I've got an dataset saved with saveAsPickleFile using pyspark -- it saves
without problems. When I try to read it back in, it fails with:
Job aborted due to stage failure: Task 401 in stage 0.0 failed 4 times, most
recent failure: Lost task 401.3 in stage 0.0 (TID 449,
e1326.hpc-lca.ethz.ch): java.lang.NegativeArraySizeException:
org.apache.hadoop.io.BytesWritable.setCapacity(BytesWritable.java:119)
org.apache.hadoop.io.BytesWritable.setSize(BytesWritable.java:98)
org.apache.hadoop.io.BytesWritable.readFields(BytesWritable.java:153)
org.apache.hadoop.io.serializer.WritableSerialization$WritableDeserializer.deserialize(WritableSerialization.java:67)
org.apache.hadoop.io.serializer.WritableSerialization$WritableDeserializer.deserialize(WritableSerialization.java:40)
org.apache.hadoop.io.SequenceFile$Reader.deserializeValue(SequenceFile.java:1875)
org.apache.hadoop.io.SequenceFile$Reader.getCurrentValue(SequenceFile.java:1848)
org.apache.hadoop.mapred.SequenceFileRecordReader.getCurrentValue(SequenceFileRecordReader.java:103)
org.apache.hadoop.mapred.SequenceFileRecordReader.next(SequenceFileRecordReader.java:78)
org.apache.spark.rdd.HadoopRDD$$anon$1.getNext(HadoopRDD.scala:219)
org.apache.spark.rdd.HadoopRDD$$anon$1.getNext(HadoopRDD.scala:188)
org.apache.spark.util.NextIterator.hasNext(NextIterator.scala:71)
org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:39)
scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:330)
org.apache.spark.api.python.PythonRDD$WriterThread$$anonfun$run$1.apply$mcV$sp(PythonRDD.scala:209)
org.apache.spark.api.python.PythonRDD$WriterThread$$anonfun$run$1.apply(PythonRDD.scala:184)
org.apache.spark.api.python.PythonRDD$WriterThread$$anonfun$run$1.apply(PythonRDD.scala:184)
org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1311)
org.apache.spark.api.python.PythonRDD$WriterThread.run(PythonRDD.scala:183)
Not really sure where to start looking for the culprit -- any suggestions
most welcome. Thanks!
Rok
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