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new 45ba9224602e [SPARK-47385] Fix tuple encoders with Option inputs
45ba9224602e is described below
commit 45ba9224602eb18fe45e339cbb8cf2e8a4924f0b
Author: Chenhao Li <chenhao...@databricks.com>
AuthorDate: Thu Mar 14 14:27:36 2024 +0800
[SPARK-47385] Fix tuple encoders with Option inputs
https://github.com/apache/spark/pull/40755 adds a null check on the input
of the child deserializer in the tuple encoder. It breaks the deserializer for
the `Option` type, because null should be deserialized into `None` rather than
null. This PR adds a boolean parameter to `ExpressionEncoder.tuple` so that
only the user that https://github.com/apache/spark/pull/40755 intended to fix
has this null check.
Unit test.
Closes #45508 from chenhao-db/SPARK-47385.
Authored-by: Chenhao Li <chenhao...@databricks.com>
Signed-off-by: Wenchen Fan <wenc...@databricks.com>
(cherry picked from commit 9986462811f160eacd766da8a4e14a9cbb4b8710)
Signed-off-by: Wenchen Fan <wenc...@databricks.com>
---
.../spark/sql/catalyst/encoders/ExpressionEncoder.scala | 10 ++++++++--
sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala | 4 +++-
.../src/test/scala/org/apache/spark/sql/DatasetSuite.scala | 12 ++++++++++++
3 files changed, 23 insertions(+), 3 deletions(-)
diff --git
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoder.scala
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoder.scala
index e6477e48fe96..2e60323bdd0d 100644
---
a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoder.scala
+++
b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/encoders/ExpressionEncoder.scala
@@ -79,8 +79,14 @@ object ExpressionEncoder {
* Given a set of N encoders, constructs a new encoder that produce objects
as items in an
* N-tuple. Note that these encoders should be unresolved so that
information about
* name/positional binding is preserved.
+ * When `useNullSafeDeserializer` is true, the deserialization result for a
child will be null if
+ * the input is null. It is false by default as most deserializers handle
null input properly and
+ * don't require an extra null check. Some of them are null-tolerant, such
as the deserializer for
+ * `Option[T]`, and we must not set it to true in this case.
*/
- def tuple(encoders: Seq[ExpressionEncoder[_]]): ExpressionEncoder[_] = {
+ def tuple(
+ encoders: Seq[ExpressionEncoder[_]],
+ useNullSafeDeserializer: Boolean = false): ExpressionEncoder[_] = {
if (encoders.length > 22) {
throw QueryExecutionErrors.elementsOfTupleExceedLimitError()
}
@@ -125,7 +131,7 @@ object ExpressionEncoder {
case GetColumnByOrdinal(0, _) => input
}
- if (enc.objSerializer.nullable) {
+ if (useNullSafeDeserializer && enc.objSerializer.nullable) {
nullSafe(input, childDeserializer)
} else {
childDeserializer
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
index 233b61926c4f..8b14ed651362 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/Dataset.scala
@@ -1173,7 +1173,9 @@ class Dataset[T] private[sql](
}
implicit val tuple2Encoder: Encoder[(T, U)] =
- ExpressionEncoder.tuple(this.exprEnc, other.exprEnc)
+ ExpressionEncoder
+ .tuple(Seq(this.exprEnc, other.exprEnc), useNullSafeDeserializer =
true)
+ .asInstanceOf[Encoder[(T, U)]]
val leftResultExpr = {
if (!this.exprEnc.isSerializedAsStructForTopLevel) {
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
b/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
index 43322b6dc972..fdbb463c0553 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/DatasetSuite.scala
@@ -2197,6 +2197,18 @@ class DatasetSuite extends QueryTest
)
assert(result == expected)
}
+
+ test("SPARK-47385: Tuple encoder with Option inputs") {
+ implicit val enc: Encoder[(SingleData, Option[SingleData])] =
+ Encoders.tuple(Encoders.product[SingleData],
Encoders.product[Option[SingleData]])
+
+ val input = Seq(
+ (SingleData(1), Some(SingleData(1))),
+ (SingleData(2), None)
+ )
+ val ds = spark.createDataFrame(input).as[(SingleData, Option[SingleData])]
+ checkDataset(ds, input: _*)
+ }
}
case class Bar(a: Int)
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