[jira] [Assigned] (SPARK-13719) Bad JSON record raises java.lang.ClassCastException
[ https://issues.apache.org/jira/browse/SPARK-13719?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-13719: Assignee: (was: Apache Spark) > Bad JSON record raises java.lang.ClassCastException > > > Key: SPARK-13719 > URL: https://issues.apache.org/jira/browse/SPARK-13719 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 1.5.2, 1.6.0 > Environment: OS X, Linux >Reporter: dmtran >Priority: Minor > > I have defined a JSON schema, using org.apache.spark.sql.types.StructType, > that expects this kind of record : > {noformat} > { > "request": { > "user": { > "id": 123 > } > } > } > {noformat} > There's a bad record in my dataset, that defines field "user" as an array, > instead of a JSON object : > {noformat} > { > "request": { > "user": [] > } > } > {noformat} > The following exception is raised because of that bad record : > {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} > Here's a code snippet that reproduces the exception : > {noformat} > import org.apache.spark.SparkContext > import org.apache.spark.rdd.RDD > import org.apache.spark.sql.{SQLContext, DataFrame} > import org.apache.spark.sql.hive.HiveContext > import org.apache.spark.sql.types.{StringType, StructField, StructType} > object Snippet { > def main(args : Array[String]): Unit = { > val sc = new SparkContext() > implicit val sqlContext = new HiveContext(sc) > val rdd: RDD[String] = sc.parallelize(Seq(badRecord)) > val df: DataFrame = sqlContext.read.schema(schema).json(rdd) > import sqlContext.implicits._ > df.select("request.user.id") > .filter($"id".isNotNull) > .count() > } > val badRecord = > s"""{ > | "request": { > |"user": [] > | } > |}""".stripMargin.replaceAll("\n", " ") // Convert the multiline > string to a signe line string > val schema = > StructType( > StructField("request", Struc
[jira] [Assigned] (SPARK-13719) Bad JSON record raises java.lang.ClassCastException
[ https://issues.apache.org/jira/browse/SPARK-13719?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] Apache Spark reassigned SPARK-13719: Assignee: Apache Spark > Bad JSON record raises java.lang.ClassCastException > > > Key: SPARK-13719 > URL: https://issues.apache.org/jira/browse/SPARK-13719 > Project: Spark > Issue Type: Bug > Components: SQL >Affects Versions: 1.5.2, 1.6.0 > Environment: OS X, Linux >Reporter: dmtran >Assignee: Apache Spark >Priority: Minor > > I have defined a JSON schema, using org.apache.spark.sql.types.StructType, > that expects this kind of record : > {noformat} > { > "request": { > "user": { > "id": 123 > } > } > } > {noformat} > There's a bad record in my dataset, that defines field "user" as an array, > instead of a JSON object : > {noformat} > { > "request": { > "user": [] > } > } > {noformat} > The following exception is raised because of that bad record : > {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$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > {noformat} > Here's a code snippet that reproduces the exception : > {noformat} > import org.apache.spark.SparkContext > import org.apache.spark.rdd.RDD > import org.apache.spark.sql.{SQLContext, DataFrame} > import org.apache.spark.sql.hive.HiveContext > import org.apache.spark.sql.types.{StringType, StructField, StructType} > object Snippet { > def main(args : Array[String]): Unit = { > val sc = new SparkContext() > implicit val sqlContext = new HiveContext(sc) > val rdd: RDD[String] = sc.parallelize(Seq(badRecord)) > val df: DataFrame = sqlContext.read.schema(schema).json(rdd) > import sqlContext.implicits._ > df.select("request.user.id") > .filter($"id".isNotNull) > .count() > } > val badRecord = > s"""{ > | "request": { > |"user": [] > | } > |}""".stripMargin.replaceAll("\n", " ") // Convert the multiline > string to a signe line string > val schema = > StructType( > Str