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https://issues.apache.org/jira/browse/BEAM-6812?focusedWorklogId=216443&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-216443
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ASF GitHub Bot logged work on BEAM-6812:
----------------------------------------
Author: ASF GitHub Bot
Created on: 20/Mar/19 20:11
Start Date: 20/Mar/19 20:11
Worklog Time Spent: 10m
Work Description: kyle-winkelman commented on pull request #8042:
[BEAM-6812]: Convert keys to ByteArray in Combine.perKey to make sure hashCode
is consistent
URL: https://github.com/apache/beam/pull/8042#discussion_r267525204
##########
File path:
runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TransformTranslator.java
##########
@@ -569,8 +569,8 @@ private static Partitioner
getPartitioner(EvaluationContext context) {
Long bundleSize =
context.getSerializableOptions().get().as(SparkPipelineOptions.class).getBundleSize();
return (bundleSize > 0)
- ? null
- : new HashPartitioner(context.getSparkContext().defaultParallelism());
+ ? new HashPartitioner(context.getSparkContext().defaultParallelism())
+ : null;
Review comment:
I agree that the old functionality seems strange, but I remember (when I had
the logic backwards) that the performance tests for the spark runner were
impacted. I think the impact was in streaming mode because if you don't use
this HashPartitioner then it actually does a double shuffle of the data. I
tried to clean this up but my I never finished getting PR #6511 merged.
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Issue Time Tracking
-------------------
Worklog Id: (was: 216443)
Time Spent: 2h 40m (was: 2.5h)
> Convert keys to ByteArray in Combine.perKey for Spark
> -----------------------------------------------------
>
> Key: BEAM-6812
> URL: https://issues.apache.org/jira/browse/BEAM-6812
> Project: Beam
> Issue Type: Bug
> Components: runner-spark
> Reporter: Ankit Jhalaria
> Assignee: Ankit Jhalaria
> Priority: Critical
> Time Spent: 2h 40m
> Remaining Estimate: 0h
>
> * During calls to Combine.perKey, we want they keys used to have consistent
> hashCode when invoked from different JVM's.
> * However, while testing this in our company we found out that when using
> protobuf as keys during combine, the hashCodes can be different for the same
> key when invoked from different JVMs. This results in duplicates.
> * `ByteArray` class in Spark has a stable has code when dealing with arrays
> as well.
> * GroupByKey correctly converts keys to `ByteArray` and uses coders for
> serialization.
> * The fix does something similar when dealing with combines.
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