ExaLogLog: Space-Efficient and Practical Approximate Distinct Counting up to the Exa-Scale
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866915174257000448 |
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| author | Ertl, Otmar |
| author_facet | Ertl, Otmar |
| contents | This work introduces ExaLogLog, a new data structure for approximate distinct counting, which has the same practical properties as the popular HyperLogLog algorithm. It is commutative, idempotent, mergeable, reducible, has a constant-time insert operation, and supports distinct counts up to the exa-scale. At the same time, as theoretically derived and experimentally verified, it requires 43% less space to achieve the same estimation error. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13726 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ExaLogLog: Space-Efficient and Practical Approximate Distinct Counting up to the Exa-Scale Ertl, Otmar Data Structures and Algorithms Databases This work introduces ExaLogLog, a new data structure for approximate distinct counting, which has the same practical properties as the popular HyperLogLog algorithm. It is commutative, idempotent, mergeable, reducible, has a constant-time insert operation, and supports distinct counts up to the exa-scale. At the same time, as theoretically derived and experimentally verified, it requires 43% less space to achieve the same estimation error. |
| title | ExaLogLog: Space-Efficient and Practical Approximate Distinct Counting up to the Exa-Scale |
| topic | Data Structures and Algorithms Databases |
| url | https://arxiv.org/abs/2402.13726 |