Taurus: Lightweight Parallel Logging for In-Memory Database Management Systems (Extended Version)

TL;DR

Taurus introduces a parallel logging scheme for in-memory DBMSs that significantly improves performance and recovery times by using multiple log streams and transaction dependency tracking.

Contribution

The paper presents Taurus, a novel parallel logging scheme compatible with data and command logging, enhancing performance and recovery speed in in-memory DBMSs.

Findings

Achieves up to 9.9x speedup over single-stream data logging.

Enables recovery up to 75.6x faster than baseline.

Outperforms state-of-the-art schemes by up to 2.8x on NVMe and 9.2x on HDDs.

Abstract

Existing single-stream logging schemes are unsuitable for in-memory database management systems (DBMSs) as the single log is often a performance bottleneck. To overcome this problem, we present Taurus, an efficient parallel logging scheme that uses multiple log streams, and is compatible with both data and command logging. Taurus tracks and encodes transaction dependencies using a vector of log sequence numbers (LSNs). These vectors ensure that the dependencies are fully captured in logging and correctly enforced in recovery. Our experimental evaluation with an in-memory DBMS shows that Taurus's parallel logging achieves up to 9.9x and 2.9x speedups over single-streamed data logging and command logging, respectively. It also enables the DBMS to recover up to 22.9x and 75.6x faster than these baselines for data and command logging, respectively. We also compare Taurus with two state-of-the-art parallel logging schemes and show that the DBMS achieves up to 2.8x better performance on NVMe drives and 9.2x on HDDs.