logstash

Logstash is an open-source data processing pipeline designed to ingest, parse, enrich, and forward data. It is a core component of the Elastic Stack and is commonly used to collect logs and metrics from diverse sources, then store them in Elasticsearch for querying and visualization in Kibana. Logstash uses a plugin-based architecture that supports a wide range of inputs, filters, and outputs, enabling flexible data processing pipelines.

At runtime, Logstash runs as a Java application and processes data through pipelines composed of three stages: input, filter, and output. Inputs collect data from sources such as files, syslog, Beats, TCP/UDP, and message queues. Filters transform and enrich data using plugins like grok (pattern-based parsing), mutate (field edits), date, geoip, and user_agent. Outputs define destinations such as Elasticsearch, files, stdout, or messaging systems like Kafka or Redis. The configuration language is declarative and supports conditional routing and multiple pipelines.

History and development: Logstash was created by Jordan Sissel in 2009 as an open-source project. It was later acquired by Elastic and integrated into the Elastic Stack, alongside Elasticsearch and Kibana. It remains widely used for centralized logging and data ingestion workflows in both on-premises and cloud environments.

Deployment and ecosystem: Logstash runs as a standalone process with multiple worker threads and can use a persisted queue to improve reliability. It can be deployed on traditional servers or in cloud environments and scaled by running multiple instances and directing data to a central Elasticsearch cluster. The ecosystem includes Beats, lightweight shippers that feed Logstash or Elasticsearch directly, and a suite of visualization capabilities via Elastic Stack components.