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Posted to issues@spark.apache.org by "Stéphane Collot (JIRA)" <ji...@apache.org> on 2017/03/24 16:34:41 UTC
[jira] [Commented] (SPARK-17557) SQL query on parquet table
java.lang.UnsupportedOperationException:
org.apache.parquet.column.values.dictionary.PlainValuesDictionary
[ https://issues.apache.org/jira/browse/SPARK-17557?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15940672#comment-15940672 ]
Stéphane Collot commented on SPARK-17557:
-----------------------------------------
Hi,
I got a similar issue with Spark 2.0.0 and it appears that I had partitioned table, and I had some different columns types inside some partitions because I was writing those manually inside the subfolders of the partitions. So reading a specific partition was working, but reading the entire table was giving this PlainValuesDictionary exception on a df.show() or on df.sort('column').show()
So it was my fault, but Spark could check if the schema in each partition is the same.
Cheers,
Stéphane
> SQL query on parquet table java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary
> -------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-17557
> URL: https://issues.apache.org/jira/browse/SPARK-17557
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Egor Pahomov
>
> Working on 1.6.2, broken on 2.0
> {code}
> select * from logs.a where year=2016 and month=9 and day=14 limit 100
> {code}
> {code}
> java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainBinaryDictionary
> at org.apache.parquet.column.Dictionary.decodeToInt(Dictionary.java:48)
> at org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.getInt(OnHeapColumnVector.java:233)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
> at org.apache.spark.scheduler.Task.run(Task.scala:86)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> {code}
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