Filebeat — Lightweight Log Shipper for the Elastic Stack

Why It Matters

Collecting logs is one thing, shipping them reliably at scale is another. Filebeat exists for exactly that role: a small agent that tails files, structures events, and forwards them into bigger systems like Elasticsearch or Logstash. It’s part of the Beats family, but by far the most widely used, because almost every infrastructure needs file logs collected somewhere.

How It Works in Practice

Filebeat runs as a lightweight daemon on servers or containers.
– It watches log files or journal sources.
– It batches events and ships them with backpressure handling, so bursts don’t overwhelm the pipeline.
– Output options usually include Elasticsearch, Logstash, or Kafka.
– Modules simplify parsing for common services (Nginx, MySQL, system logs).

In real use, teams often start small — just collecting syslog — and then extend Filebeat with modules as they discover the value of structured logs.

What It Collects

– System logs: syslog, journald, Windows event logs (via Winlogbeat, but Filebeat can wrap too).
– Application logs: web servers, databases, containers.
– Custom log files: any text-based logs with line breaks.
– JSON logs: structured events that can be parsed without regex.

Integrations and Outputs

– Elasticsearch: most common, with direct indexing.
– Logstash: for heavy parsing pipelines.
– Kafka / Redis: buffer layers before storage.
– SIEM tools: events can flow downstream into Splunk, Graylog, or custom collectors.

Deployment Notes

– Packages exist for Linux, Windows, macOS.
– Containers and Kubernetes DaemonSets are common for scaling.
– Config is YAML-based — simple at first, but can get complex with many inputs.
– Performance is high: Filebeat is designed to handle thousands of log lines per second with low memory use.

Security and Reliability

– Supports TLS and authentication for outputs.
– Backpressure ensures no logs are lost if Elasticsearch slows down.
– Minimal overhead, so it runs quietly even on busy servers.

Where It Fits Best

– As the first hop in an Elastic Stack deployment.
– Collecting container logs in Kubernetes clusters.
– Forwarding structured logs to Kafka for multi-team analytics.
– Lightweight logging in mixed Linux/Windows estates.

Known Drawbacks

– Parsing is limited — deep enrichment usually requires Logstash.
– Configuration files can become messy with many log sources.
– Monitoring Filebeat itself is sometimes overlooked until bottlenecks appear.

Snapshot Comparison

| Tool | Role | Strengths | Best Fit |
|————|———————|—————————-|———-|
| Filebeat | Log shipper | Lightweight, fast, modular | Elastic Stack users, log pipelines |
| Logstash | Log processor | Deep parsing, enrichment | Complex pipelines needing transforms |
| Fluentd | Universal collector | Huge plugin ecosystem | Multi-system environments |
| Graylog | Central log system | Built-in UI and queries | Teams wanting turnkey dashboards |