[SPARK-37001][SQL] Disable two level of map for final hash aggregatio…

…n by default

### What changes were proposed in this pull request?

This PR is to disable two level of maps for final hash aggregation by default. The feature was introduced in #32242 and we found it can lead to query performance regression when the final aggregation gets rows with a lot of distinct keys. The 1st level hash map is full so a lot of rows will waste the 1st hash map lookup and inserted into 2nd hash map. This feature still benefits query with not so many distinct keys though, so introducing a config here `spark.sql.codegen.aggregate.final.map.twolevel.enabled`, to allow query to enable the feature when seeing benefit.

### Why are the changes needed?

Fix query regression.

### Does this PR introduce _any_ user-facing change?

Yes, the introduced `spark.sql.codegen.aggregate.final.map.twolevel.enabled` config.

### How was this patch tested?

Existing unit test in `AggregationQuerySuite.scala`.

Also verified generated code for an example query in the file:

```
spark.sql(
    """
      |SELECT key, avg(value)
      |FROM agg1
      |GROUP BY key
    """.stripMargin)
```

Verified the generated code for final hash aggregation not have two level maps by default:
https://gist.github.com/c21/d4ce87ef28a22d1ce839e0cda000ce14 .

Verified the generated code for final hash aggregation have two level maps if enabling the config:
https://gist.github.com/c21/4b59752c1f3f98303b60ccff66b5db69 .

Closes #34270 from c21/agg-fix.

Authored-by: Cheng Su <chengsu@fb.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
(cherry picked from commit 3354a21)
Signed-off-by: Wenchen Fan <wenchen@databricks.com>

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/grouping.scala

@@ -217,9 +217,9 @@ case class Grouping(child: Expression) extends Expression with Unevaluable
     Examples:
       > SELECT name, _FUNC_(), sum(age), avg(height) FROM VALUES (2, 'Alice', 165), (5, 'Bob', 180) people(age, name, height) GROUP BY cube(name, height);
         Alice	0	2	165.0
-        Bob	0	5	180.0
         Alice	1	2	165.0
         NULL	3	7	172.5
+        Bob	0	5	180.0
         Bob	1	5	180.0
         NULL	2	2	165.0
         NULL	2	5	180.0

sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala

@@ -1685,6 +1685,16 @@ object SQLConf {
       .booleanConf
       .createWithDefault(true)
 
+  val ENABLE_TWOLEVEL_AGG_MAP_PARTIAL_ONLY =
+    buildConf("spark.sql.codegen.aggregate.map.twolevel.partialOnly")
+      .internal()
+      .doc("Enable two-level aggregate hash map for partial aggregate only, " +
+        "because final aggregate might get more distinct keys compared to partial aggregate. " +
+        "Overhead of looking up 1st-level map might dominate when having a lot of distinct keys.")
+      .version("3.2.1")
+      .booleanConf
+      .createWithDefault(true)
+
   val ENABLE_VECTORIZED_HASH_MAP =
     buildConf("spark.sql.codegen.aggregate.map.vectorized.enable")
       .internal()

sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala

@@ -667,7 +667,14 @@ case class HashAggregateExec(
     val isNotByteArrayDecimalType = bufferSchema.map(_.dataType).filter(_.isInstanceOf[DecimalType])
       .forall(!DecimalType.isByteArrayDecimalType(_))
 
-    isSupported && isNotByteArrayDecimalType
+    val isEnabledForAggModes =
+      if (modes.forall(mode => mode == Partial || mode == PartialMerge)) {
+        true
+      } else {
+        !conf.getConf(SQLConf.ENABLE_TWOLEVEL_AGG_MAP_PARTIAL_ONLY)
+      }
+
+    isSupported && isNotByteArrayDecimalType && isEnabledForAggModes
   }
 
   private def enableTwoLevelHashMap(ctx: CodegenContext): Unit = {