[SPARK-53524][CONNECT][SQL] Fix temporal value conversion in LiteralValueProtoConverter

heyihong · cloud-fan · commit 1817e676f1b7 · 2025-09-11T22:08:49.000+08:00
### What changes were proposed in this pull request? This PR fixes temporal value conversion issues in the `LiteralValueProtoConverter` for Spark Connect. The main changes include: 1. **Fixed temporal value conversion in `getConverter` method**: Updated the conversion logic for temporal data types (DATE, TIMESTAMP, TIMESTAMP_NTZ, DAY_TIME_INTERVAL, YEAR_MONTH_INTERVAL, TIME) to use proper utility methods from `SparkDateTimeUtils` and `SparkIntervalUtils` instead of directly returning raw protobuf values. 2. **Added comprehensive test coverage**: Extended the `PlanGenerationTestSuite` with a new test case that includes a tuple containing all temporal types to ensure proper conversion and serialization. 3. **Updated test expectations**: Modified the expected explain output and query test files to reflect the corrected temporal value handling. ### Why are the changes needed? The struct type in typedlit doesn't work well with temporal values due to bugs in type conversions. For example, the code below fails: ```scala import org.apache.spark.sql.functions.typedlit spark.sql("select 1").select(typedlit((1, java.time.LocalDate.of(2020, 10, 10)))).collect() """ org.apache.spark.SparkIllegalArgumentException: The value (18545) of the type (java.lang.Integer) cannot be converted to the DATE type. org.apache.spark.sql.catalyst.CatalystTypeConverters$DateConverter$.toCatalystImpl(CatalystTypeConverters.scala:356) org.apache.spark.sql.catalyst.CatalystTypeConverters$DateConverter$.toCatalystImpl(CatalystTypeConverters.scala:347) org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:110) org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:271) org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:251) org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:110) org.apache.spark.sql.catalyst.CatalystTypeConverters$.$anonfun$createToCatalystConverter$2(CatalystTypeConverters.scala:532) org.apache.spark.sql.connect.planner.LiteralExpressionProtoConverter$.toCatalystExpression(LiteralExpressionProtoConverter.scala:116) """ ``` ### Does this PR introduce _any_ user-facing change? **Yes.** This PR fixes temporal value conversion in LiteralValueProtoConverter, allowing the struct type in typedlit to work with temporal values. ### How was this patch tested? `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 #52270 from heyihong/SPARK-53524. Authored-by: Yihong He <heyihong.cn@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
diff --git a/sql/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala b/sql/connect/client/jvm/src/test/scala/org/apache/spark/sql/PlanGenerationTestSuite.scala
@@ -3404,6 +3404,17 @@ class PlanGenerationTestSuite
       fn.typedLit(java.time.Period.ofDays(100)),
       fn.typedLit(java.time.LocalTime.of(23, 59, 59, 999999999)),
       fn.typedLit(new CalendarInterval(2, 20, 100L)),
+      fn.typedLit(
+        (
+          java.time.LocalDate.of(2020, 10, 10),
+          java.time.Instant.ofEpochMilli(1677155519808L),
+          new java.sql.Timestamp(12345L),
+          java.time.LocalDateTime.of(2023, 2, 23, 20, 36),
+          java.sql.Date.valueOf("2023-02-23"),
+          java.time.Duration.ofSeconds(200L),
+          java.time.Period.ofDays(100),
+          java.time.LocalTime.of(23, 59, 59, 999999999),
+          new CalendarInterval(2, 20, 100L))),
 
       // Handle parameterized scala types e.g.: List, Seq and Map.
       fn.typedLit(Some(1)),
diff --git a/sql/connect/common/src/main/scala/org/apache/spark/sql/connect/common/LiteralValueProtoConverter.scala b/sql/connect/common/src/main/scala/org/apache/spark/sql/connect/common/LiteralValueProtoConverter.scala
@@ -476,7 +476,7 @@ object LiteralValueProtoConverter {
     }
   }
 
-  private def getConverter(dataType: proto.DataType): proto.Expression.Literal => Any = {
+  private def getScalaConverter(dataType: proto.DataType): proto.Expression.Literal => Any = {
     dataType.getKindCase match {
       case proto.DataType.KindCase.SHORT => v => v.getShort.toShort
       case proto.DataType.KindCase.INTEGER => v => v.getInteger
@@ -487,11 +487,18 @@ object LiteralValueProtoConverter {
       case proto.DataType.KindCase.BOOLEAN => v => v.getBoolean
       case proto.DataType.KindCase.STRING => v => v.getString
       case proto.DataType.KindCase.BINARY => v => v.getBinary.toByteArray
-      case proto.DataType.KindCase.DATE => v => v.getDate
-      case proto.DataType.KindCase.TIMESTAMP => v => v.getTimestamp
-      case proto.DataType.KindCase.TIMESTAMP_NTZ => v => v.getTimestampNtz
-      case proto.DataType.KindCase.DAY_TIME_INTERVAL => v => v.getDayTimeInterval
-      case proto.DataType.KindCase.YEAR_MONTH_INTERVAL => v => v.getYearMonthInterval
+      case proto.DataType.KindCase.DATE =>
+        v => SparkDateTimeUtils.toJavaDate(v.getDate)
+      case proto.DataType.KindCase.TIMESTAMP =>
+        v => SparkDateTimeUtils.toJavaTimestamp(v.getTimestamp)
+      case proto.DataType.KindCase.TIMESTAMP_NTZ =>
+        v => SparkDateTimeUtils.microsToLocalDateTime(v.getTimestampNtz)
+      case proto.DataType.KindCase.DAY_TIME_INTERVAL =>
+        v => SparkIntervalUtils.microsToDuration(v.getDayTimeInterval)
+      case proto.DataType.KindCase.YEAR_MONTH_INTERVAL =>
+        v => SparkIntervalUtils.monthsToPeriod(v.getYearMonthInterval)
+      case proto.DataType.KindCase.TIME =>
+        v => SparkDateTimeUtils.nanosToLocalTime(v.getTime.getNano)
       case proto.DataType.KindCase.DECIMAL => v => Decimal(v.getDecimal.getValue)
       case proto.DataType.KindCase.CALENDAR_INTERVAL =>
         v =>
@@ -646,7 +653,7 @@ object LiteralValueProtoConverter {
       }
     }
 
-    makeArrayData(getConverter(arrayType.getElementType))
+    makeArrayData(getScalaConverter(arrayType.getElementType))
   }
 
   def getProtoArrayType(array: proto.Expression.Literal.Array): proto.DataType.Array = {
@@ -691,7 +698,7 @@ object LiteralValueProtoConverter {
       }
     }
 
-    makeMapData(getConverter(mapType.getKeyType), getConverter(mapType.getValueType))
+    makeMapData(getScalaConverter(mapType.getKeyType), getScalaConverter(mapType.getValueType))
   }
 
   def getProtoMapType(map: proto.Expression.Literal.Map): proto.DataType.Map = {
@@ -724,7 +731,7 @@ object LiteralValueProtoConverter {
     val structData = Array.tabulate(struct.getElementsCount) { i =>
       val element = struct.getElements(i)
       val dataType = structType.getFields(i).getDataType
-      getConverter(dataType)(element)
+      getScalaConverter(dataType)(element)
     }
     new GenericRowWithSchema(structData, DataTypeProtoConverter.toCatalystStructType(structType))
   }
diff --git a/sql/connect/common/src/test/resources/query-tests/explain-results/function_typedLit.explain b/sql/connect/common/src/test/resources/query-tests/explain-results/function_typedLit.explain
@@ -1,2 +1,2 @@
-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, 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]
+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]
 +- LocalRelation <empty>, [id#0L, a#0, b#0]
diff --git a/sql/connect/common/src/test/resources/query-tests/queries/function_typedLit.json b/sql/connect/common/src/test/resources/query-tests/queries/function_typedLit.json
@@ -674,6 +674,101 @@
           }
         }
       }
+    }, {
+      "literal": {
+        "struct": {
+          "elements": [{
+            "date": 18545
+          }, {
+            "timestamp": "1677155519808000"
+          }, {
+            "timestamp": "12345000"
+          }, {
+            "timestampNtz": "1677184560000000"
+          }, {
+            "date": 19411
+          }, {
+            "dayTimeInterval": "200000000"
+          }, {
+            "yearMonthInterval": 0
+          }, {
+            "time": {
+              "nano": "86399999999999",
+              "precision": 6
+            }
+          }, {
+            "calendarInterval": {
+              "months": 2,
+              "days": 20,
+              "microseconds": "100"
+            }
+          }],
+          "dataTypeStruct": {
+            "fields": [{
+              "name": "_1",
+              "nullable": true
+            }, {
+              "name": "_2",
+              "nullable": true
+            }, {
+              "name": "_3",
+              "nullable": true
+            }, {
+              "name": "_4",
+              "nullable": true
+            }, {
+              "name": "_5",
+              "nullable": true
+            }, {
+              "name": "_6",
+              "dataType": {
+                "dayTimeInterval": {
+                  "startField": 0,
+                  "endField": 3
+                }
+              },
+              "nullable": true
+            }, {
+              "name": "_7",
+              "dataType": {
+                "yearMonthInterval": {
+                  "startField": 0,
+                  "endField": 1
+                }
+              },
+              "nullable": true
+            }, {
+              "name": "_8",
+              "dataType": {
+                "time": {
+                  "precision": 6
+                }
+              },
+              "nullable": true
+            }, {
+              "name": "_9",
+              "nullable": true
+            }]
+          }
+        }
+      },
+      "common": {
+        "origin": {
+          "jvmOrigin": {
+            "stackTrace": [{
+              "classLoaderName": "app",
+              "declaringClass": "org.apache.spark.sql.functions$",
+              "methodName": "typedLit",
+              "fileName": "functions.scala"
+            }, {
+              "classLoaderName": "app",
+              "declaringClass": "org.apache.spark.sql.PlanGenerationTestSuite",
+              "methodName": "~~trimmed~anonfun~~",
+              "fileName": "PlanGenerationTestSuite.scala"
+            }]
+          }
+        }
+      }
     }, {
       "literal": {
         "integer": 1
diff --git a/sql/connect/common/src/test/resources/query-tests/queries/function_typedLit.proto.bin b/sql/connect/common/src/test/resources/query-tests/queries/function_typedLit.proto.bin

Original file line number	Diff line number	Diff line change
`@@ -476,7 +476,7 @@ object LiteralValueProtoConverter {`
`476`	`476`	`}`
`477`	`477`	`}`
`478`	`478`
`479`		`- private def getConverter(dataType: proto.DataType): proto.Expression.Literal => Any = {`
	`479`	`+ private def getScalaConverter(dataType: proto.DataType): proto.Expression.Literal => Any = {`
`480`	`480`	`dataType.getKindCase match {`
`481`	`481`	`case proto.DataType.KindCase.SHORT => v => v.getShort.toShort`
`482`	`482`	`case proto.DataType.KindCase.INTEGER => v => v.getInteger`
`@@ -487,11 +487,18 @@ object LiteralValueProtoConverter {`
`487`	`487`	`case proto.DataType.KindCase.BOOLEAN => v => v.getBoolean`
`488`	`488`	`case proto.DataType.KindCase.STRING => v => v.getString`
`489`	`489`	`case proto.DataType.KindCase.BINARY => v => v.getBinary.toByteArray`
`490`		`- case proto.DataType.KindCase.DATE => v => v.getDate`
`491`		`- case proto.DataType.KindCase.TIMESTAMP => v => v.getTimestamp`
`492`		`- case proto.DataType.KindCase.TIMESTAMP_NTZ => v => v.getTimestampNtz`
`493`		`- case proto.DataType.KindCase.DAY_TIME_INTERVAL => v => v.getDayTimeInterval`
`494`		`- case proto.DataType.KindCase.YEAR_MONTH_INTERVAL => v => v.getYearMonthInterval`
	`490`	`+ case proto.DataType.KindCase.DATE =>`
	`491`	`+ v => SparkDateTimeUtils.toJavaDate(v.getDate)`
	`492`	`+ case proto.DataType.KindCase.TIMESTAMP =>`
	`493`	`+ v => SparkDateTimeUtils.toJavaTimestamp(v.getTimestamp)`
	`494`	`+ case proto.DataType.KindCase.TIMESTAMP_NTZ =>`
	`495`	`+ v => SparkDateTimeUtils.microsToLocalDateTime(v.getTimestampNtz)`
	`496`	`+ case proto.DataType.KindCase.DAY_TIME_INTERVAL =>`
	`497`	`+ v => SparkIntervalUtils.microsToDuration(v.getDayTimeInterval)`
	`498`	`+ case proto.DataType.KindCase.YEAR_MONTH_INTERVAL =>`
	`499`	`+ v => SparkIntervalUtils.monthsToPeriod(v.getYearMonthInterval)`
	`500`	`+ case proto.DataType.KindCase.TIME =>`
	`501`	`+ v => SparkDateTimeUtils.nanosToLocalTime(v.getTime.getNano)`
`495`	`502`	`case proto.DataType.KindCase.DECIMAL => v => Decimal(v.getDecimal.getValue)`
`496`	`503`	`case proto.DataType.KindCase.CALENDAR_INTERVAL =>`
`497`	`504`	`v =>`
`@@ -646,7 +653,7 @@ object LiteralValueProtoConverter {`
`646`	`653`	`}`
`647`	`654`	`}`
`648`	`655`
`649`		`- makeArrayData(getConverter(arrayType.getElementType))`
	`656`	`+ makeArrayData(getScalaConverter(arrayType.getElementType))`
`650`	`657`	`}`
`651`	`658`
`652`	`659`	`def getProtoArrayType(array: proto.Expression.Literal.Array): proto.DataType.Array = {`
`@@ -691,7 +698,7 @@ object LiteralValueProtoConverter {`
`691`	`698`	`}`
`692`	`699`	`}`
`693`	`700`
`694`		`- makeMapData(getConverter(mapType.getKeyType), getConverter(mapType.getValueType))`
	`701`	`+ makeMapData(getScalaConverter(mapType.getKeyType), getScalaConverter(mapType.getValueType))`
`695`	`702`	`}`
`696`	`703`
`697`	`704`	`def getProtoMapType(map: proto.Expression.Literal.Map): proto.DataType.Map = {`
`@@ -724,7 +731,7 @@ object LiteralValueProtoConverter {`
`724`	`731`	`val structData = Array.tabulate(struct.getElementsCount) { i =>`
`725`	`732`	`val element = struct.getElements(i)`
`726`	`733`	`val dataType = structType.getFields(i).getDataType`
`727`		`- getConverter(dataType)(element)`
	`734`	`+ getScalaConverter(dataType)(element)`
`728`	`735`	`}`
`729`	`736`	`new GenericRowWithSchema(structData, DataTypeProtoConverter.toCatalystStructType(structType))`
`730`	`737`	`}`
Original file line number	Diff line number	Diff line change
`@@ -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, 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, [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]
`2`	`2`	`+- LocalRelation <empty>, [id#0L, a#0, b#0]`