Group by upload: use repartition to increase parallelism #601
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hzding621
approved these changes
Oct 31, 2023
| @@ -190,6 +190,8 @@ abstract class JoinBase(joinConf: api.Join, | |||
| // all lazy vals - so evaluated only when needed by each case. | |||
| lazy val partitionRangeGroupBy = genGroupBy(unfilledRange) | |||
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| println(s"debug count ${partitionRangeGroupBy.inputDf.count()}") | |||
| // shuffle point: the input rdd has less number of partitions due to compact size | ||
| // when rows are converted to chronon rows, the size increases | ||
| // so we repartition it to reduce memory overhead and improve performance | ||
| val keyedInputRddRepartitioned = if (inputPartition < (parallelism / 10)) { |
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do we need to make this 10 configurable?
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yeah we can make it configurable
nikhilsimha
reviewed
Oct 31, 2023
| // so we repartition it to reduce memory overhead and improve performance | ||
| val keyedInputRddRepartitioned = if (inputPartition < (parallelism / 10)) { | ||
| keyedInputRdd | ||
| .repartition(parallelism) |
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I think this needs to be configurable (OPT_IN) before merging - we are going to add a shuffle step to ALL the upload jobs.
By default it should be opt-out
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Sounds good. Let me make it configurable.
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Summary
The group by upload input rdd has less number of partitions with compact size. It can leads to executor OOM while converting to chronon row.
Use the default parallelism to improve scalability.
Tested with Relevance team's upload job. The running time got reduced from 40+ mins to less than 15mins.
The downside is that repartition will trigger a shuffle.
Why / Goal
Improve performance.
Test Plan
Checklist
Reviewers
@nikhilsimha @hzding621