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Posted to user@spark.apache.org by anshu shukla <an...@gmail.com> on 2015/05/06 09:53:35 UTC
Creating topology in spark streaming
Please help guys, Even After going through all the examples given i have
not understood how to pass the D-streams from one bolt/logic to other
(without writing it on HDFS etc.) just like emit function in storm .
Suppose i have topology with 3 bolts(say)
*BOLT1(parse the tweets nd emit tweet using given
hashtags)=====>Bolt2(Complex logic for sentiment analysis over
tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)*
Now since Sentiment analysis will take most of the time ,we have to
increase its level of parallelism for tuning latency. Howe to increase the
levele of parallelism since the logic of topology is not clear .
--
Thanks & Regards,
Anshu Shukla
Indian Institute of Sciences
Re: Creating topology in spark streaming
Posted by anshu shukla <an...@gmail.com>.
Thanks alot Juan,
That was a great post, One more thing if u can .Any there any demo/blog
telling how to configure or create a topology of different types .. i
mean how we can decide the pipelining model in spark as done in storm for
https://storm.apache.org/documentation/images/topology.png .
On Wed, May 6, 2015 at 2:47 PM, Juan Rodríguez Hortalá <
juan.rodriguez.hortala@gmail.com> wrote:
> Hi,
>
> You can use the method repartition from DStream (for the Scala API) or
> JavaDStream (for the Java API)
>
> defrepartition(numPartitions: Int): DStream
> <https://spark.apache.org/docs/latest/api/scala/org/apache/spark/streaming/dstream/DStream.html>
> [T]
>
> Return a new DStream with an increased or decreased level of parallelism.
> Each RDD in the returned DStream has exactly numPartitions partitions.
>
> I think the post
> http://www.michael-noll.com/blog/2014/10/01/kafka-spark-streaming-integration-example-tutorial/
> on integration of Spark Streaming gives very interesting review on the
> subject, although the integration with Kafka it's not up to date with
> https://databricks.com/blog/2015/03/30/improvements-to-kafka-integration-of-spark-streaming.html
>
> Hope that helps.
>
> Greetings,
>
> Juan
>
> 2015-05-06 10:32 GMT+02:00 anshu shukla <an...@gmail.com>:
>
>> But main problem is how to increase the level of parallelism for any
>> particular bolt logic .
>>
>> suppose i want this type of topology .
>>
>> https://storm.apache.org/documentation/images/topology.png
>>
>> How we can manage it .
>>
>> On Wed, May 6, 2015 at 1:36 PM, ayan guha <gu...@gmail.com> wrote:
>>
>>> Every transformation on a dstream will create another dstream. You may
>>> want to take a look at foreachrdd? Also, kindly share your code so people
>>> can help better
>>> On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
>>>
>>>> Please help guys, Even After going through all the examples given i
>>>> have not understood how to pass the D-streams from one bolt/logic to
>>>> other (without writing it on HDFS etc.) just like emit function in storm .
>>>> Suppose i have topology with 3 bolts(say)
>>>>
>>>> *BOLT1(parse the tweets nd emit tweet using given
>>>> hashtags)=====>Bolt2(Complex logic for sentiment analysis over
>>>> tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)*
>>>>
>>>>
>>>> Now since Sentiment analysis will take most of the time ,we have to
>>>> increase its level of parallelism for tuning latency. Howe to increase the
>>>> levele of parallelism since the logic of topology is not clear .
>>>>
>>>> --
>>>> Thanks & Regards,
>>>> Anshu Shukla
>>>> Indian Institute of Sciences
>>>>
>>>
>>
>>
>> --
>> Thanks & Regards,
>> Anshu Shukla
>>
>
>
--
Thanks & Regards,
Anshu Shukla
Re: Creating topology in spark streaming
Posted by anshu shukla <an...@gmail.com>.
Thanks alot Juan,
That was a great post, One more thing if u can .Any there any demo/blog
telling how to configure or create a topology of different types .. i
mean how we can decide the pipelining model in spark as done in storm for
https://storm.apache.org/documentation/images/topology.png .
On Wed, May 6, 2015 at 2:47 PM, Juan Rodríguez Hortalá <
juan.rodriguez.hortala@gmail.com> wrote:
> Hi,
>
> You can use the method repartition from DStream (for the Scala API) or
> JavaDStream (for the Java API)
>
> defrepartition(numPartitions: Int): DStream
> <https://spark.apache.org/docs/latest/api/scala/org/apache/spark/streaming/dstream/DStream.html>
> [T]
>
> Return a new DStream with an increased or decreased level of parallelism.
> Each RDD in the returned DStream has exactly numPartitions partitions.
>
> I think the post
> http://www.michael-noll.com/blog/2014/10/01/kafka-spark-streaming-integration-example-tutorial/
> on integration of Spark Streaming gives very interesting review on the
> subject, although the integration with Kafka it's not up to date with
> https://databricks.com/blog/2015/03/30/improvements-to-kafka-integration-of-spark-streaming.html
>
> Hope that helps.
>
> Greetings,
>
> Juan
>
> 2015-05-06 10:32 GMT+02:00 anshu shukla <an...@gmail.com>:
>
>> But main problem is how to increase the level of parallelism for any
>> particular bolt logic .
>>
>> suppose i want this type of topology .
>>
>> https://storm.apache.org/documentation/images/topology.png
>>
>> How we can manage it .
>>
>> On Wed, May 6, 2015 at 1:36 PM, ayan guha <gu...@gmail.com> wrote:
>>
>>> Every transformation on a dstream will create another dstream. You may
>>> want to take a look at foreachrdd? Also, kindly share your code so people
>>> can help better
>>> On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
>>>
>>>> Please help guys, Even After going through all the examples given i
>>>> have not understood how to pass the D-streams from one bolt/logic to
>>>> other (without writing it on HDFS etc.) just like emit function in storm .
>>>> Suppose i have topology with 3 bolts(say)
>>>>
>>>> *BOLT1(parse the tweets nd emit tweet using given
>>>> hashtags)=====>Bolt2(Complex logic for sentiment analysis over
>>>> tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)*
>>>>
>>>>
>>>> Now since Sentiment analysis will take most of the time ,we have to
>>>> increase its level of parallelism for tuning latency. Howe to increase the
>>>> levele of parallelism since the logic of topology is not clear .
>>>>
>>>> --
>>>> Thanks & Regards,
>>>> Anshu Shukla
>>>> Indian Institute of Sciences
>>>>
>>>
>>
>>
>> --
>> Thanks & Regards,
>> Anshu Shukla
>>
>
>
--
Thanks & Regards,
Anshu Shukla
Re: Creating topology in spark streaming
Posted by Juan Rodríguez Hortalá <ju...@gmail.com>.
Hi,
You can use the method repartition from DStream (for the Scala API) or
JavaDStream (for the Java API)
defrepartition(numPartitions: Int): DStream
<https://spark.apache.org/docs/latest/api/scala/org/apache/spark/streaming/dstream/DStream.html>
[T]
Return a new DStream with an increased or decreased level of parallelism.
Each RDD in the returned DStream has exactly numPartitions partitions.
I think the post
http://www.michael-noll.com/blog/2014/10/01/kafka-spark-streaming-integration-example-tutorial/
on integration of Spark Streaming gives very interesting review on the
subject, although the integration with Kafka it's not up to date with
https://databricks.com/blog/2015/03/30/improvements-to-kafka-integration-of-spark-streaming.html
Hope that helps.
Greetings,
Juan
2015-05-06 10:32 GMT+02:00 anshu shukla <an...@gmail.com>:
> But main problem is how to increase the level of parallelism for any
> particular bolt logic .
>
> suppose i want this type of topology .
>
> https://storm.apache.org/documentation/images/topology.png
>
> How we can manage it .
>
> On Wed, May 6, 2015 at 1:36 PM, ayan guha <gu...@gmail.com> wrote:
>
>> Every transformation on a dstream will create another dstream. You may
>> want to take a look at foreachrdd? Also, kindly share your code so people
>> can help better
>> On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
>>
>>> Please help guys, Even After going through all the examples given i
>>> have not understood how to pass the D-streams from one bolt/logic to
>>> other (without writing it on HDFS etc.) just like emit function in storm .
>>> Suppose i have topology with 3 bolts(say)
>>>
>>> *BOLT1(parse the tweets nd emit tweet using given
>>> hashtags)=====>Bolt2(Complex logic for sentiment analysis over
>>> tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)*
>>>
>>>
>>> Now since Sentiment analysis will take most of the time ,we have to
>>> increase its level of parallelism for tuning latency. Howe to increase the
>>> levele of parallelism since the logic of topology is not clear .
>>>
>>> --
>>> Thanks & Regards,
>>> Anshu Shukla
>>> Indian Institute of Sciences
>>>
>>
>
>
> --
> Thanks & Regards,
> Anshu Shukla
>
Re: Creating topology in spark streaming
Posted by Juan Rodríguez Hortalá <ju...@gmail.com>.
Hi,
You can use the method repartition from DStream (for the Scala API) or
JavaDStream (for the Java API)
defrepartition(numPartitions: Int): DStream
<https://spark.apache.org/docs/latest/api/scala/org/apache/spark/streaming/dstream/DStream.html>
[T]
Return a new DStream with an increased or decreased level of parallelism.
Each RDD in the returned DStream has exactly numPartitions partitions.
I think the post
http://www.michael-noll.com/blog/2014/10/01/kafka-spark-streaming-integration-example-tutorial/
on integration of Spark Streaming gives very interesting review on the
subject, although the integration with Kafka it's not up to date with
https://databricks.com/blog/2015/03/30/improvements-to-kafka-integration-of-spark-streaming.html
Hope that helps.
Greetings,
Juan
2015-05-06 10:32 GMT+02:00 anshu shukla <an...@gmail.com>:
> But main problem is how to increase the level of parallelism for any
> particular bolt logic .
>
> suppose i want this type of topology .
>
> https://storm.apache.org/documentation/images/topology.png
>
> How we can manage it .
>
> On Wed, May 6, 2015 at 1:36 PM, ayan guha <gu...@gmail.com> wrote:
>
>> Every transformation on a dstream will create another dstream. You may
>> want to take a look at foreachrdd? Also, kindly share your code so people
>> can help better
>> On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
>>
>>> Please help guys, Even After going through all the examples given i
>>> have not understood how to pass the D-streams from one bolt/logic to
>>> other (without writing it on HDFS etc.) just like emit function in storm .
>>> Suppose i have topology with 3 bolts(say)
>>>
>>> *BOLT1(parse the tweets nd emit tweet using given
>>> hashtags)=====>Bolt2(Complex logic for sentiment analysis over
>>> tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)*
>>>
>>>
>>> Now since Sentiment analysis will take most of the time ,we have to
>>> increase its level of parallelism for tuning latency. Howe to increase the
>>> levele of parallelism since the logic of topology is not clear .
>>>
>>> --
>>> Thanks & Regards,
>>> Anshu Shukla
>>> Indian Institute of Sciences
>>>
>>
>
>
> --
> Thanks & Regards,
> Anshu Shukla
>
Re: Creating topology in spark streaming
Posted by anshu shukla <an...@gmail.com>.
Ohhh its filled with lot of trouble (Scala mainly) .. please please can
anyone point out me to sample topology type of code that have multistep
modular levels of logics with parallelisation controlled in each level
.
I am not finding any demo with such sample on git .
On Wed, May 6, 2015 at 4:12 PM, Evo Eftimov <ev...@isecc.com> wrote:
> The “abstraction level” of Storm or shall we call it Architecture, is
> effectively Pipelines of Nodes/Agents – Pipelines is one of the standard
> Parallel Programming Patterns which you can use on multicore CPUs as well
> as Distributed Systems – the chaps from Storm simply implemented it as a
> reusable framework for distributed systems and offered it for free.
> Effectively it you have a set of independent Agents chained in a pipeline
> as the output from the previous Agent feeds into the Input of the next
> Agent
>
>
>
> Spark Streaming (which is essentially Batch Spark but with some
> optimizations for Streaming) on the other hand is more like a Map Reduce
> framework where you always have to have a Central Job/Task Manager
> scheduling and submitting tasks to remote distributed nodes, collecting the
> results / statuses and then scheduling and sending some more tasks and so
> on
>
>
>
> “Map Reduce” is simply another Parallel Programming pattern known as Data
> Parallelism or Data Parallel Programming. Although you can also have Data
> Parallelism without a Central Scheduler
>
>
>
> *From:* Juan Rodríguez Hortalá [mailto:juan.rodriguez.hortala@gmail.com]
> *Sent:* Wednesday, May 6, 2015 11:20 AM
> *To:* Evo Eftimov
> *Cc:* anshu shukla; ayan guha; user@spark.apache.org
>
> *Subject:* Re: Creating topology in spark streaming
>
>
>
> Hi,
>
>
>
> I agree with Evo, Spark works at a different abstraction level than Storm,
> and there is not a direct translation from Storm topologies to Spark
> Streaming jobs. I think something remotely close is the notion of lineage
> of DStreams or RDDs, which is similar to a logical plan of an engine like
> Apache Pig. Here
> https://github.com/JerryLead/SparkInternals/blob/master/pdf/2-JobLogicalPlan.pdf
> is a diagram of a spark logical plan by a third party. I would suggest you
> reading the book "Learning Spark"
> https://www.safaribooksonline.com/library/view/learning-spark/9781449359034/foreword01.html
> for more on this. But in general I think that Storm has an abstraction
> level closer to MapReduce, and Spark has an abstraction level closer to
> Pig, so the correspondence between Storm and Spark notions cannot be
> perfect.
>
>
>
> Greetings,
>
>
>
> Juan
>
>
>
>
>
>
>
>
>
> 2015-05-06 11:37 GMT+02:00 Evo Eftimov <ev...@isecc.com>:
>
> What is called Bolt in Storm is essentially a combination of
> [Transformation/Action and DStream RDD] in Spark – so to achieve a higher
> parallelism for specific Transformation/Action on specific Dstream RDD
> simply repartition it to the required number of partitions which directly
> relates to the corresponding number of Threads
>
>
>
> *From:* anshu shukla [mailto:anshushukla0@gmail.com]
> *Sent:* Wednesday, May 6, 2015 9:33 AM
> *To:* ayan guha
> *Cc:* user@spark.apache.org; dev@spark.apache.org
> *Subject:* Re: Creating topology in spark streaming
>
>
>
> But main problem is how to increase the level of parallelism for any
> particular bolt logic .
>
>
>
> suppose i want this type of topology .
>
>
>
> https://storm.apache.org/documentation/images/topology.png
>
>
>
> How we can manage it .
>
>
>
> On Wed, May 6, 2015 at 1:36 PM, ayan guha <gu...@gmail.com> wrote:
>
> Every transformation on a dstream will create another dstream. You may
> want to take a look at foreachrdd? Also, kindly share your code so people
> can help better
>
> On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
>
> Please help guys, Even After going through all the examples given i have
> not understood how to pass the D-streams from one bolt/logic to other
> (without writing it on HDFS etc.) just like emit function in storm .
>
> Suppose i have topology with 3 bolts(say)
>
>
>
> *BOLT1(parse the tweets nd emit tweet using given
> hashtags)=====>Bolt2(Complex logic for sentiment analysis over
> tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)*
>
>
>
>
>
> Now since Sentiment analysis will take most of the time ,we have to
> increase its level of parallelism for tuning latency. Howe to increase the
> levele of parallelism since the logic of topology is not clear .
>
>
>
> --
>
> Thanks & Regards,
> Anshu Shukla
>
> Indian Institute of Sciences
>
>
>
>
>
> --
>
> Thanks & Regards,
> Anshu Shukla
>
>
>
--
Thanks & Regards,
Anshu Shukla
RE: Creating topology in spark streaming
Posted by Evo Eftimov <ev...@isecc.com>.
The “abstraction level” of Storm or shall we call it Architecture, is effectively Pipelines of Nodes/Agents – Pipelines is one of the standard Parallel Programming Patterns which you can use on multicore CPUs as well as Distributed Systems – the chaps from Storm simply implemented it as a reusable framework for distributed systems and offered it for free. Effectively it you have a set of independent Agents chained in a pipeline as the output from the previous Agent feeds into the Input of the next Agent
Spark Streaming (which is essentially Batch Spark but with some optimizations for Streaming) on the other hand is more like a Map Reduce framework where you always have to have a Central Job/Task Manager scheduling and submitting tasks to remote distributed nodes, collecting the results / statuses and then scheduling and sending some more tasks and so on
“Map Reduce” is simply another Parallel Programming pattern known as Data Parallelism or Data Parallel Programming. Although you can also have Data Parallelism without a Central Scheduler
From: Juan Rodríguez Hortalá [mailto:juan.rodriguez.hortala@gmail.com]
Sent: Wednesday, May 6, 2015 11:20 AM
To: Evo Eftimov
Cc: anshu shukla; ayan guha; user@spark.apache.org
Subject: Re: Creating topology in spark streaming
Hi,
I agree with Evo, Spark works at a different abstraction level than Storm, and there is not a direct translation from Storm topologies to Spark Streaming jobs. I think something remotely close is the notion of lineage of DStreams or RDDs, which is similar to a logical plan of an engine like Apache Pig. Here https://github.com/JerryLead/SparkInternals/blob/master/pdf/2-JobLogicalPlan.pdf is a diagram of a spark logical plan by a third party. I would suggest you reading the book "Learning Spark" https://www.safaribooksonline.com/library/view/learning-spark/9781449359034/foreword01.html for more on this. But in general I think that Storm has an abstraction level closer to MapReduce, and Spark has an abstraction level closer to Pig, so the correspondence between Storm and Spark notions cannot be perfect.
Greetings,
Juan
2015-05-06 11:37 GMT+02:00 Evo Eftimov <ev...@isecc.com>:
What is called Bolt in Storm is essentially a combination of [Transformation/Action and DStream RDD] in Spark – so to achieve a higher parallelism for specific Transformation/Action on specific Dstream RDD simply repartition it to the required number of partitions which directly relates to the corresponding number of Threads
From: anshu shukla [mailto:anshushukla0@gmail.com]
Sent: Wednesday, May 6, 2015 9:33 AM
To: ayan guha
Cc: user@spark.apache.org; dev@spark.apache.org
Subject: Re: Creating topology in spark streaming
But main problem is how to increase the level of parallelism for any particular bolt logic .
suppose i want this type of topology .
https://storm.apache.org/documentation/images/topology.png
How we can manage it .
On Wed, May 6, 2015 at 1:36 PM, ayan guha <gu...@gmail.com> wrote:
Every transformation on a dstream will create another dstream. You may want to take a look at foreachrdd? Also, kindly share your code so people can help better
On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
Please help guys, Even After going through all the examples given i have not understood how to pass the D-streams from one bolt/logic to other (without writing it on HDFS etc.) just like emit function in storm .
Suppose i have topology with 3 bolts(say)
BOLT1(parse the tweets nd emit tweet using given hashtags)=====>Bolt2(Complex logic for sentiment analysis over tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)
Now since Sentiment analysis will take most of the time ,we have to increase its level of parallelism for tuning latency. Howe to increase the levele of parallelism since the logic of topology is not clear .
--
Thanks & Regards,
Anshu Shukla
Indian Institute of Sciences
--
Thanks & Regards,
Anshu Shukla
Re: Creating topology in spark streaming
Posted by Juan Rodríguez Hortalá <ju...@gmail.com>.
Hi,
I agree with Evo, Spark works at a different abstraction level than Storm,
and there is not a direct translation from Storm topologies to Spark
Streaming jobs. I think something remotely close is the notion of lineage
of DStreams or RDDs, which is similar to a logical plan of an engine like
Apache Pig. Here
https://github.com/JerryLead/SparkInternals/blob/master/pdf/2-JobLogicalPlan.pdf
is a diagram of a spark logical plan by a third party. I would suggest you
reading the book "Learning Spark"
https://www.safaribooksonline.com/library/view/learning-spark/9781449359034/foreword01.html
for more on this. But in general I think that Storm has an abstraction
level closer to MapReduce, and Spark has an abstraction level closer to
Pig, so the correspondence between Storm and Spark notions cannot be
perfect.
Greetings,
Juan
2015-05-06 11:37 GMT+02:00 Evo Eftimov <ev...@isecc.com>:
> What is called Bolt in Storm is essentially a combination of
> [Transformation/Action and DStream RDD] in Spark – so to achieve a higher
> parallelism for specific Transformation/Action on specific Dstream RDD
> simply repartition it to the required number of partitions which directly
> relates to the corresponding number of Threads
>
>
>
> *From:* anshu shukla [mailto:anshushukla0@gmail.com]
> *Sent:* Wednesday, May 6, 2015 9:33 AM
> *To:* ayan guha
> *Cc:* user@spark.apache.org; dev@spark.apache.org
> *Subject:* Re: Creating topology in spark streaming
>
>
>
> But main problem is how to increase the level of parallelism for any
> particular bolt logic .
>
>
>
> suppose i want this type of topology .
>
>
>
> https://storm.apache.org/documentation/images/topology.png
>
>
>
> How we can manage it .
>
>
>
> On Wed, May 6, 2015 at 1:36 PM, ayan guha <gu...@gmail.com> wrote:
>
> Every transformation on a dstream will create another dstream. You may
> want to take a look at foreachrdd? Also, kindly share your code so people
> can help better
>
> On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
>
> Please help guys, Even After going through all the examples given i have
> not understood how to pass the D-streams from one bolt/logic to other
> (without writing it on HDFS etc.) just like emit function in storm .
>
> Suppose i have topology with 3 bolts(say)
>
>
>
> *BOLT1(parse the tweets nd emit tweet using given
> hashtags)=====>Bolt2(Complex logic for sentiment analysis over
> tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)*
>
>
>
>
>
> Now since Sentiment analysis will take most of the time ,we have to
> increase its level of parallelism for tuning latency. Howe to increase the
> levele of parallelism since the logic of topology is not clear .
>
>
>
> --
>
> Thanks & Regards,
> Anshu Shukla
>
> Indian Institute of Sciences
>
>
>
>
>
> --
>
> Thanks & Regards,
> Anshu Shukla
>
RE: Creating topology in spark streaming
Posted by Evo Eftimov <ev...@isecc.com>.
What is called Bolt in Storm is essentially a combination of [Transformation/Action and DStream RDD] in Spark – so to achieve a higher parallelism for specific Transformation/Action on specific Dstream RDD simply repartition it to the required number of partitions which directly relates to the corresponding number of Threads
From: anshu shukla [mailto:anshushukla0@gmail.com]
Sent: Wednesday, May 6, 2015 9:33 AM
To: ayan guha
Cc: user@spark.apache.org; dev@spark.apache.org
Subject: Re: Creating topology in spark streaming
But main problem is how to increase the level of parallelism for any particular bolt logic .
suppose i want this type of topology .
https://storm.apache.org/documentation/images/topology.png
How we can manage it .
On Wed, May 6, 2015 at 1:36 PM, ayan guha <gu...@gmail.com> wrote:
Every transformation on a dstream will create another dstream. You may want to take a look at foreachrdd? Also, kindly share your code so people can help better
On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
Please help guys, Even After going through all the examples given i have not understood how to pass the D-streams from one bolt/logic to other (without writing it on HDFS etc.) just like emit function in storm .
Suppose i have topology with 3 bolts(say)
BOLT1(parse the tweets nd emit tweet using given hashtags)=====>Bolt2(Complex logic for sentiment analysis over tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)
Now since Sentiment analysis will take most of the time ,we have to increase its level of parallelism for tuning latency. Howe to increase the levele of parallelism since the logic of topology is not clear .
--
Thanks & Regards,
Anshu Shukla
Indian Institute of Sciences
--
Thanks & Regards,
Anshu Shukla
Re: Creating topology in spark streaming
Posted by anshu shukla <an...@gmail.com>.
But main problem is how to increase the level of parallelism for any
particular bolt logic .
suppose i want this type of topology .
https://storm.apache.org/documentation/images/topology.png
How we can manage it .
On Wed, May 6, 2015 at 1:36 PM, ayan guha <gu...@gmail.com> wrote:
> Every transformation on a dstream will create another dstream. You may
> want to take a look at foreachrdd? Also, kindly share your code so people
> can help better
> On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
>
>> Please help guys, Even After going through all the examples given i
>> have not understood how to pass the D-streams from one bolt/logic to
>> other (without writing it on HDFS etc.) just like emit function in storm .
>> Suppose i have topology with 3 bolts(say)
>>
>> *BOLT1(parse the tweets nd emit tweet using given
>> hashtags)=====>Bolt2(Complex logic for sentiment analysis over
>> tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)*
>>
>>
>> Now since Sentiment analysis will take most of the time ,we have to
>> increase its level of parallelism for tuning latency. Howe to increase the
>> levele of parallelism since the logic of topology is not clear .
>>
>> --
>> Thanks & Regards,
>> Anshu Shukla
>> Indian Institute of Sciences
>>
>
--
Thanks & Regards,
Anshu Shukla
Re: Creating topology in spark streaming
Posted by anshu shukla <an...@gmail.com>.
But main problem is how to increase the level of parallelism for any
particular bolt logic .
suppose i want this type of topology .
https://storm.apache.org/documentation/images/topology.png
How we can manage it .
On Wed, May 6, 2015 at 1:36 PM, ayan guha <gu...@gmail.com> wrote:
> Every transformation on a dstream will create another dstream. You may
> want to take a look at foreachrdd? Also, kindly share your code so people
> can help better
> On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
>
>> Please help guys, Even After going through all the examples given i
>> have not understood how to pass the D-streams from one bolt/logic to
>> other (without writing it on HDFS etc.) just like emit function in storm .
>> Suppose i have topology with 3 bolts(say)
>>
>> *BOLT1(parse the tweets nd emit tweet using given
>> hashtags)=====>Bolt2(Complex logic for sentiment analysis over
>> tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)*
>>
>>
>> Now since Sentiment analysis will take most of the time ,we have to
>> increase its level of parallelism for tuning latency. Howe to increase the
>> levele of parallelism since the logic of topology is not clear .
>>
>> --
>> Thanks & Regards,
>> Anshu Shukla
>> Indian Institute of Sciences
>>
>
--
Thanks & Regards,
Anshu Shukla
Re: Creating topology in spark streaming
Posted by ayan guha <gu...@gmail.com>.
Every transformation on a dstream will create another dstream. You may want
to take a look at foreachrdd? Also, kindly share your code so people can
help better
On 6 May 2015 17:54, "anshu shukla" <an...@gmail.com> wrote:
> Please help guys, Even After going through all the examples given i have
> not understood how to pass the D-streams from one bolt/logic to other
> (without writing it on HDFS etc.) just like emit function in storm .
> Suppose i have topology with 3 bolts(say)
>
> *BOLT1(parse the tweets nd emit tweet using given
> hashtags)=====>Bolt2(Complex logic for sentiment analysis over
> tweets)=======>BOLT3(submit tweets to the sql database using spark SQL)*
>
>
> Now since Sentiment analysis will take most of the time ,we have to
> increase its level of parallelism for tuning latency. Howe to increase the
> levele of parallelism since the logic of topology is not clear .
>
> --
> Thanks & Regards,
> Anshu Shukla
> Indian Institute of Sciences
>