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