Github user davies commented on the pull request: https://github.com/apache/spark/pull/9383#issuecomment-153118810 After some benchmark, realized that using hashcode as prefix in timsort will cause regression in timsort and snappy compression (especially for aggregation after join, the order of records will become random). I will revert that part. benchmark code: ``` sqlContext.setConf("spark.sql.shuffle.partitions", "1") N = 1<<25 M = 1<<20 df = sqlContext.range(N).selectExpr("id", "repeat(id, 2) as s") df.show() df2 = df.select(df.id.alias('id2'), df.s.alias('s2')) j = df.join(df2, df.id==df2.id2).groupBy(df.s).max("id", "id2") n = j.count() ``` Another interesting finding is that Snappy will slowdown the spilling by 50% of end-to-end time, LZ4 will be faster than Snappy, but still 10% slower than not-compressed. Should we use `false` as the default value for `spark.shuffle.spill.compress`?(PS: tested on Mac with SSD, it may not be true on HD)
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