comphead commented on code in PR #6927:
URL: https://github.com/apache/datafusion-comet/pull/6927#discussion_r4263843661
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spark/src/main/scala/org/apache/comet/serde/aggregates.scala:
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@@ -1475,14 +1475,16 @@ object AggSerde {
/**
* Shared support level for `Min` / `Max` based on the result data type.
Floats follow Spark's
- * ordering natively, so strict floating-point mode does not apply to them.
+ * ordering natively, so strict floating-point mode does not apply to them.
Spark and the native
+ * accumulators both compare strings and binary as unsigned bytes, except
that Spark orders a
+ * string with a non-default collation by that collation.
*/
- def minMaxSupportLevel(dt: DataType): SupportLevel = {
- if (!minMaxDataTypeSupported(dt)) {
- Unsupported(Some(s"Unsupported data type: $dt"))
- } else {
- Compatible()
- }
+ def minMaxSupportLevel(dt: DataType): SupportLevel = dt match {
+ case _: StringType if isStringCollationType(dt) =>
+ Unsupported(Some(ComparisonUtils.nonDefaultCollationReason("min/max")))
+ case _: StringType | BinaryType => Compatible()
Review Comment:
Thanks for measuring this. I agree with Andy's read: plain string and binary
`min`/`max` runs as ordered sort aggregates that emit each group as soon as it
completes, so the exposure is the hash shape next to a typed imperative
aggregate. #6936 tracks the fix in DataFusion's `MinMaxBytesState`, and as of
3752f31136 the new case in `minMaxSupportLevel` points at it.
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