JkSelf commented on issue #26434: [SPARK-29544] [SQL] optimize skewed partition based on data size URL: https://github.com/apache/spark/pull/26434#issuecomment-573031981 @cloud-fan We have test this PR with the following sql in 5 node clusters. ``` spark.range(0, 100000, 1, 6).selectExpr("id % 2 as key1").createOrReplaceTempView("test1"); spark.range(0, 100000, 1, 6).selectExpr("id % 2 +1 as key2").createOrReplaceTempView("test2"); import org.apache.spark.sql.SaveMode.Overwrite spark.sql("select * from test1, test2 where key1 = key2").write.format("noop").mode(Overwrite).save() ``` The main spark configuration is: ``` spark.sql.shuffle.partitions 500 spark.sql.autoBroadcastJoinThreshold -1 spark.sql.adaptive.enabled true spark.sql.adaptive.shuffle.localShuffleReader.enabled false spark.sql.adaptive.shuffle.reducePostShufflePartitions.enabled false spark.sql.adaptive.optimizeSkewedJoin.skewedPartitionSizeThreshold 500 ``` This PR can gain about 6x performance improvement(27s vs 162s). And the following is the UI of with and without this PR. **With this PR:**  **Without this PR:** 
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