Leaper: A Learned Prefetcher for Cache Invalidation in LSM-tree based Storage Engines ∗ Lei Yang1 , Hong Wu2, Tieying Zhang2, Xuntao Cheng2, Feifei Li2, Lei Zou1, Yujie Wang2, Rongyao Chen2, Jianying Wang2, and Gui Huang2 fyang lei,
[email protected] fhong.wu, tieying.zhang, xuntao.cxt, lifeifei, zhencheng.wyj, rongyao.cry, beilou.wjy,
[email protected] Peking University1 Alibaba Group2 ABSTRACT 101 Hit ratio Frequency-based cache replacement policies that work well QPS on page-based database storage engines are no longer suffi- Latency cient for the emerging LSM-tree (Log-Structure Merge-tree) based storage engines. Due to the append-only and copy- 100 on-write techniques applied to accelerate writes, the state- of-the-art LSM-tree adopts mutable record blocks and issues Normalized value frequent background operations (i.e., compaction, flush) to reorganize records in possibly every block. As a side-effect, 0 50 100 150 200 such operations invalidate the corresponding entries in the Time (s) Figure 1: Cache hit ratio and system performance churn (QPS cache for each involved record, causing sudden drops on the and latency of 95th percentile) caused by cache invalidations. cache hit rates and spikes on access latency. Given the ob- with notable examples including LevelDB [10], HBase [2], servation that existing methods cannot address this cache RocksDB [7] and X-Engine [14] for its superior write perfor- invalidation problem, we propose Leaper, a machine learn- mance. These storage engines usually come with row-level ing method to predict hot records in an LSM-tree storage and block-level caches to buffer hot records in main mem- engine and prefetch them into the cache without being dis- ory.