[jira] [Assigned] (SPARK-21441) Incorrect Codegen in SortMergeJoinExec results failures in some cases
[
https://issues.apache.org/jira/browse/SPARK-21441?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Wenchen Fan reassigned SPARK-21441:
---
Assignee: Feng Zhu
> Incorrect Codegen in SortMergeJoinExec results failures in some cases
> -
>
> Key: SPARK-21441
> URL: https://issues.apache.org/jira/browse/SPARK-21441
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.1.0, 2.1.1, 2.2.0
>Reporter: Feng Zhu
>Assignee: Feng Zhu
> Fix For: 2.1.2, 2.2.1, 2.3.0
>
>
> We noticed that the codegen mechanism in SortMergeJoinExec caused job fails
> in some cases. The below simple example demonstrates this issue.
> The query joins two relations with conditions containing a HiveUDF (i.e.,
> base64) in OR predicates.
> {code:sql}
> SELECT ca_zip
> FROM customer, customer_address
> WHERE customer.c_current_addr_sk = customer_address.ca_address_sk
> AND (base64(ca_zip) = '85669' OR customer.c_birth_month > 2)
> {code}
> Physical plan before execution
> *Project [ca_zip#27]
> +- *SortMergeJoin [c_current_addr_sk#4], [ca_address_sk#18], Inner,
> ((HiveSimpleUDF#Base64(ca_zip#27) = 85669) || (c_birth_month#12 > 2))
>:- *Sort [c_current_addr_sk#4 ASC NULLS FIRST], false, 0
>: +- Exchange hashpartitioning(c_current_addr_sk#4, 200)
>: +- *Filter isnotnull(c_current_addr_sk#4)
>:+- HiveTableScan [c_current_addr_sk#4, c_birth_month#12],
> MetastoreRelation test, customer
>+- *Sort [ca_address_sk#18 ASC NULLS FIRST], false, 0
> +- Exchange hashpartitioning(ca_address_sk#18, 200)
> +- *Filter isnotnull(ca_address_sk#18)
> +- HiveTableScan [ca_address_sk#18, ca_zip#27], MetastoreRelation
> test, customer_address
> By default, the query will fail and throws the following exception:
> {code:java}
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
> stage 2.0 failed 4 times
> ...
> Caused by: java.lang.NegativeArraySizeException
> at
> org.apache.spark.unsafe.types.UTF8String.getBytes(UTF8String.java:229)
> at
> org.apache.spark.unsafe.types.UTF8String.toString(UTF8String.java:821)
> at java.lang.String.valueOf(String.java:2994)
> at
> scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
> at
> scala.collection.TraversableOnce$$anonfun$addString$1.apply(TraversableOnce.scala:359)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> scala.collection.TraversableOnce$class.addString(TraversableOnce.scala:357)
> at scala.collection.AbstractTraversable.addString(Traversable.scala:104)
> at
> scala.collection.TraversableOnce$class.mkString(TraversableOnce.scala:323)
> at scala.collection.AbstractTraversable.mkString(Traversable.scala:104)
> at
> scala.collection.TraversableLike$class.toString(TraversableLike.scala:600)
> at scala.collection.SeqLike$class.toString(SeqLike.scala:682)
> at scala.collection.AbstractSeq.toString(Seq.scala:41)
> at java.lang.String.valueOf(String.java:2994)
> at
> scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
> at
> org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
> at
> org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
> at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
> at org.apache.spark.sql.hive.HiveSimpleUDF.logInfo(hiveUDFs.scala:130)
> at org.apache.spark.sql.hive.HiveSimpleUDF.eval(hiveUDFs.scala:179)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$2.hasNext(WholeStageCodegenExec.scala:396)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> ...
> {code}
> However, when we close the codegen (i.e., spark.sql.codegen.wholeStage=false,
> spark.sql.codegen=false), it works well.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-21441) Incorrect Codegen in SortMergeJoinExec results failures in some cases
[
https://issues.apache.org/jira/browse/SPARK-21441?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-21441:
Assignee: Apache Spark
> Incorrect Codegen in SortMergeJoinExec results failures in some cases
> -
>
> Key: SPARK-21441
> URL: https://issues.apache.org/jira/browse/SPARK-21441
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.1.0, 2.1.1, 2.2.0
>Reporter: Feng Zhu
>Assignee: Apache Spark
>Priority: Critical
>
> We noticed that the codegen mechanism in SortMergeJoinExec caused job fails
> in some cases. The below simple example demonstrates this issue.
> The query joins two relations with conditions containing a HiveUDF (i.e.,
> base64) in OR predicates.
> {code:sql}
> SELECT ca_zip
> FROM customer, customer_address
> WHERE customer.c_current_addr_sk = customer_address.ca_address_sk
> AND (base64(ca_zip) = '85669' OR customer.c_birth_month > 2)
> {code}
> Physical plan before execution
> *Project [ca_zip#27]
> +- *SortMergeJoin [c_current_addr_sk#4], [ca_address_sk#18], Inner,
> ((HiveSimpleUDF#Base64(ca_zip#27) = 85669) || (c_birth_month#12 > 2))
>:- *Sort [c_current_addr_sk#4 ASC NULLS FIRST], false, 0
>: +- Exchange hashpartitioning(c_current_addr_sk#4, 200)
>: +- *Filter isnotnull(c_current_addr_sk#4)
>:+- HiveTableScan [c_current_addr_sk#4, c_birth_month#12],
> MetastoreRelation test, customer
>+- *Sort [ca_address_sk#18 ASC NULLS FIRST], false, 0
> +- Exchange hashpartitioning(ca_address_sk#18, 200)
> +- *Filter isnotnull(ca_address_sk#18)
> +- HiveTableScan [ca_address_sk#18, ca_zip#27], MetastoreRelation
> test, customer_address
> By default, the query will fail and throws the following exception:
> {code:java}
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
> stage 2.0 failed 4 times
> ...
> Caused by: java.lang.NegativeArraySizeException
> at
> org.apache.spark.unsafe.types.UTF8String.getBytes(UTF8String.java:229)
> at
> org.apache.spark.unsafe.types.UTF8String.toString(UTF8String.java:821)
> at java.lang.String.valueOf(String.java:2994)
> at
> scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
> at
> scala.collection.TraversableOnce$$anonfun$addString$1.apply(TraversableOnce.scala:359)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> scala.collection.TraversableOnce$class.addString(TraversableOnce.scala:357)
> at scala.collection.AbstractTraversable.addString(Traversable.scala:104)
> at
> scala.collection.TraversableOnce$class.mkString(TraversableOnce.scala:323)
> at scala.collection.AbstractTraversable.mkString(Traversable.scala:104)
> at
> scala.collection.TraversableLike$class.toString(TraversableLike.scala:600)
> at scala.collection.SeqLike$class.toString(SeqLike.scala:682)
> at scala.collection.AbstractSeq.toString(Seq.scala:41)
> at java.lang.String.valueOf(String.java:2994)
> at
> scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
> at
> org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
> at
> org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
> at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
> at org.apache.spark.sql.hive.HiveSimpleUDF.logInfo(hiveUDFs.scala:130)
> at org.apache.spark.sql.hive.HiveSimpleUDF.eval(hiveUDFs.scala:179)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$2.hasNext(WholeStageCodegenExec.scala:396)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> ...
> {code}
> However, when we close the codegen (i.e., spark.sql.codegen.wholeStage=false,
> spark.sql.codegen=false), it works well.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-21441) Incorrect Codegen in SortMergeJoinExec results failures in some cases
[
https://issues.apache.org/jira/browse/SPARK-21441?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-21441:
Assignee: (was: Apache Spark)
> Incorrect Codegen in SortMergeJoinExec results failures in some cases
> -
>
> Key: SPARK-21441
> URL: https://issues.apache.org/jira/browse/SPARK-21441
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.1.0, 2.1.1, 2.2.0
>Reporter: Feng Zhu
>Priority: Critical
>
> We noticed that the codegen mechanism in SortMergeJoinExec caused job fails
> in some cases. The below simple example demonstrates this issue.
> The query joins two relations with conditions containing a HiveUDF (i.e.,
> base64) in OR predicates.
> {code:sql}
> SELECT ca_zip
> FROM customer, customer_address
> WHERE customer.c_current_addr_sk = customer_address.ca_address_sk
> AND (base64(ca_zip) = '85669' OR customer.c_birth_month > 2)
> {code}
> Physical plan before execution
> *Project [ca_zip#27]
> +- *SortMergeJoin [c_current_addr_sk#4], [ca_address_sk#18], Inner,
> ((HiveSimpleUDF#Base64(ca_zip#27) = 85669) || (c_birth_month#12 > 2))
>:- *Sort [c_current_addr_sk#4 ASC NULLS FIRST], false, 0
>: +- Exchange hashpartitioning(c_current_addr_sk#4, 200)
>: +- *Filter isnotnull(c_current_addr_sk#4)
>:+- HiveTableScan [c_current_addr_sk#4, c_birth_month#12],
> MetastoreRelation test, customer
>+- *Sort [ca_address_sk#18 ASC NULLS FIRST], false, 0
> +- Exchange hashpartitioning(ca_address_sk#18, 200)
> +- *Filter isnotnull(ca_address_sk#18)
> +- HiveTableScan [ca_address_sk#18, ca_zip#27], MetastoreRelation
> test, customer_address
> By default, the query will fail and throws the following exception:
> {code:java}
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
> stage 2.0 failed 4 times
> ...
> Caused by: java.lang.NegativeArraySizeException
> at
> org.apache.spark.unsafe.types.UTF8String.getBytes(UTF8String.java:229)
> at
> org.apache.spark.unsafe.types.UTF8String.toString(UTF8String.java:821)
> at java.lang.String.valueOf(String.java:2994)
> at
> scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
> at
> scala.collection.TraversableOnce$$anonfun$addString$1.apply(TraversableOnce.scala:359)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> scala.collection.TraversableOnce$class.addString(TraversableOnce.scala:357)
> at scala.collection.AbstractTraversable.addString(Traversable.scala:104)
> at
> scala.collection.TraversableOnce$class.mkString(TraversableOnce.scala:323)
> at scala.collection.AbstractTraversable.mkString(Traversable.scala:104)
> at
> scala.collection.TraversableLike$class.toString(TraversableLike.scala:600)
> at scala.collection.SeqLike$class.toString(SeqLike.scala:682)
> at scala.collection.AbstractSeq.toString(Seq.scala:41)
> at java.lang.String.valueOf(String.java:2994)
> at
> scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
> at
> org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
> at
> org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
> at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
> at org.apache.spark.sql.hive.HiveSimpleUDF.logInfo(hiveUDFs.scala:130)
> at org.apache.spark.sql.hive.HiveSimpleUDF.eval(hiveUDFs.scala:179)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$2.hasNext(WholeStageCodegenExec.scala:396)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> ...
> {code}
> However, when we close the codegen (i.e., spark.sql.codegen.wholeStage=false,
> spark.sql.codegen=false), it works well.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-21441) Incorrect Codegen in SortMergeJoinExec results failures in some cases
[
https://issues.apache.org/jira/browse/SPARK-21441?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-21441:
Assignee: Apache Spark
> Incorrect Codegen in SortMergeJoinExec results failures in some cases
> -
>
> Key: SPARK-21441
> URL: https://issues.apache.org/jira/browse/SPARK-21441
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.1.0, 2.1.1, 2.2.0
>Reporter: Feng Zhu
>Assignee: Apache Spark
>Priority: Critical
>
> We noticed that the codegen mechanism in SortMergeJoinExec caused job fails
> in some cases. The below simple example demonstrates this issue.
> The query joins two relations with conditions containing a HiveUDF (i.e.,
> base64) in OR predicates.
> {code:sql}
> SELECT ca_zip
> FROM customer, customer_address
> WHERE customer.c_current_addr_sk = customer_address.ca_address_sk
> AND (base64(ca_zip) = '85669' OR customer.c_birth_month > 2)
> {code}
> Physical plan before execution
> *Project [ca_zip#27]
> +- *SortMergeJoin [c_current_addr_sk#4], [ca_address_sk#18], Inner,
> ((HiveSimpleUDF#Base64(ca_zip#27) = 85669) || (c_birth_month#12 > 2))
>:- *Sort [c_current_addr_sk#4 ASC NULLS FIRST], false, 0
>: +- Exchange hashpartitioning(c_current_addr_sk#4, 200)
>: +- *Filter isnotnull(c_current_addr_sk#4)
>:+- HiveTableScan [c_current_addr_sk#4, c_birth_month#12],
> MetastoreRelation test, customer
>+- *Sort [ca_address_sk#18 ASC NULLS FIRST], false, 0
> +- Exchange hashpartitioning(ca_address_sk#18, 200)
> +- *Filter isnotnull(ca_address_sk#18)
> +- HiveTableScan [ca_address_sk#18, ca_zip#27], MetastoreRelation
> test, customer_address
> By default, the query will fail and throws the following exception:
> {code:java}
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
> stage 2.0 failed 4 times
> ...
> Caused by: java.lang.NegativeArraySizeException
> at
> org.apache.spark.unsafe.types.UTF8String.getBytes(UTF8String.java:229)
> at
> org.apache.spark.unsafe.types.UTF8String.toString(UTF8String.java:821)
> at java.lang.String.valueOf(String.java:2994)
> at
> scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
> at
> scala.collection.TraversableOnce$$anonfun$addString$1.apply(TraversableOnce.scala:359)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> scala.collection.TraversableOnce$class.addString(TraversableOnce.scala:357)
> at scala.collection.AbstractTraversable.addString(Traversable.scala:104)
> at
> scala.collection.TraversableOnce$class.mkString(TraversableOnce.scala:323)
> at scala.collection.AbstractTraversable.mkString(Traversable.scala:104)
> at
> scala.collection.TraversableLike$class.toString(TraversableLike.scala:600)
> at scala.collection.SeqLike$class.toString(SeqLike.scala:682)
> at scala.collection.AbstractSeq.toString(Seq.scala:41)
> at java.lang.String.valueOf(String.java:2994)
> at
> scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
> at
> org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
> at
> org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
> at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
> at org.apache.spark.sql.hive.HiveSimpleUDF.logInfo(hiveUDFs.scala:130)
> at org.apache.spark.sql.hive.HiveSimpleUDF.eval(hiveUDFs.scala:179)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$2.hasNext(WholeStageCodegenExec.scala:396)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> ...
> {code}
> However, when we close the codegen (i.e., spark.sql.codegen.wholeStage=false,
> spark.sql.codegen=false), it works well.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-21441) Incorrect Codegen in SortMergeJoinExec results failures in some cases
[
https://issues.apache.org/jira/browse/SPARK-21441?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-21441:
Assignee: (was: Apache Spark)
> Incorrect Codegen in SortMergeJoinExec results failures in some cases
> -
>
> Key: SPARK-21441
> URL: https://issues.apache.org/jira/browse/SPARK-21441
> Project: Spark
> Issue Type: Bug
> Components: SQL
>Affects Versions: 2.1.0, 2.1.1, 2.2.0
>Reporter: Feng Zhu
>Priority: Critical
>
> We noticed that the codegen mechanism in SortMergeJoinExec caused job fails
> in some cases. The below simple example demonstrates this issue.
> The query joins two relations with conditions containing a HiveUDF (i.e.,
> base64) in OR predicates.
> {code:sql}
> SELECT ca_zip
> FROM customer, customer_address
> WHERE customer.c_current_addr_sk = customer_address.ca_address_sk
> AND (base64(ca_zip) = '85669' OR customer.c_birth_month > 2)
> {code}
> Physical plan before execution
> *Project [ca_zip#27]
> +- *SortMergeJoin [c_current_addr_sk#4], [ca_address_sk#18], Inner,
> ((HiveSimpleUDF#Base64(ca_zip#27) = 85669) || (c_birth_month#12 > 2))
>:- *Sort [c_current_addr_sk#4 ASC NULLS FIRST], false, 0
>: +- Exchange hashpartitioning(c_current_addr_sk#4, 200)
>: +- *Filter isnotnull(c_current_addr_sk#4)
>:+- HiveTableScan [c_current_addr_sk#4, c_birth_month#12],
> MetastoreRelation test, customer
>+- *Sort [ca_address_sk#18 ASC NULLS FIRST], false, 0
> +- Exchange hashpartitioning(ca_address_sk#18, 200)
> +- *Filter isnotnull(ca_address_sk#18)
> +- HiveTableScan [ca_address_sk#18, ca_zip#27], MetastoreRelation
> test, customer_address
> By default, the query will fail and throws the following exception:
> {code:java}
> org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in
> stage 2.0 failed 4 times
> ...
> Caused by: java.lang.NegativeArraySizeException
> at
> org.apache.spark.unsafe.types.UTF8String.getBytes(UTF8String.java:229)
> at
> org.apache.spark.unsafe.types.UTF8String.toString(UTF8String.java:821)
> at java.lang.String.valueOf(String.java:2994)
> at
> scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
> at
> scala.collection.TraversableOnce$$anonfun$addString$1.apply(TraversableOnce.scala:359)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> scala.collection.TraversableOnce$class.addString(TraversableOnce.scala:357)
> at scala.collection.AbstractTraversable.addString(Traversable.scala:104)
> at
> scala.collection.TraversableOnce$class.mkString(TraversableOnce.scala:323)
> at scala.collection.AbstractTraversable.mkString(Traversable.scala:104)
> at
> scala.collection.TraversableLike$class.toString(TraversableLike.scala:600)
> at scala.collection.SeqLike$class.toString(SeqLike.scala:682)
> at scala.collection.AbstractSeq.toString(Seq.scala:41)
> at java.lang.String.valueOf(String.java:2994)
> at
> scala.collection.mutable.StringBuilder.append(StringBuilder.scala:200)
> at
> org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
> at
> org.apache.spark.sql.hive.HiveSimpleUDF$$anonfun$eval$1.apply(hiveUDFs.scala:179)
> at org.apache.spark.internal.Logging$class.logInfo(Logging.scala:54)
> at org.apache.spark.sql.hive.HiveSimpleUDF.logInfo(hiveUDFs.scala:130)
> at org.apache.spark.sql.hive.HiveSimpleUDF.eval(hiveUDFs.scala:179)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown
> Source)
> at
> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$10$$anon$2.hasNext(WholeStageCodegenExec.scala:396)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> ...
> {code}
> However, when we close the codegen (i.e., spark.sql.codegen.wholeStage=false,
> spark.sql.codegen=false), it works well.
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
