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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/08/12 07:18:00 UTC
[jira] [Resolved] (SPARK-28680) redundant code, or possible bug in
Partitioner that could mess up check against spark.default.parallelism
[ https://issues.apache.org/jira/browse/SPARK-28680?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-28680.
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Resolution: Invalid
> redundant code, or possible bug in Partitioner that could mess up check against spark.default.parallelism
> ---------------------------------------------------------------------------------------------------------
>
> Key: SPARK-28680
> URL: https://issues.apache.org/jira/browse/SPARK-28680
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.4.3
> Environment: master = local[*]
> Reporter: Chris Bedford
> Priority: Minor
>
> This is a suggestion to reduce (what I think is) some code redundancy.
> Looking at this line of code in org.apache.spark.Partitioner:
> https://github.com/apache/spark/blob/924d794a6f5abb972fa07bf63adbb4ad544ef246/core/src/main/scala/org/apache/spark/Partitioner.scala#L83
> the first part of the && in the if condition is true if hasMaxPartitioner is non empty, which means that after a scan of rdds we found one with a partitioner whose # of partitions was > 0, and hasMaxPartitioner is the Option wrapped RDD which has the
> partitioner with greatest number of partitions.
> We then pass the rdd inside hasMaxPartitioner to isEligiblePartitioner where we
> set maxPartitions = the length of the longest partitioner in rdds and then check
> to see if
> log10(maxPartitions) - log10(hasMaxPartitioner.getNumPartitions) < 1
> It seems to me that the values inside the two calls to log10 will be equal, so subtracting these will result in 0, which is always < 1.
> So... isn't this whole block of code redundant ?
> It might even be a bug because the right hand side of the && condition after is always
> true, so we never check that
> defaultNumPartitions <= hasMaxPartitioner.get.getNumPartitions
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