[GitHub] spark pull request #14266: [SPARK-16526][SQL] Benchmarking Performance for F...

[sameeragarwal <gi...@git.apache.org>]

Github user sameeragarwal commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14266#discussion_r73040155
  
    --- Diff: sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/AggregateBenchmark.scala ---
    @@ -576,4 +576,605 @@ class AggregateBenchmark extends BenchmarkBase {
         benchmark.run()
       }
     
    -}
    +  // This test does not do any benchmark, instead it produces generated code for vectorized
    +  // and row-based hashmaps.
    +  ignore("generated code comparison for vectorized vs. rowbased") {
    +    val N = 20 << 23
    +
    +    sparkSession.conf.set("spark.sql.codegen.wholeStage", "true")
    +    sparkSession.conf.set("spark.sql.codegen.aggregate.map.columns.max", "30")
    +
    +    sparkSession.range(N)
    +      .selectExpr(
    +        "id & 1023 as k1",
    +        "cast (id & 1023 as string) as k2")
    +      .createOrReplaceTempView("test")
    +
    +    // dataframe/query
    +    val query = sparkSession.sql("select count(k1), sum(k1) from test group by k1, k2")
    +
    +    // vectorized
    +    sparkSession.conf.set("spark.sql.codegen.aggregate.map.enforce.impl", "vectorized")
    +    query.queryExecution.debug.codegen()
    +
    +    // row based
    +    sparkSession.conf.set("spark.sql.codegen.aggregate.map.enforce.impl", "rowbased")
    +    query.queryExecution.debug.codegen()
    +  }
    +
    +  ignore("1 key field, 1 value field, distinct linear keys") {
    +    val N = 20 << 22;
    +
    +    var timeStart: Long = 0L
    +    var timeEnd: Long = 0L
    +    var nsPerRow: Long = 0L
    +    var i = 0
    +    sparkSession.conf.set("spark.sql.codegen.wholeStage", "true")
    +    sparkSession.conf.set("spark.sql.codegen.aggregate.map.columns.max", "30")
    +
    +    // scalastyle:off
    +    println(Benchmark.getJVMOSInfo())
    --- End diff --
    
    nit: to minimize duplication, maybe create a small utility function that can then be reused in all test cases.


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