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

2026-03-05

AI Summary

This work addresses the high software overhead and performance bottlenecks of LSM-tree-based databases on fast storage devices, primarily caused by frequent system calls and user-kernel context switches during compaction. To tackle this, the authors propose the first integration of eBPF and io_uring into LSM-tree compaction optimization, migrating critical I/O paths into the kernel to enable zero-intrusion kernel acceleration. This approach significantly reduces software stack overhead without altering the underlying storage format or compaction algorithms. Experimental results demonstrate a 99% reduction in system calls and a 50% decrease in compaction time. Furthermore, under write-intensive workloads, the system achieves a 75% improvement in throughput and a 40% reduction in p99 latency.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Search and Optimization: Distributed Search
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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%.

Problem

• LSM-tree
• compaction
• system calls
• I/O overhead
• user-kernel transition

Innovation

• eBPF
• io_uring
• LSM-tree
• compaction
• system call overhead