Container Orchestration

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  1. Container Orchestration for MediaWiki Servers

This article provides a technical overview of container orchestration as it pertains to deploying and managing MediaWiki installations. It's geared towards system administrators and developers looking to modernize their MediaWiki infrastructure. We'll cover the benefits, common tools, and a basic configuration overview.

What is Container Orchestration?

Traditionally, deploying MediaWiki involved installing software directly onto physical or virtual servers. Containerization, using technologies like Docker, allows packaging the MediaWiki application and its dependencies into isolated units called containers. This improves consistency and portability. However, managing numerous containers across multiple servers becomes complex. *Container orchestration* solves this by automating the deployment, scaling, and management of these containers. It ensures your MediaWiki instance remains highly available, scalable, and resilient.

Benefits of Container Orchestration for MediaWiki

Employing container orchestration offers several advantages:

  • Increased Scalability: Easily scale MediaWiki components (web servers, database, caches) based on traffic demands.
  • Improved Reliability: Automatic restart of failed containers and rolling updates minimize downtime.
  • Simplified Deployment: Automated deployment pipelines reduce errors and deployment time.
  • Resource Optimization: Efficiently utilize hardware resources by dynamically allocating them to containers.
  • Portability: Containers can run consistently across different environments (development, testing, production).
  • Version Control: Container images provide version control for your MediaWiki environment.

Popular Container Orchestration Tools

Several tools are available for container orchestration. Here are a few of the most popular:

  • Kubernetes (K8s): The most widely adopted orchestration platform. It's powerful and flexible but can be complex to set up.
  • Docker Swarm: Docker’s native orchestration solution. Simpler than Kubernetes but less feature-rich.
  • HashiCorp Nomad: A simpler and more lightweight alternative to Kubernetes, focusing on operational simplicity.

We'll focus on Kubernetes in this article due to its prevalence and comprehensive feature set.

Kubernetes Architecture Overview

Kubernetes operates on a master-worker node architecture.

  • Master Node(s): Manages the cluster and schedules containers. Components include the API Server, Scheduler, Controller Manager, and etcd (a distributed key-value store).
  • Worker Nodes: Execute the containers. Each node runs a container runtime (like Docker) and a Kubelet (an agent that communicates with the master node).

Containers are grouped into *Pods*, which are the smallest deployable units in Kubernetes. These Pods are then deployed onto the worker nodes.

MediaWiki Containerization – Component Breakdown

A typical MediaWiki deployment will consist of several containers. Here’s a breakdown:

Component Container Image Purpose
Web Server (Apache/Nginx) `docker.io/httpd:latest` or `docker.io/nginx:latest` Serves MediaWiki web pages.
PHP-FPM `docker.io/php:8.2-fpm` Executes PHP code.
MariaDB/MySQL Database `docker.io/mariadb:10.6` or `docker.io/mysql:8.0` Stores MediaWiki data.
Redis (Cache) `docker.io/redis:latest` Caches frequently accessed data for performance.
Message Queue (RabbitMQ/Beanstalkd) `docker.io/rabbitmq:latest` or `docker.io/beanstalkd:latest` Handles asynchronous tasks (e.g., email sending, job queue).

These components are typically deployed as separate Pods within the Kubernetes cluster. Load balancing is essential to distribute traffic across multiple web server Pods.

Basic Kubernetes Configuration for MediaWiki

This provides a simplified example. Actual configurations can be significantly more complex. We'll focus on defining a *Deployment* for the MediaWiki web server.

Deployment YAML (mediawiki-web.yaml):

```yaml apiVersion: apps/v1 kind: Deployment metadata: 

 name: mediawiki-web

spec: 

 replicas: 3  # Run three web server instances
 selector:
   matchLabels:
     app: mediawiki-web
 template:
   metadata:
     labels:
       app: mediawiki-web
   spec:
     containers:
     - name: mediawiki-web-container
       image: docker.io/httpd:latest
       ports:
       - containerPort: 80
       volumeMounts:
       - name: mediawiki-html
         mountPath: /usr/local/apache2/htdocs/
     volumes:
     - name: mediawiki-html
       persistentVolumeClaim:
         claimName: mediawiki-data  #Reference to a Persistent Volume Claim

```

This Deployment defines how to run three replicas of a container based on the `docker.io/httpd:latest` image. It also mounts a volume to serve the MediaWiki files. Persistent Volumes are crucial for maintaining data across container restarts.

Service Configuration

A Kubernetes *Service* exposes the Deployment to the network.

Service YAML (mediawiki-web-service.yaml):

```yaml apiVersion: v1 kind: Service metadata: 

 name: mediawiki-web-service

spec: 

 selector:
   app: mediawiki-web
 ports:
   - protocol: TCP
     port: 80
     targetPort: 80
 type: LoadBalancer # Expose the service using a cloud provider's load balancer

```

This Service selects Pods labeled `app: mediawiki-web` and exposes them on port 80. The `type: LoadBalancer` instructs Kubernetes to provision a load balancer (if supported by your cloud provider) to distribute traffic.

Database Persistence and Configuration Management

Managing the database is critical. Consider using a Persistent Volume Claim to ensure data persistence. Configuration management tools like Ansible or Chef can automate the configuration of the MediaWiki environment within the containers. Using a dedicated database cluster (e.g., using a Kubernetes StatefulSet) is highly recommended for production environments.

Here's a simplified table outlining typical resource requirements:

Component CPU Memory Storage
Web Server 0.5 vCPU 1 GB RAM 10 GB
PHP-FPM 1 vCPU 2 GB RAM 5 GB
MariaDB/MySQL 2 vCPU 4 GB RAM 50 GB+ (depending on wiki size)
Redis 0.25 vCPU 512 MB RAM 2 GB

Monitoring and Logging

Implement robust monitoring and logging solutions. Tools like Prometheus and Grafana can collect metrics from the Kubernetes cluster and MediaWiki containers. Centralized logging (using tools like Elasticsearch, Logstash, and Kibana – the ELK stack) is essential for troubleshooting. Regularly review logs for errors and performance issues.

Further Resources


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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