used directly in HDFS, rather than proxying through
>> the RegionServer.
>>
>> I'd imagine that join operations do not require realtime-ness, and so
>> faster batch jobs using Hive -> frozen HBase files in HDFS could be
>> the optimal way to go?
>>
>> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <pa...@cloudera.com> wrote:
>>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com> wrote:
>>>
>>>> For my need I don't really need the general case, but even if I did I think
>>>> it can probably be done simpler.
>>>> The main problem is getting the data from both tables into the same MR job,
>>>> without resorting to lookups. So without the theoretical
>>>> MutliTableInputFormat, I could just copy all the data from both tables into
>>>> a temp table, just append the source table name to the row keys to make
>>>> sure
>>>> there are no conflicts. When all the data from both tables is in the same
>>>> temp table, run a MR job. For each row the mapper should emit a key which
>>>> is
>>>> composed of all the values of the join fields in that row (the value can be
>>>> emitted as is). This will cause all the rows from both tables, with same
>>>> join field values to arrive at the reducer together. The reducer could then
>>>> iterate over them and produce the Cartesian product as needed.
>>>>
>>>> I still don't like having to copy all the data into a temp table just
>>>> because I can't feed two tables into the MR job.
>>>>
>>>
>>> Loading the smaller table in memory is called a map join, versus a
>>> reduce-side join (a.k.a. common join). One reason to prefer a map join is
>>> you avoid the shuffle phase which potentially involves several trips to disk
>>> for the intermediate records due to spills, and also once through the
>>> network to get each intermediate KV pair to the right reducer. With a map
>>> join, everything is local, except for the part where you load the small
>>> table.
>>>
>>>
>>>>
>>>> As Jason Rutherglen mentioned above, Hive can do joins. I don't know if it
>>>> can do them for HBase and it will not suit my needs, but it would be
>>>> interesting to know how is it doing them, if anyone knows.
>>>>
>>>
>>> The Hive-HBase integration allows you to create Hive tables that are backed
>>> by HBase. You can do joins on those tables (and also with standard Hive
>>> tables). It might be worth trying out in your case as it lets you easily see
>>> the load characteristics and the job runtime without much coding investment.
>>>
>>> There are probably some specific optimizations that can be applied to your
>>> situation, but it's hard to say without knowing your use-case.
>>>
>>> Regards,
>>>
>>> - Patrick
>>>
>>>
>>>> -eran
>>>>
>>>>
>>>>
>>>> On Tue, May 31, 2011 at 22:02, Ted Dunning <td...@maprtech.com> wrote:
>>>>
>>>> > The Cartesian product often makes an honest-to-god join not such a good
>>>> > idea
>>>> > on large data.  The common alternative is co-group
>>>> > which is basically like doing the hard work of the join, but involves
>>>> > stopping just before emitting the cartesian product.  This allows
>>>> > you to inject whatever cleverness you need at this point.
>>>> >
>>>> > Common kinds of cleverness include down-sampling of problematically large
>>>> > sets of candidates.
>>>> >
>>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
>>>> > <mi...@hotmail.com>wrote:
>>>> >
>>>> > > So the underlying problem that the OP was trying to solve was how to
>>>> join
>>>> > > two tables from HBase.
>>>> > > Unfortunately I goofed.
>>>> > > I gave a quick and dirty solution that is a bit incomplete. They row
>>>> key
>>>> > in
>>>> > > the temp table has to be unique and I forgot about the Cartesian
>>>> > > product. So my solution wouldn't work in the general case.
>>>> > >
>>>> >
>>>>
>>>
>>
>

Re: How to efficiently join HBase tables?

[Jason Rutherglen <ja...@gmail.com>]

> I'd imagine that join operations do not require realtime-ness, and so
> faster batch jobs using Hive -> frozen HBase files in HDFS could be
> the optimal way to go?

In addition to lessening the load on the perhaps live RegionServer.
There's no Jira for this, I'm tempted to open one.

On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen
<ja...@gmail.com> wrote:
>> The Hive-HBase integration allows you to create Hive tables that are backed
>> by HBase
>
> In addition, HBase can be made to go faster for MapReduce jobs, if the
> HFile's could be used directly in HDFS, rather than proxying through
> the RegionServer.
>
> I'd imagine that join operations do not require realtime-ness, and so
> faster batch jobs using Hive -> frozen HBase files in HDFS could be
> the optimal way to go?
>
> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <pa...@cloudera.com> wrote:
>> On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com> wrote:
>>
>>> For my need I don't really need the general case, but even if I did I think
>>> it can probably be done simpler.
>>> The main problem is getting the data from both tables into the same MR job,
>>> without resorting to lookups. So without the theoretical
>>> MutliTableInputFormat, I could just copy all the data from both tables into
>>> a temp table, just append the source table name to the row keys to make
>>> sure
>>> there are no conflicts. When all the data from both tables is in the same
>>> temp table, run a MR job. For each row the mapper should emit a key which
>>> is
>>> composed of all the values of the join fields in that row (the value can be
>>> emitted as is). This will cause all the rows from both tables, with same
>>> join field values to arrive at the reducer together. The reducer could then
>>> iterate over them and produce the Cartesian product as needed.
>>>
>>> I still don't like having to copy all the data into a temp table just
>>> because I can't feed two tables into the MR job.
>>>
>>
>> Loading the smaller table in memory is called a map join, versus a
>> reduce-side join (a.k.a. common join). One reason to prefer a map join is
>> you avoid the shuffle phase which potentially involves several trips to disk
>> for the intermediate records due to spills, and also once through the
>> network to get each intermediate KV pair to the right reducer. With a map
>> join, everything is local, except for the part where you load the small
>> table.
>>
>>
>>>
>>> As Jason Rutherglen mentioned above, Hive can do joins. I don't know if it
>>> can do them for HBase and it will not suit my needs, but it would be
>>> interesting to know how is it doing them, if anyone knows.
>>>
>>
>> The Hive-HBase integration allows you to create Hive tables that are backed
>> by HBase. You can do joins on those tables (and also with standard Hive
>> tables). It might be worth trying out in your case as it lets you easily see
>> the load characteristics and the job runtime without much coding investment.
>>
>> There are probably some specific optimizations that can be applied to your
>> situation, but it's hard to say without knowing your use-case.
>>
>> Regards,
>>
>> - Patrick
>>
>>
>>> -eran
>>>
>>>
>>>
>>> On Tue, May 31, 2011 at 22:02, Ted Dunning <td...@maprtech.com> wrote:
>>>
>>> > The Cartesian product often makes an honest-to-god join not such a good
>>> > idea
>>> > on large data.  The common alternative is co-group
>>> > which is basically like doing the hard work of the join, but involves
>>> > stopping just before emitting the cartesian product.  This allows
>>> > you to inject whatever cleverness you need at this point.
>>> >
>>> > Common kinds of cleverness include down-sampling of problematically large
>>> > sets of candidates.
>>> >
>>> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
>>> > <mi...@hotmail.com>wrote:
>>> >
>>> > > So the underlying problem that the OP was trying to solve was how to
>>> join
>>> > > two tables from HBase.
>>> > > Unfortunately I goofed.
>>> > > I gave a quick and dirty solution that is a bit incomplete. They row
>>> key
>>> > in
>>> > > the temp table has to be unique and I forgot about the Cartesian
>>> > > product. So my solution wouldn't work in the general case.
>>> > >
>>> >
>>>
>>
>

Re: How to efficiently join HBase tables?

[Bill Graham <bi...@gmail.com>]

We use Pig to join HBase tables using HBaseStorage which has worked well. If
you're using HBase >= 0.89 you'll need to build from the trunk or the Pig
0.8 branch.


On Tue, May 31, 2011 at 5:18 PM, Jason Rutherglen <
jason.rutherglen@gmail.com> wrote:

> > The Hive-HBase integration allows you to create Hive tables that are
> backed
> > by HBase
>
> In addition, HBase can be made to go faster for MapReduce jobs, if the
> HFile's could be used directly in HDFS, rather than proxying through
> the RegionServer.
>
> I'd imagine that join operations do not require realtime-ness, and so
> faster batch jobs using Hive -> frozen HBase files in HDFS could be
> the optimal way to go?
>
> On Tue, May 31, 2011 at 1:41 PM, Patrick Angeles <pa...@cloudera.com>
> wrote:
> > On Tue, May 31, 2011 at 3:19 PM, Eran Kutner <er...@gigya.com> wrote:
> >
> >> For my need I don't really need the general case, but even if I did I
> think
> >> it can probably be done simpler.
> >> The main problem is getting the data from both tables into the same MR
> job,
> >> without resorting to lookups. So without the theoretical
> >> MutliTableInputFormat, I could just copy all the data from both tables
> into
> >> a temp table, just append the source table name to the row keys to make
> >> sure
> >> there are no conflicts. When all the data from both tables is in the
> same
> >> temp table, run a MR job. For each row the mapper should emit a key
> which
> >> is
> >> composed of all the values of the join fields in that row (the value can
> be
> >> emitted as is). This will cause all the rows from both tables, with same
> >> join field values to arrive at the reducer together. The reducer could
> then
> >> iterate over them and produce the Cartesian product as needed.
> >>
> >> I still don't like having to copy all the data into a temp table just
> >> because I can't feed two tables into the MR job.
> >>
> >
> > Loading the smaller table in memory is called a map join, versus a
> > reduce-side join (a.k.a. common join). One reason to prefer a map join is
> > you avoid the shuffle phase which potentially involves several trips to
> disk
> > for the intermediate records due to spills, and also once through the
> > network to get each intermediate KV pair to the right reducer. With a map
> > join, everything is local, except for the part where you load the small
> > table.
> >
> >
> >>
> >> As Jason Rutherglen mentioned above, Hive can do joins. I don't know if
> it
> >> can do them for HBase and it will not suit my needs, but it would be
> >> interesting to know how is it doing them, if anyone knows.
> >>
> >
> > The Hive-HBase integration allows you to create Hive tables that are
> backed
> > by HBase. You can do joins on those tables (and also with standard Hive
> > tables). It might be worth trying out in your case as it lets you easily
> see
> > the load characteristics and the job runtime without much coding
> investment.
> >
> > There are probably some specific optimizations that can be applied to
> your
> > situation, but it's hard to say without knowing your use-case.
> >
> > Regards,
> >
> > - Patrick
> >
> >
> >> -eran
> >>
> >>
> >>
> >> On Tue, May 31, 2011 at 22:02, Ted Dunning <td...@maprtech.com>
> wrote:
> >>
> >> > The Cartesian product often makes an honest-to-god join not such a
> good
> >> > idea
> >> > on large data.  The common alternative is co-group
> >> > which is basically like doing the hard work of the join, but involves
> >> > stopping just before emitting the cartesian product.  This allows
> >> > you to inject whatever cleverness you need at this point.
> >> >
> >> > Common kinds of cleverness include down-sampling of problematically
> large
> >> > sets of candidates.
> >> >
> >> > On Tue, May 31, 2011 at 11:56 AM, Michael Segel
> >> > <mi...@hotmail.com>wrote:
> >> >
> >> > > So the underlying problem that the OP was trying to solve was how to
> >> join
> >> > > two tables from HBase.
> >> > > Unfortunately I goofed.
> >> > > I gave a quick and dirty solution that is a bit incomplete. They row
> >> key
> >> > in
> >> > > the temp table has to be unique and I forgot about the Cartesian
> >> > > product. So my solution wouldn't work in the general case.
> >> > >
> >> >
> >>
> >
>