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# How to Reduce Downtime While Running Aggregata Farming Instances

This article details best practices for minimizing downtime when operating [[Aggregata]] farming instances within a MediaWiki 1.40 environment. Aggregata, a demanding process, requires careful server configuration to ensure high availability and resilience. This guide is aimed at new server engineers and system administrators.

== Understanding Aggregata and Downtime Risks ==

[[Aggregata]] farming refers to the process of computationally intensive tasks, often involving complex database queries and data processing, used for generating metrics or performing large-scale updates within the wiki. These processes can place significant strain on server resources (CPU, memory, disk I/O).  Unmitigated, this can lead to service interruptions, often manifesting as slow page loads, database lockups, or complete server crashes. Common causes of downtime during Aggregata runs include:

*   **Resource Exhaustion:** CPU, memory, or disk I/O reaching capacity.
*   **Database Lock Contention:** Multiple processes competing for access to database resources.
*   **Software Bugs:** Errors within the Aggregata scripts themselves.
*   **Hardware Failures:** Though less frequent, underlying hardware issues can exacerbate existing problems.

== Hardware Recommendations ==

The foundation of a stable Aggregata farming environment is appropriate hardware. The following table outlines minimum and recommended specifications.

{| class="wikitable"
! Component
! Minimum Specification
! Recommended Specification
|-
| CPU
| 8 Cores, 2.0 GHz
| 16+ Cores, 3.0+ GHz
|-
| RAM
| 16 GB DDR4
| 32+ GB DDR4 ECC
|-
| Storage
| 500 GB SSD
| 1 TB NVMe SSD (RAID 1 Mirroring)
|-
| Network
| 1 Gbps Ethernet
| 10 Gbps Ethernet
|}

It's crucial to monitor resource usage during Aggregata runs to identify bottlenecks.  Tools like [[Server Monitoring]] and [[System Logs]] are invaluable for this purpose.  Consider using a dedicated server for Aggregata to isolate its resource demands from the main wiki.

== Software Configuration for High Availability ==

Several software configurations can significantly reduce downtime.

*   **Database Tuning:** Optimizing the [[MySQL]] or [[MariaDB]] database is paramount.  Adjust `innodb_buffer_pool_size` to maximize caching and reduce disk I/O.  Review and optimize slow query logs to identify and address inefficient queries. See the [[Database Administration]] guide for detailed instructions.
*   **Caching:** Implement robust caching using [[Memcached]] or [[Redis]].  Cache frequently accessed data to reduce database load. Configure MediaWiki's `$wgCacheDirectory` appropriately.
*   **Web Server Configuration:**  Use a performant web server like [[Apache]] or [[Nginx]].  Configure appropriate worker processes and keep-alive settings to handle concurrent requests efficiently.
*   **Process Management:** Utilize a process manager like [[systemd]] to automatically restart Aggregata scripts if they crash.  This ensures that the process is always running, even in the event of an unexpected error.
*   **Load Balancing:** For larger deployments, consider using a [[Load Balancer]] to distribute traffic across multiple servers running Aggregata instances. This provides redundancy and improves performance.

== Implementing Rolling Updates and Backups ==

Downtime can also occur during software updates or maintenance. The following strategies minimize disruption:

*   **Rolling Updates:** Implement a rolling update strategy where Aggregata instances are updated one at a time, while others continue to serve requests.  This requires careful coordination and automation.
*   **Regular Backups:**  Perform frequent, automated backups of the wiki's files and database.  A recent backup allows for rapid restoration in the event of a catastrophic failure. The [[Backup and Restore]] guide provides detailed instructions.
*   **Staging Environment:**  Test all updates and changes in a staging environment that mirrors the production environment before deploying them to production. This helps identify and resolve potential issues before they impact users.
*	**Database Replication:** Utilize database replication to maintain a hot standby database.  In the event of a primary database failure, you can quickly failover to the standby database, minimizing downtime. See [[Database Replication]] for setup.

== Monitoring and Alerting ==

Proactive monitoring and alerting are essential for identifying and addressing potential problems before they lead to downtime.

{| class="wikitable"
! Metric
! Threshold
! Action
|-
| CPU Usage
| > 80%
| Investigate Aggregata script performance, scale resources
|-
| Memory Usage
| > 90%
| Investigate memory leaks, optimize Aggregata script, add RAM
|-
| Disk I/O
| > 80%
| Optimize database queries, upgrade storage
|-
| Database Connections
| > Max Connections
| Increase Max Connections, optimize queries
|}

Configure alerts to notify you when thresholds are exceeded.  Tools like [[Nagios]], [[Zabbix]], or [[Prometheus]] can be used for monitoring and alerting. Regularly review [[System Logs]] for errors and warnings.

== Aggregata Script Optimization ==

The efficiency of the Aggregata scripts themselves has a direct impact on downtime.

*   **Batch Processing:** Process data in batches to reduce the number of database queries.
*   **Index Optimization:** Ensure that the database tables used by the Aggregata scripts are properly indexed.
*   **Code Review:**  Conduct regular code reviews to identify and address potential performance issues and bugs.
*   **Profiling:** Use profiling tools to identify performance bottlenecks in the Aggregata scripts.
*   **Limit Concurrent Runs:** Implement mechanisms to prevent multiple Aggregata instances from running concurrently, potentially causing resource contention.

== Disaster Recovery Plan ==

Despite best efforts, unforeseen events can still cause downtime. A comprehensive [[Disaster Recovery Plan]] is crucial. This plan should include:

{| class="wikitable"
! Step
! Description
|-
| 1. Detection
| Identify the cause of the outage.
|-
| 2. Isolation
| Isolate the affected components.
|-
| 3. Restoration
| Restore from backup or failover to a standby system.
|-
| 4. Verification
| Verify that the system is functioning correctly.
|-
| 5. Post-Mortem
| Analyze the outage and implement measures to prevent recurrence.
|}

Regularly test the disaster recovery plan to ensure its effectiveness.



[[Special:Search]] can help locate more information on specific areas.  Remember to consult the [[MediaWiki Configuration]] and [[Server Security]] documentation for additional guidance.

[[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.* ⚠️