Github user sameeragarwal commented on a diff in the pull request:
https://github.com/apache/spark/pull/14176#discussion_r73037506
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala
---
@@ -459,52 +476,115 @@ case class HashAggregateExec(
}
/**
- * Using the vectorized hash map in HashAggregate is currently supported
for all primitive
- * data types during partial aggregation. However, we currently only
enable the hash map for a
- * subset of cases that've been verified to show performance
improvements on our benchmarks
- * subject to an internal conf that sets an upper limit on the maximum
length of the aggregate
- * key/value schema.
- *
+ * A required check for any fast hash map implementation (basically the
common requirements
+ * for row-based and vectorized).
+ * Currently fast hash map is supported for primitive data types during
partial aggregation.
* This list of supported use-cases should be expanded over time.
*/
- private def enableVectorizedHashMap(ctx: CodegenContext): Boolean = {
- val schemaLength = (groupingKeySchema ++ bufferSchema).length
+ private def checkIfFastHashMapSupported(ctx: CodegenContext): Boolean = {
val isSupported =
(groupingKeySchema ++ bufferSchema).forall(f =>
ctx.isPrimitiveType(f.dataType) ||
f.dataType.isInstanceOf[DecimalType] ||
f.dataType.isInstanceOf[StringType]) &&
bufferSchema.nonEmpty && modes.forall(mode => mode == Partial ||
mode == PartialMerge)
- // We do not support byte array based decimal type for aggregate
values as
- // ColumnVector.putDecimal for high-precision decimals doesn't
currently support in-place
- // updates. Due to this, appending the byte array in the vectorized
hash map can turn out to be
- // quite inefficient and can potentially OOM the executor.
+ // Acting conservative and do not support byte array based decimal
type for aggregate values
+ // for now.
--- End diff --
let's keep the old comment
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