2008-08-26

hadoop in the cloud - patterns for automation

A very good use for Hadoop is to manage large "batch" jobs, such as log file analysis. A very good use for elastic server resources -- such as Amazon EC2, GoGrid, AppNexus, Flexiscale, etc. -- is to handle large resource needs which are periodic but not necessarily 24/7 requirements, such as large batch jobs.

Running a framework like Hadoop on elastic server resources seems natural. Fire up a few dozen servers, crunch the data, bring down the (relatively smaller) results, and you're done!

What also seems natural is the need to have important batch jobs automated. However, the mechanics of launching a Hadoop cluster on remote resources, monitoring for job completing, downloading results, etc., are far from being automated. At least, not from what we've been able to find in our research.

Corporate security policies come into play. For example, after a Hadoop cluster running for several hours to obtain results, your JobTracker out on EC2 may not be able to initiate a download back to your corporate data center. Policy restrictions may require that to be initiated from the other side.

There may be several steps involved in running a large Hadoop job. Having a VPN running between the corporate data center and the remote resources would be ideal, and allow for simple file transfers. Great, if you can swing it.

Another problem is time: part of the cost-effectiveness of this approach is to run the elastic resources only as long as you need them -- in other words, while the MR jobs are running. That may make the security policies and server automation difficult to manage.

One approach would be to use message queues on either side: in the data center, or in the cloud. The scripts which launch Hadoop could then synchronize with processes on your premises via queues. A queue poller on the corporate data center side could initiate data transfers, for example.

Would be very interested to hear how others are approach this issue.

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