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Posted to issues@spark.apache.org by "nirav patel (JIRA)" <ji...@apache.org> on 2018/07/09 18:58:00 UTC
[jira] [Commented] (SPARK-23822) Improve error message for Parquet
schema mismatches
[ https://issues.apache.org/jira/browse/SPARK-23822?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16537405#comment-16537405 ]
nirav patel commented on SPARK-23822:
-------------------------------------
Does the fix pinpoint what column it fails to merge schema?
> Improve error message for Parquet schema mismatches
> ---------------------------------------------------
>
> Key: SPARK-23822
> URL: https://issues.apache.org/jira/browse/SPARK-23822
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.0
> Reporter: Yuchen Huo
> Assignee: Yuchen Huo
> Priority: Major
> Fix For: 2.3.1, 2.4.0
>
>
> If a user attempts to read Parquet files with mismatched schemas and schema merging is disabled then this may result in a very confusing UnsupportedOperationException and ParquetDecodingException errors from Parquet.
> e.g.
> {code:java}
> Seq(("bcd")).toDF("a").coalesce(1).write.mode("overwrite").parquet(s"$path/")
> Seq((1)).toDF("a").coalesce(1).write.mode("append").parquet(s"$path/")
> spark.read.parquet(s"$path/").collect()
> {code}
> Would result in
> {code:java}
> Caused by: java.lang.UnsupportedOperationException: Unimplemented type: IntegerType
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBinaryBatch(VectorizedColumnReader.java:474)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedColumnReader.readBatch(VectorizedColumnReader.java:214)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextBatch(VectorizedParquetRecordReader.java:261)
> at org.apache.spark.sql.execution.datasources.parquet.VectorizedParquetRecordReader.nextKeyValue(VectorizedParquetRecordReader.java:159)
> at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:106)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.nextIterator(FileScanRDD.scala:182)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:106)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.scan_nextBatch$(Unknown Source)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$11$$anon$1.hasNext(WholeStageCodegenExec.scala:617)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:253)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:830)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
> at org.apache.spark.scheduler.Task.run(Task.scala:109)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:748)
> {code}
>
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