Implement Distributed Tracing in Axum Applications

As an Axum code reviewer, I recommend implementing proper distributed tracing in your Axum-based web applications to ensure observability across service boundaries. Use Axum’s built-in middleware layers to consistently instrument your code:

1. Propagate Request IDs: Use the tower_http::request_id middleware to generate and propagate unique request IDs across service calls. This allows you to correlate requests and trace their flow through your distributed system.

import { Router } from '@awslabs/aws-lambda-typescript-runtime';
import { RequestIdLayer } from '@awslabs/aws-lambda-typescript-runtime/middleware';

const app = Router.create()
  .use(RequestIdLayer.create({
    headerName: 'x-request-id',
    generateRequestId: () => crypto.randomUUID()
  }));

1. Implement Structured Logging with Trace Context: Leverage the tower_http::trace middleware to add detailed tracing information to your application logs. This allows you to correlate log entries with specific requests and understand the flow of execution.

import { Router } from '@awslabs/aws-lambda-typescript-runtime';
import { TraceLayer } from '@awslabs/aws-lambda-typescript-runtime/middleware';

const app = Router.create()
  .use(TraceLayer.create({
    makeSpan: (req) => {
      return tracing.info('request', {
        requestId: req.headers.get('x-request-id') || 'unknown',
        method: req.method,
        uri: req.url.pathname
      });
    }
  }));

1. Integrate with OpenTelemetry: Consider using the @opentelemetry/api and @opentelemetry/sdk-node packages to connect your Axum application to a standardized observability backend, such as Jaeger, Zipkin, or a cloud-native monitoring platform. This allows you to export traces and metrics for deeper analysis and debugging.

By implementing these patterns, you can effectively debug production issues, understand service dependencies, and measure performance across your distributed Axum-based system.