TierBase: A Workload-Driven Cost-Optimized Key-Value Store
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909606447415296 |
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| author | Shen, Zhitao Yang, Shiyu Chen, Weibo Wang, Kunming Li, Yue Jin, Jiabao Jia, Wei Chen, Junwei Su, Yuan Duan, Xiaoxia Chen, Wei Wang, Lei Song, Jie Ruan, Ruoyi Lin, Xuemin |
| author_facet | Shen, Zhitao Yang, Shiyu Chen, Weibo Wang, Kunming Li, Yue Jin, Jiabao Jia, Wei Chen, Junwei Su, Yuan Duan, Xiaoxia Chen, Wei Wang, Lei Song, Jie Ruan, Ruoyi Lin, Xuemin |
| contents | In the current era of data-intensive applications, the demand for high-performance, cost-effective storage solutions is paramount. This paper introduces a Space-Performance Cost Model for key-value store, designed to guide cost-effective storage configuration decisions. The model quantifies the trade-offs between performance and storage costs, providing a framework for optimizing resource allocation in large-scale data serving environments. Guided by this cost model, we present TierBase, a distributed key-value store developed by Ant Group that optimizes total cost by strategically synchronizing data between cache and storage tiers, maximizing resource utilization and effectively handling skewed workloads. To enhance cost-efficiency, TierBase incorporates several optimization techniques, including pre-trained data compression, elastic threading mechanisms, and the utilization of persistent memory. We detail TierBase's architecture, key components, and the implementation of cost optimization strategies. Extensive evaluations using both synthetic benchmarks and real-world workloads demonstrate TierBase's superior cost-effectiveness compared to existing solutions. Furthermore, case studies from Ant Group's production environments showcase TierBase's ability to achieve up to 62% cost reduction in primary scenarios, highlighting its practical impact in large-scale online data serving. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06556 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TierBase: A Workload-Driven Cost-Optimized Key-Value Store Shen, Zhitao Yang, Shiyu Chen, Weibo Wang, Kunming Li, Yue Jin, Jiabao Jia, Wei Chen, Junwei Su, Yuan Duan, Xiaoxia Chen, Wei Wang, Lei Song, Jie Ruan, Ruoyi Lin, Xuemin Databases Distributed, Parallel, and Cluster Computing In the current era of data-intensive applications, the demand for high-performance, cost-effective storage solutions is paramount. This paper introduces a Space-Performance Cost Model for key-value store, designed to guide cost-effective storage configuration decisions. The model quantifies the trade-offs between performance and storage costs, providing a framework for optimizing resource allocation in large-scale data serving environments. Guided by this cost model, we present TierBase, a distributed key-value store developed by Ant Group that optimizes total cost by strategically synchronizing data between cache and storage tiers, maximizing resource utilization and effectively handling skewed workloads. To enhance cost-efficiency, TierBase incorporates several optimization techniques, including pre-trained data compression, elastic threading mechanisms, and the utilization of persistent memory. We detail TierBase's architecture, key components, and the implementation of cost optimization strategies. Extensive evaluations using both synthetic benchmarks and real-world workloads demonstrate TierBase's superior cost-effectiveness compared to existing solutions. Furthermore, case studies from Ant Group's production environments showcase TierBase's ability to achieve up to 62% cost reduction in primary scenarios, highlighting its practical impact in large-scale online data serving. |
| title | TierBase: A Workload-Driven Cost-Optimized Key-Value Store |
| topic | Databases Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2505.06556 |