Usage of transform for code reuse between Streaming and Batch job affects the performance ?

[swetha <sw...@gmail.com>]

Hi,

I have the following code for code reuse between the batch and the streaming
job

*  val groupedAndSortedSessions =
sessions.transform(rdd=>JobCommon.getGroupedAndSortedSessions(rdd))*

The same code without code reuse between the batch and the streaming has the
following. 

* val groupedSessions = sessions.groupByKey();

    val sortedSessions  = groupedSessions.mapValues[(List[(Long,
String)])](iter => iter.toList.sortBy(_._1))
*

Does use of transform for code reuse affect groupByKey performance?


Thanks,
Swetha



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Re: Usage of transform for code reuse between Streaming and Batch job affects the performance ?

[Adrian Tanase <at...@adobe.com>]

It shouldn't, as lots of the streaming operations delegate to transform under the hood. Easiest way to make sure is to look at the source code - with a decent IDE navigating around should be a breeze.

As a matter of fact, for more advanced operations where you may want to control the partitioning (e.g. unioning 2 DStreams or a simple flatMap) you will be forced to use transform as the DStreams hide away some of the control.

-adrian

Sent from my iPhone

> On 05 Oct 2015, at 03:59, swetha <sw...@gmail.com> wrote:
> 
> Hi,
> 
> I have the following code for code reuse between the batch and the streaming
> job
> 
> *  val groupedAndSortedSessions =
> sessions.transform(rdd=>JobCommon.getGroupedAndSortedSessions(rdd))*
> 
> The same code without code reuse between the batch and the streaming has the
> following. 
> 
> * val groupedSessions = sessions.groupByKey();
> 
>    val sortedSessions  = groupedSessions.mapValues[(List[(Long,
> String)])](iter => iter.toList.sortBy(_._1))
> *
> 
> Does use of transform for code reuse affect groupByKey performance?
> 
> 
> Thanks,
> Swetha
> 
> 
> 
> --
> View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Usage-of-transform-for-code-reuse-between-Streaming-and-Batch-job-affects-the-performance-tp24920.html
> Sent from the Apache Spark User List mailing list archive at Nabble.com.
> 
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> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
> For additional commands, e-mail: user-help@spark.apache.org
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