DDSketch: A fast and fully-mergeable quantile sketch with relative-error guarantees
| Autor: | Homin K. Lee, Jee E. Rim, Charles Masson |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer science Data stream mining Rank (computer programming) Order statistic General Engineering Databases (cs.DB) 02 engineering and technology Sketch Arbitrarily large Computer Science - Databases Bounding overwatch Approximation error 020204 information systems Computer Science - Data Structures and Algorithms 0202 electrical engineering electronic engineering information engineering Data Structures and Algorithms (cs.DS) Algorithm Quantile |
| DOI: | 10.48550/arxiv.1908.10693 |
| Popis: | Summary statistics such as the mean and variance are easily maintained for large, distributed data streams, but order statistics (i.e., sample quantiles) can only be approximately summarized. There is extensive literature on maintaining quantile sketches where the emphasis has been on bounding the rank error of the sketch while using little memory. Unfortunately, rank error guarantees do not preclude arbitrarily large relative errors, and this often occurs in practice when the data is heavily skewed. Given the distributed nature of contemporary large-scale systems, another crucial property for quantile sketches is mergeablility, i.e., several combined sketches must be as accurate as a single sketch of the same data. We present the first fully-mergeable, relative-error quantile sketching algorithm with formal guarantees. The sketch is extremely fast and accurate, and is currently being used by Datadog at a wide-scale. Comment: 11 pages, 11 figures, VLDB |
| Databáze: | OpenAIRE |
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