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Microsoft REEF, new open source big data framework


REEF (Retainable Evaluator Execution Framework) is a big data framework that sits on top of Hadoop new YARN resource manager, and is especially well suited for building machine learning jobs.



GigaOM, By Derrick Harris, Aug 12, 2013.

MicrosoftMicrosoft has developed a big data framework called REEF (a graciously simple acronym for Retainable Evaluator Execution Framework) that the company intends to open source in about a month. REEF is designed to run on top of YARN, the next-generation resource manager for Hadoop, and is particularly well suited for building machine learning jobs.

Microsoft Technical Fellow and CTO of Information Services Raghu Ramakrishnan explained REEF and Microsoft’s plans to open source it during a Monday morning keynote at KDD-2013, the ACM Knowledge Discovery and Data Mining conference, taking place in Chicago.

YARN is a resource manager developed as part of the Apache Hadoop project that lets users run and manage multiple types of jobs (e.g., batch MapReduce, stream processing with Storm and/or a graph-processing package) atop the same cluster of physical machines. This makes it possible not only to consolidate the number of systems that an organization has to manage, but also to run different types of analysis on top of the same data from the same place. In some cases, the entire data workflow can be carried out on just one cluster of machines.

Read more.

See also Big Data at Microsoft, a 27-page PDF by Raghu Ramakrishnan, which explains REEF and YARN.

From KDD-2013 Keynote by Raghu Ramakrishnan, Technical Fellow and CTO Information Services, Microsoft

Hadoop has become a key building block in the new generation of scale-out systems. Early versions of analytic tools over Hadoop, such as Hive and Pig for SQL-like queries, were implemented by translation into Map-Reduce computations. This approach has inherent limitations, and the emergence of resource managers such as YARN and Mesos has opened the door for newer analytic tools to bypass the Map-Reduce layer.

This trend is especially significant for iterative computations such as graph analytics and machine learning, for which Map-Reduce is widely recognized to be a poor fit. In this talk, I will examine this architectural trend, and argue that resource managers are a first step in re-factoring the early implementations of Map-Reduce, and that more work is needed if we wish to support a variety of analytic tools on a common scale-out computational fabric.

I will then present REEF, which runs on top of resource managers like YARN and provides support for task monitoring and restart, data movement and communications, and distributed state management. Finally, I will illustrate the value of using REEF to implement iterative algorithms for graph analytics and machine learning.


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