Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.01250 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911900613214208 |
|---|---|
| author | Zhuang, Zhutao Zeng, Xinqi Chen, Zhiguang |
| author_facet | Zhuang, Zhutao Zeng, Xinqi Chen, Zhiguang |
| contents | Key\-value separation is used in LSM\-tree to stored large value in separate log files to reduce write amplification, but requires garbage collection to garbage collect invalid values. Existing garbage collection techniques in LSM\-tree typically adopt static parameter based garbage collection to garbage collect obsolete values which struggles to achieve low write amplification and it's challenging to find proper parameter for garbage collection triggering. In this work we introduce DumpKV, which introduces learning based lifetime aware garbage collection with dynamic lifetime adjustment to do efficient garbage collection to achieve lower write amplification. DumpKV manages large values using trained lightweight model with features suitable for various application based on past write access information of keys to give lifetime prediction for each individual key to enable efficient garbage collection. To reduce interference to write throughput DumpKV conducts feature collection during L0\-L1 compaction leveraging the fact that LSM\-tree is small under KV separation. Experimental results show that DumpKV achieves lower write amplification by 38\%\-73\% compared to existing key\-value separation garbage collection LSM\-tree stores with small feature storage overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01250 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DumpKV: Learning based lifetime aware garbage collection for key value separation in LSM-tree Zhuang, Zhutao Zeng, Xinqi Chen, Zhiguang Databases Artificial Intelligence Machine Learning Key\-value separation is used in LSM\-tree to stored large value in separate log files to reduce write amplification, but requires garbage collection to garbage collect invalid values. Existing garbage collection techniques in LSM\-tree typically adopt static parameter based garbage collection to garbage collect obsolete values which struggles to achieve low write amplification and it's challenging to find proper parameter for garbage collection triggering. In this work we introduce DumpKV, which introduces learning based lifetime aware garbage collection with dynamic lifetime adjustment to do efficient garbage collection to achieve lower write amplification. DumpKV manages large values using trained lightweight model with features suitable for various application based on past write access information of keys to give lifetime prediction for each individual key to enable efficient garbage collection. To reduce interference to write throughput DumpKV conducts feature collection during L0\-L1 compaction leveraging the fact that LSM\-tree is small under KV separation. Experimental results show that DumpKV achieves lower write amplification by 38\%\-73\% compared to existing key\-value separation garbage collection LSM\-tree stores with small feature storage overhead. |
| title | DumpKV: Learning based lifetime aware garbage collection for key value separation in LSM-tree |
| topic | Databases Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2406.01250 |