viirya commented on code in PR #56334:
URL: https://github.com/apache/spark/pull/56334#discussion_r3487468545
##########
sql/api/src/main/scala/org/apache/spark/sql/util/ArrowUtils.scala:
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@@ -38,6 +38,50 @@ private[sql] object ArrowUtils {
// todo: support more types.
+ /**
+ * Check if a Spark DataType is supported by Arrow. This recursively checks
complex types
+ * (Array, Struct, Map).
+ *
+ * Note: This checks compatibility with toArrowField(), not toArrowType().
Types like
+ * GeometryType, GeographyType, and VariantType are not supported by
toArrowType() (which only
+ * handles primitive Arrow types), but ARE supported by toArrowField() which
converts them to
+ * Arrow Struct representations with metadata. Since Arrow cache uses
toArrowField() via
+ * toArrowSchema() to create the schema, these types are supported.
+ */
+ def isSupportedByArrow(dt: DataType): Boolean = {
+ dt match {
+ // Primitive types
+ case BooleanType | ByteType | ShortType | IntegerType | LongType |
FloatType | DoubleType |
+ _: StringType | BinaryType | NullType =>
+ true
+
+ // Decimal
+ case _: DecimalType => true
+
+ // Temporal types
+ case DateType | TimestampType | TimestampNTZType | _: TimeType => true
Review Comment:
Follow-up filed: SPARK-57735 / https://github.com/apache/spark/pull/56842
adds nanosecond-timestamp support to the default in-memory cache
(`DefaultCachedBatchSerializer`), which is the prerequisite -- once that lands,
the Arrow cache can route these types through the same statistics machinery
rather than rejecting them here.
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