AWS Lambda Observability - Structured Logging, Tracing & Metrics
"It works on my machine" doesn't help when when users see errors or experience slowness with your app that uses Lambda. How will you know what is causing the issue? Today, we're implementing observability with structured logging, distributed tracing and custom metrics.
We'll transform a Lambda function from basic print statements into production-grade observability using AWS Lambda Powertools. We'll implement structured JSON logging for machine-readable logs, distributed tracing with AWS X-Ray to visualize request paths, and custom business metrics to track what actually matters.
We'll see how to query logs in CloudWatch Logs Insights, use X-Ray traces to correlate errors across multiple services and create CloudWatch alarms for business-critical metrics.
Source code: https://github.com/prime-computing-la...
References:
AWS Lambda Powertools Python: https://docs.powertools.aws.dev/lambd...
AWS X-Ray: https://docs.aws.amazon.com/xray/late...
CloudWatch Logs Insights: https://docs.aws.amazon.com/AmazonClo...
Lambda Layers: https://docs.aws.amazon.com/lambda/la...