Performance Monitoring
# Performance Monitoring
This article details performance monitoring strategies for a MediaWiki 1.40 installation. Effective monitoring is crucial for ensuring a stable and responsive wiki experience for all users. We will cover key metrics, tools, and configuration aspects.
Understanding Key Metrics
Before diving into tools, it's essential to understand *what* to monitor. Several metrics directly impact MediaWiki performance. These can be broadly categorized as server-level, database-level, and application-level.
Server Metrics
These metrics reflect the overall health of the server hosting MediaWiki.
| Metric | Description | Importance |
|---|---|---|
| CPU Usage | Percentage of CPU time utilized. High sustained usage indicates a potential bottleneck. | High |
| Memory Usage (RAM) | Amount of RAM currently in use. Insufficient RAM leads to swapping, severely degrading performance. | High |
| Disk I/O | Rate of data read from and written to the disk. Slow disk I/O is a common bottleneck. | Medium |
| Network Traffic | Amount of data transferred over the network. High traffic can indicate a DDoS attack or simply a very busy wiki. | Medium |
| Load Average | Number of processes waiting to run. A high load average suggests the server is overloaded. | High |
Database Metrics
These metrics relate to the performance of the database server (typically MySQL or PostgreSQL).
| Metric | Description | Importance |
|---|---|---|
| Query Execution Time | Time taken to execute database queries. Slow queries are a major performance killer. Database replication can improve read performance. | High |
| Connection Count | Number of active connections to the database. Exceeding the maximum allowed connections will cause errors. | Medium |
| Cache Hit Ratio | Percentage of queries served from the database cache. A low hit ratio indicates inefficient caching. | Medium |
| Table Locks | Number of tables currently locked. Excessive locking can lead to contention. | Low |
| Slow Query Log | Records queries exceeding a certain execution time. A critical tool for identifying performance issues. | High |
Application Metrics
These metrics are specific to MediaWiki itself.
| Metric | Description | Importance |
|---|---|---|
| Page Generation Time | Time taken to generate a wiki page. Directly impacts user experience. Caching is vital here. | High |
| API Request Time | Time taken to process API requests. Important for extensions and mobile apps. | Medium |
| Job Queue Length | Number of jobs waiting to be processed by the Job queue. A long queue indicates a backlog. | Medium |
| Parser Cache Hits | Percentage of pages served from the parser cache. Impacts page rendering speed. | Medium |
| Session Count | Number of active user sessions. | Low |
Monitoring Tools
Several tools can be used to monitor these metrics. The best choice depends on your server environment and technical expertise.
- Nagios/Icinga: Powerful, open-source monitoring systems. Require significant configuration but offer comprehensive monitoring capabilities.
- Zabbix: Another robust, open-source monitoring solution. Similar to Nagios/Icinga in terms of features and complexity.
- Prometheus/Grafana: A popular combination for time-series data monitoring and visualization. Excellent for tracking trends.
- Netdata: A real-time performance and health monitoring tool. Easy to install and provides a wealth of information.
- `top` / `htop` / `vmstat` / `iostat`: Command-line tools available on most Linux systems. Useful for quick diagnostics.
- phpMyAdmin / pgAdmin: Database administration tools which can provide database performance metrics.
- **`$wgDebugLogFile`**: Specifies a file to log debug information. Useful for tracking errors and performance bottlenecks.
- **`$wgDebugToolbar`**: Enables a debug toolbar that displays performance information during page rendering. Disable in production
* **`$wgShowExceptionDetails`**: Displays detailed error messages. Useful for debugging but should be disabled in production for security reasons. - **`$wgProfiler`**: Enables profiling of PHP code execution. Useful for identifying slow functions.
- **Enable Slow Query Logging in MySQL/PostgreSQL**: Configure your database server to log slow queries. This is crucial for identifying database performance issues. See Database performance for details.
- **Regularly Check the Job queue**: Monitor the job queue to ensure tasks are being processed efficiently. Long queues indicate potential problems.
- **Use a Cache**: Implement Caching strategies such as Memcached or Redis to reduce database load and improve page generation time.
- **Establish Baselines**: Record normal performance metrics during periods of low traffic. This will help you identify deviations that indicate problems.
- **Look for Trends**: Monitor metrics over time to identify patterns. For example, a gradual increase in CPU usage may indicate a memory leak.
- **Correlate Metrics**: Look for relationships between different metrics. For example, high CPU usage and slow query times may indicate a database bottleneck.
- **Investigate Anomalies**: When you see an unusual spike or drop in a metric, investigate the cause immediately.
- **Regularly Review Logs**: Examine the MediaWiki debug log, database error logs, and server system logs for errors and warnings. Logging is crucial.
- Manual:Configuration settings
- Database performance
- Caching
- Job queue
- Manual:Upgrading (Performance improvements often accompany upgrades)
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Configuring MediaWiki for Monitoring
MediaWiki provides several configuration options that aid in performance monitoring.
Analyzing Monitoring Data
Collecting data is only the first step. You must also analyze it to identify and resolve performance issues.
Further Reading
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️