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Execution Primitives for Scalable Joins and Aggregations in Map Reduce

Summary: Proposes execution primitives to scale complex joins and aggregations on MapReduce. Introduces a calculation data unit model driven by user-defined cost functions, with new operators and skew-aware load balancing; implemented in Rubix at LinkedIn for TB-scale data, delivering speedups and cost reductions. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10807
Venue
VLDB
Year
2014
Pagerank
4.5846987e-05
Overall Rank
8,108 | 43.60%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
1,939 From Theory to Practice: Efficient Join Query Evaluation in a Parallel Database System 2015 SIGMOD 0.00010025655
11,835 An Efficient MapReduce Cube Algorithm for Varied Data Distributions 2016 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank Cited Paper Year Venue Pagerank
913 Tenzing A SQL Implementation On The MapReduce Framework 2011 VLDB 0.00015408131
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