Continuum Analytics: Documentation

# How to run PySpark as 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 a PySpark job.

## 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 `spark-basic.py example script` to the cluster node where you submit Spark jobs.

For this example you will 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 `spark-basic.py example script`, open the file in a text editor on your cluster. Replace `HEAD_NODE_HOSTNAME` with the hostname or IP address of the Spark master as defined in your Hadoop configuration.

    # 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 and prints the first 10 results.

The fourth code block runs the calculation on the Spark cluster using the NumPy library from Anaconda on the Spark workers and prints the results of the calculation.

NOTE: You may need to install NumPy on the cluster nodes using `adam scale -n my-cluster 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 spark-basic.py

The output from the above command shows the first ten values that were returned from the `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 Help and Support page.

## Further information

See the Spark and PySpark documentation pages for more information.