Effective logging is crucial for diagnosing and resolving issues, especially during unexpected production incidents. Many developers realize their logging practices are inadequate only when faced with a 3:00 a.m. crisis, sifting through disorganized logs to find the root cause of a problem. To prevent this, it’s essential to adopt strategic logging practices that ensure clarity, structure, and actionable insights.

1. Establish Clear Objectives

Before adding log statements to your code, define your logging strategy with clear objectives. Use the following questions to guide your approach:

• What are the main goals of your application?
• Which critical operations need to be monitored?
• What Key Performance Indicators (KPIs) are most relevant?

For example, error logs shouldn’t just indicate that something went wrong—they should provide enough context to identify and fix the issue quickly.

💡 Pro Tip: Anticipate what information you’ll need during debugging. Initially, you might over-log, but it’s easier to reduce unnecessary data later than to add missing context once in production. Regularly review your logs to identify what’s useful and eliminate noise.

2. Understand Log Levels

Logging at appropriate levels helps organize and prioritize information. Here are four common log levels:

A. Info

Captures routine operations, such as successful user logins or completed transactions.

Example:
INFO: User login successful - UserID: 12345

B. Warning

Indicates something unusual but non-critical, like a delay in payment processing.

Example:
WARNING: Payment processing delayed - OrderID: 67890

C. Error

Logs serious issues, such as a failed payment or a service crash.

Example:
ERROR: Database connection failed - Service: Checkout

D. Fatal

Represents catastrophic issues, such as an "out of memory" error causing the application to shut down.

Example:
FATAL: Application out of memory - Shutting down

In production, the default log level is often set to INFO to avoid excessive verbosity. However, during troubleshooting, increase the log level to capture more detailed information.

3. Implement Structured Logging

Structured logging formats log entries into easily searchable fields, making it simpler for both humans and machines to process and analyze them. Instead of a vague error message, structured logs should answer key questions like who, what, where, and why.

Include the following details in your structured logs:

• Request IDs: To trace activity across microservices.
• User IDs: For session context.
• System State: Database status, memory usage, or other relevant metrics.
• Error Context: Detailed messages, including stack traces when applicable.

Example of a structured log entry:

{
  "timestamp": "2024-12-29T03:00:00Z",
  "level": "ERROR",
  "message": "Payment processing failed",
  "orderID": "67890",
  "userID": "12345",
  "errorDetails": "Timeout while connecting to payment gateway"
}

4. Use Log Sampling for Cost Efficiency

High-traffic systems generate enormous amounts of log data, leading to high storage and processing costs. Log sampling can reduce these costs while preserving essential information.

How log sampling works:

• Retain a subset of routine logs, such as a 20% sample of successful login attempts.
• Always keep critical logs, such as errors or warnings.
• Adjust sampling rates during peak traffic or for specific operations.

Example: In an authentication service handling 1,000 logins per second, a 10% sampling rate would save 90% of storage space while maintaining enough data for analysis.

Conclusion

Effective logging is more than just adding print statements—it’s about creating a reliable system for tracking and diagnosing issues. By setting clear objectives, using appropriate log levels, implementing structured logging, and employing log sampling, you can ensure your logs are both actionable and cost-effective.

When an issue arises at 3:00 a.m., these practices will save you from hours of frustration, helping you pinpoint and resolve problems swiftly.