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[FLINK-12665] [table-planner-blink] Introduce MiniBatchIntervalInferRule and watermark assigner operators #8562

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merged 5 commits into from Jul 4, 2019

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godfreyhe
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What is the purpose of the change

Introduce MiniBatchIntervalInferRule and watermark assigner operators

Brief change log

  • Introduce MiniBatchIntervalInferRule
  • Introduce watermark assigner operators, including: WatermarkAssignerOperator, MiniBatchedWatermarkAssignerOperator, MiniBatchAssignerOperator

Verifying this change

This change added tests and can be verified as follows:

  • Added operators tests for watermark assigner operators
  • Added MiniBatchIntervalInferTest that validates the plan after MiniBatchIntervalInferRule is applied

Does this pull request potentially affect one of the following parts:

  • Dependencies (does it add or upgrade a dependency): (yes / no)
  • The public API, i.e., is any changed class annotated with @Public(Evolving): (yes / no)
  • The serializers: (yes / no / don't know)
  • The runtime per-record code paths (performance sensitive): (yes / no / don't know)
  • Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Yarn/Mesos, ZooKeeper: (yes / no / don't know)
  • The S3 file system connector: (yes / no / don't know)

Documentation

  • Does this pull request introduce a new feature? (yes / no)
  • If yes, how is the feature documented? (not applicable / docs / JavaDocs / not documented)

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@cshuo cshuo left a comment

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Looks good overall. I've left some minor comments.


private final long watermarkDelay;

// timezone watermarkDelay.
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use "offset" here may be more precise.

.toTable(tEnv, 'a, 'b, 'c, 'd, 'e, 'rowtime)
val t = failingDataSource(TestData.tupleData5.map {
case (a, b, c, d, e) => (b, a, c, d, e)
}).assignTimestampsAndWatermarks(
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It's ok here as mini-batch window is supported now. But it's necessary to construct a StreamExecWatermarkAssigner node to fully verify the correctness of mini-batch watermark.

val tableName = scan.getTable.getQualifiedName.mkString(".")
val inputBlocks = block.children.filter(b => tableName.equals(b.getOutputTableName))
Preconditions.checkArgument(inputBlocks.size <= 1)
if (inputBlocks.size == 1) {
val mergedInterval = FlinkRelOptUtil.mergeMiniBatchInterval(
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traits of sinkBlock have already been initialized before the first round of optimization, so miniBatchInterval can be ignored if the block is sink block.

val updatedTrait = rel match {
case _: StreamExecGroupWindowAggregate =>
// TODO introduce mini-batch window aggregate later
MiniBatchIntervalTrait.NONE
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It's not proper to use MiniBatchIntervalTrait.NONE here, since we can not distinguish window without miniBatch with other cases which yield MiniBatchIntervalTrait.NONE.
A specific value may be needed.

+- LogicalTableScan(table=[[_DataStreamTable_3]])

== Optimized Logical Plan ==
Calc(select=[id1, rowtime AS ts, text], updateAsRetraction=[true], accMode=[Acc], reuse_id=[1]): rowcount = , cumulative cost = {rows, cpu, io, network, memory}
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It's necessary to add cases with multi-layer blocks to validate the correctness of trait propagating among blocks.

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cshuo commented Jul 4, 2019

Looks good. 👍

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@KurtYoung KurtYoung left a comment

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+1, verifying this locally and merge

@KurtYoung KurtYoung merged commit 4fbf323 into apache:master Jul 4, 2019
@godfreyhe godfreyhe deleted the FLINK-12665 branch May 20, 2020 02:07
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