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From "Xiao Li (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-20479) Performance degradation for large number of hash-aggregated columns
Date Wed, 26 Apr 2017 19:23:04 GMT

     [ https://issues.apache.org/jira/browse/SPARK-20479?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Xiao Li updated SPARK-20479:
----------------------------
    Affects Version/s:     (was: 2.3.0)
                       2.2.0

> Performance degradation for large number of hash-aggregated columns
> -------------------------------------------------------------------
>
>                 Key: SPARK-20479
>                 URL: https://issues.apache.org/jira/browse/SPARK-20479
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.2.0
>            Reporter: Kazuaki Ishizaki
>
> In comment of SPARK-20184, [~maropu] revealed that performance is degraded when # of
aggregated columns get large with whole-stage codegen.
> {code}
> ./bin/spark-shell --master local[1] --conf spark.driver.memory=2g --conf spark.sql.shuffle.partitions=1
-v
> def timer[R](f: => {}): Unit = {
>   val count = 9
>   val iters = (0 until count).map { i =>
>     val t0 = System.nanoTime()
>     f
>     val t1 = System.nanoTime()
>     val elapsed = t1 - t0 + 0.0
>     println(s"#$i: ${elapsed / 1000000000.0}")
>     elapsed
>   }
>   println("Elapsed time: " + ((iters.sum / count) / 1000000000.0) + "s")
> }
> val numCols = 80
> val t = s"(SELECT id AS key1, id AS key2, ${((0 until numCols).map(i => s"id AS c$i")).mkString(",
")} FROM range(0, 100000, 1, 1))"
> val sqlStr = s"SELECT key1, key2, ${((0 until numCols).map(i => s"SUM(c$i)")).mkString(",
")} FROM $t GROUP BY key1, key2 LIMIT 100"
> // Elapsed time: 2.3084404905555553s
> sql("SET spark.sql.codegen.wholeStage=true")
> timer { sql(sqlStr).collect }
> // Elapsed time: 0.527486733s
> sql("SET spark.sql.codegen.wholeStage=false")
> timer { sql(sqlStr).collect }
> {code}



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