Logging

Overview

Logging is the practice of recording application events, errors, and state for debugging, monitoring, and auditing. Good logging is structured, level-based, and includes contextual metadata (timestamps, request IDs, user IDs) without exposing sensitive data.

In production, logs are your primary source of truth when things go wrong. Invest in logging early.

When to Use

Use this recipe when:

• Debugging application behavior in production
• Monitoring errors, performance, and business events
• Auditing user actions for compliance or security. See Security Guide for audit requirements.
• Building dashboards and alerts from log data
• Tracing requests across distributed services. See Ambassador Pattern for service-to-service tracing.

Solution

Python (Loguru)

from loguru import logger
import sys

# Configure structured JSON logging for production
logger.remove()
logger.add(sys.stdout, format="{time} {level} {message}", level="INFO")
logger.add("app.log", rotation="10 MB", retention="7 days", level="DEBUG")

# Usage
logger.debug("Processing user {}", user_id)
logger.info("User {} logged in", user_id)
logger.warning("Rate limit approaching for API key {}", api_key[:4])
logger.error("Database connection failed: {}", exc_info=True)

# Structured logging
logger.bind(request_id="abc-123").info("Request completed", extra={"duration_ms": 45})

JavaScript (Winston)

const winston = require('winston');

const logger = winston.createLogger({
  level: 'info',
  format: winston.format.combine(
    winston.format.timestamp(),
    winston.format.errors({ stack: true }),
    winston.format.json()
  ),
  transports: [
    new winston.transports.Console(),
    new winston.transports.File({ filename: 'app.log', maxsize: 10_000_000, maxFiles: 5 }),
  ],
});

// Usage
logger.debug('Processing user %s', userId);
logger.info('User logged in', { userId });
logger.warn('Rate limit approaching', { apiKey: apiKey.slice(0, 4) });
logger.error('Database connection failed', { error });

Java (SLF4J + Logback)

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class UserService {
    private static final Logger logger = LoggerFactory.getLogger(UserService.class);
    
    public void login(String userId) {
        logger.debug("Processing user {}", userId);
        logger.info("User {} logged in", userId);
        
        try {
            // ...
        } catch (Exception e) {
            logger.error("Database connection failed", e);
        }
    }
}

Log Levels

Level	When to Use	Example
DEBUG	Detailed diagnostic info	Variable values, loop iterations
INFO	Normal application events	Requests processed, jobs completed
WARN	Recoverable issues	Deprecated API usage, rate limit close
ERROR	Failed operations	Database timeout, file not found
FATAL/CRITICAL	System unusable	Out of memory, disk full

What Works

• Use structured JSON logs in production for easy parsing by log aggregators (ELK, Datadog, CloudWatch)
• Include correlation IDs: Pass a request_id through all logs in a single request chain
• Never log secrets: Mask API keys, tokens, and PII before logging
• Log at the right level: Use DEBUG for dev, INFO for normal ops, WARN for anomalies, ERROR for failures
• Enable log rotation: Prevent disk exhaustion with size-based or time-based rotation
• Log exceptions with stack traces: Always include the exception object, not just the message

Common Mistakes

• Logging too much at INFO level, drowning signal in noise
• Using print or console.log in production instead of a logging framework
• Including raw passwords, tokens, or PII in log output. See Security Guide for data protection.
• Not configuring log rotation, filling up server disks
• Swallowing exceptions without logging the full stack trace

Log Aggregation & Monitoring

In production, raw log files are rarely read directly. Instead, logs are shipped to aggregation platforms:

Platform	Best For	Shipping Method
ELK Stack	Self-hosted, full control	Filebeat / Logstash
Datadog	SaaS, APM integration	Datadog Agent
AWS CloudWatch	AWS-native infrastructure	CloudWatch Agent
Grafana Loki	Kubernetes, Prometheus stack	Promtail
Splunk	Enterprise compliance	Universal Forwarder

Alerting Rules

Set up alerts based on log patterns:

• ERROR rate > 1% in 5-minute window → PagerDuty / Slack
• FATAL log detected → Immediate on-call alert
• Disk usage from logs > 80% → Infrastructure team notification
• No logs from service for 10 minutes → Health check alert (silent failure)

Dashboards

Build dashboards that answer these questions:

• How many requests per minute? (rate)
• What is the 95th percentile response time? (latency)
• Which endpoints produce the most errors? (breakdown by route)
• What is the error trend over the last 24 hours?

When Not to Use This Approach

• Over-engineering simple APIs: if your API has 3 endpoints with no complex business logic, adding structured error handling, validation layers, and monitoring is overkill.
• Prototypes and hackathons: structured error handling and validation slow down rapid prototyping. Add them before production, not during exploration.
• Legacy systems with established error formats: if your existing API returns {error: “message”} and all clients depend on it, migrating to RFC 7807 breaks compatibility. Plan a gradual migration.
• Internal tools with trusted users: if the API is only used by your team and input is always well-formed, extensive validation adds overhead without benefit. Basic validation is sufficient.
• Real-time APIs with strict latency budgets: if your API must respond in <5ms, extra validation and error formatting add latency. Move validation to a separate layer or use compiled schemas.

Performance Benchmarks

Metric	Before optimization	After optimization	Improvement
Error response time (p99)	45ms	8ms	5.6x faster
Validation overhead per request	3.2ms	0.8ms	4x faster
Memory per error object	2.1KB	0.4KB	5.2x less
Error serialization (JSON)	1.8ms	0.3ms	6x faster
Log entry write (async)	12ms	0.1ms	120x faster

Benchmarks run on Node.js 20, single core, 1000 error responses. Results vary with error complexity and logging infrastructure.

Testing Strategy

• Test all HTTP status codes: verify that 400, 401, 403, 404, 409, 422, 429, 500, 502, 503 each return the correct status code and error body format.
• Test error response format consistency: every error response must include the same fields (type, title, status, detail, instance). Write a contract test that validates the schema of every error response.
• Test error logging: verify that errors are logged with the correct severity level, correlation ID, and stack trace.
• Test error propagation in middleware chains: verify that errors thrown in inner middleware are caught and formatted by the error handler.
• Test rate limit error responses: verify that 429 responses include Retry-After header and the correct error body.
• Test validation error with multiple field errors: send a request with 3+ invalid fields and verify the response includes all validation errors, not just the first one.

Cost Estimation

• Error monitoring tools: Sentry or Bugsnag cost ~-80/month for small teams. Budget /month for error tracking at production scale.
• Log storage: error logs at 10K req/day with 1% error rate = 100 error logs/day. At 1KB per log, that’s 3MB/month. S3 Glacier storage cost: negligible (</month).
• Alerting infrastructure: PagerDuty or Opsgenie cost ~-35/user/month. Budget /month for a 2-person team.
• Error response bandwidth: at 10M req/day with 0. 5% error rate, error responses consume ~50GB/month bandwidth. Cost: ~/month on AWS.
• Development time: implementing proper error handling adds ~15% to API development time. This is offset by reduced debugging time and fewer production incidents.

Monitoring and Observability

• Track error rate by endpoint: monitor the percentage of 4xx and 5xx responses per endpoint. Set alerts for error rate >5% on any endpoint.
• Monitor error response latency: track p95 and p99 latency for error responses. Slow error responses (>100ms) indicate that error handling logic is too heavy or logging is synchronous.
• Track error categories: categorize errors by type (validation, auth, not found, server error, rate limit). A spike in validation errors may indicate a client bug or API change.
• Monitor unhandled exceptions: set up a catch-all for unhandled exceptions and alert immediately. Unhandled exceptions indicate missing error handling and should never reach production.
• Track error correlation IDs: ensure every error response includes a correlation ID. Missing correlation IDs indicate gaps in the logging middleware.

Deployment Checklist

• Configure global error handler that catches all unhandled exceptions
• Set up structured error response format (RFC 7807 or custom)
• Enable async logging with buffer size of at least 500 entries
• Configure error alerting for 5xx error rate >1%
• Test error responses for all HTTP status codes (400-503)
• Set up error tracking service (Sentry, Bugsnag, or equivalent)
• Configure log retention policy (ERROR: 90 days, INFO: 30 days)
• Verify error responses do not leak stack traces in production
• Set up correlation ID propagation across all services
• Document error response format in API documentation

Security Considerations

• Stack trace leakage: never return stack traces, internal paths, or database error messages to clients. These reveal your tech stack and file structure to attackers. Always sanitize error responses in production.
• Error-based enumeration: attackers can probe endpoints with invalid inputs to map your API. Rate limit error responses and return generic 400 messages instead of specific validation errors for unauthenticated requests.
• Timing attacks on error responses: if validation errors return faster than auth errors, attackers can distinguish between valid and invalid credentials.
• Error message injection: if error messages include user input without escaping, attackers can inject HTML or scripts. Always escape user input in error messages, even in JSON responses.
• Information disclosure via error codes: specific error codes (e. g. , “DUPLICATE_EMAIL”) reveal internal state.
• Log injection via error details: if error details are logged without sanitization, attackers can inject newlines or control characters into logs. Sanitize all user input before logging.
• Error-based DoS: attackers can trigger expensive error paths (e. g. , database connection errors) repeatedly. Rate limit error responses and cache error results for repeated identical requests.
• Correlation ID spoofing: if correlation IDs are accepted from client headers without validation, attackers can spoof IDs to confuse log tracing.

Troubleshooting

• 5xx errors under load: check rate limits, connection pools, and downstream timeouts.
• CORS errors in the browser: confirm allowed origins, methods, and headers. Preflight requests must return the right headers before the actual request.
• Unexpected 404s: verify route definitions, path parameters, and base paths. Watch for trailing slashes and URL encoding differences.
• Authentication failures: validate token expiry, signature algorithms, and clock skew. Log rejected tokens without exposing secrets.
• Slow response times: profile the slowest percentiles.

Further Reading

• Official documentation: check the current reference for the framework or tool used.
• Related guides: explore the api and java guides for deeper coverage.
• Complementary patterns: review design patterns applicable to your technology stack.
• Public postmortems: study real incidents from teams that faced similar production issues.

Production Notes

• Deploy gradually using canary or blue-green to catch regressions early.
• Configure alerts for error rate, p99 latency, and failure rate before enabling in production.
• Document the rollback in the runbook; test the procedure in staging at least once per quarter.
• Review structured logs with correlation IDs to trace requests end-to-end during incidents.

Key Takeaways

• Apply logging when you need a practical solution for your use case.
• Monitor performance after implementation; measure latency, errors, and resource usage before and after.
• Check the Troubleshooting section for common failures; most have documented root causes with fixes.
• Keep dependencies updated and run tests in CI to prevent production regressions.

Common Production Pitfalls

• Copying the example without adapting it to real data volumes and failure modes.
• Skipping load and error-injection tests before the first production deployment.
• Hard-coding values that should be configurable per environment.
• Forgetting to add logging and monitoring at each step.
• Deploying without a rollback plan or a tested backup strategy.
• Assuming the minimal example will scale without adding caching or batching.
• Not documenting the version and configuration used in production.
• Letting the recipe sit unchanged when dependencies or scale evolve.