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https://issues.apache.org/jira/browse/SPARK-13764?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-13764:
------------------------------------
Assignee: Apache Spark
> Parse modes in JSON data source
> -------------------------------
>
> Key: SPARK-13764
> URL: https://issues.apache.org/jira/browse/SPARK-13764
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 2.0.0
> Reporter: Hyukjin Kwon
> Assignee: Apache Spark
> Priority: Minor
>
> Currently, JSON data source just fails to read if some JSON documents are
> malformed.
> Therefore, if there are two JSON documents below:
> {noformat}
> {
> "request": {
> "user": {
> "id": 123
> }
> }
> }
> {noformat}
> {noformat}
> {
> "request": {
> "user": []
> }
> }
> {noformat}
> This will fail emitting the exception below :
> {noformat}
> Exception in thread "main" org.apache.spark.SparkException: Job aborted due
> to stage failure: Task 7 in stage 0.0 failed 4 times, most recent failure:
> Lost task 7.3 in stage 0.0 (TID 10, 192.168.1.170):
> java.lang.ClassCastException: org.apache.spark.sql.types.GenericArrayData
> cannot be cast to org.apache.spark.sql.catalyst.InternalRow
> at
> org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow$class.getStruct(rows.scala:50)
> at
> org.apache.spark.sql.catalyst.expressions.GenericMutableRow.getStruct(rows.scala:247)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate$$anonfun$create$2.apply(GeneratePredicate.scala:67)
> at
> org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate$$anonfun$create$2.apply(GeneratePredicate.scala:67)
> at
> org.apache.spark.sql.execution.Filter$$anonfun$4$$anonfun$apply$4.apply(basicOperators.scala:117)
> at
> org.apache.spark.sql.execution.Filter$$anonfun$4$$anonfun$apply$4.apply(basicOperators.scala:115)
> at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:390)
> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1.org$apache$spark$sql$execution$aggregate$TungstenAggregate$$anonfun$$executePartition$1(TungstenAggregate.scala:97)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
> at
> org.apache.spark.sql.execution.aggregate.TungstenAggregate$$anonfun$doExecute$1$$anonfun$2.apply(TungstenAggregate.scala:119)
> at
> org.apache.spark.rdd.MapPartitionsWithPreparationRDD.compute(MapPartitionsWithPreparationRDD.scala:64)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:300)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:264)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
> at org.apache.spark.scheduler.Task.run(Task.scala:88)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
> 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)
> {noformat}
> So, just like the parse modes in CSV data source, (See
> https://github.com/databricks/spark-csv), it would be great if there are some
> parse modes so that users do not have to filter or pre-process themselves.
> This happens only when custom schema is set. when this uses inferred schema,
> then it infers the type as {{StringType}} which reads the data successfully
> anyway.
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