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https://issues.apache.org/jira/browse/SPARK-20184?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15976251#comment-15976251
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Takeshi Yamamuro commented on SPARK-20184:
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When #aggregated columns gets large, it seems we get the possibly similar
regression even in spark-shell
{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}
> performance regression for complex/long sql when enable whole stage codegen
> ---------------------------------------------------------------------------
>
> Key: SPARK-20184
> URL: https://issues.apache.org/jira/browse/SPARK-20184
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 1.6.0, 2.1.0
> Reporter: Fei Wang
>
> The performance of following SQL get much worse in spark 2.x in contrast
> with codegen off.
> SELECT
> sum(COUNTER_57)
> ,sum(COUNTER_71)
> ,sum(COUNTER_3)
> ,sum(COUNTER_70)
> ,sum(COUNTER_66)
> ,sum(COUNTER_75)
> ,sum(COUNTER_69)
> ,sum(COUNTER_55)
> ,sum(COUNTER_63)
> ,sum(COUNTER_68)
> ,sum(COUNTER_56)
> ,sum(COUNTER_37)
> ,sum(COUNTER_51)
> ,sum(COUNTER_42)
> ,sum(COUNTER_43)
> ,sum(COUNTER_1)
> ,sum(COUNTER_76)
> ,sum(COUNTER_54)
> ,sum(COUNTER_44)
> ,sum(COUNTER_46)
> ,DIM_1
> ,DIM_2
> ,DIM_3
> FROM aggtable group by DIM_1, DIM_2, DIM_3 limit 100;
> Num of rows of aggtable is about 35000000.
> whole stage codegen on(spark.sql.codegen.wholeStage = true): 40s
> whole stage codegen off(spark.sql.codegen.wholeStage = false): 6s
> After some analysis i think this is related to the huge java method(a java
> method of thousand lines) which generated by codegen.
> And If i config -XX:-DontCompileHugeMethods the performance get much
> better(about 7s).
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