Serverless computing has revolutionized how we build and deploy applications. The promise of infinite scalability, pay-per-use pricing, and reduced operational overhead is compelling. But as many developers have discovered, with great power comes a significant challenge: when things go wrong, how do you debug a system that's distributed, ephemeral, and seemingly opaque?
Wading through mountains of logs from dozens of different function invocations is a frustrating and time-consuming process. You're looking for a needle in a haystack, without a clear picture of how one event led to another.
This is where distributed tracing comes in. It transforms the "black box" of serverless into a transparent, observable system. And with a tool like trace.do, implementing it is easier than you think.
Traditional debugging methods fall short in a serverless world. The core challenges are:
These issues lead to longer resolution times, frustrated developers, and unhappy users. The solution is to shift from reactive log-diving to proactive observability with distributed tracing.
Imagine you're tracking a package from a warehouse to your doorstep. You can see when it left the warehouse, when it arrived at the local distribution center, when it was loaded onto the truck, and when it was delivered.
Distributed tracing does the same thing for your application requests.
It provides a unified view of a request's entire lifecycle as it travels through your services. This complete journey is called a trace. Each step in the journey—an API call, a database query, or a function execution—is a span.
By connecting these spans, trace.do builds a detailed, chronological map of your request flow. This allows you to instantly pinpoint errors, identify performance bottlenecks, and understand the real-world behavior of your system.
Let's see how trace.do brings effortless observability to a common serverless pattern: an order processing workflow.
Imagine we have a function, processOrder, that is triggered when a customer places an order. This function then calls two other services (which could be other functions or microservices): one to handle payment and another to dispatch the shipment.
Our initial AWS Lambda handler might look something like this:
// services.ts
import { completePayment, dispatchShipment } from './downstream-services';
// handler.ts
export async function handler(event: any) {
const { orderId } = JSON.parse(event.body);
console.log(`Starting to process order: ${orderId}`);
try {
const payment = await completePayment(orderId);
console.log(`Payment processed for order: ${orderId}`);
await dispatchShipment(orderId);
console.log(`Shipment dispatched for order: ${orderId}`);
return { statusCode: 200, body: JSON.stringify({ success: true }) };
} catch (error) {
console.error(`Failed to process order ${orderId}:`, error);
return { statusCode: 500, body: JSON.stringify({ success: false }) };
}
}
If an order fails, we have to sift through CloudWatch logs to piece together what happened. If it's just slow, we have no easy way of knowing whether the delay was in the payment service or the shipping service.
Now, let's add trace.do. Our approach becomes one of Observability as Code. We use a simple trace.span() wrapper to automatically capture the entire execution and add meaningful context.
// Import the trace.do SDK
import { trace } from '@do/trace';
import { completePayment, dispatchShipment } from './downstream-services';
export async function handler(event: any) {
const { orderId } = JSON.parse(event.body);
// Automatically trace the entire handler execution
return trace.span('processOrderHandler', async (span) => {
// Add custom attributes to the span for easy filtering and analysis
span.setAttribute('order.id', orderId);
span.setAttribute('cloud.region', process.env.AWS_REGION);
try {
// The trace context is automatically propagated to downstream calls
const payment = await completePayment(orderId);
// Add events to mark specific points in time within the span
span.addEvent('Payment processed', { paymentId: payment.id });
await dispatchShipment(orderId);
span.addEvent('Shipment dispatched');
return { statusCode: 200, body: JSON.stringify({ success: true }) };
} catch (error) {
// Errors are automatically captured on the span
span.recordException(error);
span.setStatus({ code: 'error', message: error.message });
throw error; // Re-throw to let Lambda handle the failure
}
});
}
With just a few lines of code, we've gained complete clarity:
Now, instead of guessing, you can look at the trace and see:
The bottleneck is obvious. You know exactly where to focus your optimization efforts.
Worrying about vendor lock-in? Don't be. trace.do is built on OpenTelemetry (OTel), the open-source industry standard for observability. This means you get:
Stop flying blind. Serverless applications demand a modern approach to debugging and performance monitoring. By embracing distributed tracing with trace.do, you turn complex, opaque workflows into clear, actionable insights.
The code-driven, agentic workflow of trace.do integrates observability directly into your development process, making it a natural part of building resilient, high-performance applications.
Ready to bring complete clarity to your services? Explore trace.do today and transform your debugging workflow.