Software logging is a crucial practice. Logs provide a detailed record of what happened leading up to an issue or problem. Well-designed logs significantly speed up debugging and troubleshooting, reducing downtime and improving system reliability.

Below are the guidelines I follow in day-to-day software development:

Use Clear and Descriptive Language

Log messages should be informative and provide context about what’s happening. Avoid generic messages that are ambiguous to align with execution flow, or cryptic messages that are hard to interpret.

Avoid Excessive Logging

Detailed logs are valuable, but excessive logging can degrade software performance and bury important entries. Too much noise makes troubleshooting harder, not easier.

Choose Appropriate Log Levels

Use log levels to categorize the severity and significance of messages. This allows troubleshooting in multiple passes: start with higher-level logs for a quick diagnosis, then drill down into more detailed logs if necessary. This approach only works if log levels are applied consistently throughout the codebase.

Consider Alternatives to Logging

If the intended audience is other developers, logging may not always be the best communication tool.

In Python, for example, the warnings module is commonly used by libraries to alert application developers.

For errors, prefer raising exceptions rather than only logging error messages. Exceptions provide clearer and more structured error handling and are supported in most modern languages.

Include Dynamic Information

Log runtime states/values that may provide meaningful insights during troubleshooting.

Avoid Sensitive Information

Never log sensitive data such as passwords, tokens, or personally identifiable information.

Defer Log Message String Formatting

String manipulation is one of the most common operations in software development, and inefficient handling can become a significant performance cost, especially in logging.

Avoid eager string interpolation or templates, even if they are concise and readable. Instead, use a pattern that defers string formatting:

This ensures that formatting only occurs if the log message will actually be emitted.

Empower Structured Logging

Most modern logging frameworks support structured logging, which makes logs easier to parse and analyze programmatically.

In Python, when using printf-style formatting, always prefer mapping keys. For smoother analysis, aim for globally unique mapping keys.

Regularly Curate Logs

Periodically review logs to ensure these guidelines are consistently applied. Identify inaccurate, irrelevant, or obsolete entries, and remove log statements that no longer add value. This keeps logs clear, actionable, and easier to maintain.