The Three Pillars in One Place: Unifying Logs, Metrics, and Traces

In modern software development, especially with the rise of microservices and complex distributed systems, understanding what's happening inside your application can feel like navigating a maze in the dark. To find our way, we rely on observability. But true observability isn't just about collecting data; it's about understanding the story that data tells.

This story is written using the three pillars of observability: Logs, Metrics, and Traces.

For years, developers and SREs have treated these as separate tools for separate jobs. You check a metrics dashboard for high-level health, dig through logs to find specific errors, and (if you’re lucky) look at a trace to understand a request flow. This constant context-switching is slow, inefficient, and frustrating.

What if you could have all three, perfectly correlated, in a single, unified view? The real power isn't in having the pillars, but in bringing them together.

A Quick Refresher: The Three Pillars

Before we unify them, let's quickly recap what each pillar represents.

1. Logs: The "What Happened"

Logs are timestamped, structured or unstructured text records of discrete events. They are the most granular form of insight into your application.

• What they are: A detailed, chronological diary of your application's life.
• Examples: User '123' logged in, ERROR: Database connection failed, Order 'XYZ' created.
• Strength: Excellent for capturing detailed, specific context around an event.
• Weakness: Can be overwhelmingly noisy ("needle in a haystack") and difficult to correlate across multiple services without a shared context.

2. Metrics: The "How Are We Doing"

Metrics are numerical measurements aggregated over time. They provide a high-level, quantitative view of your system's health and performance.

• What they are: Aggregatable numbers you can plot on a graph.
• Examples: CPU utilization, memory usage, request latency (p99), error rate per minute.
• Strength: Ideal for dashboards, monitoring trends, and setting up alerts. They tell you that a problem exists.
• Weakness: Lack specific context. A spike in latency tells you something is slow, but it doesn't tell you what request was slow or why.

3. Traces: The "Where and Why"

A distributed trace represents the end-to-end journey of a single request as it travels through multiple services, queues, and databases in your system. Each step in the journey is a "span."

• What they are: A complete, contextual map of a single operation.
• Examples: Tracking a user's API call from the load balancer, through the authentication service, to the order processing service, and its subsequent database queries.
• Strength: Unbeatable for debugging latency issues, understanding service dependencies, and performing root cause analysis in distributed architectures.
• Weakness: Historically complex to implement and can be overwhelming without the context provided by logs and metrics.

The Pain of Silos: A Debugging Story

Imagine an alert fires: "API p99 latency is over 2 seconds!"

Your current workflow might look like this:

1. Metrics: You see the spike on your dashboard. Okay, it started at 2:15 PM.
2. Logs: You jump to your logging platform and filter for logs around 2:15 PM with level=error. You find a few timeout errors but have no idea if they are the cause or a symptom. Which user requests were affected? What was the full call chain?
3. Traces: You switch to your separate tracing tool. You search for traces around 2:15 PM that have a long duration. You find one! But now you have to manually correlate the traceId with the vague error messages you found in the logs.

This frantic shuffle between tabs is where precious time is lost. The clues are scattered, and it's up to you to piece the puzzle together.

The Power of Unification with trace.do

The solution isn't a better logging tool or a prettier metrics dashboard. The solution is to tear down the walls between them. This is where trace.do changes the game by treating Observability as Code.

Instead of retrofitting observability with separate agents and complex configurations, trace.do allows you to define it programmatically within your application logic. The result? Logs, metrics, and traces that are inherently connected from the moment they are created.

Let's look at how this works. With a simple API wrapper, you can instrument a function and get all three pillars automatically.

import { trace } from '@do/trace';

async function processOrder(orderId: string) {
  // This automatically creates a TRACE SPAN for the entire function
  return trace.span('processOrder', async (span) => {
    // This attribute acts like a STRUCTURED LOG and can be aggregated into a METRIC
    span.setAttribute('order.id', orderId);

    // Context is automatically passed to downstream calls
    const payment = await completePayment(orderId);

    // This is a time-stamped LOG event tied directly to this trace
    span.addEvent('Payment processed', { paymentId: payment.id });

    await dispatchShipment(orderId);
    span.addEvent('Shipment dispatched');

    return { success: true };
  });
}

In this single block of code:

• Traces: The trace.span() call creates a parent span for the processOrder operation, automatically capturing its duration and context. Any instrumented functions it calls, like completePayment, will appear as child spans.
• Logs: span.addEvent() and span.setAttribute() create contextual, structured logs that are permanently attached to the trace. You're no longer searching for logs; they are presented to you as part of the request's story.
• Metrics: Attributes like 'order.id' and the overall duration and error count of the span can be automatically aggregated into powerful metrics. You can instantly ask questions like, "What is the success rate for this function?" or "Show me the p99 latency of processOrder."

Built on the open standard of OpenTelemetry (OTel), trace.do ensures you're not locked into a proprietary ecosystem. It works with your existing frameworks and can export data to platforms you already use, like Jaeger, Prometheus, or Datadog.

From Silos to Solutions

Unifying the three pillars transforms your debugging workflow from a reactive scavenger hunt to a proactive investigation. The journey becomes:

1. An alert fires, linking you directly to the offending traces.
2. The trace waterfall view instantly shows you the slow span (e.g., dispatchShipment).
3. Attached to that span are the exact logs (span.addEvent) and attributes (span.setAttribute) needed to understand the failure, giving you complete clarity.

Stop juggling tabs and start solving problems. When logs, metrics, and traces are born together, they tell a single, coherent story—one that leads you directly to the solution.

Ready to achieve effortless observability and complete clarity? Discover how trace.do can unify your observability stack today.