E-Log: Fine-Grained Elastic Log-Based Anomaly Detection and Diagnosis for Databases

Database Management Systems (DBMS) form the backbone of modern large-scale software systems, where reliable anomaly detection and diagnosis are essential for ensuring system availability. However, existing log-based methods often impose significant performance overhead by collecting large volumes of logs, which is impractical for DBMS requiring high read/write throughput. This paper addresses a critical yet underexplored challenge: how to balance logging granularity with runtime efficiency for effective anomaly management in databases. We present E-Log, a novel fine-grained elastic log-based framework for anomaly detection and diagnosis. E-Log intelligently adjusts the amount and detail of logging based on system state—maintaining lightweight logging during normal operation for efficient anomaly detection, and triggering rich, informative logging only upon anomaly suspicion for accurate diagnosis. This adaptive strategy significantly reduces runtime overhead while preserving diagnostic precision. We implement E-Log on Apache IoTDB and evaluate it using benchmarks including TSBS, TPCx-IoT, and IoT-Bench. Experimental results show that E-Log improves anomaly detection accuracy by 3.15% and diagnosis performance by 9.32% compared to state-of-the-art methods. Moreover, it reduces log storage size by 43.53% and increases average write throughput by 26.22%. These results highlight E-Log's potential to enable efficient, accurate, and scalable anomaly management in high-performance database systems.