[
https://issues.apache.org/jira/browse/SPARK-17601?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15514019#comment-15514019
]
Gang Wu commented on SPARK-17601:
---------------------------------
[~hyukjin.kwon] Yes I agree. I just created these JIRAs for issues we met in
production. I think there definitely can be more issues for ORC, Parquet, etc.
Schema evolution is always painful to tackle with. Seems that you are working
on this. Do you mind telling a little bit more about what's your plan there?
I'd like to know. Thanks!
> SparkSQL vectorization cannot handle schema evolution for parquet tables when
> parquet files use Int whereas DataFrame uses Long
> -------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-17601
> URL: https://issues.apache.org/jira/browse/SPARK-17601
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Gang Wu
>
> This is a JIRA related to SPARK-17477.
> When using SparkSession to read a Hive table which is stored as parquet
> files. If there has been a schema evolution from int to long of a column.
> There are some old parquet files use int for the column while some new
> parquet files use long. In Hive metastore, the type is long (bigint). If we
> use vectorization in SparkSQL then we will get following exception:
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1450)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1438)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1437)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1437)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:811)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:811)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1659)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1618)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1607)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:632)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1871)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1884)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1897)
> at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:347)
> at
> org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:39)
> at
> org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2183)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
> at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2532)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2182)
> at
> org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2189)
> at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1925)
> at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:1924)
> at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2562)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:1924)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:2139)
> at org.apache.spark.sql.Dataset.showString(Dataset.scala:239)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:526)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:486)
> at org.apache.spark.sql.Dataset.show(Dataset.scala:495)
> ... 48 elided
> Caused by: java.lang.NullPointerException
> at
> org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.getLong(OnHeapColumnVector.java:272)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$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:784)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:784)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
> at org.apache.spark.scheduler.Task.run(Task.scala:85)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> 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:745)
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
