How to invoke a Pig script from a PDI job.

Prerequisites

In order follow along with this how-to guide you will need the following:

• MapR
• Pig
• Pentaho Data Integration

Sample Files

The sample data file needed for this guide is:

NOTE:If you have completed the Using Pentaho MapReduce to Parse Weblog Data in MapR guide, then the necessary files will already be in the proper location.
This file should be placed in the /weblogs/parse directory of the CLDB using the following commands.

hadoop fs -mkdir /weblogs
hadoop fs -mkdir /weblogs/parse
hadoop fs -put weblogs_parse.txt /weblogs/parse/part-00000

Step-by-Step

Create a Pig Script

In this task you are going to create a Pig Script that you will call from within a PDI job.

1. Create Pig Script: Using a text editor create a new file containing the following PigLatin script:

   weblogs = LOAD '/weblogs/parse/part*' USING PigStorage('\t')
           AS (
   client_ip:chararray,
   full_request_date:chararray,
   day:int,
   month:chararray,
   month_num:int,
   year:int,
   hour:int,
   minute:int,
   second:int,
   timezone:chararray,
   http_verb:chararray,
   uri:chararray,
   http_status_code:chararray,
   bytes_returned:chararray,
   referrer:chararray,
   user_agent:chararray
   );
   
   weblog_group = GROUP weblogs by (client_ip, year, month_num);
   weblog_count = FOREACH weblog_group GENERATE group.client_ip, group.year, group.month_num,  COUNT_STAR(weblogs) as pageviews;
   
   STORE weblog_count INTO '/weblogs/aggregate_pig';
   

   
   
2. Save Script: Save the script as aggregate_pig.pig in a folder of your choice.

Create a Job to Aggregate Web Log Data Using a Pig Script

In this task you will create a job that runs the created Pig script to build an aggregate file of weblog data. The file will contain a count of pageviews for each IP address by month and year.

1. Start PDI on your desktop. Once it is running choose 'File' -> 'New' -> 'Job' from the menu system or click on the 'New file' icon on the toolbar and choose the 'Job' option.

   Speed Tip

   You can download the Kettle Job aggregate_pig.kjb already completed

   
   
2. Add a Start Job Entry: You need to tell PDI where to start the job, so expand the 'General' section of the Design palette and drag a 'Start' node onto the job canvas. Your canvas should look like:
   
   
   
3. Add a Pig Script Executor Job Entry: You are going to execute a Pig script in this job, so expand the 'Big Data' section of the Design palette and drag a 'Pig Script Executor' node onto the job canvas. Your canvas should look like:
   
   
   
4. Connect the Start and Pig Script steps: Hover the mouse over the 'Start' node and a tooltip will appear. Click on the output connector (the green arrow pointing to the right) and drag a connector arrow to the 'Pig Script Executor' node. Your canvas should look like this:
   
   
   
5. Edit the Pig Script Job Entry: Double-click on the 'Pig Script Executor' node to edit its properties. Enter this information:
   1. Hadoop distribution: Select 'MapR'
   2. HDFS hostname, HDFS port, Job tracker hostname, Job tracker port: your MapR connection information. For local single node clusters these may be left empty.
   3. Pig script: Browse to the Pig Script you just created and select it.
   4. Check 'Enable blocking'
      When you are done your window should look like:
      
      Click 'OK' to close the window.
      
      
6. Save the Job: Choose 'File' -> 'Save as...' from the menu system. Save the transformation as 'aggregate_pig.kjb' into a folder of your choice.
   
   
7. Run the Job: Choose 'Action' -> 'Run' from the menu system or click on the green run button on the job toolbar. A 'Execute a job' window will open. Click on the 'Launch' button. An 'Execution Results' panel will open at the bottom of the PDI window and it will show you the progress of the job as it runs. After a few seconds the job should finish successfully:
   
   If any errors occurred the job step that failed will be highlighted in red and you can use the 'Logging' tab to view error messages.

Check MapR for the Pig Generated File

1. If you have mounted your MapR CLDB onto your local machine you may verify the file loaded by navigating to the MapR directory.

   head /mapr/my.cluster.com/weblogs/aggregate_pig/part-r-00000
   

   This should return the first few rows of the aggregated file.
   
2. If you have not mounted your MapR CLDB onto your local machine you may alternatively check MapR by:

   hadoop fs -cat /weblogs/aggregate_pig/part-r-00000 | head
   

   This should return the first few rows of the aggregated file.

Summary

During this guide you learned how to invoke a Pig Script from a PDI Job.