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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2405.08411 |
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| _version_ | 1866913519037841408 |
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| author | Xiong, Tao Wang, Yong |
| author_facet | Xiong, Tao Wang, Yong |
| contents | Online controlled experiment (also called A/B test or experiment) is the most important tool for decision-making at a wide range of data-driven companies like Microsoft, Google, Meta, etc. Metric computation is the core procedure for reaching a conclusion during an experiment. With the growth of experiments and metrics in an experiment platform, computing metrics efficiently at scale becomes a non-trivial challenge. This work shows how metric computation in WeChat experiment platform can be done efficiently using bit-sliced index (BSI) arithmetic. This approach has been implemented in a real world system and the performance results are presented, showing that the BSI arithmetic approach is very suitable for large-scale metric computation scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08411 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large-Scale Metric Computation in Online Controlled Experiment Platform Xiong, Tao Wang, Yong Distributed, Parallel, and Cluster Computing Online controlled experiment (also called A/B test or experiment) is the most important tool for decision-making at a wide range of data-driven companies like Microsoft, Google, Meta, etc. Metric computation is the core procedure for reaching a conclusion during an experiment. With the growth of experiments and metrics in an experiment platform, computing metrics efficiently at scale becomes a non-trivial challenge. This work shows how metric computation in WeChat experiment platform can be done efficiently using bit-sliced index (BSI) arithmetic. This approach has been implemented in a real world system and the performance results are presented, showing that the BSI arithmetic approach is very suitable for large-scale metric computation scenarios. |
| title | Large-Scale Metric Computation in Online Controlled Experiment Platform |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2405.08411 |