Practical Parsing Apache Logs
Let’s put it all together with a complete example. Suppose you want to ingest and parse Apache web server logs and send the data to Elasticsearch. Here’s a full 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" ]
}
}
output {
elasticsearch {
hosts => ["http://localhost:9200"]
index => "apache-logs"
}
stdout {
codec => rubydebug
}
}
Explanation
- Input Section: Reads from the Apache access log file.
- Filter Section:
- Grok Filter: Parses each log entry using the COMMONAPACHELOG pattern.
- Date Filter: Converts the timestamp field to a date object that Elasticsearch can use.
- Output Section:
- Elasticsearch Output: Sends the parsed data to Elasticsearch, indexing it under apache-logs.
- Stdout Output: Prints the parsed data to the console for debugging purposes.
Introduction to Logstash for Data Ingestion
Logstash is a powerful data processing pipeline tool in the Elastic Stack (ELK Stack), which also includes Elasticsearch, Kibana, and Beats. Logstash collects, processes, and sends data to various destinations, making it an essential component for data ingestion.
This article provides a comprehensive introduction to Logstash, explaining its features, and how it works, and offering practical examples to help you get started.
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