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Apache Spark commented on SPARK-13764: -------------------------------------- User 'HyukjinKwon' has created a pull request for this issue: https://github.com/apache/spark/pull/11756 > 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 > 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. -- 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