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M3R: Increased Performance for In-Memory Hadoop Jobs

Summary: M3R is an in-memory Hadoop MapReduce engine for online analytics on memory-resident clusters, sacrificing resilience for speed. Unchanged HMR execution (Pig/Jaql/SystemML, BigSheets) with large speedups (≈45× for sparse mat-vec) and API extensions that accelerate workloads without altering Hadoop semantics. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10412
Venue
VLDB
Year
2012
Pagerank
7.0242945e-05
Overall Rank
3,511 | 75.60%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
2,714 Minimal MapReduce Algorithms 2013 SIGMOD 8.2426646e-05
3,425 General Incremental Sliding-Window Aggregation 2015 VLDB 7.1042928e-05
5,803 Lifetime-Based Memory Management for Distributed Data Processing Systems 2016 VLDB 5.3208219e-05
7,510 Hone: "Scaling Down" Hadoop on Shared-Memory Systems 2013 VLDB 4.7135369e-05
11,980 Palette: Enabling Scalable Analytics for Big-Memory, Multicore Machines 2014 SIGMOD 4.1905499e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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