Apache Spark Component Guide
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Chapter 7. Automating Spark Jobs with Oozie Spark Action

If you use Apache Spark as part of a complex workflow with multiple processing steps, triggers, and interdependencies, consider using Apache Oozie to automate jobs. Oozie is a workflow engine that executes sequences of actions structured as directed acyclic graphs (DAGs). Each action is an individual unit of work, such as a Spark job or Hive query.

The Oozie "Spark action" runs a Spark job as part of an Oozie workflow. The workflow waits until the Spark job completes before continuing to the next action.

For additional information about Spark action, see the Apache Oozie Spark Action Extension documentation. For general information about Oozie, see Using HDP for Workflow and Scheduling with Oozie. For general information about using Workflow Manager, see the Workflow Management Guide.

[Note]Note

In HDP 2.6, Oozie works with either Spark 1 or Spark 2 (not side-by-side deployments), but Spark 2 support for Oozie Spark action is available as a technical preview; it is not ready for production deployment. Configuration is through manual steps (not Ambari).

Support for yarn-client execution mode for Oozie Spark action will be removed in a future release. Oozie will continue to support yarn-cluster execution mode for Oozie Spark action.

Configuring Oozie Spark Action for Spark 1

To place a Spark job into an Oozie workflow, you need two configuration files:

  • A workflow XML file that defines workflow logic and parameters for running the Spark job. Some of the elements in a Spark action are specific to Spark; others are common to many types of actions.

  • A job.properties file for configuring the Oozie job.

You can configure a Spark action manually, or on an Ambari-managed cluster you can use the Spark action editor in the Ambari Oozie Workflow Manager (WFM). The Workflow Manager is designed to help build powerful workflows.

For two examples that use Oozie Workflow Manager--one that creates a new Spark action, and another that imports and runs an existing Spark workflow--see the Hortonworks Community Connection article Apache Ambari Workflow Manager View for Apache Oozie: Part 7 ( Spark Action & PySpark).

Here is the basic structure of a workflow definition XML file for a Spark action:

<workflow-app name="[WF-DEF-NAME]" xmlns="uri:oozie:workflow:0.3">
    ...
    <action name="[NODE-NAME]">
        <spark xmlns="uri:oozie:spark-action:0.1">
            <job-tracker>[JOB-TRACKER]</job-tracker>
            <name-node>[NAME-NODE]</name-node>
            <prepare>
               <delete path="[PATH]"/>
               ...
               <mkdir path="[PATH]"/>
               ...
            </prepare>
            <job-xml>[SPARK SETTINGS FILE]</job-xml>
            <configuration>
                <property>
                    <name>[PROPERTY-NAME]</name>
                    <value>[PROPERTY-VALUE]</value>
                </property>
                ...
            </configuration>
            <master>[SPARK MASTER URL]</master>
            <mode>[SPARK MODE]</mode>
            <name>[SPARK JOB NAME]</name>
            <class>[SPARK MAIN CLASS]</class>
            <jar>[SPARK DEPENDENCIES JAR / PYTHON FILE]</jar>
            <spark-opts>[SPARK-OPTIONS]</spark-opts>
            <arg>[ARG-VALUE]</arg>
                ...
            <arg>[ARG-VALUE]</arg>
            ...
        </spark>
        <ok to="[NODE-NAME]"/>
        <error to="[NODE-NAME]"/>
    </action>
    ...
</workflow-app>

The following examples show a workflow definition XML file and an Oozie job configuration file for running a SparkPi job (Spark version 1.x).

Sample Workflow.xml file for SparkPi app:

<workflow-app xmlns=’uri:oozie:workflow:0.5′ name=’SparkWordCount’>
      <start to=’spark-node’ />
        <action name=’spark-node’>
          <spark xmlns="uri:oozie:spark-action:0.1">
            <job-tracker>${jobTracker}</job-tracker>
            <name-node>${nameNode}</name-node>
            <prepare>
              <delete path="${nameNode}/user/${wf:user()}/${examplesRoot}/output-data"/>
            </prepare>
            <master>${master}</master>
            <name>SparkPi</name>
            <class>org.apache.spark.examples.SparkPi</class>
            <jar>lib/spark-examples.jar</jar>
            <spark-opts>--executor-memory 20G --num-executors 50</spark-opts>
            <arg>value=10</arg>
          </spark>
          <ok to="end" />
          <error to="fail" />
        </action>
        <kill name="fail">
          <message>Workflow failed, error
            message[${wf:errorMessage(wf:lastErrorNode())}] </message>
        </kill>
        <end name=’end’ />
    </workflow-app>

Sample Job.properties file for SparkPi app:

nameNode=hdfs://host:8020
jobTracker=host:8050
queueName=default
examplesRoot=examples
oozie.use.system.libpath=true
oozie.wf.application.path=${nameNode}/user/${user.name}/${examplesRoot}/apps/pyspark
master=yarn-cluster

Configuring Oozie Spark Action for Spark 2

To use Oozie Spark action with Spark 2 jobs, create a spark2 ShareLib directory, copy associated files into it, and then point Oozie to spark2. (The Oozie ShareLib is a set of libraries that allow jobs to run on any node in a cluster.)

  1. Create a spark2 ShareLib directory under the Oozie ShareLib directory associated with the oozie service user:

    hdfs dfs -mkdir /user/oozie/share/lib/lib_<ts>/spark2
  2. Copy spark2 jar files from the spark2 jar directory to the Oozie spark2 ShareLib:

    hdfs dfs -put \
        /usr/hdp/<version>/spark2/jars/* \
        /user/oozie/share/lib/lib_<ts>/spark2/
  3. Copy the oozie-sharelib-spark jar file from the spark ShareLib directory to the spark2 ShareLib directory:

    hdfs dfs -cp \
        /user/oozie/share/lib/lib_<ts>/spark/oozie-sharelib-spark-<version>.jar \
        /user/oozie/share/lib/lib_<ts>/spark2/
  4. Copy the hive-site.xml file from the current spark ShareLib to the spark2 ShareLib:

    hdfs dfs -cp \
        /user/oozie/share/lib/lib_<ts>/spark/hive-site.xml \
        /user/oozie/share/lib/lib_<ts>/spark2/
  5. Copy Python libraries to the spark2 ShareLib:

    hdfs dfs -put \
        /usr/hdp/<version>/spark2/python/lib/py* \
        /user/oozie/share/lib/lib_<ts>/spark2/
  6. Run the Oozie sharelibupdate command:

    oozie admin –sharelibupdate

To verify the configuration, run the Oozie shareliblist command. You should see spark2 in the results.

oozie admin –shareliblist spark2

To run a Spark job with the spark2 ShareLib, add the action.sharelib.for.spark property to the job.properties file, and set its value to spark2:

oozie.action.sharelib.for.spark=spark2

The following examples show a workflow definition XML file, an Oozie job configuration file, and a Python script for running a Spark2-Pi job.

Sample Workflow.xml file for spark2-Pi:

<workflow-app xmlns='uri:oozie:workflow:0.5' name='SparkPythonPi'>
          <start to='spark-node' />
          
          <action name='spark-node'>
            <spark xmlns="uri:oozie:spark-action:0.1">
              <job-tracker>${jobTracker}</job-tracker>
              <name-node>${nameNode}</name-node>
              <master>${master}</master>
              <name>Python-Spark-Pi</name>
              <jar>pi.py</jar>
            </spark>
            <ok to="end" />
            <error to="fail" />
          </action>
          
          <kill name="fail">
            <message>Workflow failed, error message [${wf:errorMessage(wf:lastErrorNode())}]</message>
          </kill>
          <end name='end' />
        </workflow-app>

Sample Job.properties file for spark2-Pi:

nameNode=hdfs://host:8020
jobTracker=host:8050
queueName=default
examplesRoot=examples
oozie.use.system.libpath=true
oozie.wf.application.path=${nameNode}/user/${user.name}/${examplesRoot}/apps/pyspark
master=yarn-cluster
oozie.action.sharelib.for.spark=spark2

Sample Python script, lib/pi.py:

import sys
from random import random
from operator import add
from pyspark import SparkContext
        
if __name__ == "__main__":
"""
Usage: pi [partitions]
"""
sc = SparkContext(appName="Python-Spark-Pi")
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(range(1, n + 1), partitions).map(f).reduce(add)
print("Pi is roughly %f" % (4.0 * count / n))
        
sc.stop()