Name
Abstract | ||
|---|---|---|
Database architectures typically process queries one-at-a-time, executing concurrent queries in independent execution contexts. Often, such a design leads to unpredictable performance and poor scalability. One approach to circumvent the problem is to take advantage of sharing opportunities across concurrently running queries. In this paper we propose Many-Query Join (MQJoin), a novel method for sharing the execution of a join that can efficiently deal with hundreds of concurrent queries. This is achieved by minimizing redundant work and making efficient use of main-memory bandwidth and multi-core architectures. Compared to existing proposals, MQJoin is able to efficiently handle larger workloads regardless of the schema by exploiting more sharing opportunities. We also compared MQJoin to two commercial main-memory column-store databases. For a TPC-H based workload, we show that MQJoin provides 2-5x higher throughput with significantly more stable response times. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.14778/2904121.2904124 | PROCEEDINGS OF THE VLDB ENDOWMENT |
Field | DocType | Volume |
Joins,Workload,Computer science,Bandwidth (signal processing),Throughput,Schema (psychology),Database,Scalability,Distributed computing | Journal | 9 |
Issue | ISSN | Citations |
6 | 2150-8097 | 4 |
PageRank | References | Authors |
0.41 | 18 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Darko Makreshanski | 1 | 33 | 3.50 |
| Georgios Giannikis | 2 | 80 | 5.10 |
| Gustavo Alonso | 3 | 5476 | 612.79 |
| Donald Kossmann | 4 | 6220 | 603.55 |