In today's world of microservices, serverless functions, and complex AI workflows, understanding your application's behavior is more challenging than ever. When a user request fails or slows to a crawl, the old methods of tailing log files on a single server just don't cut it. The problem isn't isolated to one place; it's spread across a distributed system. The solution? A modern approach that gives you complete clarity: distributed tracing.
But traditional tracing tools often come with a steep learning curve and require tedious, manual instrumentation that can quickly become a maintenance nightmare. What if you could define your application's observability right within your code, just as you define its logic?
This is the promise of Observability as Code, a developer-centric paradigm that turns monitoring into a simple, programmatic, and automated workflow. And trace.do is the platform built to make it a reality.
Observability is built on three pillars: logs, metrics, and traces. While logs tell you what happened at a point in time and metrics give you aggregated data, traces tell you the why and where. A trace follows a single request as it travels through multiple services, painting a complete picture of its journey, including timing, dependencies, and errors.
However, setting this up has historically been painful:
Observability as Code treats your monitoring setup as part of your application's source code. It’s version-controlled, testable, and lives right alongside the business logic it describes. This philosophy is at the core of trace.do.
Instead of complex configurations, you use a simple, elegant API to wrap your code. This brings powerful benefits:
trace.do was designed from the ground up to provide effortless observability and complete clarity. It achieves this by providing a powerful SDK that makes implementing automated distributed tracing incredibly simple.
Let’s look at a real-world example. Imagine you have a function to process a customer order, which involves calling a payment service and a shipping service. Here’s how you’d implement tracing with trace.do:
import { trace } from '@do/trace';
async function processOrder(orderId: string) {
// Automatically trace the entire function execution
return trace.span('processOrder', async (span) => {
span.setAttribute('order.id', orderId);
// The trace context is automatically propagated
const payment = await completePayment(orderId);
span.addEvent('Payment processed', { paymentId: payment.id });
await dispatchShipment(orderId);
span.addEvent('Shipment dispatched');
return { success: true };
});
}
Let's break down how simple and powerful this is:
Worried about vendor lock-in or compatibility with your existing tools? Don't be. trace.do is built on OpenTelemetry (OTel), the open-source industry standard for observability.
This means you get the best of both worlds: a simplified, code-driven instrumentation experience with trace.do, and the flexibility to send your data to any OTel-compatible backend you choose, including:
You are in complete control of your data, ensuring that trace.do fits seamlessly into your existing observability and monitoring stack.
Stop guessing where performance bottlenecks are and start seeing the full story. By embracing Observability as Code, you empower your developers to build more resilient, performant, and debuggable systems.
trace.do makes this transition seamless. It provides the automated distributed tracing and agentic workflow platform you need to monitor, debug, and optimize your services with unparalleled ease.
Ready for effortless observability and complete clarity? Visit trace.do to learn more and get started.