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Yang Jie edited comment on SPARK-40303 at 9/2/22 4:58 AM: ---------------------------------------------------------- Run use Java 8 with `-XX:+PrintCompilation`, I found the following logs: {code:java} 64350 21862 4 org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1::hashAgg_doConsume_0$ (2051 bytes) COMPILE SKIPPED: unsupported incoming calling sequence (not retryable) 137245 22141 % 4 org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1::hashAgg_doAggregateWithKeys_0$ @ 38 (2712 bytes) COMPILE SKIPPED: unsupported calling sequence (not retryable) {code} [~rednaxelafx] In this case, will compilation optimization degenerate to Level 2 or give up when use Java 8? was (Author: luciferyang): Run use Java 8 with `-XX:+PrintCompilation`, I found the following logs: {code:java} 64350 21862 4 org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1::hashAgg_doConsume_0$ (2051 bytes) COMPILE SKIPPED: unsupported incoming calling sequence (not retryable) 137245 22141 % 4 org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1::hashAgg_doAggregateWithKeys_0$ @ 38 (2712 bytes) COMPILE SKIPPED: unsupported calling sequence (not retryable) {code} [~rednaxelafx] In this case, will compilation optimization degenerate to Level 2 or give up? > The performance will be worse after codegen > ------------------------------------------- > > Key: SPARK-40303 > URL: https://issues.apache.org/jira/browse/SPARK-40303 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 3.4.0 > Reporter: Yuming Wang > Priority: Major > > {code:scala} > import org.apache.spark.benchmark.Benchmark > val dir = "/tmp/spark/benchmark" > val N = 2000000 > val columns = Range(0, 100).map(i => s"id % $i AS id$i") > spark.range(N).selectExpr(columns: _*).write.mode("Overwrite").parquet(dir) > // Seq(1, 2, 5, 10, 15, 25, 40, 60, 100) > Seq(60).foreach{ cnt => > val selectExps = columns.take(cnt).map(_.split(" ").last).map(c => > s"count(distinct $c)") > val benchmark = new Benchmark("Benchmark count distinct", N, minNumIters = > 1) > benchmark.addCase(s"$cnt count distinct with codegen") { _ => > withSQLConf( > "spark.sql.codegen.wholeStage" -> "true", > "spark.sql.codegen.factoryMode" -> "FALLBACK") { > spark.read.parquet(dir).selectExpr(selectExps: > _*).write.format("noop").mode("Overwrite").save() > } > } > benchmark.addCase(s"$cnt count distinct without codegen") { _ => > withSQLConf( > "spark.sql.codegen.wholeStage" -> "false", > "spark.sql.codegen.factoryMode" -> "NO_CODEGEN") { > spark.read.parquet(dir).selectExpr(selectExps: > _*).write.format("noop").mode("Overwrite").save() > } > } > benchmark.run() > } > {code} > {noformat} > Java HotSpot(TM) 64-Bit Server VM 1.8.0_281-b09 on Mac OS X 10.15.7 > Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz > Benchmark count distinct: Best Time(ms) Avg Time(ms) > Stdev(ms) Rate(M/s) Per Row(ns) Relative > ------------------------------------------------------------------------------------------------------------------------ > 60 count distinct with codegen 628146 628146 > 0 0.0 314072.8 1.0X > 60 count distinct without codegen 147635 147635 > 0 0.0 73817.5 4.3X > {noformat} -- This message was sent by Atlassian Jira (v8.20.10#820010) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org