Persistent key-value stores have emerged as a main component in the data access path of modern data processing systems. However, they exhibit high CPU and I/O overhead. Nowadays, due to power limitations, it is important to reduce CPU overheads for data processing. In this article, we propose Kreon , a key-value store that targets servers with flash-based storage, where CPU overhead and I/O amplification are more significant bottlenecks compared to I/O randomness. We first observe that two significant sources of overhead in key-value stores are: (a) The use of compaction in Log-Structured Merge-Trees (LSM-Tree) that constantly perform merging and sorting of large data segments and (b) the use of an I/O cache to access devices, which incurs overhead even for data that reside in memory. To avoid these, Kreon performs data movement from level to level by using partial reorganization instead of full data reorganization via the use of a full index per-level. Kreon uses memory-mapped I/O via a custom kernel path to avoid a user-space cache. For a large dataset, Kreon reduces CPU cycles/op by up to 5.8×, reduces I/O amplification for inserts by up to 4.61×, and increases insert ops/s by up to 5.3×, compared to RocksDB.

中文翻译：

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克雷翁

持久键值存储已成为现代数据处理系统的数据访问路径中的主要组件。但是，它们表现出高 CPU 和 I/O 开销。如今，由于功率限制，减少用于数据处理的 CPU 开销非常重要。在本文中，我们提出克雷翁，一种键值存储，针对具有基于闪存的存储的服务器，与 I/O 随机性相比，CPU 开销和 I/O 放大是更重要的瓶颈。我们首先观察到键值存储中两个重要的开销来源是：（a）在不断执行大数据段的合并和排序的日志结构合并树（LSM-Tree）中使用压缩；（b）使用 I/O 缓存来访问设备，即使对于驻留在内存中的数据也会产生开销。为了避免这些，克雷翁通过使用部分重组而不是通过使用每个级别的完整索引来进行完全数据重组，执行从级别到级别的数据移动。克雷翁通过自定义内核路径使用内存映射 I/O 来避免用户空间缓存。对于大型数据集，克雷翁与 RocksDB 相比，CPU 周期/操作最多可减少 5.8 倍，插入的 I/O 放大最多可减少 4.61 倍，插入操作/秒最多可增加 5.3 倍。