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[SPARK-53553][CONNECT] Fix handling of null values in LiteralValueProtoConverter
### What changes were proposed in this pull request? This PR fixes the handling of null literal values in `LiteralValueProtoConverter` for Spark Connect. The main changes include: 1. **Added proper null value handling**: Created a new `setNullValue` method that correctly sets null values in proto literals with appropriate data type information. 2. **Reordered pattern matching**: Moved null and Option handling to the top of the pattern matching in `toLiteralProtoBuilderInternal` to ensure null values are processed before other type-specific logic. 3. **Fixed converter logic**: Updated the `getScalaConverter` method to properly handle null values by checking `hasNull` before applying type-specific conversion logic. ### Why are the changes needed? The previous implementation had several issues with null value handling: 1. **Incorrect null processing order**: Null values were being processed after type-specific logic, which could lead to exceptions. 2. **Missing null checks in converters**: The converter functions didn't properly check for null values before applying type-specific conversion logic. ### Does this PR introduce _any_ user-facing change? **Yes**. This PR fixes a bug where null values in literals (especially in arrays and maps) were not being properly handled in Spark Connect. Users who were experiencing issues with null value serialization in complex types should now see correct behavior. ### How was this patch tested? `build/sbt "connect-client-jvm/testOnly *ClientE2ETestSuite -- -z SPARK-53553"` `build/sbt "connect/testOnly *LiteralExpressionProtoConverterSuite"` `build/sbt "connect-client-jvm/testOnly org.apache.spark.sql.PlanGenerationTestSuite"` `build/sbt "connect/testOnly org.apache.spark.sql.connect.ProtoToParsedPlanTestSuite"` ### Was this patch authored or co-authored using generative AI tooling? Generated-by: Cursor 1.5.11 Closes #52310 from heyihong/SPARK-53553. Authored-by: Yihong He <heyihong.cn@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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‎sql/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala‎

Lines changed: 4 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -3419,8 +3419,12 @@ class PlanGenerationTestSuite
34193419
// Handle parameterized scala types e.g.: List, Seq and Map.
34203420
fn.typedLit(Some(1)),
34213421
fn.typedLit(Array(1, 2, 3)),
3422+
fn.typedLit[Array[Integer]](Array(null, null)),
3423+
fn.typedLit[Array[(Int, String)]](Array(null, null, (1, "a"), (2, null))),
3424+
fn.typedLit[Array[Option[(Int, String)]]](Array(None, None, Some((1, "a")))),
34223425
fn.typedLit(Seq(1, 2, 3)),
34233426
fn.typedLit(mutable.LinkedHashMap("a" -> 1, "b" -> 2)),
3427+
fn.typedLit(mutable.LinkedHashMap[String, Integer]("a" -> null, "b" -> null)),
34243428
fn.typedLit(("a", 2, 1.0)),
34253429
fn.typedLit[Option[Int]](None),
34263430
fn.typedLit[Array[Option[Int]]](Array(Some(1))),

‎sql/connect/client/jvm/src/test/scala/org/apache/spark/sql/connect/ClientE2ETestSuite.scala‎

Lines changed: 7 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -1785,6 +1785,13 @@ class ClientE2ETestSuite
17851785
assert(observation.get.contains("map"))
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assert(observation.get("map") === Map("count" -> 10))
17871787
}
1788+
1789+
test("SPARK-53553: null value handling in literals") {
1790+
val df = spark.sql("select 1").select(typedlit(Array[Integer](1, null)).as("arr_col"))
1791+
val result = df.collect()
1792+
assert(result.length === 1)
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assert(result(0).getAs[Array[Integer]]("arr_col") === Array(1, null))
1794+
}
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}
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private[sql] case class ClassData(a: String, b: Int)

‎sql/connect/common/src/main/scala/org/apache/spark/sql/connect/common/LiteralValueProtoConverter.scala‎

Lines changed: 23 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -40,6 +40,19 @@ import org.apache.spark.unsafe.types.CalendarInterval
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object LiteralValueProtoConverter {
4242

43+
private def setNullValue(
44+
builder: proto.Expression.Literal.Builder,
45+
dataType: DataType,
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needDataType: Boolean): proto.Expression.Literal.Builder = {
47+
if (needDataType) {
48+
builder.setNull(toConnectProtoType(dataType))
49+
} else {
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// No need data type but still set the null type to indicate that
51+
// the value is null.
52+
builder.setNull(ProtoDataTypes.NullType)
53+
}
54+
}
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4356
private def setArrayTypeAfterAddingElements(
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ab: proto.Expression.Literal.Array.Builder,
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elementType: DataType,
@@ -275,6 +288,14 @@ object LiteralValueProtoConverter {
275288
}
276289

277290
(literal, dataType) match {
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case (v: Option[_], _) =>
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if (v.isDefined) {
293+
toLiteralProtoBuilderInternal(v.get, dataType, options, needDataType)
294+
} else {
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setNullValue(builder, dataType, needDataType)
296+
}
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case (null, _) =>
298+
setNullValue(builder, dataType, needDataType)
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case (v: mutable.ArraySeq[_], ArrayType(_, _)) =>
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toLiteralProtoBuilderInternal(v.array, dataType, options, needDataType)
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case (v: immutable.ArraySeq[_], ArrayType(_, _)) =>
@@ -287,12 +308,6 @@ object LiteralValueProtoConverter {
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builder.setMap(mapBuilder(v, keyType, valueType, valueContainsNull))
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case (v, structType: StructType) =>
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builder.setStruct(structBuilder(v, structType))
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case (v: Option[_], _: DataType) =>
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if (v.isDefined) {
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toLiteralProtoBuilderInternal(v.get, options, needDataType)
293-
} else {
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builder.setNull(toConnectProtoType(dataType))
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}
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case (v: LocalTime, timeType: TimeType) =>
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builder.setTime(
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builder.getTimeBuilder
@@ -477,7 +492,7 @@ object LiteralValueProtoConverter {
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}
478493

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private def getScalaConverter(dataType: proto.DataType): proto.Expression.Literal => Any = {
480-
dataType.getKindCase match {
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val converter: proto.Expression.Literal => Any = dataType.getKindCase match {
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case proto.DataType.KindCase.SHORT => v => v.getShort.toShort
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case proto.DataType.KindCase.INTEGER => v => v.getInteger
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case proto.DataType.KindCase.LONG => v => v.getLong
@@ -513,6 +528,7 @@ object LiteralValueProtoConverter {
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case _ =>
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throw InvalidPlanInput(s"Unsupported Literal Type: ${dataType.getKindCase}")
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}
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v => if (v.hasNull) null else converter(v)
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}
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private def getInferredDataType(

‎sql/connect/common/src/test/resources/query-tests/explain-results/function_typedLit.explain‎

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@@ -1,2 +1,2 @@
1-
Project [id#0L, id#0L, 1 AS 1#0, null AS NULL#0, true AS true#0, 68 AS 68#0, 9872 AS 9872#0, -8726532 AS -8726532#0, 7834609328726532 AS 7834609328726532#0L, 2.718281828459045 AS 2.718281828459045#0, -0.8 AS -0.8#0, 89.97620 AS 89.97620#0, 89889.7667231 AS 89889.7667231#0, connect! AS connect!#0, T AS T#0, ABCDEFGHIJ AS ABCDEFGHIJ#0, 0x78797A7B7C7D7E7F808182838485868788898A8B8C8D8E AS X'78797A7B7C7D7E7F808182838485868788898A8B8C8D8E'#0, 0x0806 AS X'0806'#0, [8,6] AS ARRAY(8, 6)#0, null AS NULL#0, 2020-10-10 AS DATE '2020-10-10'#0, 8.997620 AS 8.997620#0, 2023-02-23 04:31:59.808 AS TIMESTAMP '2023-02-23 04:31:59.808'#0, 1969-12-31 16:00:12.345 AS TIMESTAMP '1969-12-31 16:00:12.345'#0, 2023-02-23 20:36:00 AS TIMESTAMP_NTZ '2023-02-23 20:36:00'#0, 2023-02-23 AS DATE '2023-02-23'#0, INTERVAL '0 00:03:20' DAY TO SECOND AS INTERVAL '0 00:03:20' DAY TO SECOND#0, INTERVAL '0-0' YEAR TO MONTH AS INTERVAL '0-0' YEAR TO MONTH#0, 23:59:59.999999999 AS TIME '23:59:59.999999999'#0, 2 months 20 days 0.0001 seconds AS INTERVAL '2 months 20 days 0.0001 seconds'#0, [18545,1677155519808000,12345000,1677184560000000,19411,200000000,0,86399999999999,2 months 20 days 0.0001 seconds] AS NAMED_STRUCT('_1', DATE '2020-10-10', '_2', TIMESTAMP '2023-02-23 04:31:59.808', '_3', TIMESTAMP '1969-12-31 16:00:12.345', '_4', TIMESTAMP_NTZ '2023-02-23 20:36:00', '_5', DATE '2023-02-23', '_6', INTERVAL '0 00:03:20' DAY TO SECOND, '_7', INTERVAL '0-0' YEAR TO MONTH, '_8', TIME '23:59:59.999999999', '_9', INTERVAL '2 months 20 days 0.0001 seconds')#0, 1 AS 1#0, [1,2,3] AS ARRAY(1, 2, 3)#0, [1,2,3] AS ARRAY(1, 2, 3)#0, map(keys: [a,b], values: [1,2]) AS MAP('a', 1, 'b', 2)#0, [a,2,1.0] AS NAMED_STRUCT('_1', 'a', '_2', 2, '_3', 1.0D)#0, null AS NULL#0, [1] AS ARRAY(1)#0, map(keys: [1], values: [0]) AS MAP(1, 0)#0, map(keys: [1], values: [0]) AS MAP(1, 0)#0, map(keys: [1], values: [0]) AS MAP(1, 0)#0, [[1,2,3],[4,5,6],[7,8,9]] AS ARRAY(ARRAY(1, 2, 3), ARRAY(4, 5, 6), ARRAY(7, 8, 9))#0, [keys: [a,b], values: [1,2],keys: [a,b], values: [3,4],keys: [a,b], values: [5,6]] AS ARRAY(MAP('a', 1, 'b', 2), MAP('a', 3, 'b', 4), MAP('a', 5, 'b', 6))#0, [keys: [a,b], values: [[1,2],[3,4]],keys: [a,b], values: [[5,6],[7,8]],keys: [a,b], values: [[],[]]] AS ARRAY(MAP('a', ARRAY('1', '2'), 'b', ARRAY('3', '4')), MAP('a', ARRAY('5', '6'), 'b', ARRAY('7', '8')), MAP('a', ARRAY(), 'b', ARRAY()))#0, map(keys: [1,2], values: [keys: [a,b], values: [1,2],keys: [a,b], values: [3,4]]) AS MAP(1, MAP('a', 1, 'b', 2), 2, MAP('a', 3, 'b', 4))#0, [[1,2,3],keys: [a,b], values: [1,2],[a,keys: [1,2], values: [a,b]]] AS NAMED_STRUCT('_1', ARRAY(1, 2, 3), '_2', MAP('a', 1, 'b', 2), '_3', NAMED_STRUCT('_1', 'a', '_2', MAP(1, 'a', 2, 'b')))#0]
1+
Project [id#0L, id#0L, 1 AS 1#0, null AS NULL#0, true AS true#0, 68 AS 68#0, 9872 AS 9872#0, -8726532 AS -8726532#0, 7834609328726532 AS 7834609328726532#0L, 2.718281828459045 AS 2.718281828459045#0, -0.8 AS -0.8#0, 89.97620 AS 89.97620#0, 89889.7667231 AS 89889.7667231#0, connect! AS connect!#0, T AS T#0, ABCDEFGHIJ AS ABCDEFGHIJ#0, 0x78797A7B7C7D7E7F808182838485868788898A8B8C8D8E AS X'78797A7B7C7D7E7F808182838485868788898A8B8C8D8E'#0, 0x0806 AS X'0806'#0, [8,6] AS ARRAY(8, 6)#0, null AS NULL#0, 2020-10-10 AS DATE '2020-10-10'#0, 8.997620 AS 8.997620#0, 2023-02-23 04:31:59.808 AS TIMESTAMP '2023-02-23 04:31:59.808'#0, 1969-12-31 16:00:12.345 AS TIMESTAMP '1969-12-31 16:00:12.345'#0, 2023-02-23 20:36:00 AS TIMESTAMP_NTZ '2023-02-23 20:36:00'#0, 2023-02-23 AS DATE '2023-02-23'#0, INTERVAL '0 00:03:20' DAY TO SECOND AS INTERVAL '0 00:03:20' DAY TO SECOND#0, INTERVAL '0-0' YEAR TO MONTH AS INTERVAL '0-0' YEAR TO MONTH#0, 23:59:59.999999999 AS TIME '23:59:59.999999999'#0, 2 months 20 days 0.0001 seconds AS INTERVAL '2 months 20 days 0.0001 seconds'#0, [18545,1677155519808000,12345000,1677184560000000,19411,200000000,0,86399999999999,2 months 20 days 0.0001 seconds] AS NAMED_STRUCT('_1', DATE '2020-10-10', '_2', TIMESTAMP '2023-02-23 04:31:59.808', '_3', TIMESTAMP '1969-12-31 16:00:12.345', '_4', TIMESTAMP_NTZ '2023-02-23 20:36:00', '_5', DATE '2023-02-23', '_6', INTERVAL '0 00:03:20' DAY TO SECOND, '_7', INTERVAL '0-0' YEAR TO MONTH, '_8', TIME '23:59:59.999999999', '_9', INTERVAL '2 months 20 days 0.0001 seconds')#0, 1 AS 1#0, [1,2,3] AS ARRAY(1, 2, 3)#0, [null,null] AS ARRAY(CAST(NULL AS INT), CAST(NULL AS INT))#0, [null,null,[1,a],[2,null]] AS ARRAY(NULL, NULL, NAMED_STRUCT('_1', 1, '_2', 'a'), NAMED_STRUCT('_1', 2, '_2', CAST(NULL AS STRING)))#0, [null,null,[1,a]] AS ARRAY(NULL, NULL, NAMED_STRUCT('_1', 1, '_2', 'a'))#0, [1,2,3] AS ARRAY(1, 2, 3)#0, map(keys: [a,b], values: [1,2]) AS MAP('a', 1, 'b', 2)#0, map(keys: [a,b], values: [null,null]) AS MAP('a', CAST(NULL AS INT), 'b', CAST(NULL AS INT))#0, [a,2,1.0] AS NAMED_STRUCT('_1', 'a', '_2', 2, '_3', 1.0D)#0, null AS NULL#0, [1] AS ARRAY(1)#0, map(keys: [1], values: [null]) AS MAP(1, CAST(NULL AS INT))#0, map(keys: [1], values: [null]) AS MAP(1, CAST(NULL AS INT))#0, map(keys: [1], values: [null]) AS MAP(1, CAST(NULL AS INT))#0, [[1,2,3],[4,5,6],[7,8,9]] AS ARRAY(ARRAY(1, 2, 3), ARRAY(4, 5, 6), ARRAY(7, 8, 9))#0, [keys: [a,b], values: [1,2],keys: [a,b], values: [3,4],keys: [a,b], values: [5,6]] AS ARRAY(MAP('a', 1, 'b', 2), MAP('a', 3, 'b', 4), MAP('a', 5, 'b', 6))#0, [keys: [a,b], values: [[1,2],[3,4]],keys: [a,b], values: [[5,6],[7,8]],keys: [a,b], values: [[],[]]] AS ARRAY(MAP('a', ARRAY('1', '2'), 'b', ARRAY('3', '4')), MAP('a', ARRAY('5', '6'), 'b', ARRAY('7', '8')), MAP('a', ARRAY(), 'b', ARRAY()))#0, map(keys: [1,2], values: [keys: [a,b], values: [1,2],keys: [a,b], values: [3,4]]) AS MAP(1, MAP('a', 1, 'b', 2), 2, MAP('a', 3, 'b', 4))#0, [[1,2,3],keys: [a,b], values: [1,2],[a,keys: [1,2], values: [a,b]]] AS NAMED_STRUCT('_1', ARRAY(1, 2, 3), '_2', MAP('a', 1, 'b', 2), '_3', NAMED_STRUCT('_1', 'a', '_2', MAP(1, 'a', 2, 'b')))#0]
22
+- LocalRelation <empty>, [id#0L, a#0, b#0]

‎sql/connect/common/src/test/resources/query-tests/queries/function_typedLit.json‎

Lines changed: 248 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -77,7 +77,8 @@
7777
}, {
7878
"literal": {
7979
"null": {
80-
"null": {
80+
"string": {
81+
"collation": "UTF8_BINARY"
8182
}
8283
}
8384
},
@@ -821,6 +822,206 @@
821822
}
822823
}
823824
}
825+
}, {
826+
"literal": {
827+
"array": {
828+
"elements": [{
829+
"null": {
830+
"integer": {
831+
}
832+
}
833+
}, {
834+
"null": {
835+
"null": {
836+
}
837+
}
838+
}],
839+
"dataType": {
840+
"containsNull": true
841+
}
842+
}
843+
},
844+
"common": {
845+
"origin": {
846+
"jvmOrigin": {
847+
"stackTrace": [{
848+
"classLoaderName": "app",
849+
"declaringClass": "org.apache.spark.sql.functions$",
850+
"methodName": "typedLit",
851+
"fileName": "functions.scala"
852+
}, {
853+
"classLoaderName": "app",
854+
"declaringClass": "org.apache.spark.sql.PlanGenerationTestSuite",
855+
"methodName": "~~trimmed~anonfun~~",
856+
"fileName": "PlanGenerationTestSuite.scala"
857+
}]
858+
}
859+
}
860+
}
861+
}, {
862+
"literal": {
863+
"array": {
864+
"elements": [{
865+
"null": {
866+
"struct": {
867+
"fields": [{
868+
"name": "_1",
869+
"dataType": {
870+
"integer": {
871+
}
872+
}
873+
}, {
874+
"name": "_2",
875+
"dataType": {
876+
"string": {
877+
"collation": "UTF8_BINARY"
878+
}
879+
},
880+
"nullable": true
881+
}]
882+
}
883+
}
884+
}, {
885+
"null": {
886+
"null": {
887+
}
888+
}
889+
}, {
890+
"struct": {
891+
"elements": [{
892+
"integer": 1
893+
}, {
894+
"string": "a"
895+
}],
896+
"dataTypeStruct": {
897+
"fields": [{
898+
"name": "_1"
899+
}, {
900+
"name": "_2",
901+
"dataType": {
902+
"string": {
903+
"collation": "UTF8_BINARY"
904+
}
905+
},
906+
"nullable": true
907+
}]
908+
}
909+
}
910+
}, {
911+
"struct": {
912+
"elements": [{
913+
"integer": 2
914+
}, {
915+
"null": {
916+
"string": {
917+
"collation": "UTF8_BINARY"
918+
}
919+
}
920+
}],
921+
"dataTypeStruct": {
922+
"fields": [{
923+
"name": "_1"
924+
}, {
925+
"name": "_2",
926+
"nullable": true
927+
}]
928+
}
929+
}
930+
}],
931+
"dataType": {
932+
"containsNull": true
933+
}
934+
}
935+
},
936+
"common": {
937+
"origin": {
938+
"jvmOrigin": {
939+
"stackTrace": [{
940+
"classLoaderName": "app",
941+
"declaringClass": "org.apache.spark.sql.functions$",
942+
"methodName": "typedLit",
943+
"fileName": "functions.scala"
944+
}, {
945+
"classLoaderName": "app",
946+
"declaringClass": "org.apache.spark.sql.PlanGenerationTestSuite",
947+
"methodName": "~~trimmed~anonfun~~",
948+
"fileName": "PlanGenerationTestSuite.scala"
949+
}]
950+
}
951+
}
952+
}
953+
}, {
954+
"literal": {
955+
"array": {
956+
"elements": [{
957+
"null": {
958+
"struct": {
959+
"fields": [{
960+
"name": "_1",
961+
"dataType": {
962+
"integer": {
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}
964+
}
965+
}, {
966+
"name": "_2",
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"dataType": {
968+
"string": {
969+
"collation": "UTF8_BINARY"
970+
}
971+
},
972+
"nullable": true
973+
}]
974+
}
975+
}
976+
}, {
977+
"null": {
978+
"null": {
979+
}
980+
}
981+
}, {
982+
"struct": {
983+
"elements": [{
984+
"integer": 1
985+
}, {
986+
"string": "a"
987+
}],
988+
"dataTypeStruct": {
989+
"fields": [{
990+
"name": "_1"
991+
}, {
992+
"name": "_2",
993+
"dataType": {
994+
"string": {
995+
"collation": "UTF8_BINARY"
996+
}
997+
},
998+
"nullable": true
999+
}]
1000+
}
1001+
}
1002+
}],
1003+
"dataType": {
1004+
"containsNull": true
1005+
}
1006+
}
1007+
},
1008+
"common": {
1009+
"origin": {
1010+
"jvmOrigin": {
1011+
"stackTrace": [{
1012+
"classLoaderName": "app",
1013+
"declaringClass": "org.apache.spark.sql.functions$",
1014+
"methodName": "typedLit",
1015+
"fileName": "functions.scala"
1016+
}, {
1017+
"classLoaderName": "app",
1018+
"declaringClass": "org.apache.spark.sql.PlanGenerationTestSuite",
1019+
"methodName": "~~trimmed~anonfun~~",
1020+
"fileName": "PlanGenerationTestSuite.scala"
1021+
}]
1022+
}
1023+
}
1024+
}
8241025
}, {
8251026
"literal": {
8261027
"array": {
@@ -891,6 +1092,52 @@
8911092
}
8921093
}
8931094
}
1095+
}, {
1096+
"literal": {
1097+
"map": {
1098+
"keys": [{
1099+
"string": "a"
1100+
}, {
1101+
"string": "b"
1102+
}],
1103+
"values": [{
1104+
"null": {
1105+
"integer": {
1106+
}
1107+
}
1108+
}, {
1109+
"null": {
1110+
"null": {
1111+
}
1112+
}
1113+
}],
1114+
"dataType": {
1115+
"keyType": {
1116+
"string": {
1117+
"collation": "UTF8_BINARY"
1118+
}
1119+
},
1120+
"valueContainsNull": true
1121+
}
1122+
}
1123+
},
1124+
"common": {
1125+
"origin": {
1126+
"jvmOrigin": {
1127+
"stackTrace": [{
1128+
"classLoaderName": "app",
1129+
"declaringClass": "org.apache.spark.sql.functions$",
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"methodName": "typedLit",
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"fileName": "functions.scala"
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}, {
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"classLoaderName": "app",
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"declaringClass": "org.apache.spark.sql.PlanGenerationTestSuite",
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"methodName": "~~trimmed~anonfun~~",
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"fileName": "PlanGenerationTestSuite.scala"
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}]
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}
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}
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}
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}, {
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"literal": {
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"struct": {

‎sql/connect/common/src/test/resources/query-tests/queries/function_typedLit.proto.bin‎

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‎sql/connect/server/src/test/scala/org/apache/spark/sql/connect/planner/LiteralExpressionProtoConverterSuite.scala‎

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@@ -53,7 +53,11 @@ class LiteralExpressionProtoConverterSuite extends AnyFunSuite { // scalastyle:i
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}
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}
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// The goal of this test is to check that converting a Scala value -> Proto -> Catalyst value
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// is equivalent to converting a Scala value directly to a Catalyst value.
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Seq[(Any, DataType)](
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(Array[String](null, "a", null), ArrayType(StringType)),
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(Map[String, String]("a" -> null, "b" -> null), MapType(StringType, StringType)),
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(
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(1, "string", true),
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StructType(

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