Tips and Tricks #212: Implement Structured Logging for Observability

Use structured JSON logging for better searchability and analysis in cloud environments.

Code Snippet

import json
import logging
from datetime import datetime

class StructuredLogger:
    def __init__(self, service_name: str):
        self.service = service_name
        self.logger = logging.getLogger(service_name)
    
    def _log(self, level: str, message: str, **kwargs):
        log_entry = {
            "timestamp": datetime.utcnow().isoformat(),
            "level": level,
            "service": self.service,
            "message": message,
            **kwargs
        }
        print(json.dumps(log_entry))
    
    def info(self, message: str, **kwargs):
        self._log("INFO", message, **kwargs)
    
    def error(self, message: str, **kwargs):
        self._log("ERROR", message, **kwargs)

# Usage
logger = StructuredLogger("order-service")
logger.info("Order created", order_id="12345", user_id="u-789", amount=99.99)
# {"timestamp":"2024-01-15T10:30:00","level":"INFO","service":"order-service","message":"Order created","order_id":"12345","user_id":"u-789","amount":99.99}

Why This Helps

• Easy to query in CloudWatch, Datadog, etc.
• Enables correlation across services
• Machine-parseable for alerting

How to Test

• Verify JSON output format
• Test queries in log aggregation tool

When to Use

All cloud-native applications. Essential for microservices and distributed systems.

Performance/Security Notes

Include correlation IDs for request tracing. Consider using OpenTelemetry for full observability.

References

• https://opentelemetry.io/docs/concepts/signals/logs/

Try this tip in your next project and share your results in the comments!