RESYSTANCE: Unleashing Hidden Performance of Compaction in LSM-trees via eBPF

TL;DR

RESYSTANCE leverages eBPF and io_uring to significantly reduce system call overhead and improve performance in LSM-tree-based databases by handling compaction within the kernel.

Contribution

It introduces a kernel-based approach using eBPF and io_uring to optimize compaction in LSM-trees without modifying the database structure.

Findings

99% reduction in system call invocations during compaction

50% decrease in compaction time

up to 75% throughput improvement in write workloads

Abstract

The development of high-speed storage devices such as NVMe SSDs has shifted the primary I/O bottleneck from hardware to software. Modern database systems also rely on kernel-based I/O paths, where frequent system call invocations and kernel-user space transitions lead to relatively large overheads and performance degradation. This issue is particularly pronounced in Log-Structured Merge-tree (LSM-tree)-based NoSQL databases. We identified that, in particular, the background compaction process generates a large number of read system calls, causing significant overhead. To address this problem, we propose RESYSTANCE, which leverages eBPF and io_uring to free compaction from system calls and unlock hidden performance potential. RESYSTANCE improves disk I/O efficiency during read operations via io uring and significantly reduces software stack overhead by handling compaction directly inside the kernel through eBPF. Moreover, RESYSTANCE minimizes user-kernel transitions by offloading key I/O routines into the kernel without modifying the LSM-tree structure or compaction algorithm. RESYSTANCE was extensively evaluated using db_bench, YCSB, and OLTP workloads. Compared to baseline RocksDB, it reduced the average number of system call invocations during compaction by 99% and shortened compaction time by 50%. Consequently, in write-intensive workloads, RESYSTANCE improved throughput by up to 75% and reduced the p99 latency by 40%.