Skip to main content

Log Monitoring

Log monitoring refers to the continuous process of collecting, storing, and analyzing log data generated by applications, servers, containers, and other infrastructure components. Logs contain time-stamped records of events that help developers and operators understand system behavior, troubleshoot issues, and ensure security and compliance.

Log monitoring serves various purposes, such as:

  • Troubleshooting & Debugging: Logs help identify errors, stack traces, and performance bottlenecks.
  • Performance Monitoring: Monitoring log patterns can detect slow responses or failed transactions.
  • Security: Detect unauthorized access or anomalies..
  • Audit Trails: Logs act as a historical record of system and user activities.
  • Automation & Alerting: Integrates with systems to automatically trigger alerts or actions based on specific log entries.

There are many tools available for log monitoring. One popular option is the Elastic stack (or ELK stack), an open-source suite consisting of three main components:

  • Elasticsearch: A distributed search and analytics engine that stores and indexes log data for fast querying.
  • Logstash: A data processing pipeline that ingests, transforms, and forwards logs from various sources to Elasticsearch.
  • Kibana: A visualization tool that enables users to explore and analyze log data stored in Elasticsearch through dashboards and charts.

These tools are covered in an excellent manner in the YouTube playlists Mini Beginner's Crash Course to Elasticsearch and Kibana or Elasticsearch 101 Course by Official Elastic Community.

Logging from applications​

To create effective log monitoring, applications should produce structured logs that can be easily parsed and analyzed by log management tools. Structured logging means that log messages are formatted in a consistent, machine-readable format such as JSON. This enables automated systems to extract fields like timestamps, log levels, service names, and contextual data for filtering, searching, and visualization.

Different programming languages have their own popular logging libraries that support structured logging and integration with monitoring tools. For example:

  • Node.js: Winston is a widely used, flexible logging library.
  • Java: Logback is a logging framework commonly used in enterprise applications.
  • Python: structlog provides structured logging capabilities.

Choosing the right library for your language and environment helps ensure your logs are consistent, structured, and compatible with modern log monitoring solutions.

Below, you can see one example of structured log message:

JSON log message (Node.js with Winston)
{
  "timestamp": "2025-06-01T12:34:56.789Z",
  "level": "info",
  "service": "auth-service",
  "message": "User login successful",
  "userId": "12345"
}

By implementing structured logging and using standard libraries, you ensure that your logs are compatible with log monitoring solutions.

Example​