Creating a Hadoop Container Job

You can create a Hadoop Container job similar to the sample job, which contains four Hadoop steps that correspond to four rows of tables and transformations.

The two rows at the top of the tab are used for the Transfer To and Transfer From steps. Note that the first row contains a text source table and Hadoop target table, where the second row contains a Hadoop source table and a text target table. The two rows at the bottom of the tab are used for the Map Reduce and Pig steps. Note that each row begins with a text source and a Transfer To Hadoop transformation that creates the Hadoop source table for the Hadoop Container transformation. Both rows feed steps that send output to Hadoop target tables.

The steps processed in the Hadoop Container transformation are listed in a table on the Hadoop Steps tab. You can add, edit, reorder, and delete steps by clicking the buttons in the toolbar at the top of the tab.

You can click a row in the table to review its name, description, input, and output in the Details panel at the bottom of the tab. If a step has multiple inputs or outputs, you can use the drop-down arrow to select the object that you need.

You can select a step and click the Properties button to configure, review, and edit its properties.

The Transfer To and Transfer From steps are configured in a window that contains panes that cover Transfer Options and Statement Options.

The Map Reduce step enables you to submit your own Map Reduce code in the context of a job. It is assumed that you know Map Reduce well enough to use it in a production environment. Your Hadoop installation usually includes an example Map Reduce program. You must create your own Map Reduce program in Java and save it to a JAR file. You then specify this JAR file in the properties window for the Map Reduce step, along with some relevant arguments.

Use the Additional Map Reduce jars field to specify a JAR file that contains your user-written Map Reduce code. Use the other fields to specify relevant arguments from that code. The Map Reduce Options section of the Advanced Options window also contains several other options that are not set in the sample job.

Note that the All files in this location check box must be selected on the File Location tab in the properties window for the Map Reduce target table. This step enables you to see the data in the table after the job has completed successfully.

The Pig step contains the following statements:

one = load '/user/test/nodecontainer/NodeContainerPigSource2.txt' using PigStorage(); 
generated = FOREACH one GENERATE $0, $2; 
store generated into '/user/test/nodecontainer/NodeContainerPigTarget.txt'  
USING PigStorage(',')

Note that the All files in this location check box must be selected in the File Location tab in the properties window for the Pig target table. This step enables you to see the data in the table after the job has completed successfully.

The Hadoop Options tab enables you to set options such as server selection, output deletion, pre- and post-process code, and configuration overrides for all of the steps. Note that the pre- and post-process code on this tab is run against the Hadoop server only. This code is not the standard pre- and post-process code that is run on the SAS workspace server. Therefore, SAS code is not appropriate input for these fields.

Run the job and verify that the job completes without error. Then, review the output. You should see the following results: