Small Selectivities Matter: Lifting the Burden of Empty Samples
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Every year more and more advanced approaches to cardinality estimation are published, using learned models or other data and workload specific synopses. In contrast, the majority of commercial in-memory systems still relies on sampling. It is arguably the most general and easiest estimator to implement. While most methods do not seem to improve much over sampling-based estimators in the presence of non-selective queries, sampling struggles with highly selective queries due to limitations of the sample size. Especially in situations where no sample tuple qualifies, optimizers fall back to basic heuristics that ignore attribute correlations and lead to large estimation errors. In this work, we present a novel approach, dealing with these 0-Tuple Situations. It is ready to use in any DBMS capable of sampling, showing a negligible impact on optimization time. Our experiments on real world and synthetic data sets demonstrate up to two orders of magnitude reduced estimation errors. Enumerating single filter predicates according to our estimates reveals 1.3 to 1.8 times faster query responses for complex filters.
Details
Original language | English |
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Title of host publication | SIGMOD '21: Proceedings of the 2021 International Conference on Management of Data |
Publisher | Association for Computing Machinery, Inc |
Pages | 697-709 |
Number of pages | 13 |
Publication status | Published - 2021 |
Peer-reviewed | Yes |
Conference
Title | 2021 International Conference on Management of Data, SIGMOD 2021 |
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Duration | 20 - 25 June 2021 |
City | Virtual, Online |
Country | China |
External IDs
Scopus | 85108979368 |
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ORCID | /0000-0001-8107-2775/work/142253441 |
Keywords
ASJC Scopus subject areas
Keywords
- beta distribution, filter predicate ordering, in-memory, olap, sampling, small selectivity