Abstract
Analytical queries virtually always involve aggregation and statistics. SQL offers a wide range of functionalities to summarize data such as associative aggregates, distinct aggregates, ordered-set aggregates, grouping sets, and window functions. In this work, we propose a unified framework for advanced statistics that composes all flavors of complex SQL aggregates from low-level plan operators. These operators can reuse materialized intermediate results, which decouples monolithic aggregation logic and speeds up complex multi-expression queries. The contribution is therefore twofold: our framework modularizes aggregate implementations, and outperforms traditional systems whenever multiple aggregates are combined. We integrated our approach into the high-performance database system Umbra and experimentally show that we compute complex aggregates faster than the state-of-the-art HyPer system.
Original language | English |
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Pages (from-to) | 1001-1013 |
Number of pages | 13 |
Journal | Proceedings of the ACM SIGMOD International Conference on Management of Data |
DOIs | |
State | Published - 2021 |
Event | 2021 International Conference on Management of Data, SIGMOD 2021 - Virtual, Online, China Duration: 20 Jun 2021 → 25 Jun 2021 |
Keywords
- database systems
- query optimization
- query processing