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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