Apache Spark jobs

Prerequisites

Apache Spark job definition

For more information about creating jobs using the various supported product interfaces, see Defining a job.

Scheduling and stopping a job in HCL Workload Automation

You schedule HCL Workload Automation Apache Spark jobs by defining them in job streams. Add the job to a job stream with all the necessary scheduling arguments and submit the job stream.

You can submit jobs by using the Dynamic Workload Console, Application Lab or the conman command line. See Scheduling and submitting jobs and job streams for information about how to schedule and submit jobs and job streams using the various interfaces.

After submission, when the job is running and is reported in EXEC status in HCL Workload Automation, you can stop it if necessary, by using the kill command. This action stops also the program execution on the Apache Spark server.

Monitoring a job

If the HCL Workload Automation agent stops when you submit the Apache Spark job, or while the job is running, the job restarts automatically as soon as the agent restarts.

For information about how to monitor jobs using the different product interfaces available, see Monitoring HCL Workload Automation jobs.

ApacheSparkJobExecutor.properties

The properties file is automatically generated either when you perform a "Test Connection" from the Dynamic Workload Console in the job definition panels, or when you submit the job to run the first time. Once the file has been created, you can customize it. This is especially useful when you need to schedule several jobs of the same type. You can specify the values in the properties file and avoid having to provide information such as credentials and other information, for each job. You can override the values in the properties files by defining different values at job definition time.

 url= http://<SPARK_SERVER>:8080/json
 sparkurl= http://<SPARK_SERVER>:6066
drivercores=1
drivermemory=1
executorcores=1
executormemory=1
timeout=36000

The timeout property represents the time, in seconds, that HCL Workload Automation waits for a reply from Apache Spark server. When the timeout expires with no reply, the job terminates with abend status. The timeout property can be specified only in the properties file.

For a description of each property, see the corresponding job attribute description in Required and optional attributes for the definition of an Apache Spark job.

Job properties

While the job is running, you can track the status of the job and analyze the properties of the job. In particular, in the Extra Information section, if the job contains variables, you can verify the value passed to the variable from the remote system. Some job streams use the variable passing feature, for example, the value of a variable specified in job 1, contained in job stream A, is required by job 2 in order to run in the same job stream.

conman sj <job_name>;props

The properties are listed in the Extra Information section of the output command.

For information about passing job properties, see Passing job properties from one job to another in the same job stream instance.

 <?xml version="1.0" encoding="UTF-8"?>
jsdl:jobDefinition xmlns:jsdl="http://www.ibm.com/xmlns/prod/scheduling/1.0/jsdl"
xmlns:jsdlapachespark="http://www.ibm.com/xmlns/prod/scheduling/1.0/jsdlapachespark" name="APACHESPARK">
 <jsdl:application name="apachespark">
 <jsdlapachespark:apachespark>
 <jsdlapachespark:ApacheSparkParameters>
 <jsdlapachespark:Connection>
 <jsdlapachespark:connectionInfo>
 <jsdlapachespark:url>{url}</jsdlapachespark:url>
 <jsdlapachespark:sparkurl>{sparkurl}</jsdlapachespark:sparkurl>
 </jsdlapachespark:connectionInfo>
 </jsdlapachespark:Connection>
 <jsdlapachespark:Action>
 <jsdlapachespark:ResourceProperties>
 <jsdlapachespark:resourcename>{resourcename}</jsdlapachespark:resourcename>
 <jsdlapachespark:resourcetype>{resourcetype}</jsdlapachespark:resourcetype>
 <jsdlapachespark:mainclass>{mainclass}</jsdlapachespark:mainclass>
 <jsdlapachespark:arguments>{arguments}</jsdlapachespark:arguments>
 </jsdlapachespark:ResourceProperties>
 <jsdlapachespark:SparkProperties>
 <jsdlapachespark:appname>{appname}</jsdlapachespark:appname>
 <jsdlapachespark:jars>{jars}</jsdlapachespark:jars>
 <jsdlapachespark:deploymode>{deploymode}</jsdlapachespark:deploymode>
 <jsdlapachespark:sparkmaster>{sparkmaster}</jsdlapachespark:sparkmaster>
 <jsdlapachespark:drivercores>{drivercores}</jsdlapachespark:drivercores>
 <jsdlapachespark:drivermemory>{drivermemory}</jsdlapachespark:drivermemory>
 <jsdlapachespark:executorcores>{executorcores}</jsdlapachespark:executorcores>
			            <jsdlapachespark:executormemory>{executormemory}
                                    </jsdlapachespark:executormemory>
				</jsdlapachespark:SparkProperties>
				<jsdlapachespark:EnvVariables>
				<jsdlapachespark:variablelistValues pairsList="</jsdlapachespark:variablelistValue">
                        	</jsdlapachespark:variablelistValues>
				</jsdlapachespark:EnvVariables>
                       </jsdlapachespark:Action>
	           </jsdlapachespark:ApacheSparkParameters>
	</jsdlapachespark:apachespark>
</jsdl:application>
</jsdl:jobDefinition>

Job log content

For information about how to display the job log from the various supported interfaces, see Analyzing the job log.

For example, you can see the job log content by running conman sj <job_name>;stdlist, where <job_name> is the Apache Spark job name.

See also

From the Dynamic Workload Console you can perform the same task as described in

Creating job definitions.

For more information about how to create and edit scheduling objects, see

Designing your Workload.