Intermediate

Log aggregation

Analysis

Elasticsearch

Logstash

Kibana

Data reporting dashboard on a laptop screen.

Implement a Log Aggregation and Analysis Platform

Use Elasticsearch, Logstash, and Kibana (ELK) to build a log aggregation and analysis platform

In this project, we will be implementing a log aggregation and analysis platform using Elasticsearch, Logstash, and Kibana (ELK). ELK is a popular open-source stack for collecting, storing, and analyzing log data from distributed applications.

Project Checklist

  • Install and set up Elasticsearch, Logstash, and Kibana on a local or cloud-based infrastructure
  • Configure Logstash to collect and parse logs from the applications and servers being monitored
  • Ingest the logs into Elasticsearch for storage and analysis
  • Use Kibana to create dashboards and visualizations to make it easier to track and analyze the collected logs

Bonus Project Checklist Items

  • Implement alerting rules to trigger notifications when certain events or patterns are detected in the logs
  • Integrate the log aggregation platform with a monitoring and alerting system for a more comprehensive view of the application's health and performance
  • Implement log retention policies to ensure that important logs are kept for a certain period of time, while less important logs are purged

Inspiration (Any companies/libraries similar)

  • Elastic
  • Splunk
  • Sumo Logic

Hint/Code snippet to start

To get started, you can use the following code snippet to set up a basic Logstash configuration for collecting logs from an Apache webserver:
input {
  file {
    path => "/var/log/apache2/*.log"
    start_position => "beginning"
  }
}

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

output {
  elasticsearch {
    hosts => ["localhost:9200"]
  }
}