Performance Monitoring

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  1. 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.

Configuring MediaWiki for Monitoring

MediaWiki provides several configuration options that aid in performance monitoring.

  • **`$wgDebugLogFile`**: Specifies a file to log debug information. Useful for tracking errors and performance bottlenecks.
  • **`$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.

Analyzing Monitoring Data

Collecting data is only the first step. You must also analyze it to identify and resolve performance issues.

  • **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.

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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