viirya commented on code in PR #56842:
URL: https://github.com/apache/spark/pull/56842#discussion_r3487463894
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sql/core/src/test/scala/org/apache/spark/sql/execution/columnar/InMemoryColumnarQuerySuite.scala:
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@@ -222,6 +222,36 @@ class InMemoryColumnarQuerySuite extends
SharedSparkSession with AdaptiveSparkPl
}
}
+ test("cache nanosecond-precision timestamp types") {
+ withSQLConf(SQLConf.TIMESTAMP_NANOS_TYPES_ENABLED.key -> "true") {
+ Seq("TIMESTAMP_NTZ(9)", "TIMESTAMP_LTZ(9)").foreach { typeName =>
+ Seq("false", "true").foreach { vectorized =>
+ withSQLConf(SQLConf.CACHE_VECTORIZED_READER_ENABLED.key ->
vectorized) {
Review Comment:
You're right, thanks. Nanosecond timestamps are non-primitive for the
default cache, and `DefaultCachedBatchSerializer.supportsColumnarOutput`
returns `true` only for the primitive types, so they always read back through
the row path -- the `CACHE_VECTORIZED_READER_ENABLED=true` case exercised the
same path as `false`. I've dropped the loop and test the single (row) path,
with a comment noting why (same as `CalendarInterval`/`Variant`/`Decimal`).
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