Can I read M3R: increased performance for in-memory Hadoop jobs on EtoBox?

M3R: increased performance for in-memory Hadoop jobs by Avraham Shinnar; David Cunningham; Vijay Saraswat; Benjamin Herta is a Computer Science article available to read on EtoBox.

What is M3R: increased performance for in-memory Hadoop jobs about?

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.

Who reads M3R: increased performance for in-memory Hadoop jobs?

Author

Avraham Shinnar; David Cunningham; Vijay Saraswat; Benjamin Herta

Publisher

ACM

Published

2012

Language

EN

Field

Computer Science (Physical Sciences)