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Monitoring dashboard examples

# Monitoring Dashboard Examples

This article provides examples of monitoring dashboards useful for maintaining a healthy and performant MediaWiki installation. Effective monitoring is crucial for proactive identification and resolution of issues, ensuring a smooth experience for your users. We'll cover examples focusing on server resources, MediaWiki-specific metrics, and database performance. These dashboards can be implemented using tools such as Prometheus, Grafana, Nagios, or similar monitoring solutions. This article assumes a basic understanding of server administration and monitoring concepts.

1. Server Resource Monitoring

Monitoring server resources provides a foundational view of system health. Key metrics include CPU usage, memory consumption, disk I/O, and network traffic. Without sufficient resources, MediaWiki performance will degrade significantly.

Metric Description Recommended Threshold Action if Exceeded
CPU Usage (%) Percentage of CPU time currently in use. 80% Investigate processes consuming high CPU, consider scaling up CPU resources. See Scaling MediaWiki.
Memory Usage (%) Percentage of RAM currently in use. 90% Identify memory leaks or processes with high memory consumption. Consider increasing RAM. Review PHP memory limits.
Disk I/O (MB/s) Rate of data transfer to/from disk. 80% of disk capacity Investigate slow queries or processes performing excessive disk writes. Examine Database maintenance.
Network Traffic (Mbps) Rate of data transfer over the network. 90% of network capacity Investigate network congestion or unusual traffic patterns. Check Server security.

These metrics should be tracked over time to establish baselines and identify anomalies. Alerts should be configured to notify administrators when thresholds are exceeded. Consider separate dashboards for each server role (web server, database server, cache server).

2. MediaWiki Specific Metrics

Beyond basic server resources, monitoring specific MediaWiki metrics offers deeper insight into application performance. These metrics are often exposed through the API, or can be gathered using custom scripts.

Metric Description Data Source Importance
Page Views per Second Number of pages viewed by users per second. Web server logs, API High
API Request Latency (ms) Time taken to process API requests. API, Web server logs High
Edit Attempts per Minute Number of attempted edits per minute. MediaWiki logs Medium
Failed Login Attempts per Minute Number of failed login attempts per minute. MediaWiki logs High (indicates potential security issues)
Job Queue Length Number of jobs waiting to be processed by the Job queue. MediaWiki status page, API Medium (long queue length indicates performance issues)

Analyzing these metrics can help pinpoint performance bottlenecks within the MediaWiki application itself. For example, a consistently high API request latency might indicate slow database queries or inefficient code. Regularly checking the Special:Statistics page can provide some of this information, but automated monitoring is preferred.

3. Database Performance Monitoring

The database server is often the biggest bottleneck in a MediaWiki installation. Monitoring its performance is critical. This section assumes a MySQL or MariaDB database, but similar principles apply to other database systems.

Metric Description Recommended Threshold Action if Exceeded
Queries per Second (QPS) Number of database queries executed per second. Dependent on hardware, establish baseline Investigate slow queries, optimize indexes. See Database optimization.
Slow Query Count Number of queries exceeding a defined execution time. 0 (ideally) Analyze slow query log, optimize queries and indexes. Use EXPLAIN to understand query plans.
Connection Usage Number of active database connections. Dependent on max_connections setting Investigate connection leaks or excessive connection requests. Review PHP configuration.
Table Locks Number of tables currently locked. 0 (ideally) Investigate long-running transactions or contention for table access.
InnoDB Buffer Pool Hit Ratio (%) Percentage of data found in the InnoDB buffer pool. > 95% Increase InnoDB buffer pool size if ratio is low.

Regular database maintenance tasks, such as Database backups and Database replication, are essential for ensuring data integrity and availability, and should be monitored as well. Monitoring tools can often integrate with database servers to provide detailed performance insights. Checking the Special:Statistics page can give a quick overview of database performance.

Further Considerations

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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