Building Advanced SQL Analytics from Low-Level Plan Operators

André Kohn, Viktor Leis, Thomas Neumann

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations

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 languageEnglish
Pages (from-to)1001-1013
Number of pages13
JournalProceedings of the ACM SIGMOD International Conference on Management of Data
DOIs
StatePublished - 2021
Event2021 International Conference on Management of Data, SIGMOD 2021 - Virtual, Online, China
Duration: 20 Jun 202125 Jun 2021

Keywords

  • database systems
  • query optimization
  • query processing

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