Hyukjin Kwon created SPARK-13764:
------------------------------------
Summary: 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.
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