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Giraph : Large-scale graph processing on Hadoop

Web and online social graphs have been rapidly growing in size and
scale during the past decade.  In 2008, Google estimated that the
number of web pages reached over a trillion.  Online social networking
and email sites, including Yahoo!, Google, Microsoft, Facebook,
LinkedIn, and Twitter, have hundreds of millions of users and are
expected to grow much more in the future.  Processing these graphs
plays a big role in relevant and personalized information for users,
such as results from a search engine or news in an online social
networking site.

Graph processing platforms to run large-scale algorithms (such as page
rank, shared connections, personalization-based popularity, etc.) have
become quite popular.  Some recent examples include Pregel and HaLoop.
For general-purpose big data computation, the map-reduce computing
model has been well adopted and the most deployed map-reduce
infrastructure is Apache Hadoop.  We have implemented a
graph-processing framework that is launched as a typical Hadoop job to
leverage existing Hadoop infrastructure, such as Amazon’s EC2.  Giraph
builds upon the graph-oriented nature of Pregel but additionally adds
fault-tolerance to the coordinator process with the use of ZooKeeper
as its centralized coordination service and is in the process of being
open-sourced.

Giraph follows the bulk-synchronous parallel model relative to graphs
where vertices can send messages to other vertices during a given
superstep.  Checkpoints are initiated by the Giraph infrastructure at
user-defined intervals and are used for automatic application restarts
when any worker in the application fails.  Any worker in the
application can act as the application coordinator and one will
automatically take over if the current application coordinator fails.

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Building and testing:

You will need the following:
- Java 1.6
- Maven 2 or higher

Use the maven commands to:
- compile (i.e mvn compile)
- package (i.e. mvn package)
- test (i.e. mvn test)
-- For testing, one can submit the test to a running Hadoop instance
   (i.e. mvn test -Dprop.mapred.job.tracker=localhost:50300)

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Running:

You will need the following:
- Hadoop 0.20.203 supported, other versions may work as well