Py4JJavaError in spark wordcount python 3.5 on jupyter notebook

Question

I am trying a simple network word count program on spark streaming in python with code as

from pyspark import SparkContext
from pyspark.streaming import StreamingContext


sc = SparkContext("local[2]", "NetworkWordCount")
ssc = StreamingContext(sc, 1)


lines = ssc.socketTextStream("localhost", 9999)

words = lines.flatMap(lambda line: line.split(" "))

pairs = words.map( lambda word : (word,1))
wordCount = pairs.reduceByKey( lambda x, y : (x+y))
wordCount.pprint()
ssc.start()
ssc.awaitTermination()

its running all good till ssc.start() but its giving an error at ssc.awaitTermination()

Py4JJavaError                             Traceback (most recent call last)
<ipython-input-2-18f3db416f1c> in <module>()
      1 ssc.start()
----> 2 ssc.awaitTermination()

/usr/local/lib/python3.5/dist-packages/pyspark/streaming/context.py in awaitTermination(self, timeout)
    204         """
    205         if timeout is None:
--> 206             self._jssc.awaitTermination()
    207         else:
    208             self._jssc.awaitTerminationOrTimeout(int(timeout * 1000))

/usr/local/lib/python3.5/dist-packages/py4j/java_gateway.py in __call__(self, *args)
   1158         answer = self.gateway_client.send_command(command)
   1159         return_value = get_return_value(
-> 1160             answer, self.gateway_client, self.target_id, self.name)
   1161 
   1162         for temp_arg in temp_args:

/usr/local/lib/python3.5/dist-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    318                 raise Py4JJavaError(
    319                     "An error occurred while calling {0}{1}{2}.\n".
--> 320                     format(target_id, ".", name), value)
    321             else:
    322                 raise Py4JError(

Py4JJavaError: An error occurred while calling o22.awaitTermination.
: org.apache.spark.SparkException: An exception was raised by Python:
Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/pyspark/streaming/util.py", line 65, in call
    r = self.func(t, *rdds)
  File "/usr/local/lib/python3.5/dist-packages/pyspark/streaming/dstream.py", line 171, in takeAndPrint
    taken = rdd.take(num + 1)
  File "/usr/local/lib/python3.5/dist-packages/pyspark/rdd.py", line 1358, in take
    res = self.context.runJob(self, takeUpToNumLeft, p)
  File "/usr/local/lib/python3.5/dist-packages/pyspark/context.py", line 1001, in runJob
    port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
  File "/usr/local/lib/python3.5/dist-packages/py4j/java_gateway.py", line 1160, in __call__
    answer, self.gateway_client, self.target_id, self.name)
  File "/usr/local/lib/python3.5/dist-packages/py4j/protocol.py", line 320, in get_return_value
    format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2.0 (TID 1, localhost, executor driver): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/home/blaze/spark/spark-2.2.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 123, in main
    ("%d.%d" % sys.version_info[:2], version))
Exception: Python in worker has different version 2.7 than that in driver 3.5, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1517)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1505)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1504)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1504)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1732)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1687)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1676)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2050)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2069)
    at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:455)
    at org.apache.spark.api.python.PythonRDD.runJob(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:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/home/blaze/spark/spark-2.2.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/worker.py", line 123, in main
    ("%d.%d" % sys.version_info[:2], version))
Exception: Python in worker has different version 2.7 than that in driver 3.5, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    ... 1 more


    at org.apache.spark.streaming.api.python.TransformFunction.callPythonTransformFunction(PythonDStream.scala:95)
    at org.apache.spark.streaming.api.python.TransformFunction.apply(PythonDStream.scala:78)
    at org.apache.spark.streaming.api.python.PythonDStream$$anonfun$callForeachRDD$1.apply(PythonDStream.scala:179)
    at org.apache.spark.streaming.api.python.PythonDStream$$anonfun$callForeachRDD$1.apply(PythonDStream.scala:179)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply$mcV$sp(ForEachDStream.scala:51)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1$$anonfun$apply$mcV$sp$1.apply(ForEachDStream.scala:51)
    at org.apache.spark.streaming.dstream.DStream.createRDDWithLocalProperties(DStream.scala:416)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply$mcV$sp(ForEachDStream.scala:50)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
    at org.apache.spark.streaming.dstream.ForEachDStream$$anonfun$1.apply(ForEachDStream.scala:50)
    at scala.util.Try$.apply(Try.scala:192)
    at org.apache.spark.streaming.scheduler.Job.run(Job.scala:39)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply$mcV$sp(JobScheduler.scala:257)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler$$anonfun$run$1.apply(JobScheduler.scala:257)
    at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
    at org.apache.spark.streaming.scheduler.JobScheduler$JobHandler.run(JobScheduler.scala:256)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

spark version: 2.2.1 python version: 3.5 java version: 1.8.0_162 pyspark version: 2.3.0

Thanks.

​

Answer 1

Are you using standalone spark?

Your error is : Exception: Python in worker has different version 2.7 than that in driver 3.5, PySpark cannot run with different minor versions.Please check environment variables PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON are correctly set.

Your error has been addressed here : How do I set the driver's python version in spark?

Answer 2

UPDATE THE SPARK ENVIRONMENT TO USE PYTHON 3.7:

Open a new terminal and type the following command: export PYSPARK_PYTHON=python3.7 This will ensure that the worker nodes use Python 3.7 (same as the Driver) and not the default Python 3.4

DEPENDING ON VERSIONS OF PYTHON YOU HAVE, YOU MAY HAVE TO DO SOME INSTALL/UPDATE ANACONDA:

(To install see: https://www.digitalocean.com/community/tutorials/how-to-install-anaconda-on-ubuntu-18-04-quickstart )

Make sure you have anaconda 4.1.0 or higher. Open a new terminal and check your conda version by typing into a new terminal:

conda --version

checking conda version

if you are below anaconda 4.1.0, type conda update conda

2. Next we check to see if we have the library nb_conda_kernels by typing

conda list

Checking if we have nb_conda_kernels

3. If you don't see nb_conda_kernels type

conda install nb_conda_kernels

Installing nb_conda_kernels

4. If you are using Python 2 and want a separate Python 3 environment please type the following

conda create -n py36 python=3.6 ipykernel

py35 is the name of the environment. You could literally name it anything you want.

Alternatively, If you are using Python 3 and want a separate Python 2 environment, you could type the following.

conda create -n py27 python=2.7 ipykernel

py27 is the name of the environment. It uses python 2.7.

5. Ensure the versions of python are installed successfully and close the terminal. Open a new terminal and type pyspark . You should see the new environments appearing.