Runtime Optimization of Join Location in Parallel Data Management Systems
Summary: Per-key runtime choice between map-side and reduce-side joins in parallel storage, accounting for UDFs and transfer costs. Extends ski-rental with multi-resource load balancing (CPU, network, I/O) and worst-case guarantees; implemented on Hadoop, Spark, and Muppet; yields throughput gains. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Bikash Chandra
- 2. S. Sudarshan
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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 |
|---|---|---|---|---|
| 168 | Approximate Frequency Counts over Data Streams | 2002 | VLDB | 0.0003915627 |
| 287 | Storm @Twitter | 2014 | SIGMOD | 0.00028917909 |
| 330 | An Architecture for Parallel Topic Models | 2010 | VLDB | 0.00027271063 |
| 548 | Practical Skew Handling in Parallel Joins | 1992 | VLDB | 0.00020369531 |
| 831 | Finding Frequent Items in Data Streams | 2008 | VLDB | 0.00016094846 |
| 2,612 | Muppet: MapReduce-Style Processing of Fast Data | 2012 | VLDB | 8.4566845e-05 |
| 3,312 | Automatic Partitioning of Database Applications | 2012 | VLDB | 7.2356116e-05 |
| 4,945 | Lifting the Burden of History from Adaptive Query Processing | 2004 | VLDB | 5.8115261e-05 |
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