Continuum Analytics: Documentation

# How to Run a Spark Standalone Job

## Overview

This is a minimal Spark script that imports PySpark, initializes a SparkContext and performs a distributed calculation on a Spark cluster in standalone mode.

## Who is this for?

This how-to is for users of a Spark cluster that has been configured in standalone mode who wish to run Python code.

## Spark Standalone Summary

1. Before you start
2. Modifying the script
3. Running the job
4. Troubleshooting
5. Further information

## Before you start

To execute this example, download the `cluster-spark-basic.py example script` to the cluster node where you submit Spark jobs.

For this example, you’ll need Spark running with the standalone scheduler. You can install Spark using an enterprise Hadoop distribution such as Cloudera CDH or Hortonworks HDP. Some additional configuration might be necessary to use Spark in standalone mode.

## Modifying the script

After downloading the `cluster-spark-basic.py example script` open the file in a text editor on your cluster. Replace `HEAD_NODE_HOSTNAME` with the hostname of the head node of the Spark cluster.

    # cluster-spark-basic.py
    from pyspark import SparkConf
    from pyspark import SparkContext

    conf = SparkConf()
    conf.setMaster('spark://HEAD_NODE_HOSTNAME:7077')
    conf.setAppName('spark-basic')
    sc = SparkContext(conf=conf)

    def mod(x):
        import numpy as np
        return (x, np.mod(x, 2))

    rdd = sc.parallelize(range(1000)).map(mod).take(10)
    print rdd

Let’s analyze the contents of the `spark-basic.rst example script`. The first code block contains imports from PySpark.

The second code block initializes the SparkContext and sets the application name.

The third code block contains the analysis code that calculates the modulus of a range of numbers up to 1000 using the NumPy package and returns/prints the first 10 results.

Note: you may have to install NumPy with `acluster conda install numpy`.

## Running the job

You can run this script by submitting it to your cluster for execution using spark-submit or by running this command

    python cluster-spark-basic.py

The output from the above command shows the first ten values that were returned from the `cluster-spark-basic.py` script.

    16/05/05 22:26:53 INFO spark.SparkContext: Running Spark version 1.6.0

    [...]

    16/05/05 22:27:03 INFO scheduler.TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, localhost, partition 0,PROCESS_LOCAL, 3242 bytes)
    16/05/05 22:27:04 INFO storage.BlockManagerInfo: Added broadcast_0_piece0 in memory on localhost:46587 (size: 2.6 KB, free: 530.3 MB)
    16/05/05 22:27:04 INFO scheduler.TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 652 ms on localhost (1/1)
    16/05/05 22:27:04 INFO cluster.YarnScheduler: Removed TaskSet 0.0, whose tasks have all completed, from pool
    16/05/05 22:27:04 INFO scheduler.DAGScheduler: ResultStage 0 (runJob at PythonRDD.scala:393) finished in 4.558 s
    16/05/05 22:27:04 INFO scheduler.DAGScheduler: Job 0 finished: runJob at PythonRDD.scala:393, took 4.951328 s
    [(0, 0), (1, 1), (2, 0), (3, 1), (4, 0), (5, 1), (6, 0), (7, 1), (8, 0), (9, 1)]

## Troubleshooting

If something goes wrong consult the FAQ / Known issues page.

## Further information

See the Spark and PySpark documentation pages for more information.