Your application is slow. Users are complaining. Your error alerts are quiet, but you know something is wrong. You're stuck in the frustrating guessing game of performance debugging: Is it the database? A third-party API? A new piece of code?
Stop guessing. Traditional monitoring might tell you that a service is slow, but it won't tell you why. To get to the root cause, you need to see the entire journey of a request as it travels through your system. This is the power of distributed tracing, and with a tool like trace.do, you can pinpoint the exact source of latency in minutes, not hours.
Let's explore five of the most common performance bottlenecks and see how you can instantly identify them with automated distributed tracing.
Before we dive in, a quick primer. Distributed tracing follows a single request from the moment it hits your frontend, through every microservice, database call, and API it touches, until a response is sent back. This entire journey is called a trace. Each individual operation within that journey (like an API call or a database query) is called a span.
By visualizing these traces in a waterfall chart, you can see exactly how long each span takes and how they connect, making bottlenecks stand out immediately.
This is the classic culprit. A single, unoptimized query can bring a service to its knees, causing cascading delays across your application.
This bottleneck is craftier. It’s not one slow query, but a storm of tiny, fast queries that collectively create a massive performance drag. This often happens when you loop through a list of items and perform a separate database lookup for each one.
Your service might be perfectly optimized, but if it depends on a slow external API, your users will still experience delays. You have no control over the third party's performance, but you need to know when they are the problem.
In modern asynchronous applications, a piece of code that unexpectedly blocks the main execution thread can be disastrous. This could be anything from writing a large file to disk synchronously to a poorly configured library call.
Serverless functions are incredibly efficient, but the first request to an inactive function can be slow due to a "cold start," where the cloud provider has to provision resources. While unavoidable, it's crucial to monitor its impact.
Finding these bottlenecks is easy when you have the right data. But doesn't collecting that data require complex, manual instrumentation?
Not with trace.do. We believe in Observability as Code. Instead of wrestling with agents and configuration files, you use a simple, powerful SDK to get instant visibility.
Here’s how easy it is to trace a function in your application:
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 to downstream calls
const payment = await completePayment(orderId);
span.addEvent('Payment processed', { paymentId: payment.id });
await dispatchShipment(orderId);
span.addEvent('Shipment dispatched');
return { success: true };
});
}
With one trace.span wrapper, you automatically capture the function's execution time, link it to the parent request, and propagate the context to the completePayment and dispatchShipment functions. No manual context passing, no complex setup. Just clear, code-driven observability.
Because trace.do is built on OpenTelemetry (OTel) standards, it works seamlessly with your existing frameworks and can export data to platforms like Jaeger, Datadog, and Honeycomb.
Stop guessing and start seeing. Get the complete clarity you need to monitor, debug, and optimize your services.
Ready to find and fix your application bottlenecks? Get started with trace.do today.