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Tag Archives: online aggregation

oscilloHadoop MapReduce is a batch-processing system.  Why?  Because that’s the way Google described their MapReduce implementation.

But it doesn’t have to be that way. Introducing HOP: the Hadoop Online Prototype [updated link to final NSDI ’10 version]. With modest changes to the structure of Hadoop, we were able to convert it from a batch-processing system to an interactive, online system that can provide features like “early returns” from big jobs, and continuous data stream processing, while preserving the simple MapReduce programming and fault tolerance models popularized by Google and Hadoop.  And by the way, it exposes pipeline parallelism that can even make batch jobs finish faster.  This is a project led by Tyson Condie, in collaboration with folks at Berkeley and Yahoo! Research.

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One thing I plan to do here is jot down ideas I don’t have time to work on myself. Here’s the first installment in what will hopefully be a running series of “Research Gimme‘s”. Anybody who wants to run with this, I’d love to hear what you’re up to.

So…. who’s going to re-examine Online Aggregation in the Hadoop context? Goodness knows it’d be useful. It will require moving Hadoop beyond a slavish implementation of the Google MapReduce paper. That’s got to be a good thing… Here’s the start of the program:

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