What is logging?

Logging is the practice of automatically recording events, actions, and messages that happen inside a computer program or system. Think of it like a diary where the software writes down what it did, any errors it encountered, and important information about its operation.

Let's break it down

  • Log entry: A single line of text that describes one event (e.g., “User logged in” or “File not found”).
  • Log level: A label that shows how important the entry is, such as DEBUG, INFO, WARN, ERROR, and CRITICAL.
  • Log file: A text file (or series of files) where the entries are stored.
  • Logger: The part of the code that creates and sends log entries to the log file or another destination.
  • Log rotation: A process that archives old log files and starts new ones to keep file sizes manageable.

Why does it matter?

  • Troubleshooting: When something goes wrong, logs show the steps leading up to the problem, helping developers find the cause quickly.
  • Monitoring: Teams can watch logs in real time to detect issues, performance bottlenecks, or security threats.
  • Auditing: Logs provide a record of who did what and when, which is essential for compliance and security investigations.
  • Improvement: Analyzing logs over time reveals patterns that can guide performance tuning and feature enhancements.

Where is it used?

  • Web servers (e.g., Apache, Nginx) to record requests, errors, and access details.
  • Applications of any kind-mobile apps, desktop software, cloud services-use logging to track internal behavior.
  • Operating systems (Windows Event Log, Linux syslog) to capture system events and hardware issues.
  • Containers and orchestration platforms (Docker, Kubernetes) generate logs for each container and pod.
  • Security tools (firewalls, intrusion detection systems) log traffic and alerts.

Good things about it

  • Visibility: Gives developers and operators a clear view into what the software is doing.
  • Speed: Automated logs are faster and more reliable than trying to reproduce problems manually.
  • Scalability: Modern logging frameworks can handle huge volumes of data across many servers.
  • Flexibility: Logs can be written to files, databases, cloud services, or streamed to monitoring dashboards.
  • Standardization: Common log levels and formats make it easy to share and interpret logs across teams.

Not-so-good things

  • Performance impact: Excessive or poorly configured logging can slow down an application, especially if it writes large amounts of data synchronously.
  • Storage cost: Logs can grow quickly, requiring careful rotation, archiving, or deletion strategies.
  • Noise: Too many low‑level (DEBUG) messages can hide important warnings or errors, making analysis harder.
  • Security risk: Logs may contain sensitive information (passwords, personal data) if not filtered or protected properly.
  • Complexity: Setting up centralized logging, parsing formats, and maintaining retention policies can be challenging for small teams.