error with pyspark - dynamodb

[hadoop@ip-172-31-1-78 ~]$ pyspark --jars /usr/share/aws/emr/ddb/lib/emr-ddb-hadoop.jar
Python 2.7.12 (default, Sep  1 2016, 22:14:00)
[GCC 4.8.3 20140911 (Red Hat 4.8.3-9)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/02/08 10:45:00 WARN Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
17/02/08 10:45:22 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
Welcome to
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   /__ / .__/\_,_/_/ /_/\_\   version 2.1.0
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Using Python version 2.7.12 (default, Sep  1 2016 22:14:00)
SparkSession available as 'spark'.
>>> conf = {
...     "dynamodb.servicename": "dynamodb",
...     "dynamodb.input.tableName": "users",
...     "dynamodb.endpoint": "https://dynamodb.eu-central-1.amazonaws.com",
...     "dynamodb.regionid": "eu-central-1",
...     "mapred.output.format.class": "org.apache.hadoop.dynamodb.write.DynamoDBOutputFormat",
...     "mapred.input.format.class": "org.apache.hadoop.dynamodb.read.DynamoDBInputFormat"
... }
>>> conf
{'dynamodb.regionid': 'eu-central-1', 'dynamodb.servicename': 'dynamodb', 'mapred.input.format.class': 'org.apache.hadoop.dynamodb.read.DynamoDBInputFormat', 'mapred.output.format.class': 'org.apache.hadoop.dynamodb.write.DynamoDBOutputFormat', 'dynamodb.input.tableName': 'users', 'dynamodb.endpoint': 'https://dynamodb.eu-central-1.amazonaws.com'}
>>> sc.hadoopRDD(
...     inputFormatClass='org.apache.hadoop.dynamodb.read.DynamoDBInputFormat',
...     keyClass='org.apache.hadoop.io.Text',
...     valueClass='org.apache.hadoop.dynamodb.DynamoDBItemWritable',
...     conf=conf
... )
17/02/08 10:45:49 WARN TaskCalculator: The calculated max number of concurrent map tasks is less than 1. Use 1 instead.
17/02/08 10:45:52 ERROR TaskSetManager: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.hadoop.dynamodb.DynamoDBItemWritable
Serialization stack:
	- object not serializable (class: org.apache.hadoop.dynamodb.DynamoDBItemWritable, value: {"id":{"n":"10"},"login":{"s":"some_log_in_124324"}})
	- field (class: scala.Tuple2, name: _2, type: class java.lang.Object)
	- object (class scala.Tuple2, (,{"id":{"n":"10"},"login":{"s":"some_log_in_124324"}}))
	- element of array (index: 0)
	- array (class [Lscala.Tuple2;, size 1); not retrying
17/02/08 10:45:52 WARN ExecutorAllocationManager: No stages are running, but numRunningTasks != 0
Traceback (most recent call last):
  File "<stdin>", line 5, in <module>
  File "/usr/lib/spark/python/pyspark/context.py", line 732, in hadoopRDD
    jconf, batchSize)
  File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
  File "/usr/lib/spark/python/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.hadoopRDD.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0.0 in stage 0.0 (TID 0) had a not serializable result: org.apache.hadoop.dynamodb.DynamoDBItemWritable
Serialization stack:
	- object not serializable (class: org.apache.hadoop.dynamodb.DynamoDBItemWritable, value: {"id":{"n":"10"},"login":{"s":"some_log_in_124324"}})
	- field (class: scala.Tuple2, name: _2, type: class java.lang.Object)
	- object (class scala.Tuple2, (,{"id":{"n":"10"},"login":{"s":"some_log_in_124324"}}))
	- element of array (index: 0)
	- array (class [Lscala.Tuple2;, size 1)
	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
	at scala.Option.foreach(Option.scala:257)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
	at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1353)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
	at org.apache.spark.rdd.RDD.take(RDD.scala:1326)
	at org.apache.spark.api.python.SerDeUtil$.pairRDDToPython(SerDeUtil.scala:203)
	at org.apache.spark.api.python.PythonRDD$.hadoopRDD(PythonRDD.scala:651)
	at org.apache.spark.api.python.PythonRDD.hadoopRDD(PythonRDD.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:280)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:214)
	at java.lang.Thread.run(Thread.java:745)