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Running PySpark as standalone application

You can run Spark applications locally or distributed across a cluster, either by using an interactive shell or by submitting an application.

Submitting Spark Applications

To submit an application consisting of a Python file you can use the spark-submit script.

Symplified spark-submit Syntax

spark-submit --option value python file [application arguments]


Option Description
python file Path to a Python file containing a Spark application. For the client deployment mode, the path must point to a local file. For the cluster deployment mode, the path can be either a local file or a URL globally visible inside your cluster;
application arguments Arguments to pass to the main method of your application.

Selected spark-submit Options

Option Description
deploy-mode Deployment mode: cluster and client. In cluster mode, the driver runs on worker hosts. In client mode, the driver runs locally as an external client. Use cluster mode with production jobs; client mode is more appropriate for interactive and debugging uses, where you want to see your application output immediately.
Default: client.
driver-cores Number of cores used by the driver in cluster mode.
Default: 1.
driver-memory Maximum heap size (represented as a JVM string; for example 1024m, 2g, and so on) to allocate to the driver. Alternatively, you can use the spark.driver.memory property.
master The location to run the application.

Master Values

Master Description
local Run Spark locally with one worker thread (that is, no parallelism).
local[K] Run Spark locally with K worker threads. (Ideally, set this to the number of cores on your host.)
local[*] Run Spark locally with as many worker threads as logical cores on your host.
yarn Run using a YARN cluster manager.


For more information you can refer to Apache Submitting Applications documentation.


Let's create a file which contains code for counting of intensity datapoints within a time window:

from import DevicePropertyDataQuery
    from pyspark import SparkContext
    from pyspark.conf import SparkConf
    from pyspark.sql import SparkSession

conf = (SparkConf()
            .set('spark.driver.memory', '16G')
    sc = SparkContext(conf=conf)
    spark = SparkSession(sc)

    intensity = DevicePropertyDataQuery.builder(spark).system("CMW") \
        .startTime("2018-06-17 00:00:00.000").endTime("2018-06-20 00:00:00.000") \

    print("Found %d intensity data points." % intensity.count())

Alternatively spark session can be initialized using a session builder:

spark = SparkSession.builder.master("local").appName('intensity_example').getOrCreate()

In order to submit a spark job spark-submit must be invoked from our Spark bundle for NXCALS directory:


Certain Spark pramaters can be specified as arguments. Lets run our test application on a YARN cluster on 8 cores and using 4GB of memory:

./bin/spark-submit --master yarn --executor-memory 4G --total-executor-cores 8