BTW, I think Json Parser should verify the json format at least when
inferring the schema of json.
On Wed, Oct 21, 2015 at 12:59 PM, Jeff Zhang <zjf...@gmail.com> wrote:
> I think this is due to the json file format. DataFrame can only accept
> json file with one valid record per line. Multiple line per record is
> invalid for DataFrame.
>
>
> On Tue, Oct 6, 2015 at 2:48 AM, Davies Liu <dav...@databricks.com> wrote:
>
>> Could you create a JIRA to track this bug?
>>
>> On Fri, Oct 2, 2015 at 1:42 PM, balajikvijayan
>> <balaji.k.vija...@gmail.com> wrote:
>> > Running Windows 8.1, Python 2.7.x, Scala 2.10.5, Spark 1.4.1.
>> >
>> > I'm trying to read in a large quantity of json data in a couple of
>> files and
>> > I receive a scala.MatchError when I do so. Json, Python and stack trace
>> all
>> > shown below.
>> >
>> > Json:
>> >
>> > {
>> > "dataunit": {
>> > "page_view": {
>> > "nonce": 438058072,
>> > "person": {
>> > "user_id": 5846
>> > },
>> > "page": {
>> > "url": "http://mysite.com/blog";
>> > }
>> > }
>> > },
>> > "pedigree": {
>> > "true_as_of_secs": 1438627992
>> > }
>> > }
>> >
>> > Python:
>> >
>> > import pyspark
>> > sc = pyspark.SparkContext()
>> > sqlContext = pyspark.SQLContext(sc)
>> > pageviews = sqlContext.read.json("[Path to folder containing file with
>> above
>> > json]")
>> > pageviews.collect()
>> >
>> > Stack Trace:
>> > Py4JJavaError: An error occurred while calling
>> > z:org.apache.spark.api.python.PythonRDD.collectAndServe.
>> > : org.apache.spark.SparkException: Job aborted due to stage failure:
>> Task 1
>> > in stage 32.0 failed 1 times, most recent failure: Lost task 1.0 in
>> stage
>> > 32.0 (TID 133, localhost): scala.MatchError:
>> > (VALUE_STRING,ArrayType(StructType(),true)) (of class scala.Tuple2)
>> > at
>> >
>> org.apache.spark.sql.json.JacksonParser$.convertField(JacksonParser.scala:49)
>> > at
>> >
>> org.apache.spark.sql.json.JacksonParser$$anonfun$parseJson$1$$anonfun$apply$1.apply(JacksonParser.scala:201)
>> > at
>> >
>> org.apache.spark.sql.json.JacksonParser$$anonfun$parseJson$1$$anonfun$apply$1.apply(JacksonParser.scala:193)
>> > at
>> scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
>> > at
>> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>> > at
>> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>> > at
>> >
>> org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.hasNext(SerDeUtil.scala:116)
>> > at scala.collection.Iterator$class.foreach(Iterator.scala:727)
>> > at
>> >
>> org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.foreach(SerDeUtil.scala:111)
>> > at
>> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
>> > at
>> >
>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
>> > at
>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
>> > at scala.collection.TraversableOnce$class.to
>> (TraversableOnce.scala:273)
>> > at
>> >
>> org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.to(SerDeUtil.scala:111)
>> > at
>> >
>> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
>> > at
>> >
>> org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.toBuffer(SerDeUtil.scala:111)
>> > at
>> >
>> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
>> > at
>> >
>> org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.toArray(SerDeUtil.scala:111)
>> > at
>> >
>> org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:885)
>> > at
>> >
>> org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:885)
>> > at
>> >
>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1767)
>> > at
>> >
>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1767)
>> > at
>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
>> > at org.apache.spark.scheduler.Task.run(Task.scala:70)
>> > at
>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
>> > at
>> >
>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>> > at
>> >
>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>> > at java.lang.Thread.run(Thread.java:745)
>> >
>> > Driver stacktrace:
>> > at
>> > org.apache.spark.scheduler.DAGScheduler.org
>> $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1273)
>> > at
>> >
>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1264)
>> > at
>> >
>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1263)
>> > at
>> >
>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>> > at
>> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>> > at
>> >
>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1263)
>> > at
>> >
>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730)
>> > at
>> >
>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730)
>> > at scala.Option.foreach(Option.scala:236)
>> > at
>> >
>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:730)
>> > at
>> >
>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1457)
>> > at
>> >
>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1418)
>> > at
>> org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
>> >
>> > It seems like this issue has been resolved in scala per SPARK-3390
>> > <https://issues.apache.org/jira/browse/SPARK-3390> ; any thoughts on
>> the
>> > root cause of this in pyspark?
>> >
>> >
>> >
>> > --
>> > View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/Reading-JSON-in-Pyspark-throws-scala-MatchError-tp24911.html
>> > Sent from the Apache Spark User List mailing list archive at Nabble.com.
>> >
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>
>
> --
> Best Regards
>
> Jeff Zhang
>
--
Best Regards
Jeff Zhang
