Parsing Logs with Apache Pig and Elastic MapReduce

Sample Code & Libraries>Elastic MapReduce>Parsing Logs with Apache Pig and Elastic MapReduce
This tutorial shows you how to develop a simple, log parsing application using Pig and Amazon Elastic MapReduce. The tutorial walks you through using Pig interactively (via SSH) on a subset of your data, which enables you to prototype your script quickly. The tutorial then takes you through uploading the script to Amazon S3 and running on a larger set of input data.


Submitted By: Ian@AWS
AWS Products Used: Amazon Elastic MapReduce
Created On: August 5, 2009 3:57 PM GMT
Last Updated: March 20, 2014 3:30 PM GMT

This article is outdated. For information on how to use Pig to analyze data on EMR, please see our documentation:

Pig is an Apache library that interprets scripts written in a language called Pig Latin and then runs them on a Hadoop cluster. The Pig Latin language is a high level data transformation language that allows you to concentrate on the data transformations you require rather than begin concerned with individual map and reduce functions.

You can find the Pig project home page at and the documentation at Location of Pig Script s3://elasticmapreduce/samples/pig-apache/do-reports2.pig Sample Dataset s3://elasticmapreduce/samples/pig-apache/input Source License Apache License, Version 2.0

Section 1 - Setting up for SSH

If you are already familiar with Amazon EC2 and setting up SSH to access Amazon EC2 instances then you can jump to Section 2.

1.1 - Setting up SSH on your Machine

To launch an interactive Pig job flow, you must have SSH set up on your client PC. If you are using Linux/OSX then likely you already have ssh installed and can type ssh on the command line.

On Microsoft Windows, if you are familiar with Linux, then you can install Cygwin and use "ssh" from the command line. Otherwise you can use PuTTY, which requires additional configuration (described below).

1.2 - Setting up an SSH Key

The next step is setting up an SSH key. You can do this using the AWS Management Console.

  1. Go to and sign in.
  2. Click the "Amazon Elastic EC2" tab.
  3. Click the "Key Pairs" link.
  4. Click the "Create Key Pair" button.
  5. Enter a name and save the key file. Record this name and path you will need it later.
  6. If you are using PuTTY, you will further have to transform this key file into PuTTY format. For more information got to and look under "Private Key Format."

Section 2 - Starting an Interactive Job Flow

2.1 - Starting an Interactive Job Flow from the Console

You are now ready to start your interactive pig job flow.
  1. Go to and sign in.
  2. Click the "Amazon Elastic MapReduce" tab.
  3. Click the "Create New Job Flow" button.

    Figure 2.1.1: New Job Flow Wizard

  4. In the "Job Flow Name" field type a name such as "Pig Interactive Job Flow"
  5. In the "Type" field select "Pig Program", and then click "Continue".
    The console displays the following dialog box.

    Figure 2.1.2: Choosing Interactive Session

  6. Select "Start an Interactive Pig Session" and click "Continue".
    The console displays the following dialog box.

    Figure 2.1.3: Selecting Instance Configuration

  7. Set "Number of Instances" to "1" and set "Type of Instances" to "m1.small" You are using only once instance because you are working on only a small amount of data.
  8. In the "Amazon EC2 Key Pair" chooser, select the name of the key you created in Section 1, and click Continue.
    The console displays the following confirmation screen.

    Figure 2.1.4: Reviewing Job Flow Configuration

  9. Make sure everything looks good and then click "Create Job Flow". This will start your job flow and show you a confirmation dialog. Note that unlike other job flow created using the AWS Management Console this job flow will not terminate until you terminate manually. To terminate the job flow manually, select the job flow and click the "Terminate" button.

2.2 - Waiting for the Job Flow to Start

On the main Elastic MapReduce console screen you should see the job flow you just launched. Click it to get job flow details in the bottom pane.

Figure 2.2.1: Waiting for JobFlow to Start

You will now be returned to the main Elastic MapReduce screen and you should see the job flow you just created in the grid. After the job flow you created has transitioned to waiting click on it and you should get the bottom pane to show its details like this:

Figure 2.2.2: Job Flow is Ready

2.3 - SSH to the Master Instance

You now need to SSH to the master instance. On the Elastic MapReduce console, note the value for Master Public DNS Name. This is the instance you will SSH to.

If the Master Public DNS Name is blank then most likely the Job Flow is still in the STARTING state. Wait for the job flow to transition to WAITING and then click on the job flow to update the detail pane. The Master Public DNS Name field should now be populated.

If you are using the ssh command, to SSH into the master node, use the following command. You should replace and with location of the keypair file you created in Section 1.2 and name of the master node you noted in the console.

  $ ssh -o "ServerAliveInterval 10" -i  hadoop@

For information about using PuTTY go to and follow the instructions under "SSH with PuTTY"

Section 3 - Accessing Pig when in Interactive Mode

3.1 - Starting an Interactive Pig Session

You should now be at the SSH prompt on the master instance. To start using Pig interactively, launch the Pig Grunt shell by typing:

  $ pig

Having run pig should see the Grunt prompt:


3.2 Using the Filesystems

You're going to start by looking at the different file systems you can talk to using Pig. Pig supports the commands "pwd", "cd", "ls" and "cp" for interacting with file systems. So, start by looking at where you currently are:

  grunt> pwd


You can look at directories and files there. You can also cd into Amazon S3 buckets:

  grunt> cd hdfs:///
  grunt> ls


You see that you are now at the root of the hdfs file system. You can look at directories and files there. You can also cd into Amazon S3 buckets:

  grunt> cd s3://elasticmapreduce/samples/pig-apache/input/
  grunt> ls

  s3://elasticmapreduce/samples/pig-apache/input/access_log_1        8803772
  s3://elasticmapreduce/samples/pig-apache/input/access_log_2        8803772
  s3://elasticmapreduce/samples/pig-apache/input/access_log_3        8803772
  s3://elasticmapreduce/samples/pig-apache/input/access_log_4        8803772

The at the end of the file names is Pig telling us that these files have replication factor 1. These are apache access logs that you want our script to process. Eventually, you will want to process all of them, but when you are developing a job flow you only want a portion of the data. That way you can learn quickly if your commands are wrong.

3.4 - The Piggybank

You are now ready to start processing the data. To help with this, you are going to use an add-on library to Pig called the Piggybank. People contribute user defined functions to this open source library. The functions are written in Java but can be called by Pig scripts to do special types of processing. As part of supporting Pig, Amazon has added a lot of functions to the Piggybank to help with String and Datetime processing.

As part of setting up Pig, Elastic MapReduce copies down the Piggybank onto the local hard disk. To set it up for use type:

  grunt> register file:/home/hadoop/lib/pig/piggybank.jar

This command loads the jar. You also need to DEFINE aliases for any of the classes you want to use. The class you use in this tutorial is EXTRACT, which you can define using an alias:

  grunt> DEFINE EXTRACT org.apache.pig.piggybank.evaluation.string.EXTRACT;

3.5 - Loading and Illustrating the Data

You are going to use an internal Pig function that loads each line of the source file as a tuple with a single element, TextLoader:

  grunt> RAW_LOGS = LOAD 's3://elasticmapreduce/samples/pig-apache/input/access_log_1' USING TextLoader as (line:chararray);

Use the ILLUSTRATE command to make Pig process a few lines of the input data and display results:

  grunt> illustrate RAW_LOGS;
  | RAW_LOGS     | line: chararray                                                                                                                                                                                                                                                                                                       |
  |              | - - [21/Jul/2009:13:14:17 -0700] "GET / HTTP/1.1" 200 35942 "-" "Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 6.0; Trident/4.0; SLCC1; .NET CLR 2.0.50727; .NET CLR 3.5.21022; InfoPath.2; .NET CLR 3.5.30729; .NET CLR 3.0.30618; OfficeLiveConnector.1.3; OfficeLivePatch.1.3; MSOffice 12)" |

This is a little hard to read because of the wrapping. What you should see is that Pig is loading the line into a tuple with just a single element --- the line itself. You now need to split the line into fields. To do this, use the EXTRACT Piggybank function, which applies a regular expression to the input and extracts the matched groups as elements of a tuple. The regular expression is a little tricky because the Apache log defines a couple of fields with quotes. What you get is:

  '^(\S+) (\S+) (\S+) \[([\w:/]+\s[+\-]\d{4})\] "(.+?)" (\S+) (\S+) "([^"]*)" "([^"]*)"'

Unfortunately, you can't use this as is because in Pig strings all backslashes must be escaped with a backslash. Adding this change, you get:

  '^(\\S+) (\\S+) (\\S+) \\[([\\w:/]+\\s[+\\-]\\d{4})\\] "(.+?)" (\\S+) (\\S+) "([^"]*)" "([^"]*)"'

Using this expression within a Pig FOREACH statement to generate a new bag, you get:

      EXTRACT(line, '^(\\S+) (\\S+) (\\S+) \\[([\\w:/]+\\s[+\\-]\\d{4})\\] "(.+?)" (\\S+) (\\S+) "([^"]*)" "([^"]*)"')
    as (
      remoteAddr:    chararray, 
      remoteLogname: chararray, 
      user:          chararray, 
      time:          chararray, 
      request:       chararray, 
      status:        int, 
      bytes_string:  chararray, 
      referrer:      chararray, 
      browser:       chararray

Now illustrating this and looking at the LOGS_BASE portion:

  grunt> illustrate LOGS_BASE;
  | LOGS_BASE     | remoteAddr: chararray | remoteLogname: chararray | user: chararray | time: chararray            | request: chararray               | status: int | bytes_string: chararray | referrer: chararray | browser: chararray                                            |
  |               |           | -                        | -               | 21/Jul/2009:18:04:54 -0700 | GET /gwidgets/alexa.xml HTTP/1.1 | 200         | 2969                    | -                   | FeedFetcher-Google; (+ |

You can see that you have split the input into a tuple.

3.4 - Writing a Query

The query you will write determines the top 50 search terms used to refer to our website, Thus, you should look at the referrer element in the tuple. The first thing you do is create a bag containing tuples with just this element:


You want to see what types of things are in this bag. ILLUSTRATE is not good enough --- you want to see more tuples of data. So, you use the DUMP command instead. The DUMP command outputs the complete contents of a bag to the screen. There is usually too much data to display so you have to add a LIMIT instruction:

  grunt> DUMP TEMP;
  2009-08-04 05:52:31,794 [main] INFO  org.apache.pig.backend.local.executionengine.LocalPigLauncher - Successfully stored result in: "file:/tmp/temp1295722813/tmp-2054682351"
  2009-08-04 05:52:31,794 [main] INFO  org.apache.pig.backend.local.executionengine.LocalPigLauncher - Records written : 10
  2009-08-04 05:52:31,794 [main] INFO  org.apache.pig.backend.local.executionengine.LocalPigLauncher - Bytes written : 0
  2009-08-04 05:52:31,794 [main] INFO  org.apache.pig.backend.local.executionengine.LocalPigLauncher - 100% complete!
  2009-08-04 05:52:31,794 [main] INFO  org.apache.pig.backend.local.executionengine.LocalPigLauncher - Success!!

You get a few log messages as the command runs, then the output. You will notice a lot of dash (-) values in the requests that don't have referrers. You also note the which is just our site referring to itself. So let's dive down on the search engines and use FILTER to include only those referrers that have 'bing' or 'google' in them.

  grunt> FILTERED = FILTER REFERRER_ONLY BY referrer matches '.*bing.*' OR referrer matches '.*google.*';
  grunt> TEMP = LIMIT FILTERED 10;
  grunt> DUMP TEMP;


You see that both search engines signify the query terms in the query string using a key of "q" and then separating them with "+". To extract these, the first step is to use our EXTRACT function to grab everything from the "q=" up to the end of a string or an ampersand (&). You then FILTER out any string that does not match our regular expression. Together you get:

  grunt> SEARCH_TERMS = FOREACH FILTERED GENERATE FLATTEN(EXTRACT(referrer, '.*[&\\?]q=([^&]+).*')) as terms:chararray;

Finally, you want to count the search terms. To do this, you use GROUP and COUNT, sort by count.


3.5 - Storing the Data

Now that you have the computation for the data you need to save it to disk. To do that, you use the STORE command to store the data:

  grunt> STORE SEARCH_TERMS_COUNT_SORTED into 'hdfs:///home/hadoop/output/run0';

When you type STORE, Pig blocks everything to run the query. Once it completes, you can see the output using CAT:

  grunt> CAT hdfs:///home/hadoop/output/run0

Section 4: Parameterizing the Script and Uploading it to Amazon S3

4.1 - Converting to Using Parameters

The next step is to add parameters for INPUT and OUTPUT to the script. This will allow you to set the input and output locations of the script while executing it in batch mode.

Change the load and store instructions to use variables $INPUT and $OUTPUT:

  grunt> RAW_LOGS = LOAD '$INPUT' USING TextLoader as (line:chararray);


To supply values for the parameters when running in batch mode use the '-p' option.

  $ pig -p INPUT=s3://elasticmapreduce/samples/pig-apache/input/access_log_1 -p OUTPUT=hdfs:///home/hadoop/output/run1  s3://mybucket/scripts/myscript.pig

Here is the complete pig script.

  register file:/home/hadoop/lib/pig/piggybank.jar
  DEFINE EXTRACT org.apache.pig.piggybank.evaluation.string.EXTRACT;
  RAW_LOGS  = LOAD '$INPUT' USING TextLoader as (line:chararray);
  LOGS_BASE = foreach RAW_LOGS generate 
      EXTRACT (line, '^(\\S+) (\\S+) (\\S+) \\[([\\w:/]+\\s[+\\-]\\d{4})\\] "(.+?)" (\\S+) (\\S+) "([^"]*)" "([^"]*)"')
    as (
      remoteAddr:chararray, remoteLogname:chararray, user:chararray, time:chararray, 
      request:chararray, status:int, bytes_string:chararray, referrer:chararray, 
  FILTERED      = FILTER REFERRER_ONLY BY referrer matches '.*bing.*' OR referrer matches '.*google.*';
  SEARCH_TERMS = FOREACH FILTERED GENERATE FLATTEN(EXTRACT(referrer, '.*[&\\?]q=([^&]+).*')) as terms:chararray;

4.2 - Save to file and Run from the Command Line

Lets quit out of Grunt and save this text to a file. At the grunt shell type

  grunt> quit

To save the grunt script as a file on the box you can from the shell do the following

  $ cat > /home/hadoop/tutorial.pig <

Or if you are familiar with vim or nano you use them to create the file. You could also save the file on your desktop and then scp the file across to the master node.

You can now test that the script runs by invoking pig on it from the command line on the script, setting values for the input and output parameters using the '-p' option, as follows:

  $ pig -p INPUT=s3://elasticmapreduce/samples/pig-apache/input/access_log_1 -p OUTPUT=hdfs:///home/hadoop/output/run2 file:///home/hadoop/tutorial.pig

This will now run the script, and when it finishes the output should be in 'hdfs:///home/hadoop/output/run2'.

4.3 - Upload to Amazon S3

In order to upload the script to Amazon S3 you need to have an Amazon S3 bucket. If you don't have a bucket then as long as you choose a unique name the tool will create the bucket for you. For more information about the limitations on bucket names see

The file can be uploaded using the "hadoop dfs" command, which despite its name can be used for interacting with any filesystem:

  $ hadoop dfs -copyFromLocal /home/hadoop/tutorial.pig s3://

4.4 Terminating the Interactive Job Flow

You are now finished with your interactive job flow, and so need to terminate it. Return to the Elastic MapReduce tab in the Console, then click the job flow, and then click the "Terminate" button.

Then click "OK" to terminate the cluster.

Section 5: Running Script through Console on all the data

You are now up to the final step: running the script on the full set of data through the console. To do this we go to the AWS Console and click "Create New Job Flow" as before, and again select "Pig Program" as the type, after typing a name Press "Continue" This takes you once more to the Pig Details screen:

Figure 5.1: Starting a Batch Mode Pig Session

Enter the following:

Script Location The exact same place you uploaded the script to in 4.4. e.g. s3://
Input Location s3://elasticmapreduce/samples/pig-apache/input
Output Location A location in your bucket that you want the output to go to. e.g. s3://
Extra args leave blank

Elastic MapReduce automatically sets the INPUT and OUTPUT parameters specified above when calling your script. Continue with the defaults through the next step and click the Create Job Flow to launch your job flow.

Your job flow will now launch the cluster, automatically run your script, and then when it completes shut down the cluster. This process should take about 15 minutes. When it is done your output will be in the Amazon S3 bucket you specified. A good, free tool for downloading this data is the Amazon S3 Organizer Firefox plug-in, which you can download from

Section 6: Further Reading and Resources

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