[
https://issues.apache.org/jira/browse/SPARK-47918?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Lionia Vasilev updated SPARK-47918:
-----------------------------------
Description:
Hello, hope you are well. We have flaky tests that fail randomly with the error
in the title. Please see the stack trace and the reproducible example below.
The executions plans of the action that threw the exception, that did not throw
the exception and with AQE disabled looks the same. I was told on GitHub that
SPARK-33823 was fixed in 3.2.0, but SPARK-39551 that was fixed in 3.2.2 looks
relevant as well. We use mostly AWS EMR and Databricks, and we have a
dependency that is build for 3.2.1.
{code:java}
java.lang.UnsupportedOperationException: BroadcastExchange does not support the
execute() code path.
at
org.apache.spark.sql.errors.QueryExecutionErrors$.executeCodePathUnsupportedError(QueryExecutionErrors.scala:1655)
at
org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecute(BroadcastExchangeExec.scala:203)
at
org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:184)
at
org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:222)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:219)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:180)
at
org.apache.spark.sql.execution.adaptive.QueryStageExec.doExecute(QueryStageExec.scala:119)
at
org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:184)
at
org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:222)
{code}
{code:scala}
val df1 = Seq(
(1, 100, 1, 1.0, 1.0, "00000000-0000-0000-0000-000000000001", "a", "x", 0),
(2, 100, 1, 1.0, 1.0, "00000000-0000-0000-0000-000000000002", "b", "x", 0)
).toDF()
val df2 = Seq(
(1, 3, 3, 1, 1, 1, 0, 1, "2024-01-01", 1.0, 1.0),
(2, 4, 3, 1, 1, 1, 0, 1, "2024-01-01", 1.0, 1.0)
).toDF()
val df3 = Seq(
(3, 100, Array[Byte](0, 0, 0, 0), "foo0", "", "foobarfooba", "Foobarfoob",
"FO", "foobafooba"),
(4, 100, Array[Byte](0, 0, 0, 1), "foo0", "", "foobarfooba", "Foobarfoob",
"FO", "foobafooba")
).toDF()
val df4 = df1.filter($"_7" =!= "a").cache()
val df5 = df2.join(df4.select("_1"), Seq("_1"), "left_semi").cache()
val df6 = df3.join(df5.select("_2"), df3("_1") === df5("_2"), "left_semi")
assert(df4.count() == 1)
assert(df5.count() == 1)
assert(df5.first().getAs[Int]("_1") == 2)
assert(df6.count() == 1) // This line throws the error.
assert(df6.first().getAs[Int]("_1") == 4){code}
{code:java}
== Parsed Logical Plan ==
Join LeftSemi, (_1#23495 = _2#16732)
:- LocalRelation [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499, _6#23500,
_7#23501, _8#23502, _9#23503]
+- Project [_2#16732]
+- Project [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735, _6#16736,
_7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
+- Join LeftSemi, (_1#16731 = _1#48)
:- LocalRelation [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735,
_6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
+- Project [_1#48]
+- Filter NOT (_7#80 = a)
+- LocalRelation [_1#48, _2#73, _3#74, _4#75, _5#77, _6#79,
_7#80, _8#81, _9#82]
== Analyzed Logical Plan ==
_1: int, _2: int, _3: binary, _4: string, _5: string, _6: string, _7: string,
_8: string, _9: string
Join LeftSemi, (_1#23495 = _2#16732)
:- LocalRelation [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499, _6#23500,
_7#23501, _8#23502, _9#23503]
+- Project [_2#16732]
+- Project [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735, _6#16736,
_7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
+- Join LeftSemi, (_1#16731 = _1#48)
:- LocalRelation [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735,
_6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
+- Project [_1#48]
+- Filter NOT (_7#80 = a)
+- LocalRelation [_1#48, _2#73, _3#74, _4#75, _5#77, _6#79,
_7#80, _8#81, _9#82]
== Optimized Logical Plan ==
Join LeftSemi, (_1#23495 = _2#16732)
:- LocalRelation [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499, _6#23500,
_7#23501, _8#23502, _9#23503]
+- Project [_2#16732]
+- InMemoryRelation [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735,
_6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741],
StorageLevel(disk, memory, deserialized, 1 replicas)
+- *(1) BroadcastHashJoin [_1#16731], [_1#48], LeftSemi, BuildRight,
false
:- *(1) LocalTableScan [_1#16731, _2#16732, _3#16733, _4#16734,
_5#16735, _6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0,
int, false] as bigint)),false), [id=#463]
+- InMemoryTableScan [_1#48]
+- InMemoryRelation [_1#48, _2#73, _3#74, _4#75, _5#77,
_6#79, _7#80, _8#81, _9#82], StorageLevel(disk, memory, deserialized, 1
replicas)
+- LocalTableScan [_1#48, _2#73, _3#74, _4#75,
_5#77, _6#79, _7#80, _8#81, _9#82]
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- BroadcastHashJoin [_1#23495], [_2#16732], LeftSemi, BuildRight, false
:- LocalTableScan [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499,
_6#23500, _7#23501, _8#23502, _9#23503]
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int,
false] as bigint)),false), [id=#3111]
+- InMemoryTableScan [_2#16732]
+- InMemoryRelation [_1#16731, _2#16732, _3#16733, _4#16734,
_5#16735, _6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741],
StorageLevel(disk, memory, deserialized, 1 re
plicas)
+- *(1) BroadcastHashJoin [_1#16731], [_1#48], LeftSemi,
BuildRight, false
:- *(1) LocalTableScan [_1#16731, _2#16732, _3#16733,
_4#16734, _5#16735, _6#16736, _7#16737, _8#16738, _9#16739, _10#16740,
_11#16741]
+- BroadcastExchange
HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false),
[id=#463]
+- InMemoryTableScan [_1#48]
+- InMemoryRelation [_1#48, _2#73, _3#74, _4#75,
_5#77, _6#79, _7#80, _8#81, _9#82], StorageLevel(disk, memory, deserialized, 1
replicas)
+- LocalTableScan [_1#48, _2#73, _3#74,
_4#75, _5#77, _6#79, _7#80, _8#81, _9#82]
{code}
was:
Hello, hope you are well. We have tests that fail randomly with the error in
the title. Please see the stack trace and the reproducible example below. The
executions plans of the action that threw the exception, that did not throw the
exception and with AQE disabled looks the same. I was told on GitHub that
SPARK-33823 was fixed in 3.2.0, but SPARK-39551 that was fixed in 3.2.2 looks
relevant as well. We use mostly AWS EMR and Databricks, and we have a
dependency that is build for 3.2.1.
{code:java}
java.lang.UnsupportedOperationException: BroadcastExchange does not support the
execute() code path.
at
org.apache.spark.sql.errors.QueryExecutionErrors$.executeCodePathUnsupportedError(QueryExecutionErrors.scala:1655)
at
org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecute(BroadcastExchangeExec.scala:203)
at
org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:184)
at
org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:222)
at
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:219)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:180)
at
org.apache.spark.sql.execution.adaptive.QueryStageExec.doExecute(QueryStageExec.scala:119)
at
org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:184)
at
org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:222)
{code}
{code:scala}
val df1 = Seq(
(1, 100, 1, 1.0, 1.0, "00000000-0000-0000-0000-000000000001", "a", "x", 0),
(2, 100, 1, 1.0, 1.0, "00000000-0000-0000-0000-000000000002", "b", "x", 0)
).toDF()
val df2 = Seq(
(1, 3, 3, 1, 1, 1, 0, 1, "2024-01-01", 1.0, 1.0),
(2, 4, 3, 1, 1, 1, 0, 1, "2024-01-01", 1.0, 1.0)
).toDF()
val df3 = Seq(
(3, 100, Array[Byte](0, 0, 0, 0), "foo0", "", "foobarfooba", "Foobarfoob",
"FO", "foobafooba"),
(4, 100, Array[Byte](0, 0, 0, 1), "foo0", "", "foobarfooba", "Foobarfoob",
"FO", "foobafooba")
).toDF()
val df4 = df1.filter($"_7" =!= "a").cache()
val df5 = df2.join(df4.select("_1"), Seq("_1"), "left_semi").cache()
val df6 = df3.join(df5.select("_2"), df3("_1") === df5("_2"), "left_semi")
assert(df4.count() == 1)
assert(df5.count() == 1)
assert(df5.first().getAs[Int]("_1") == 2)
assert(df6.count() == 1) // This line throws the error.
assert(df6.first().getAs[Int]("_1") == 4){code}
{code}
== Parsed Logical Plan ==
Join LeftSemi, (_1#23495 = _2#16732)
:- LocalRelation [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499, _6#23500,
_7#23501, _8#23502, _9#23503]
+- Project [_2#16732]
+- Project [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735, _6#16736,
_7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
+- Join LeftSemi, (_1#16731 = _1#48)
:- LocalRelation [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735,
_6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
+- Project [_1#48]
+- Filter NOT (_7#80 = a)
+- LocalRelation [_1#48, _2#73, _3#74, _4#75, _5#77, _6#79,
_7#80, _8#81, _9#82]
== Analyzed Logical Plan ==
_1: int, _2: int, _3: binary, _4: string, _5: string, _6: string, _7: string,
_8: string, _9: string
Join LeftSemi, (_1#23495 = _2#16732)
:- LocalRelation [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499, _6#23500,
_7#23501, _8#23502, _9#23503]
+- Project [_2#16732]
+- Project [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735, _6#16736,
_7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
+- Join LeftSemi, (_1#16731 = _1#48)
:- LocalRelation [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735,
_6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
+- Project [_1#48]
+- Filter NOT (_7#80 = a)
+- LocalRelation [_1#48, _2#73, _3#74, _4#75, _5#77, _6#79,
_7#80, _8#81, _9#82]
== Optimized Logical Plan ==
Join LeftSemi, (_1#23495 = _2#16732)
:- LocalRelation [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499, _6#23500,
_7#23501, _8#23502, _9#23503]
+- Project [_2#16732]
+- InMemoryRelation [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735,
_6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741],
StorageLevel(disk, memory, deserialized, 1 replicas)
+- *(1) BroadcastHashJoin [_1#16731], [_1#48], LeftSemi, BuildRight,
false
:- *(1) LocalTableScan [_1#16731, _2#16732, _3#16733, _4#16734,
_5#16735, _6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0,
int, false] as bigint)),false), [id=#463]
+- InMemoryTableScan [_1#48]
+- InMemoryRelation [_1#48, _2#73, _3#74, _4#75, _5#77,
_6#79, _7#80, _8#81, _9#82], StorageLevel(disk, memory, deserialized, 1
replicas)
+- LocalTableScan [_1#48, _2#73, _3#74, _4#75,
_5#77, _6#79, _7#80, _8#81, _9#82]
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- BroadcastHashJoin [_1#23495], [_2#16732], LeftSemi, BuildRight, false
:- LocalTableScan [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499,
_6#23500, _7#23501, _8#23502, _9#23503]
+- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int,
false] as bigint)),false), [id=#3111]
+- InMemoryTableScan [_2#16732]
+- InMemoryRelation [_1#16731, _2#16732, _3#16733, _4#16734,
_5#16735, _6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741],
StorageLevel(disk, memory, deserialized, 1 re
plicas)
+- *(1) BroadcastHashJoin [_1#16731], [_1#48], LeftSemi,
BuildRight, false
:- *(1) LocalTableScan [_1#16731, _2#16732, _3#16733,
_4#16734, _5#16735, _6#16736, _7#16737, _8#16738, _9#16739, _10#16740,
_11#16741]
+- BroadcastExchange
HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false),
[id=#463]
+- InMemoryTableScan [_1#48]
+- InMemoryRelation [_1#48, _2#73, _3#74, _4#75,
_5#77, _6#79, _7#80, _8#81, _9#82], StorageLevel(disk, memory, deserialized, 1
replicas)
+- LocalTableScan [_1#48, _2#73, _3#74,
_4#75, _5#77, _6#79, _7#80, _8#81, _9#82]
{code}
> BroadcastExchange does not support the execute() code path
> ----------------------------------------------------------
>
> Key: SPARK-47918
> URL: https://issues.apache.org/jira/browse/SPARK-47918
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.2.1
> Environment: MacOS, Linux.
> Reporter: Lionia Vasilev
> Priority: Major
>
> Hello, hope you are well. We have flaky tests that fail randomly with the
> error in the title. Please see the stack trace and the reproducible example
> below. The executions plans of the action that threw the exception, that did
> not throw the exception and with AQE disabled looks the same. I was told on
> GitHub that SPARK-33823 was fixed in 3.2.0, but SPARK-39551 that was fixed in
> 3.2.2 looks relevant as well. We use mostly AWS EMR and Databricks, and we
> have a dependency that is build for 3.2.1.
> {code:java}
> java.lang.UnsupportedOperationException: BroadcastExchange does not support
> the execute() code path.
> at
> org.apache.spark.sql.errors.QueryExecutionErrors$.executeCodePathUnsupportedError(QueryExecutionErrors.scala:1655)
> at
> org.apache.spark.sql.execution.exchange.BroadcastExchangeExec.doExecute(BroadcastExchangeExec.scala:203)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:184)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:222)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:219)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:180)
> at
> org.apache.spark.sql.execution.adaptive.QueryStageExec.doExecute(QueryStageExec.scala:119)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:184)
> at
> org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:222)
> {code}
> {code:scala}
> val df1 = Seq(
> (1, 100, 1, 1.0, 1.0, "00000000-0000-0000-0000-000000000001", "a", "x", 0),
> (2, 100, 1, 1.0, 1.0, "00000000-0000-0000-0000-000000000002", "b", "x", 0)
> ).toDF()
> val df2 = Seq(
> (1, 3, 3, 1, 1, 1, 0, 1, "2024-01-01", 1.0, 1.0),
> (2, 4, 3, 1, 1, 1, 0, 1, "2024-01-01", 1.0, 1.0)
> ).toDF()
> val df3 = Seq(
> (3, 100, Array[Byte](0, 0, 0, 0), "foo0", "", "foobarfooba", "Foobarfoob",
> "FO", "foobafooba"),
> (4, 100, Array[Byte](0, 0, 0, 1), "foo0", "", "foobarfooba", "Foobarfoob",
> "FO", "foobafooba")
> ).toDF()
> val df4 = df1.filter($"_7" =!= "a").cache()
> val df5 = df2.join(df4.select("_1"), Seq("_1"), "left_semi").cache()
> val df6 = df3.join(df5.select("_2"), df3("_1") === df5("_2"), "left_semi")
> assert(df4.count() == 1)
> assert(df5.count() == 1)
> assert(df5.first().getAs[Int]("_1") == 2)
> assert(df6.count() == 1) // This line throws the error.
> assert(df6.first().getAs[Int]("_1") == 4){code}
> {code:java}
> == Parsed Logical Plan ==
>
>
> Join LeftSemi, (_1#23495 = _2#16732)
>
>
> :- LocalRelation [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499, _6#23500,
> _7#23501, _8#23502, _9#23503]
>
> +- Project [_2#16732]
>
>
> +- Project [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735, _6#16736,
> _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
>
> +- Join LeftSemi, (_1#16731 = _1#48)
>
>
> :- LocalRelation [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735,
> _6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
>
> +- Project [_1#48]
> +- Filter NOT (_7#80 = a)
> +- LocalRelation [_1#48, _2#73, _3#74, _4#75, _5#77, _6#79,
> _7#80, _8#81, _9#82]
> == Analyzed Logical Plan ==
> _1: int, _2: int, _3: binary, _4: string, _5: string, _6: string, _7: string,
> _8: string, _9: string
> Join LeftSemi, (_1#23495 = _2#16732)
> :- LocalRelation [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499, _6#23500,
> _7#23501, _8#23502, _9#23503]
> +- Project [_2#16732]
> +- Project [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735, _6#16736,
> _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
> +- Join LeftSemi, (_1#16731 = _1#48)
> :- LocalRelation [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735,
> _6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
> +- Project [_1#48]
> +- Filter NOT (_7#80 = a)
> +- LocalRelation [_1#48, _2#73, _3#74, _4#75, _5#77, _6#79,
> _7#80, _8#81, _9#82]
> == Optimized Logical Plan ==
> Join LeftSemi, (_1#23495 = _2#16732)
> :- LocalRelation [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499, _6#23500,
> _7#23501, _8#23502, _9#23503]
> +- Project [_2#16732]
> +- InMemoryRelation [_1#16731, _2#16732, _3#16733, _4#16734, _5#16735,
> _6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741],
> StorageLevel(disk, memory, deserialized, 1 replicas)
> +- *(1) BroadcastHashJoin [_1#16731], [_1#48], LeftSemi, BuildRight,
> false
> :- *(1) LocalTableScan [_1#16731, _2#16732, _3#16733, _4#16734,
> _5#16735, _6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741]
> +- BroadcastExchange
> HashedRelationBroadcastMode(List(cast(input[0, int, false] as
> bigint)),false), [id=#463]
> +- InMemoryTableScan [_1#48]
> +- InMemoryRelation [_1#48, _2#73, _3#74, _4#75, _5#77,
> _6#79, _7#80, _8#81, _9#82], StorageLevel(disk, memory, deserialized, 1
> replicas)
> +- LocalTableScan [_1#48, _2#73, _3#74, _4#75,
> _5#77, _6#79, _7#80, _8#81, _9#82]
> == Physical Plan ==
> AdaptiveSparkPlan isFinalPlan=false
> +- BroadcastHashJoin [_1#23495], [_2#16732], LeftSemi, BuildRight, false
> :- LocalTableScan [_1#23495, _2#23496, _3#23497, _4#23498, _5#23499,
> _6#23500, _7#23501, _8#23502, _9#23503]
> +- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int,
> false] as bigint)),false), [id=#3111]
> +- InMemoryTableScan [_2#16732]
> +- InMemoryRelation [_1#16731, _2#16732, _3#16733, _4#16734,
> _5#16735, _6#16736, _7#16737, _8#16738, _9#16739, _10#16740, _11#16741],
> StorageLevel(disk, memory, deserialized, 1 re
> plicas)
> +- *(1) BroadcastHashJoin [_1#16731], [_1#48], LeftSemi,
> BuildRight, false
> :- *(1) LocalTableScan [_1#16731, _2#16732, _3#16733,
> _4#16734, _5#16735, _6#16736, _7#16737, _8#16738, _9#16739, _10#16740,
> _11#16741]
> +- BroadcastExchange
> HashedRelationBroadcastMode(List(cast(input[0, int, false] as
> bigint)),false), [id=#463]
> +- InMemoryTableScan [_1#48]
> +- InMemoryRelation [_1#48, _2#73, _3#74,
> _4#75, _5#77, _6#79, _7#80, _8#81, _9#82], StorageLevel(disk, memory,
> deserialized, 1 replicas)
> +- LocalTableScan [_1#48, _2#73, _3#74,
> _4#75, _5#77, _6#79, _7#80, _8#81, _9#82]
> {code}
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
