Best Practices for Logging in Spring Boot Applications

Logging is a vital aspect of modern application development and operation. It provides lines of sight into how applications perform, helps diagnose issues, and facilitates monitoring and auditing. Without a structured approach, logging can either be excessive and noisy or insufficient, leading to missed insights and difficult troubleshooting. This blog post outlines best practices for logging in Spring Boot applications to ensure logs are actionable, secure, and efficient.

Choosing the Right Log Level for Different Use Cases

Log levels determine how much detail is captured in the logs and serve as a powerful filter to control verbosity. Selecting the correct log level is essential for generating meaningful log files and avoiding unnecessary overhead. Below are the primary log levels and their typical use cases:

• TRACE – Used sparingly for the most detailed events. Ideal for system tracing but generally too noisy for production environments.
• DEBUG – Valuable during development for diagnosing issues and inspecting variable states. For example:

   logger.debug("Fetching records from database with parameters: {}", parameters);

• INFO – Reserved for important, high-level operational messages such as business logic events, e.g.,:

   logger.info("Order {} has been processed successfully.", orderId);

• WARN – Highlights potential problems that aren’t necessarily errors, such as deprecated API usage or high resource consumption:

   logger.warn("API response took longer than expected for service {}.", serviceName);

• ERROR – Reserved for serious issues that affect the application’s functionality, like unhandled exceptions:

   logger.error("Unable to process payment for order {} due to {}", orderId, exception.getMessage());

Choosing the correct log levels ensures that relevant information is captured without overwhelming the logs or incurring unnecessary performance costs.

Never Log Sensitive Data

Data security and compliance are paramount in logging. Logging sensitive information—such as passwords, API keys, tokens, or personally identifiable information (PII)—can lead to severe security breaches.

For instance, avoid logging raw authentication payloads:

   // ❌ Risky logging example
   logger.info("User login request payload = {}", loginPayload);

Instead, use redacted data when necessary:

   // ✅ Safer logging recommendation
   logger.info("User {} attempted login.", user.getUsername());

Adhering to regulations like GDPR or HIPAA may also require additional anonymization mechanisms.

Use INFO for Business-Level Logs

Business-level events should use the INFO log level to communicate significant workflow steps. For instance, log order processing or status changes:

   logger.info("User {} has completed payment for order {}.", userId, orderId);

These logs are essential for both monitoring routine operations and auditing purposes.

Use DEBUG for Development, Disable It in Production

While DEBUG logs are indispensable for developers during code testing and problem tracing, they should be disabled in production environments to:

• Avoid unnecessary verbosity.
• Reduce storage and transmission costs.
• Prevent leakage of sensitive technical details.

Spring Boot allows easy log level configuration via application.properties:

   # Development
   logging.level.com.example=DEBUG

   # Production
   logging.level.com.example=INFO

Alternatively, use Spring Profiles to load environment-specific settings.

Leverage MDC for Traceability

Mapped Diagnostic Context (MDC) provides context for log entries, such as request IDs or user identifiers. By tagging logs with contextual data, MDC enables high traceability in distributed applications.

Example:

   MDC.put("requestId", requestId);
   MDC.put("userId", user.getId());
   logger.info("Processing operation...");
   MDC.clear(); // Always clear MDC after the request lifecycle.

Enhance your log patterns in logback-spring.xml to include MDC fields:

   <pattern>%d{yyyy-MM-dd HH:mm:ss} [%X{requestId}] [%X{userId}] %-5level %logger{36} - %msg%n</pattern>

Externalize Log Configuration

Avoid hardcoding logging configurations in code. Instead, externalize them to keep application artifacts clean and adaptable for various environments.

Example Configuration:

   logging:
     file:
       name: logs/application.log
     pattern:
       console: "%d{yyyy-MM-dd HH:mm:ss} %-5level [%thread] %logger{36} - %msg%n"

Externalizing configurations allows changes without needing redeployment and ensures environment-specific tuning.

Log Exceptions with Context

When catching exceptions, always include context about the circumstances of the failure. Simply printing the stack trace isn’t informative enough.

Example:

   try {
       performOperation();
   } catch (CustomException e) {
       logger.error("Failed to perform operation for user {} due to {}", userId, e.getMessage(), e);
   }

This approach combines exception stack traces with meaningful context, improving diagnosability.

Add Correlation IDs Using Spring Cloud Sleuth

Correlation IDs trace requests across distributed systems, making it easier to pinpoint errors and analyze performance. Spring Cloud Sleuth automatically propagates correlation IDs between services.

Setting Up Sleuth:

Add necessary dependencies:

   <dependency>
       <groupId>org.springframework.cloud</groupId>
       <artifactId>spring-cloud-starter-sleuth</artifactId>
   </dependency>

Sleuth integrates with MDC to generate and propagate trace IDs:

   2025-06-21 12:00:00 INFO [traceId=abcd1234, spanId=5678efgh] Processing request...

Monitor Logs with Tools

Log analysis tools help centralize, visualize, and monitor logs. Popular options include:

• Grafana and Loki – Cloud-native solutions for metrics and logs.
• ELK Stack (Elasticsearch, Logstash, Kibana) – Provides indexing and visualization.
• Splunk – Offers advanced analytics for enterprise environments.

These tools support querying, alerting, and dashboards to derive actionable insights from complex logs.

Summary Checklist for Log Hygiene

To ensure clean and effective logging, follow this checklist:

• Choose appropriate log levels (DEBUG, INFO, WARN, ERROR).
• Avoid logging sensitive or personal data.
• Use INFO for business workflows.
• Use DEBUG logs for development, but disable them in production.
• Implement MDC to enhance traceability.
• Externalize log settings in application.properties or logback-spring.xml.
• Combine exceptions with relevant context.
• Add correlation IDs with Spring Cloud Sleuth for distributed tracing.
• Monitor logs using tools like ELK, Grafana, or Splunk.

Final Thoughts

Structured and thoughtful logging in Spring Boot applications not only simplifies debugging and monitoring but also ensures security and scalability. By applying these best practices, you can build a logging strategy that serves your team’s needs while promoting sustainable operations.