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Runtime Optimization of Join Location in Parallel Data Management Systems

Summary: Chooses map- versus reduce-side joins per key at runtime, balancing skew, transfer, replication, and UDF costs across system resources. An extended ski-rental algorithm provides worst-case guarantees; Hadoop, Spark, and Muppet implementations improve throughput. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11621
Venue
VLDB
Year
2017
Pagerank
5.093636e-05
Overall Rank
12,001 | 17.67%
DOI
10.14778/3137628.3137645

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Authors

BibTeX Citation

@article{chandra_vldb17,
        title = {{Runtime Optimization of Join Location in Parallel Data Management Systems}},
        author = {Chandra, Bikash and Sudarshan, S.},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {11},
        pages = {1490--1503},
        doi = {10.14778/3137628.3137645},
        url = {https://doi.org/10.14778/3137628.3137645},
        year = {2017}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 8 of 8 cited papers.

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

Rank Cited Paper Year Venue Pagerank
122 Approximate Frequency Counts over Data Streams 2002 VLDB 0.00031260115
220 Storm @Twitter 2014 SIGMOD 0.00024244587
452 An Architecture for Parallel Topic Models 2010 VLDB 0.00018146809
481 Practical Skew Handling in Parallel Joins 1992 VLDB 0.00017780716
885 Finding Frequent Items in Data Streams 2008 VLDB 0.00013419017
2,357 Muppet: MapReduce-Style Processing of Fast Data 2012 VLDB 8.7013171e-05
2,941 Automatic Partitioning of Database Applications 2012 VLDB 7.937125e-05
4,283 Lifting the Burden of History from Adaptive Query Processing 2004 VLDB 6.7836167e-05
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