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Building an Efficient Put-Intensive Key-Value Store with Skip-Tree

机译：使用Skip-Tree构建高效的密集型键值存储

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摘要

Multi-component based Log-Structured Merge-tree (LSM-tree) has been becoming one of the mainstream indexes. LSM-tree adopts component-by-component KV item flowing down mechanism to push each KV item from one smaller component to the adjacent larger component during compaction procedures until the KV items reach the largest component. This process incurs significant write amplification and limits the write throughput. In this paper, we propose one multi-component Skip-tree to aggressively push the KV items to the non-adjacent larger components via skipping some components and then make the KV items’ top-down move more efficient. We develop adaptive and reliable KV item movements among components. By reducing the number of steps during the flowing process from memory-resident component to the disk-resident largest component, Skip-tree can effectively reduce the write amplification and thus improve the system throughput. We design and implement one high performance key-value store, named SkipStore, based on Skip-tree. The experiments demonstrate that SkipStore outperforms the state-of-the-art open-sourced system RocksDB in Facebook by 66.5 percent under HDD and 61 percent under SSD.
机译：基于多组件的日志结构合并树（LSM-tree）已成为主流索引之一。 LSM树采用逐组件KV项目向下流动机制，以在压缩过程中将每个KV项目从一个较小的组件推到相邻的较大组件，直到KV条目达到最大组件。此过程导致显着的写放大并限制写吞吐量。在本文中，我们提出了一种多组件跳过树，通过跳过某些组件将KV项积极地推到不相邻的较大组件，然后使KV项的自顶向下移动更加有效。我们开发组件之间的自适应且可靠的KV项目移动。通过减少从驻留内存的组件到驻留磁盘的最大组件的流程中的步骤数，跳过树可以有效地减少写放大，从而提高系统吞吐量。我们基于Skip-tree设计并实现了一个名为SkipStore的高性能键值存储。实验表明，SkipStore在Facebook上的性能优于在Facebook上最先进的开源系统RocksDB，在HDD下超过了61.5％，在SSD下超过了61％。

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