Implementation of the Isolation Forest Algorithm for Mysql Query Performance Anomaly Detection Based on Data Performance Schema
Why this work is in the frame
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Bibliographic record
Abstract
Monitoring query performance in database systems is often a manual and reactive process, proving inefficient for the early detection of issues that can impact application stability. This research aims to design and implement a system for automated and proactive query performance anomaly detection. This system utilizes data from MySQL's Performance Schema and applies an unsupervised machine learning algorithm, namely Isolation Forest, to identify queries with unusual behavior based on eight researcher-selected performance metrics. The detection process is implemented to run periodically in the background and send early notifications via email. Experiments were conducted by varying the contamination parameter, with the model's performance evaluated using Precision, Recall, and F1-Score metrics. The experimental results indicate that the configuration with contamination=0.1 yielded the most optimal performance, achieving an F1-Score of 0.39 and a Recall of 100% for the anomaly class. The developed system successfully demonstrated its ability to detect various types of anomalies, including the N+1 query problem, and offers an efficient solution to proactively improve database system performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it