Minor Description
when adaptive query execution is enabled the following expression should support coalescing of partitions:
dataframe.repartition(col("somecolumn"))
currently it does not because it simply calls the repartition implementation where number of partitions is specified:
def repartition(partitionExprs: Column*): Dataset[T] = {
repartition(sparkSession.sessionState.conf.numShufflePartitions, partitionExprs: _*)
}
and repartition with the number of partitions specified does now allow for coalescing of partitions (since this breaks the user's expectation that it will have the number of partitions specified).
for more context see the discussion here:
https://github.com/apache/spark/pull/27986
a simple test to confirm that repartition by key does not support coalescing of partitions can be added in AdaptiveQueryExecSuite like this (it currently fails):
test("SPARK-32056 repartition has less partitions for small data when adaptiveExecutionEnabled") { Seq(true, false).foreach { enableAQE => withSQLConf( SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> enableAQE.toString, SQLConf.SHUFFLE_PARTITIONS.key -> "50", SQLConf.COALESCE_PARTITIONS_INITIAL_PARTITION_NUM.key -> "50", SQLConf.SHUFFLE_PARTITIONS.key -> "50") { val partitionsNum = (1 to 10).toDF.repartition($"value") .rdd.collectPartitions().length if (enableAQE) { assert(partitionsNum < 50) } else { assert(partitionsNum === 50) } } } }
Attachments
Issue Links
- relates to
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SPARK-31220 repartition obeys spark.sql.adaptive.coalescePartitions.initialPartitionNum when spark.sql.adaptive.enabled
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- Resolved
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- links to
