[jira] [Created] (SPARK-26381) Pickle Serialization Error Causing Crash

["Ryan (JIRA)" <ji...@apache.org>]

Ryan created SPARK-26381:
----------------------------

             Summary: Pickle Serialization Error Causing Crash
                 Key: SPARK-26381
                 URL: https://issues.apache.org/jira/browse/SPARK-26381
             Project: Spark
          Issue Type: Bug
          Components: PySpark
    Affects Versions: 2.4.0, 2.3.1
         Environment: Tested on two environments:
 * Spark 2.4.0 - single machine only
 * Spark 2.3.1 - YARN installation with 5 nodes and files on HDFS

 

The error occurs in both environments.
            Reporter: Ryan


There is a pickle serialization error when I try and use AllenNLP for doing NER within a Spark worker - it is causing a crash. When running on just the Spark driver or in a standalone program, everything works as expected.

 
{code:java}
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last): 
 File "/data/disk12/yarn/local/usercache/raclancy/appcache/application_1543437939000_1040/container_1543437939000_1040_01_000002/pyspark.zip/pyspark/worker.py", line 217, in main 
   func, profiler, deserializer, serializer = read_command(pickleSer, infile) 
 File "/data/disk12/yarn/local/usercache/raclancy/appcache/application_1543437939000_1040/container_1543437939000_1040_01_000002/pyspark.zip/pyspark/worker.py", line 61, in read_command 
   command = serializer.loads(command.value) 
 File "/data/disk12/yarn/local/usercache/raclancy/appcache/application_1543437939000_1040/container_1543437939000_1040_01_000002/pyspark.zip/pyspark/serializers.py", line 559, in loads 
   return pickle.loads(obj, encoding=encoding) 
TypeError: __init__() missing 3 required positional arguments: 'non_padded_namespaces', 'padding_token', and 'oov_token' 

       at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:298) 
       at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:438) 
       at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRunner.scala:421) 
       at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:252) 
       at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) 
       at scala.collection.Iterator$class.foreach(Iterator.scala:893) 
       at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) 
       at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59) 
       at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104) 
       at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48) 
       at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310) 
       at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) 
       at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302) 
       at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) 
       at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289) 
       at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) 
       at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:939) 
       at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$12.apply(RDD.scala:939) 
       at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2074) 
       at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2074) 
       at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) 
       at org.apache.spark.scheduler.Task.run(Task.scala:109) 
       at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345) 
       at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) 
       at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) 
       ... 1 more


{code}



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