[GitHub] [spark] huaxingao commented on a change in pull request #34445: [SPARK-36646][SQL] Push down group by partition column for aggregate

[GitBox <gi...@apache.org>]

huaxingao commented on a change in pull request #34445:
URL: https://github.com/apache/spark/pull/34445#discussion_r741315128



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File path: sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/FileSourceAggregatePushDownSuite.scala
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@@ -261,6 +261,63 @@ trait FileSourceAggregatePushDownSuite
     }
   }
 
+  test("aggregate with partition group by can be pushed down") {
+    withTempPath { dir =>
+      spark.range(10).selectExpr("id", "id % 3 as p")
+        .write.partitionBy("p").format(format).save(dir.getCanonicalPath)
+      withTempView("tmp") {
+        spark.read.format(format).load(dir.getCanonicalPath).createOrReplaceTempView("tmp");
+        Seq("false", "true").foreach { enableVectorizedReader =>
+          withSQLConf(aggPushDownEnabledKey -> "true",
+            vectorizedReaderEnabledKey -> enableVectorizedReader) {
+            val df = sql("SELECT count(*), count(id), p, max(id), p, p, max(id), min(id), p" +

Review comment:
       I added `count(p)`, but didn't add `max(p)` since it won't be pushed down




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