Capacity planning
- Capacity Planning
Introduction
Capacity planning is a critical process in any system – be it a manufacturing plant, a computer network, or, importantly for our context, a wiki. It's the process of determining the production capacity needed by an organization to meet changing demands for its products. In the context of a MediaWiki installation, "production" refers to the ability of the wiki to handle user requests – viewing pages, editing, searching, uploading files, and all other interactions. Effective capacity planning ensures a smooth user experience, prevents downtime, and allows the wiki to grow sustainably. Ignoring capacity planning can lead to slow response times, errors, and ultimately, a frustrating experience for your users, potentially driving them away. This article aims to provide a comprehensive introduction to capacity planning specifically tailored for MediaWiki administrators and those responsible for managing wiki infrastructure.
Why is Capacity Planning Important for MediaWiki?
MediaWikis, even seemingly small ones, can experience rapid growth. A new topic gains popularity, a project attracts contributors, or a community forms around the wiki – all of these can lead to a sudden increase in traffic and load. Without adequate capacity planning, this growth can quickly overwhelm the server, leading to:
- **Slow Page Load Times:** Users experience delays when viewing pages, making the wiki less usable. This is a major deterrent to participation.
- **Database Bottlenecks:** The database is the heart of a MediaWiki. Insufficient database resources (CPU, memory, disk I/O) will cripple performance.
- **Server Errors:** The web server (typically Apache or Nginx) may become overloaded, resulting in 500 errors or other server-side failures.
- **Wiki Downtime:** The worst-case scenario – the wiki becomes inaccessible to all users.
- **Editing Conflicts:** High concurrency can lead to frequent editing conflicts, frustrating contributors.
- **Search Inefficiency:** A strained search indexer can make searching the wiki slow and inaccurate.
- **File Upload Failures:** Insufficient disk space or upload limits can prevent users from adding valuable media.
- **Replication Delays:** If you are using database replication, increased load can cause delays in propagating changes to replica servers.
Proactive capacity planning mitigates these risks, ensuring the wiki remains responsive and reliable even during peak usage.
Understanding Key Concepts
Before diving into specific strategies, let’s define some key concepts:
- **Capacity:** The maximum amount of work that a system can perform in a given period. In a MediaWiki context, this is measured in requests per second, concurrent users, or database queries per second.
- **Demand:** The amount of work that the system is actually being asked to do. This fluctuates throughout the day, week, and year. Usage statistics are vital for understanding demand.
- **Load:** The actual amount of work that the system is handling at a given time. Load is influenced by both capacity and demand. High load relative to capacity indicates a problem.
- **Utilization:** The percentage of available capacity that is currently being used. High utilization can be a warning sign of impending bottlenecks.
- **Throughput:** The amount of work completed successfully within a specific timeframe. High throughput is a desirable outcome of effective capacity planning.
- **Response Time:** The time it takes for the system to respond to a user request. Low response time is crucial for a good user experience.
- **Scalability:** The ability of the system to handle increasing demand. MediaWiki can be scaled both vertically (increasing resources on a single server) and horizontally (adding more servers).
- **Elasticity:** The ability of the system to automatically adjust its resources to meet fluctuating demand. This is often achieved through cloud computing.
Assessing Current Capacity
The first step in capacity planning is to understand your current capacity. This involves:
1. **Hardware Inventory:** Document the specifications of your server(s): CPU, RAM, disk space, network bandwidth. 2. **Software Configuration:** Record the versions of MediaWiki, PHP, the web server (Apache or Nginx), and the database server (MySQL/MariaDB or PostgreSQL). Configuration settings can significantly impact performance. 3. **Baseline Performance Monitoring:** Use tools to monitor key metrics under normal load. Here are some useful tools:
* **Server Monitoring:** `top`, `htop`, `vmstat`, `iostat` (Linux); Performance Monitor (Windows). These provide real-time insights into CPU usage, memory usage, disk I/O, and network activity. * **Web Server Monitoring:** Apache Status module, Nginx Amplify, or third-party tools like New Relic or Datadog. * **Database Monitoring:** MySQL Workbench, phpMyAdmin (for simple monitoring), or dedicated database monitoring tools. Look at query performance, connection counts, and resource usage. * **MediaWiki Extensions:** Extensions like PerformanceFootprints can help identify slow-running code within MediaWiki itself.
4. **Load Testing:** Simulate user traffic to determine the breaking point of your system. Tools like Apache JMeter, Locust, or Siege can be used for load testing. This will help you identify bottlenecks. Consider using realistic user scenarios during testing.
Predicting Future Demand
Once you understand your current capacity, you need to predict future demand. This is more challenging, but here are some approaches:
1. **Historical Data Analysis:** Analyze your wiki's traffic logs (typically Apache or Nginx logs) to identify trends and patterns. Look for:
* **Daily/Weekly/Monthly Cycles:** Are there specific days or times when traffic is consistently higher? * **Growth Rate:** How quickly is your wiki growing in terms of page views, edits, and registered users? * **Seasonal Variations:** Does traffic increase during certain times of the year (e.g., during school holidays)?
2. **Growth Forecasting:** Use statistical methods (e.g., linear regression, exponential smoothing) to project future growth based on historical data. [Time series analysis](https://en.wikipedia.org/wiki/Time_series_analysis) can be very useful. 3. **Business Projections:** If your wiki is tied to a specific project or organization, consider any planned changes that might affect demand (e.g., marketing campaigns, new product launches). 4. **User Surveys:** Ask your users about their expectations for the wiki. This can provide valuable insights into future needs. 5. **Competitor Analysis:** If you have competitors, analyze their traffic and growth patterns to get a sense of potential demand. [Alexa Rank](https://www.alexa.com/) and [SimilarWeb](https://www.similarweb.com/) can provide some data (though not always perfectly accurate). 6. **Consider the 'Long Tail'**: Don't just focus on popular pages. Many small, less-visited pages collectively contribute to overall load.
Capacity Planning Strategies
Based on your assessment of current capacity and prediction of future demand, you can implement various capacity planning strategies:
- **Vertical Scaling (Scaling Up):** Increase the resources of your existing server(s). This includes adding more CPU, RAM, or faster storage. This is often the simplest approach, but it has limits. There's only so much you can upgrade a single server.
- **Horizontal Scaling (Scaling Out):** Add more servers to distribute the load. This requires a load balancer to distribute traffic across the servers. This is more complex than vertical scaling, but it offers greater scalability. Load balancing is essential.
- **Database Optimization:** Optimize your database schema, indexes, and queries to improve performance. Slow queries are a common bottleneck. [Database indexing](https://en.wikipedia.org/wiki/Database_index) is critical.
- **Caching:** Implement caching mechanisms to reduce the load on the database and web server. MediaWiki includes built-in caching features. [Memcached](https://memcached.org/) and [Redis](https://redis.io/) are popular caching systems. Consider using a [Content Delivery Network (CDN)](https://en.wikipedia.org/wiki/Content_delivery_network) to cache static assets (images, CSS, JavaScript) closer to your users.
- **Code Optimization:** Identify and optimize slow-running code within MediaWiki itself. Use extensions like PerformanceFootprints to help with this. [Profiling tools](https://www.xdebug.org/) can also be useful.
- **Asynchronous Tasks:** Move long-running tasks (e.g., image processing, email sending) to background processes. This prevents them from blocking user requests. [Job queues](https://en.wikipedia.org/wiki/Job_queue) are a common solution.
- **Database Replication:** Use database replication to distribute read load across multiple servers. This can significantly improve performance for read-heavy wikis. [Master-slave replication](https://en.wikipedia.org/wiki/Master%E2%80%93slave_replication) is a common configuration.
- **Sharding:** Partition your database across multiple servers. This is a more advanced technique that requires careful planning. [Database sharding](https://en.wikipedia.org/wiki/Database_sharding) can provide massive scalability.
- **Cloud Computing:** Leverage cloud computing services (e.g., AWS, Azure, Google Cloud) to automatically scale your resources up or down as needed. This provides elasticity and cost savings. [Auto-scaling](https://aws.amazon.com/autoscaling/) is a key feature of cloud platforms.
- **Content Optimization:** Optimize images and other media files to reduce their size. Large files can significantly slow down page load times. [Image compression](https://tinypng.com/) tools can help.
- **Extension Management:** Disable or remove unnecessary extensions. Each extension adds overhead to the system. [Extension analysis](https://www.mediawiki.org/wiki/Extension_analysis) can help identify resource-intensive extensions.
Monitoring and Continuous Improvement
Capacity planning is not a one-time event. It's an ongoing process. You need to continuously monitor your system's performance and adjust your capacity planning strategies as needed.
- **Regular Performance Reviews:** Schedule regular reviews of your performance monitoring data.
- **Alerting:** Set up alerts to notify you when key metrics exceed predefined thresholds. [Nagios](https://www.nagios.org/) and [Prometheus](https://prometheus.io/) are popular monitoring and alerting tools.
- **Capacity Planning Meetings:** Hold regular meetings with stakeholders to discuss capacity planning issues and make decisions.
- **Documentation:** Document your capacity planning strategies and configurations.
- **Stay Updated:** Keep up-to-date with the latest MediaWiki releases and best practices.
Advanced Considerations
- **Caching Strategies**: Explore different caching levels - browser caching, server-side caching (Varnish, Nginx caching), object caching (Memcached, Redis), and database query caching.
- **Connection Pooling**: Utilize connection pooling for database connections to reduce overhead.
- **HTTP/2 and HTTP/3**: Enable these newer HTTP protocols for improved performance.
- **PHP Opcode Caching**: Use a PHP opcode cache (e.g., OpCache) to improve PHP performance.
- **Rate Limiting**: Implement rate limiting to protect against denial-of-service attacks and abusive behavior.
- **Geographic Distribution**: Consider distributing your wiki across multiple geographic regions to reduce latency for users in different parts of the world.
- **Security Considerations**: Capacity planning must also consider security. Ensure your servers are properly secured and protected against attacks. [Web Application Firewalls (WAFs)](https://en.wikipedia.org/wiki/Web_application_firewall) can help.
- **Disaster Recovery**: Have a disaster recovery plan in place to ensure business continuity in the event of a server failure or other disaster.
By following these guidelines, you can ensure that your MediaWiki remains responsive, reliable, and scalable, providing a positive experience for your users. Remember to tailor your capacity planning strategies to the specific needs of your wiki and its community.
Database administration Server administration MediaWiki performance Load balancer Caching Traffic analysis Error logging Extension management Security Disaster recovery
[[Capacity planning for websites](https://www.digitalocean.com/community/tutorials/capacity-planning-for-websites)] [[Website capacity planning](https://www.stackpath.com/blog/website-capacity-planning/)] [[Understanding web server capacity](https://www.keycdn.com/blog/web-server-capacity/)] [[Database scalability](https://www.percona.com/blog/2018/04/26/database-scalability-understanding-and-approaches/)] [[Load testing tools](https://www.blazemeter.com/blog/load-testing-tools)] [[Monitoring tools](https://www.datadoghq.com/)] [[CDN services](https://www.cloudflare.com/)] [[PHP performance tuning](https://www.php.net/manual/en/performance.php)] [[MySQL performance tuning](https://www.percona.com/blog/2019/09/23/mysql-performance-tuning-primer/)] [[Redis documentation](https://redis.io/documentation)] [[Memcached documentation](https://memcached.org/documentation)] [[Time series forecasting](https://www.statsmodels.org/stable/timeseries.html)] [[Elasticsearch for log analysis](https://www.elastic.co/elasticsearch/)] [[Prometheus monitoring](https://prometheus.io/docs/introduction/overview/)] [[Grafana dashboards](https://grafana.com/)] [[Nagios monitoring](https://www.nagios.org/)] [[HTTP/2 documentation](https://http2.github.io/)] [[HTTP/3 documentation](https://http3-explained.github.io/)] [[Web Application Firewall (WAF)](https://owasp.org/www-project-web-application-firewall/)] [[Amazon Auto Scaling](https://aws.amazon.com/autoscaling/)] [[Google Cloud Autoscaling](https://cloud.google.com/compute/docs/autoscaler)] [[Azure Autoscaling](https://docs.microsoft.com/en-us/azure/virtual-machines/autoscaling-overview)]
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