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=== Performance Tuning for Browser-Based Farming Applications ===

This article details performance tuning strategies for server configurations hosting browser-based farming applications. These applications, characterized by numerous concurrent users performing repetitive actions (clicking, harvesting, building), place unique demands on server resources. This guide is aimed at system administrators and server engineers new to optimizing for this workload.  We will cover database tuning, web server configuration, and caching strategies.  Understanding the specific needs of these applications is crucial for providing a smooth user experience.  Consider reading about [[Server Administration]] before proceeding.

=== Understanding the Workload ===

Browser-based farming games typically generate a high volume of small, frequent database queries.  Users are constantly interacting with the game state, triggering updates.  The application logic itself is often relatively simple, but the sheer concurrency can quickly overwhelm standard server configurations.  Poor performance manifests as lag, slow loading times, and potentially even connection errors.  It's vital to differentiate between client-side lag (network issues, browser performance) and server-side lag, using tools like [[Server Monitoring]].  

=== Database Tuning ===

The database is the most crucial component.  MySQL/MariaDB are common choices.  Here's a basic configuration baseline. Remember to adjust values based on your server’s hardware and the number of concurrent users.

{| class="wikitable"
! Parameter
! Recommended Value (Example)
! Description
|-
| `innodb_buffer_pool_size`
| 50-80% of RAM
| The amount of memory allocated to cache InnoDB data and indexes.  Critical for performance.
|-
| `innodb_log_file_size`
| 256M - 1G
| Size of each InnoDB redo log file. Affects recovery time and write performance.
|-
| `innodb_flush_log_at_trx_commit`
| 2
| Controls how often InnoDB flushes log data to disk.  `2` offers a good balance between durability and performance.
|-
| `query_cache_size`
| 0 (Disable)
| The query cache is often detrimental to write-heavy workloads.  Disable it and use more advanced caching (see below).
|-
| `max_connections`
| 150-500 (Adjust based on load)
| Maximum number of concurrent database connections.
|}

It is highly recommended to use a connection pooling mechanism within your application code (e.g., using a library in your chosen programming language) to avoid the overhead of repeatedly establishing and tearing down database connections.  Regular database [[Database Backups]] are essential.  Indexing is *extremely* important.  Ensure appropriate indexes are in place for all frequently queried columns. Use `EXPLAIN` to analyze query performance and identify missing indexes.

=== Web Server Configuration (Apache/Nginx) ===

The web server handles incoming requests and serves static content. Nginx is generally preferred for high concurrency due to its event-driven architecture.  Apache can be used, but requires more tuning.

{| class="wikitable"
! Parameter (Nginx)
! Recommended Value (Example)
! Description
|-
| `worker_processes`
| Number of CPU cores
| Number of worker processes to handle connections.
|-
| `worker_connections`
| 1024-4096
| Maximum number of concurrent connections per worker process.
|-
| `keepalive_timeout`
| 60s
| Time a keep-alive connection remains open.
|-
| `gzip`
| On
| Enable gzip compression for static content.
|-
| `proxy_cache_path`
| Configured with appropriate size and levels
| Enable caching of frequently accessed resources.
|}

For Apache, ensure `mod_php` is configured with a sufficient number of processes (using `MaxRequestWorkers`) and consider using `mod_cache` for static content. Regularly review [[Web Server Logs]] for errors and performance bottlenecks.  Consider using a reverse proxy like [[Reverse Proxy Setup]] for load balancing and security.

=== Caching Strategies ===

Caching is vital to reduce database load.  Implement multiple layers of caching:

*   **Browser Caching:** Configure appropriate `Cache-Control` headers for static assets (images, CSS, JavaScript).
*   **Server-Side Caching:** Utilize a caching system like [[Memcached]] or [[Redis]] to store frequently accessed game data (e.g., user statistics, item definitions).  Cache invalidation is a critical aspect to manage data consistency.
*   **Opcode Caching (PHP):** If using PHP, enable an opcode cache like [[OPcache]] to reduce the overhead of compiling PHP code.
*   **Full Page Caching:** For pages that don't change frequently (e.g., help pages, static information), consider full page caching.

{| class="wikitable"
! Caching Layer
! Technology
! Data Cached
|-
| Browser
| HTTP Headers
| Static Assets (CSS, JS, Images)
|-
| Server-Side
| Memcached/Redis
| User Data, Item Definitions, Game State
|-
| Application
| OPcache (PHP)
| Compiled PHP Code
|}

=== Load Balancing and Scaling ===

As the number of users grows, a single server will eventually become a bottleneck. Implement load balancing to distribute traffic across multiple servers.  Horizontal scaling (adding more servers) is generally more effective than vertical scaling (upgrading a single server).  Consider using a load balancer like [[HAProxy]] or a cloud-based load balancing service.  Database replication is also crucial for high availability and read scalability.  

=== Monitoring and Analysis ===

Continuous monitoring is essential to identify and address performance issues.  Use tools like [[Nagios]], [[Zabbix]], or cloud provider monitoring services to track key metrics:

*   CPU Usage
*   Memory Usage
*   Disk I/O
*   Network Traffic
*   Database Query Performance
*   Web Server Request Latency

Regularly analyze these metrics to identify trends and proactively address potential bottlenecks.  [[Performance Profiling]] can pinpoint specific code sections that are causing performance problems.

[[Server Security]] is also a critical consideration alongside performance.

[[Network Configuration]] plays a key role too.

[[Disaster Recovery Planning]] should be in place.

[[Virtualization Technologies]] can assist with server management.

[[Cloud Computing Basics]] can offer scalable solutions.

[[Category:Server Hardware]]


== Intel-Based Server Configurations ==
{| class="wikitable"
! 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 ==
{| class="wikitable"
! 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.* ⚠️