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https://issues.apache.org/jira/browse/BEAM-4783?focusedWorklogId=148801&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-148801
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ASF GitHub Bot logged work on BEAM-4783:
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Author: ASF GitHub Bot
Created on: 27/Sep/18 16:12
Start Date: 27/Sep/18 16:12
Worklog Time Spent: 10m
Work Description: kyle-winkelman commented on issue #6181: [BEAM-4783]
Add bundleSize for splitting BoundedSources.
URL: https://github.com/apache/beam/pull/6181#issuecomment-425152159
I don't know how to proceed.
I am convinced that in batch mode my proposal is the correct way to proceed.
Another example of a silly interaction that occurs do to using
defaultParallelism in SourceRDD is reading 2 different files. If one of the two
files is a couple of orders of magnitude larger you will need to allocate
enough resources to the job to read the larger file, lets say n cores, then the
smaller file will get split into n pieces which will result in the smaller file
being broken up into many very small bundles.
The issue is I do not understand the repercussions this change will have on
the streaming mode. Maybe we will need to have two different approaches to the
groupBy logic, one for each mode.
I am ok with this being experimental and only working if you supply the
--bundleSize to the pipeline options. I would like an answer to the last
question I asked to understand if in batch mode I can always use the new
experimental groupByKeyOnlyDefaultPartitioner because I believe it will not
cause a double shuffle in batch mode.
Other than that I believe I need a code review and make sure everyone agrees
with the approach.
If this is not agreed upon I would hope someone could give me some advice on
how to get the SparkRunner to work with dynamicAllocation. (Starting with 2
cores and spinning up more if the files are large and are split into more
bundles.)
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Issue Time Tracking
-------------------
Worklog Id: (was: 148801)
Time Spent: 3h 10m (was: 3h)
> Spark SourceRDD Not Designed With Dynamic Allocation In Mind
> ------------------------------------------------------------
>
> Key: BEAM-4783
> URL: https://issues.apache.org/jira/browse/BEAM-4783
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Affects Versions: 2.5.0
> Reporter: Kyle Winkelman
> Assignee: Jean-Baptiste Onofré
> Priority: Major
> Labels: newbie
> Time Spent: 3h 10m
> Remaining Estimate: 0h
>
> When the spark-runner is used along with the configuration
> spark.dynamicAllocation.enabled=true the SourceRDD does not detect this. It
> then falls back to the value calculated in this description:
> // when running on YARN/SparkDeploy it's the result of max(totalCores,
> 2).
> // when running on Mesos it's 8.
> // when running local it's the total number of cores (local = 1,
> local[N] = N,
> // local[*] = estimation of the machine's cores).
> // ** the configuration "spark.default.parallelism" takes precedence
> over all of the above **
> So in most cases this default is quite small. This is an issue when using a
> very large input file as it will only get split in half.
> I believe that when Dynamic Allocation is enable the SourceRDD should use the
> DEFAULT_BUNDLE_SIZE and possibly expose a SparkPipelineOptions that allows
> you to change this DEFAULT_BUNDLE_SIZE.
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