Practical Enriching Data with GeoIP

A common use case for Logstash is enriching data with geographic information. Here’s how you can use the geoip filter to add location data based on an IP address in the log:

Configuration File

input {
  file {
    path => "/var/log/apache2/access.log"
    start_position => "beginning"
  }
}

filter {
  grok {
    match => { "message" => "%{COMMONAPACHELOG}" }
  }
  date {
    match => [ "timestamp" , "dd/MMM/yyyy:HH:mm:ss Z" ]
  }
  geoip {
    source => "clientip"
  }
}

output {
  elasticsearch {
    hosts => ["http://localhost:9200"]
    index => "apache-logs"
  }
  stdout {
    codec => rubydebug
  }
}

Explanation

Grok Filter: Parses the log entry.
Date Filter: Converts the timestamp field.
GeoIP Filter: Adds geographic information based on the clientip field.

Running the Pipeline

Run Logstash with this configuration:

bin/logstash -f logstash.conf

Expected Output:

The enriched log entries in Elasticsearch will include additional fields with geographic data, such as geoip.location, geoip.country_name, and more.

Configuring Logstash Pipeline for Data Processing

Logstash, a key component of the Elastic Stack, is designed to collect, transform, and send data from multiple sources to various destinations. Configuring a Logstash pipeline is essential for effective data processing, ensuring that data flows smoothly from inputs to outputs while undergoing necessary transformations along the way.

This article will guide you through the process of configuring a Logstash pipeline, providing detailed examples and outputs to help you get started.