CODEWITHSUNDEEP
pythonapache-spark
sandip divekar
sandip divekar

Reputation: 1826

how to run python script in spark job?

I have setup spark on 3 machines using tar file method. I have not done any advanced configuration, I have edited slaves file and started master and workers. I am able to see sparkUI on 8080 port. Now I want to run simple python script on spark cluster.

import sys
from random import random
from operator import add

from pyspark import SparkContext


if __name__ == "__main__":
    """
        Usage: pi [partitions]
    """
    sc = SparkContext(appName="PythonPi")
    partitions = int(sys.argv[1]) if len(sys.argv) > 1 else 2
    n = 100000 * partitions

    def f(_):
        x = random() * 2 - 1
        y = random() * 2 - 1
        return 1 if x ** 2 + y ** 2 < 1 else 0

    count = sc.parallelize(xrange(1, n + 1), partitions).map(f).reduce(add)
    print "Pi is roughly %f" % (4.0 * count / n)

    sc.stop()

I am running this command

spark-submit --master spark://IP:7077 pi.py 1

But getting following error 

14/12/22 18:31:23 INFO scheduler.TaskSchedulerImpl: Adding task set 0.0 with 1 tasks
14/12/22 18:31:38 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
14/12/22 18:31:43 INFO client.AppClient$ClientActor: Connecting to master spark://10.77.36.243:7077...
14/12/22 18:31:53 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
14/12/22 18:32:03 INFO client.AppClient$ClientActor: Connecting to master spark://10.77.36.243:7077...
14/12/22 18:32:08 WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory
14/12/22 18:32:23 ERROR cluster.SparkDeploySchedulerBackend: Application has been killed. Reason: All masters are unresponsive! Giving up.
14/12/22 18:32:23 INFO scheduler.TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
14/12/22 18:32:23 INFO scheduler.TaskSchedulerImpl: Cancelling stage 0
14/12/22 18:32:23 INFO scheduler.DAGScheduler: Failed to run reduce at /opt/pi.py:21
Traceback (most recent call last):
  File "/opt/pi.py", line 21, in <module>
    count = sc.parallelize(xrange(1, n + 1), partitions).map(f).reduce(add)
  File "/usr/local/spark/python/pyspark/rdd.py", line 759, in reduce
    vals = self.mapPartitions(func).collect()
  File "/usr/local/spark/python/pyspark/rdd.py", line 723, in collect
    bytesInJava = self._jrdd.collect().iterator()
  File "/usr/local/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py", line 538, in __call__
  File "/usr/local/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line 300, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o26.collect.
: org.apache.spark.SparkException: Job aborted due to stage failure: All masters are unresponsive! Giving up.
        at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1185)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1174)
        at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1173)
        at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
        at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)

does anyone facing same issue. Plz help in this.

Upvotes: 6

Views: 14973

Answers (1)

Def_Os
Def_Os

Reputation: 5467

This:

WARN scheduler.TaskSchedulerImpl: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient memory

suggests that the cluster does not have any available resources.

Check the status of your cluster and inspect cores and RAM (http://www.datastax.com/dev/blog/common-spark-troubleshooting).

Also, double check your IP addresses.

For more ideas: Running a Job on Spark 0.9.0 throws error

Upvotes: 4

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