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https://issues.apache.org/jira/browse/SPARK-20184?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15974682#comment-15974682
]
Kazuaki Ishizaki commented on SPARK-20184:
------------------------------------------
I succeeded to reproduce this...
{code}
% git log | head -2
commit 773754b6c1516c15b64846a00e491535cbcb1007
Author: Liang-Chi Hsieh <vii...@gmail.com>
% bin/spark-submit --driver-memory 16g --class
org.apache.spark.sql.execution.benchmark.SPARK20184
sql/core/target/spark-sql_2.11-2.2.0-SNAPSHOT-tests.jar
...
OpenJDK 64-Bit Server VM 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13 on Linux
4.4.0-66-generic
Intel(R) Xeon(R) CPU E5-2667 v3 @ 3.20GHz
SPARK-20184: Best/Avg Time(ms) Rate(M/s) Per
Row(ns) Relative
------------------------------------------------------------------------------------------------
codegen = T 2840 / 3008 0.0
2839940132.0 1.0X
codegen = F 792 / 841 0.0
792284833.0 3.6X
{code}
{code}
package org.apache.spark.sql.execution.benchmark
import org.apache.spark.SparkConf
import org.apache.spark.sql.SparkSession
import org.apache.spark.util.Benchmark
object SPARK20184 {
val conf =
new SparkConf()
.setMaster("local[1]")
.setAppName("test")
.set("spark.driver.memory", "16g")
val spark = SparkSession.builder.config(conf).getOrCreate()
import spark.implicits._
def main(args: Array[String]): Unit = {
val df = (1 to 500000)
.map(x => (x.toString, x.toString, x, x, x, x, x, x, x, x,
x, x, x, x, x, x, x, x, x, x, x, x))
.toDF("dim_1", "dim_2", "c1", "c2", "c3", "c4", "c5", "c6", "c7", "c8",
"c9", "c10",
"c11", "c12", "c13", "c14", "c15", "c16", "c17", "c18", "c19",
"c20")
df.write.saveAsTable("sum_table_50w_3")
val query = "select dim_1, dim_2," +
"sum(c1), sum(c2), sum(c3), sum(c4), sum(c5), sum(c6), sum(c7), sum(c8),
sum(c9), sum(c10)," +
"sum(c11), sum(c12), sum(c13), sum(c14), sum(c15), sum(c16), sum(c17),
sum(c18), sum(c19)," +
"sum(c20) from sum_table_50w_3 group by dim_1, dim_2 limit 100"
val benchmark = new Benchmark("SPARK-20184", 1, 20, outputPerIteration =
true)
benchmark.addCase("codegen = T") { i =>
spark.conf.set("spark.sql.codegen.wholeStage", "true")
spark.sql(query).collect
}
benchmark.addCase("codegen = F") { i =>
spark.conf.set("spark.sql.codegen.wholeStage", "false")
spark.sql(query).collect
}
benchmark.run()
}
}
{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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