Boosting Compaction In B-Tree Based Key-Value Store By Exploiting Parallel Reads In Flash Ssds

Append-only B-tree based key-value stores provide superior search and update performance based on their structural characteristics; however, they periodically require the compaction task that incurs significant I/O overhead. In this paper, we present that the compaction's degraded read performance deteriorates the overall performance in ForestDB, a representative append-only B-tree engine. We demonstrate that despite the exceptional performance of the SSD, the cause of the slow read performance is the underutilization of the SSD's internal parallelism due to the read operations using synchronous I/O. Furthermore, this paper proposes a novel compaction method that improves the compaction's read performance by exploiting SSD's internal parallelism by requesting multiple read operations in a batch using the asynchronous I/O technique. We implemented our proposed methods on ForestDB using two Linux asynchronous I/O interfaces, AIO and io_uring. The evaluation results confirm that our method drastically improves the compaction's read performance up to ten times compared to the conventional compaction method. In particular, we confirmed that the proposed method using io_uring, the latest asynchronous I/O interface, is effective regardless of the file I/O mode and outperforms the others in all cases.