Log Analysis (ELK Stack)

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  1. Log Analysis (ELK Stack)

This article details the configuration and use of the ELK Stack (Elasticsearch, Logstash, and Kibana) for centralized log analysis on our MediaWiki servers. Effective log management is crucial for troubleshooting, performance monitoring, and security auditing. This guide is intended for system administrators and developers.

Introduction to the ELK Stack

The ELK Stack is a popular open-source solution for collecting, processing, and visualizing logs.

  • Elasticsearch: A distributed, RESTful search and analytics engine. It stores and indexes the logs.
  • Logstash: A data processing pipeline that ingests data from various sources, transforms it, and sends it to Elasticsearch.
  • Kibana: A visualization dashboard for Elasticsearch data. It allows you to explore, analyze, and visualize logs using charts, graphs, and dashboards.

System Requirements

The ELK Stack requires significant resources, especially as log volume increases. The following table outlines the minimum recommended specifications for each component. These specs are for a modest-sized MediaWiki installation (approximately 50 active users). Larger installations will require scaling.

Component CPU Memory Disk Space
Elasticsearch 2 cores 4GB RAM 50GB SSD
Logstash 1 core 2GB RAM 20GB SSD
Kibana 1 core 2GB RAM 10GB SSD

It is highly recommended to use SSDs for all components to improve performance. The [Operating System](https://www.mediawiki.org/wiki/Manual:Configuration_form) should be a modern Linux distribution (e.g., Ubuntu Server 22.04, CentOS Stream 9). Consider using a dedicated server or virtual machines for each component for better isolation and scalability. See also [Server Requirements](https://www.mediawiki.org/wiki/Manual:Server_requirements) for general MediaWiki needs.

Installation and Configuration

The installation process varies depending on your Linux distribution. The following outlines a general approach. Refer to the official documentation for detailed instructions: [Elasticsearch Documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html), [Logstash Documentation](https://www.elastic.co/guide/en/logstash/current/index.html), [Kibana Documentation](https://www.elastic.co/guide/en/kibana/current/index.html).

1. Install Java: Elasticsearch requires Java. Ensure you have a compatible version installed (Java 11 or later is recommended). 2. Install Elasticsearch: Download and install the Elasticsearch package. Configure `elasticsearch.yml` to set the cluster name, network settings, and other parameters. 3. Install Logstash: Download and install the Logstash package. Configure `logstash.conf` to define input, filter, and output plugins. 4. Install Kibana: Download and install the Kibana package. Configure `kibana.yml` to connect to your Elasticsearch instance.

Logstash Configuration for MediaWiki

Logstash is the key to collecting and parsing MediaWiki logs. A sample configuration file (`logstash.conf`) is shown below:

``` input { 

 file {
   path => "/var/log/mediawiki/*log"
   start_position => "beginning"
 }

}

filter { 

 grok {
   match => { "message" => "%{COMBINEDAPACHELOG}" }
 }
 date {
   match => [ "timestamp", "dd/MMM/yyyy:HH:mm:ss Z" ]
 }

}

output { 

 elasticsearch {
   hosts => ["http://elasticsearch:9200"]
   index => "mediawiki-%{+YYYY.MM.dd}"
 }

} ```

This configuration reads logs from the `/var/log/mediawiki` directory, parses them using the `grok` filter (which requires a [Grok pattern](https://grokdebug.herokuapp.com/) to correctly interpret the log format), extracts the timestamp, and sends the processed data to Elasticsearch. The index name is dynamically generated based on the date. Adjust the `path` and `hosts` settings to match your environment. See also [Apache Log Analysis](https://www.example.com/apache_logs) for related techniques.

Elasticsearch Index Management

Elasticsearch uses indices to store data. Managing indices effectively is crucial for performance and storage. Consider the following:

Index Setting Description Recommended Value
Number of Shards Determines how data is distributed across nodes. 1-3 (depending on cluster size)
Number of Replicas Provides redundancy and improves read performance. 1-2
Refresh Interval Controls how frequently data is made searchable. 30s - 1m

Implement a [Index Lifecycle Management (ILM)](https://www.elastic.co/guide/en/elasticsearch/reference/current/ilms.html) policy to automatically rotate, delete, and optimize indices based on age and size. This prevents Elasticsearch from running out of storage and maintains performance. Regularly [optimize indices](https://www.elastic.co/guide/en/elasticsearch/reference/current/optimize-index.html) to reduce storage space and improve search speed.

Kibana Visualization and Dashboards

Kibana provides a powerful interface for visualizing Elasticsearch data. You can create charts, graphs, and dashboards to monitor key metrics. Some useful visualizations for MediaWiki logs include:

  • HTTP Status Code Distribution: Identify errors and performance issues.
  • Page View Counts: Track popular pages and user activity.
  • Error Log Analysis: Monitor errors and exceptions.
  • Slow Query Log Analysis: Identify performance bottlenecks in the database.

Use Kibana's [Discover](https://www.elastic.co/guide/en/kibana/current/discover.html) feature to explore raw log data and identify patterns. Create [Dashboards](https://www.elastic.co/guide/en/kibana/current/dashboards.html) to combine multiple visualizations into a single view. See [Kibana Tutorials](https://www.example.com/kibana_tutorials) for advanced techniques.


Security Considerations

Secure your ELK Stack deployment to protect sensitive data.

  • Enable Authentication: Protect Elasticsearch and Kibana with username/password authentication.
  • Use TLS/SSL: Encrypt communication between components using TLS/SSL.
  • Restrict Network Access: Limit access to the ELK Stack to authorized hosts and networks.
  • Regularly Update: Keep Elasticsearch, Logstash, and Kibana up to date with the latest security patches. See also [Security Best Practices](https://www.example.com/security_best_practices).


Manual:Configuration_form Manual:Server_requirements Elasticsearch Documentation Logstash Documentation Kibana Documentation Apache Log Analysis Grok pattern Index Lifecycle Management (ILM) optimize indices Discover Dashboards Kibana Tutorials Security Best Practices Database Maintenance Performance Tuning Troubleshooting Guide System Monitoring Log Rotation


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

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