Intermediate
Log aggregation
Analysis
Elasticsearch
Logstash
Kibana
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"]
}
}