MapReduce for the Masses: Zero to Hadoop in Five Minutes with Common Crawl

Common Crawl aims to change the big data game with our repository of over 40 terabytes of high-quality web crawl information into the Amazon cloud, the net total of 5 billion crawled pages. In this blog post, we’ll show you how you can harness the power of MapReduce data analysis against the Common Crawl dataset with nothing more than five minutes of your time, a bit of local configuration, and 25 cents.

When Google unveiled its MapReduce algorithm to the world in an academic paper in 2004, it shook the very foundations of data analysis. By establishing a basic pattern for writing data analysis code that can run in parallel against huge datasets, speedy analysis of data at massive scale finally became a reality, turning many orthodox notions of data analysis on their head.

With the advent of the Hadoop project, it became possible for those outside the Googleplex to tap into the power of the MapReduce pattern, but one outstanding question remained: where do we get the source data to feed this unbelievably powerful tool?

This is the very question we hope to answer with this blog post, and the example we’ll use to demonstrate how is a riff on the canonical Hadoop Hello World program, a simple word counter, but the twist is that we’ll be running it against the Internet.

When you’ve got a taste of what’s possible when open source meets open data, we’d like to whet your appetite by asking you to remix this code. Show us what you can do with Common Crawl and stay tuned as we feature some of the results!

Ready to get started? Watch our screencast and follow along below:

Step 1 – Install Git and Eclipse

We first need to install a few important tools to get started:

Eclipse (for writing Hadoop code)

How to install (Windows and OS X):

Download the “Eclipse IDE for Java developers” installer package located at:

http://www.eclipse.org/downloads/

How to install (Linux):

Run the following command in a terminal:

RHEL/Fedora

# sudo yum install eclipse

Ubuntu/Debian

# sudo apt-get install eclipse

Git (for retrieving our sample application)

How to install (Windows)

Install the latest .EXE from:

http://code.google.com/p/msysgit/downloads/list

How to install (OS X)

Install the appropriate .DMG from:

http://code.google.com/p/git-osx-installer/downloads/list

How to install (Linux)

Run the following command in a terminal:

RHEL/Fedora

# sudo yum install git

Ubuntu/Debian

# sudo apt-get install git

Step 2 – Check out the code and compile the HelloWorld JAR

Now that you’ve installed the packages you need to play with our code, run the following command from a terminal/command prompt to pull down the code:

# git clone git://github.com/ssalevan/cc-helloworld.git

Next, start Eclipse. Open the File menu then select “Project” from the “New” menu. Open the “Java” folder and select “Java Project from Existing Ant Buildfile”. Click Browse, then locate the folder containing the code you just checked out (if you didn’t change the directory when you opened the terminal, it should be in your home directory) and select the “build.xml” file. Eclipse will find the right targets, and tick the “Link to the buildfile in the file system” box, as this will enable you to share the edits you make to it in Eclipse with git.

We now need to tell Eclipse how to build our JAR, so right click on the base project folder (by default it’s named “Hello World”) and select “Properties” from the menu that appears. Navigate to the Builders tab in the left hand panel of the Properties window, then click “New”. Select “Ant Builder” from the dialog which appears, then click OK.

To configure our new Ant builder, we need to specify three pieces of information here: where the buildfile is located, where the root directory of the project is, and which ant build target we wish to execute. To set the buildfile, click the “Browse File System” button under the “Buildfile:” field, and find the build.xml file which you found earlier. To set the root directory, click the “Browse File System” button under the “Base Directory:” field, and select the folder into which you checked out our code. To specify the target, enter “dist” without the quotes into the “Arguments” field. Click OK and close the Properties window.

Finally, right click on the base project folder and select “Build Project”, and Ant will assemble a JAR, ready for use in Elastic MapReduce.

Step 3 – Get an Amazon Web Services account (if you don’t have one already) and find your security credentials

If you don’t already have an account with Amazon Web Services, you can sign up for one at the following URL:

https://aws-portal.amazon.com/gp/aws/developer/registration/index.html

Once you’ve registered, visit the following page and copy down your Access Key ID and Secret Access Key:

https://aws-portal.amazon.com/gp/aws/developer/account/index.html?action=access-key

This information can be used by any Amazon Web Services client to authorize things that cost money, so be sure to keep this information in a safe place.

Step 4 – Upload the HelloWorld JAR to Amazon S3

Uploading the JAR we just built to Amazon S3 is a lot simpler than it sounds. First, visit the following URL:

https://console.aws.amazon.com/s3/home

Next, click “Create Bucket”, give your bucket a name, and click the “Create” button. Select your new S3 bucket in the left-hand pane, then click the “Upload” button, and select the JAR you just built. It should be located here:

<your checkout dir>/dist/lib/HelloWorld.jar

Step 5 – Create an Elastic MapReduce job based on your new JAR

Now that the JAR is uploaded into S3, all we need to do is to point Elastic MapReduce to it, and as it so happens, that’s pretty easy to do too! Visit the following URL:

https://console.aws.amazon.com/elasticmapreduce/home

and click the “Create New Job Flow” button. Give your new flow a name, and tick the “Run your own application” box. Select “Custom JAR” from the “Choose a Job Type” menu and click the “Continue” button.

The next field in the wizard will ask you which JAR to use and what command-line arguments to pass to it. Add the following location:

s3n://<your bucket name>/HelloWorld.jar

then add the following arguments to it:

org.commoncrawl.tutorial.HelloWorld <your aws secret key id> <your aws secret key> 2010/01/07/18/1262876244253_18.arc.gz s3n://<your bucket name>/helloworld-out

CommonCrawl stores its crawl information as GZipped ARC-formatted files (http://www.archive.org/web/researcher/ArcFileFormat.php), and each one is indexed using the following strategy:

/YYYY/MM/DD/the hour that the crawler ran in 24-hour format/*.arc.gz

Thus, by passing these arguments to the JAR we uploaded, we’re telling Hadoop to:

1. Run the main() method in our HelloWorld class (located at org.commoncrawl.tutorial.HelloWorld)

2. Log into Amazon S3 with your AWS access codes

3. Count all the words taken from a chunk of what the web crawler downloaded at 6:00PM on January 7th, 2010

4. Output the results as a series of CSV files into your Amazon S3 bucket (in a directory called helloworld-out)

Edit 12/21/11: Updated to use directory prefix notation instead of glob notation (thanks Petar!)

If you prefer to run against a larger subset of the crawl, you can use directory prefix notation to specify a more inclusive set of data. For instance:

2010/01/07/18 – All files from this particular crawler run (6PM, January 7th 2010)

2010/ – All crawl files from 2010

Don’t worry about the continue fields for now, just accept the default values. If you’re offered the opportunity to use debugging, I recommend enabling it to be able to see your job in action. Once you’ve clicked through them all, click the “Create Job Flow” button and your Hadoop job will be sent to the Amazon cloud.

Step 6 – Watch the show

Now just wait and watch as your job runs through the Hadoop flow; you can look for errors by using the Debug button. Within about 10 minutes, your job will be complete. You can view results in the S3 Browser panel, located here. If you download these files and load them into a text editor, you can see what came out of the job. You can take this sort of data and add it into a database, or create a new Hadoop OutputFormat to export into XML which you can render into HTML with an XSLT, the possibilities are pretty much endless.

Step 7 – Start playing!

If you find something cool in your adventures and want to share it with us, we’ll feature it on our site if we think it’s cool too. To submit a remix, push your codebase to GitHub or Gitorious and send a message to our user group about it: we promise we’ll look at it.