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arXiv · 1208.4168

M3R: Increased performance for in-memory Hadoop jobs

Avraham Shinnar·David Cunningham·Benjamin Herta·Vijay Saraswat
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Abstract

Main Memory Map Reduce (M3R) is a new implementation of the Hadoop Map Reduce (HMR) API targeted at online analytics on high mean-time-to-failure clusters. It does not support resilience, and supports only those workloads which can fit into cluster memory. In return, it can run HMR jobs unchanged -- including jobs produced by compilers for higher-level languages such as Pig, Jaql, and SystemML and interactive front-ends like IBM BigSheets -- while providing significantly better performance than the Hadoop engine on several workloads (e.g. 45x on some input sizes for sparse matrix vector multiply). M3R also supports extensions to the HMR API which can enable Map Reduce jobs to run faster on the M3R engine, while not affecting their performance under the Hadoop engine.

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Avraham Shinnar, David Cunningham, Benjamin Herta, Vijay Saraswat. 2012-08-21. M3R: Increased performance for in-memory Hadoop jobs. https://arxiv.org/abs/1208.4168

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