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  1. Spark
  2. SPARK-21977

SinglePartition optimizations break certain Streaming Stateful Aggregation requirements

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    • Type: Bug
    • Status: Resolved
    • Priority: Major
    • Resolution: Fixed
    • Affects Version/s: 2.2.0
    • Fix Version/s: 2.3.0
    • Component/s: Structured Streaming
    • Labels:
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    • Target Version/s:

      Description

      This is a bit hard to explain as there are several issues here, I'll try my best. Here are the requirements:

      1. A StructuredStreaming Source that can generate empty RDDs with 0 partitions
      2. A StructuredStreaming query that uses the above source, performs a stateful aggregation (mapGroupsWithState, groupBy.count, ...), and coalesce's by 1

      The crux of the problem is that when a dataset has a `coalesce(1)` call, it receives a `SinglePartition` partitioning scheme. This scheme satisfies most required distributions used for aggregations such as HashAggregateExec. This causes a world of problems:

      Symptom 1. If the input RDD has 0 partitions, the whole lineage will receive 0 partitions, nothing will be executed, the state store will not create any delta files. When this happens, the next trigger fails, because the StateStore fails to load the delta file for the previous trigger

      Symptom 2. Let's say that there was data. Then in this case, if you stop your stream, and change `coalesce(1)` with `coalesce(2)`, then restart your stream, your stream will fail, because `spark.sql.shuffle.partitions - 1` number of StateStores will fail to find its delta files.

      To fix the issues above, we must check that the partitioning of the child of a `StatefulOperator` satisfies:
      If the grouping expressions are empty:
      a) AllTuple distribution
      b) Single physical partition
      If the grouping expressions are non empty:
      a) Clustered distribution
      b) spark.sql.shuffle.partition # of partitions
      whether or not coalesce(1) exists in the plan, and whether or not the input RDD for the trigger has any data.

      Once you fix the above problem by adding an Exchange to the plan, you come across the following bug:

      If you call `coalesce(1).groupBy().count()` on a Streaming DataFrame, and if you have a trigger with no data, `StateStoreRestoreExec` doesn't return the prior state. However, for this specific aggregation, `HashAggregateExec` after the restore returns a (0, 0) row, since we're performing a count, and there is no data. Then this data gets stored in `StateStoreSaveExec` causing the previous counts to be overwritten and lost.

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              • Assignee:
                brkyvz Burak Yavuz
                Reporter:
                brkyvz Burak Yavuz
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                • Created:
                  Updated:
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