Intro
Logging is one of those fundamental practices that separates maintainable software from debugging nightmares. Yet despite its importance, logging is often treated as an afterthought—something developers sprinkle throughout their code without much consideration. This guide explores the different logging levels, best practices, and common pitfalls to help you build better observability into your applications.
Understanding Log Levels
Most logging frameworks provide a hierarchy of log levels, each serving a distinct purpose. Understanding when to use each level is crucial for creating useful logs.
TRACE
The most granular level of logging, TRACE captures extremely detailed information about program execution. This level is typically used for diagnostic purposes during development and is rarely enabled in production environments.
Use TRACE when you need to follow the exact path of execution through your code, including entry and exit points of methods, loop iterations, or detailed state transitions. For example, you might log every iteration of a data processing loop or every step in a complex algorithm.
DEBUG
DEBUG level provides detailed information useful for debugging during development and troubleshooting in production. Unlike TRACE, DEBUG logs should focus on meaningful events and state changes rather than exhaustive execution details.
Use DEBUG for variable values, conditional branch decisions, intermediate calculation results, and other information that helps you understand why your application behaved a certain way. For instance, logging the parameters passed to a function or the result of a database query would be appropriate at this level.
INFO
INFO is the standard logging level for recording normal application behavior. These logs document significant events in your application's lifecycle and business logic execution.
Use INFO for application startup and shutdown, configuration values being loaded, successful completion of major operations, user actions, and state transitions that matter to your business logic. INFO logs should tell the story of what your application is doing under normal circumstances.
WARN
WARN indicates something unexpected happened, but the application can continue functioning. These are potential problems that don't prevent the current operation from completing but might cause issues later.
Use WARN for deprecated API usage, fallback to default values when configuration is missing, recoverable errors like temporary network issues with retry logic, performance degradation, or approaching resource limits. A warning suggests that while things are working now, someone should probably investigate.
ERROR
ERROR indicates a serious problem that prevented a specific operation from completing. The application can usually continue running, but something definitely went wrong.
Use ERROR for exceptions that prevent business logic from completing, failed database transactions, failed external API calls without a fallback, invalid user input that can't be processed, or missing required resources. ERROR logs should always provide enough context to understand what went wrong and why.
Common Mistakes
Logging Too Much or Too Little
One of the most common mistakes is finding the wrong balance. Too much logging creates noise that obscures important information and can impact performance. Too little logging leaves you blind when problems occur.
Avoid logging inside tight loops at INFO level or above, logging the same information repeatedly, or creating massive log files that are expensive to store and process. Equally problematic is having no logs around critical business logic, catching exceptions without logging them, or failing to log enough context to diagnose issues.
Poor Log Messages
Vague messages like "Error occurred" or "Processing complete" provide almost no value. Your logs should tell a clear story to someone who wasn't present when the code was written.
Include relevant context such as identifiers, parameters, and state information. Use structured logging with key-value pairs rather than string concatenation. Avoid including sensitive information like passwords, tokens, credit card numbers, or personally identifiable information unless you have proper redaction in place.
Inconsistent Formatting
When every developer on your team uses different logging styles, searching and parsing logs becomes a nightmare. Establish conventions for your team and stick to them.
Inconsistencies appear in timestamp formats, how exceptions are logged, naming conventions for log fields, and the structure of log messages. Using a structured logging framework can help enforce consistency automatically.
Logging at the Wrong Level
Misusing log levels makes it difficult to filter logs effectively. If everything is logged at ERROR, you can't distinguish real problems from routine events. If business-critical events are logged at DEBUG, they'll be invisible in production.
A common mistake is logging every caught exception as ERROR when many exceptions represent expected business logic outcomes. Similarly, logging successful operations at WARN or DEBUG undermines the purpose of INFO level logging.
Performance Impact
Logging isn't free. String concatenation, formatting, and I/O operations all consume resources. Logging inside performance-critical code paths without consideration can significantly impact application performance.
Be especially careful about logging in tight loops, constructing expensive log messages unconditionally even when that log level is disabled, and synchronous logging in high-throughput code paths. Use asynchronous logging and guard clauses to check if a log level is enabled before constructing expensive messages.
Best Practices
Use Structured Logging
Modern applications should embrace structured logging formats like JSON. Structured logs are machine-readable, making them much easier to search, filter, and analyze at scale.
Instead of logging "User John logged in from 192.168.1.1", structure it as key-value pairs with fields for user, action, and IP address. This allows you to easily query all login events or all actions by a specific user.
Include Correlation IDs
In distributed systems, tracking a single request across multiple services is essential. Include correlation or trace IDs in your logs to connect related events across service boundaries.
When a request enters your system, generate or extract a correlation ID and include it in every log statement related to that request. This allows you to reconstruct the complete journey of a request through your infrastructure.
Log Contextual Information
Every log statement should include enough context for someone to understand what was happening without reading the code. This includes relevant identifiers, operation being performed, input parameters, and environmental context.
However, balance this with privacy and security concerns. Use redaction libraries to automatically scrub sensitive data, and ensure your logging practices comply with regulations like GDPR.
Set Appropriate Default Levels
Configure production environments to log at INFO by default, with the ability to dynamically increase verbosity to DEBUG when troubleshooting. Development environments can default to DEBUG. TRACE should almost never be enabled in production due to its volume and performance impact.
Implement the ability to change log levels without restarting your application. This allows you to increase verbosity when investigating issues without downtime.
Make Logs Actionable
Every ERROR log should suggest a clear path forward. If a database connection fails, log not just the error but also which database, what operation was attempted, and relevant configuration details.
Similarly, WARN logs should indicate what the potential impact is and what action might be needed. A warning about approaching disk space limits is only useful if it includes how much space remains and where.
Use Logging Frameworks
Don't write logs directly to stdout or files. Use established logging frameworks like Log4j, Logback, Serilog, or Python's logging module. These frameworks provide features like log rotation, filtering, asynchronous logging, and multiple output destinations.
Logging frameworks also allow you to configure logging behavior without changing code, making it easier to adjust logging in different environments.
Sample High-Volume Logs
For very high-traffic operations, consider sampling your logs. You don't need to log every single request at INFO level if you're handling millions per minute. Logging 1% of successful requests with full DEBUG logging for errors provides good observability without overwhelming your log storage.
Implement intelligent sampling that always logs errors and warnings while sampling routine successful operations based on configured rates.
Integration with Observability
Modern logging doesn't exist in isolation. Integrate your logs with metrics and tracing to build comprehensive observability.
When an error occurs, your logs should reference relevant metric names and trace IDs. When investigating a performance issue, you should be able to jump from a slow trace to the corresponding detailed logs. This integration transforms logs from isolated events into part of a cohesive narrative about your application's behavior.
Use log aggregation tools like the ELK stack (Elasticsearch, Logstash, Kibana), Splunk, or cloud-native solutions like CloudWatch or Stackdriver. These tools make it possible to search across millions of log entries, create alerts based on log patterns, and visualize trends over time.
Conclusion
Effective logging is a skill that develops over time. It requires understanding your application's behavior, anticipating what information will be valuable during troubleshooting, and balancing detail with readability and performance.
Start by establishing clear conventions for your team, use structured logging from the beginning, and regularly review your logs to ensure they're providing the value you need. Good logging transforms debugging from guesswork into detective work, where the clues you need are already waiting in your log files.
Remember that logs are written once but read many times, usually under stressful circumstances when something is broken. Invest the time to make them clear, consistent, and useful. Your future self, your teammates, and your on-call rotation will thank you.
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