The AHA-Tree: An Adaptive Index for HTAP Workloads
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929384871428096 |
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| author | Xing, Lu Aref, Walid G. |
| author_facet | Xing, Lu Aref, Walid G. |
| contents | In this demo, we realize data indexes that can morph from being write-optimized at times to being read-optimized at other times nonstop with zero-down time during the workload transitioning. These data indexes are useful for HTAP systems (Hybrid Transactional and Analytical Processing Systems), where transactional workloads are write-heavy while analytical workloads are read-heavy. Traditional indexes, e.g., B+-tree and LSM-Tree, although optimized for one kind of workload, cannot perform equally well under all workloads. To migrate from the write-optimized LSM-Tree to a read-optimized B+-tree is costly and mandates some system down time to reorganize data. We design adaptive indexes that can dynamically morph from a pure LSM-tree to a pure buffered B-tree back and forth, and has interesting states in-between. There are two challenges: allowing concurrent operations and avoiding system down time. This demo benchmarks the proposed AHA-Tree index under dynamic workloads and shows how the index evolves from one state to another without blocking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08746 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The AHA-Tree: An Adaptive Index for HTAP Workloads Xing, Lu Aref, Walid G. Databases In this demo, we realize data indexes that can morph from being write-optimized at times to being read-optimized at other times nonstop with zero-down time during the workload transitioning. These data indexes are useful for HTAP systems (Hybrid Transactional and Analytical Processing Systems), where transactional workloads are write-heavy while analytical workloads are read-heavy. Traditional indexes, e.g., B+-tree and LSM-Tree, although optimized for one kind of workload, cannot perform equally well under all workloads. To migrate from the write-optimized LSM-Tree to a read-optimized B+-tree is costly and mandates some system down time to reorganize data. We design adaptive indexes that can dynamically morph from a pure LSM-tree to a pure buffered B-tree back and forth, and has interesting states in-between. There are two challenges: allowing concurrent operations and avoiding system down time. This demo benchmarks the proposed AHA-Tree index under dynamic workloads and shows how the index evolves from one state to another without blocking. |
| title | The AHA-Tree: An Adaptive Index for HTAP Workloads |
| topic | Databases |
| url | https://arxiv.org/abs/2406.08746 |