How Logs, Metrics, and Traces Tell the Full Story

When an alert fires at 3 AM, the first question is always “why?”. Answering that question quickly is often the difference between a minor incident and a major outage.

The key lies in observability and its three “pillars”: logs, metrics, and traces. You can think of them as three complementary views into your system’s behavior:

• Metrics reflect the system’s overall health and performance trends.
• Traces show the complete journey of a request as it moves through services.
• Logs capture a detailed commentary of individual events.

All three are important, but observability goes beyond simply collecting them. It’s about connecting them through shared context and using them to ask “what’s happening?” and actually get a clear, actionable answer.

This article aims to be your definitive resource for solving the logs vs. metrics vs. traces puzzle by showing you how to use them together to achieve full observability of your systems.

What are metrics?

Metrics are numerical values that represent the current state of a system at a given point in time.

Each metric is timestamped and often enriched with labels (also known as dimensions). These are key-value pairs that provide context and enable effective filtering, grouping, and analysis.

One of the defining strengths of metrics is their aggregatable nature. They can be averaged, summed, or processed over time and across dimensions, giving them a predictable storage footprint.

This makes metrics significantly more cost-effective to store and query compared to logs or traces, making them ideal for powering dashboards that visualize trends and system behavior.

Metrics are also central to traditional monitoring, where the goal is to detect known failure modes by triggering alerts when a system crosses a predefined threshold.

In infrastructure monitoring, this often involves tracking vital signs like resource utilization and saturation. For instance, an alert might be configured to fire when CPU usage consistently surpasses 90% or when available disk space drops below a critical 10% level, signaling an impending problem.

At the application level, metrics are used to track key performance indicators (KPIs) such as request and error rates, throughput, and latency distributions which cover the core questions behind most service-level concerns.

Despite their advantages, metrics have a few inherent limitations. Chief among them is the lack of contextual detail about what is being monitored. A spike in error rate might be easy to detect, but identifying the root cause often requires more detailed data that typically isn’t available.

While metric dimensions (labels) can provide some context, the high-cardinality values needed to understand the system are typically impractical to use at scale in metrics-based systems due to their performance and cost impact.

Another limitation is that you must decide what to measure in advance. If a problem occurs that your current metrics don’t capture (an “unknown unknown”), you’ll have to add new ones, deploy it, and then wait for the issue to happen again to collect relevant data.

And while metrics are excellent for visualizing high-level trends, they often miss edge cases. For instance, you might open your dashboard, see an average latency of 200ms and think everything is fine. But averages can lie, often hiding the painful truth that a small group of users are experiencing 3-second response times.

So, you get smarter and use percentiles (p95Copy, p99Copy). Now you can see the outliers, but you’re still left with a question: what makes those specific requests so slow?

To get that answer and follow one of those slow journeys from start to finish, you need more than an aggregate. You need a trace.

What are traces?

While metrics show the overall health of a system, traces tell the story of a single request from start to finish.

They capture the complete journey of a request as it flows through the various services, databases, and message queues that make up a modern distributed system to provide a detailed, end-to-end view of how that specific request was processed.

Each trace is composed of one or more spans, which represent individual, timed units of work. A span might correspond to an HTTP request, a database query, or the execution of a function.

The strength of distributed tracing lies in its ability to uncover causal relationships. Since spans are organized in a parent-child hierarchy, tracing tools can visualize how a request traverses through a system, and which components contribute to its total latency.

For example, if one service takes significantly longer than others, it becomes immediately visible in the trace. This is what makes tracing indispensable for diagnosing slowdowns and failures in complex microservice architectures.

Unlike metrics, traces thrive with high-cardinality data. Since spans can be richly annotated with request-level details like transaction IDs, specific query parameters, or span events (which act as contextual footnotes on a span’s timeline), the crucial context needed to understand the exact conditions of an outlier request is readily available.

Traces also help optimize system architecture by revealing inefficient call patterns or redundant dependencies that might otherwise go unnoticed. For instance, you might see that a single user request triggers multiple sequential calls to the same service, which could be refactored into a single batch call to improve performance.

While traces offer deep insight required for observing modern systems, they also come with a few practical considerations.

To achieve a complete, end-to-end view, every service involved in handling a request must be properly instrumented. This means each service must be configured to create spans and propagate context so that its work can be correctly linked to the overall trace. If even one service in the chain is not instrumented, the trace becomes broken and potentially misleading.

Another issue is that tracing every single request can create a significant performance and cost overhead due to the large volume of data generated. To manage this, systems typically employ sampling.

This involves strategically selecting a representative subset of traces to collect—either at the start of a request (head-based) or after it has completed (tail-based).

While tools like OpenTelemetry have made both instrumentation and sampling significantly easier, it still involves a fair amount of integration effort.

A distributed trace can show which service or operation is failing, but to understand why, you may need to zoom in further. That’s where logs come in with detailed error messages, stack traces, and other runtime context.

What are logs?

Logs are the most traditional form of telemetry: timestamped records of discrete events that provide a detailed, chronological commentary on your system’s behavior

Each log entry captures the context of a specific event, ranging from critical errors to routine operational messages like service startups or configuration changes.

Modern observability practices strongly favor structured logging where machine-parseable formats like JSON are used to format log entries in a consistent way, making them significantly easier to query and analyze compared to older, unstructured text-based approaches.

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{
  "level": "info",
  "message": "incoming GET request to /",
  "method": "GET",
  "request_id": "0f914569-0e06-4ab0-ba48-0dc800ebfb1b",
  "timestamp": "2025-05-28T09:25:37.582Z",
  "url": "/",
  "user_agent": "curl/8.5.0"
}

The primary strength of logs is providing the deep, contextual detail needed for root cause analysis. While metrics show what is wrong and traces show where it happened, logs provide the detailed context needed to understand why.

Since structured logs can capture highly cardinal values across a large number of dimensions (unique properties), they are immensely helpful for investigating the specific conditions of a rare bug, isolating a single user’s activity, or debugging complex edge cases that are invisible in other signals.

However, the utility of logs is not automatic; it is a direct reflection of the quality and foresight of the developer who wrote the logging code. This manifests in several significant ways:

1. The challenge of foresight

For logging to be effective, you need to anticipate future failure modes. You must ask: “When this code fails, what pieces of information will be essential for a quick diagnosis?”. If a crucial variable isn’t logged, that information is lost forever for past events.

2. Balancing signal and noise

Finding the right amount of detail is a constant struggle. Under-logging results in useless messages like Error: Failed to process record, which lack the context to be actionable. The opposite extreme, over-logging, creates “log spam” that buries critical errors in noise and incurs significant ingestion and storage costs.

3. The challenge of standardization

Without enforced standards, one service might log a user identifier as userIdCopy while another uses user_idCopy. This inconsistency makes cross-service analysis difficult and undermines the value of centralized logging.

Inconsistent use of log levels (e.g., FATALCopy vs. CRITICALCopy) can also cause confusion about an event’s severity.

4. Logging needs maintenance

As code evolves and business logic changes, logging statements can become outdated or misleading. They might log variables that no longer exist, provide misleading context, or fail to capture new, critical states.

Just like any other code, logs require regular review and updating to ensure that they stay useful and accurate.

Ultimately, treating logging as an afterthought is the root of its limitations. Effective logging is a deliberate engineering practice that requires a culture that recognizes logging not just as debugging output, but as the creation of a valuable signal for observability.

How OpenTelemetry enhances your telemetry

Modern observability isn’t just about collecting these three signals in isolation. It’s about connecting them, enriching them with shared context, and building a cohesive picture of your system’s behavior.

A single log entry from a service is helpful. That same log entry automatically linked to the distributed trace that caused it is transformative. When metrics from that trace are also connected, you gain a complete, contextual view of what happened, where it happened, and how it impacted your system.

This is precisely the problem OpenTelemetry (OTel) was designed to solve. Its fundamental goal is to make your telemetry data consistent, correlated, and portable.

It achieves this with a complete toolkit: vendor-agnostic SDKs for all major languages, automatic instrumentation for popular frameworks, and a powerful Collector to process and route the data.

This unified approach ensures that context is shared across signals and gives you the freedom to choose your backend tools without being locked in.

Let’s take a look at a few concrete benefits of adopting OpenTelemetry as your observability framework.

1. Baseline coverage with automatic instrumentation

One of the biggest hurdles to observability is the instrumentation effort required to gather the signals in the first place. OpenTelemetry addresses this common challenge with automatic (or zero-code) instrumentation.

For popular languages like Java, Python, Node.js, and .NET, OTel provides agents and libraries that can be enabled with little to no code changes.

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# This is all that's needed to instrument any Node.js app for tracing and metrics
npm install --save @opentelemetry/api
npm install --save @opentelemetry/auto-instrumentations-node
OTEL_SERVICE_NAME=myapp node--require @opentelemetry/auto-instrumentations-node/register index.js

These agents automatically instrument common libraries like web frameworks, database drivers, and HTTP clients to provide immediate visibility into service interactions and request flows.

This creates a powerful baseline of observability for any application, which can then be enhanced with targeted manual instrumentation for specific business logic.

2. Automatic cross-signal correlation

When a request enters a system instrumented with OpenTelemetry, a traceID is generated. As that request travels across services, this traceID (and the current spanID) is automatically injected into the request headers through process known as context propagation.

The result is that any log message generated during that operation, using an OTel-aware logging library (or bridge), can be automatically stamped with the correct IDs.

This creates an immediate, out-of-the-box correlation, allowing you to instantly jump from a trace span to all the logs generated during that operation, or from a critical error log directly to the full distributed trace that caused it.

3. Consistent enrichment with resource attributes

Knowing a request failed is one thing; knowing it failed in the payment-serviceCopy, running in the eu-west-1Copy region, on version v1.2.3Copy of your application provides actionable insight.

OpenTelemetry allows you to define a set of common resource attributes once for your application. This metadata is then automatically attached to every every log, metric, and trace that leaves that application, ensuring consistency and eliminating guesswork.

This also allows you to focus only adding event-specific context at the point of instrumentation, knowing that the foundational metadata is already handled.

4. A unified pipeline with the SDK, Protocol, and Collector

The power of OpenTelemetry lies not just in standardizing signals, but in unifying the entire telemetry pipeline, from generation inside your application to eventual delivery to an observability backend.

Instead of using separate APIs for metrics (like Prometheus), traces (like Jaeger), and logs, OpenTelemetry provides a single, vendor-agnostic SDK that produces these signals in a single standard format (known as OTLP) that’s completely decoupled from any specific backend.

But generating standardized data is only half the battle. The other half is delivering it reliably and flexibly. This is where the OpenTelemetry Collector comes in. Its main job is to receive data, process it, and forward it to any backend you choose without touching your application code.

It gives you backend independence by allowing you to switch or send data to multiple observability platforms without changing your application code.

It also enables smooth, incremental adoption, as the Collector can receive legacy formats like Jaeger and Prometheus alongside native OTel data, acting as a bridge from older systems to a modern observability stack.

In summary, OpenTelemetry provides a complete framework. It ensures telemetry is “born correlated” through context propagation, consistently enriched with metadata, and delivered flexibly through a unified pipeline, simplifying how you manage and evolve your observability strategy at scale.

How logs, metrics, and traces work together

Understanding logs vs. metrics vs. traces isn’t about choosing one over the others; it’s about using them in concert. They form a powerful trinity, where each provides a different lens into your system, and their correlation is what unlocks rapid insights.

To see this in practice, let’s walk through a common troubleshooting scenario.

Imagine you’re running an e-commerce platform. Your day might begin with an alert: “Error rate for /api/recommendationsCopy endpoint has exceeded 5% for the last 5 minutes”.

A quick glance at your metrics dashboard confirms a sharp increase in latency, and you also notice a corresponding spike in the error rate for the recommendationserviceCopy.

At this stage, metrics have told you WHAT is wrong (the recommendations service is failing and getting slower) and WHEN it started.

The immediate next question is where the delay is occurring. To answer this, you examine distributed traces for to find slow requests from the incident period.

The trace visualization clearly shows that there’s a notable increase in slow requests and errors from /api/recommendationsCopy. Digging deeper into the problematic spans identifies ListRecommendationsCopy as the primary contributor. Now, traces have shown you WHERE the problem is located in the request flow.

With the problematic service pinpointed, you need the final piece of the puzzle: why did it fail? If your trace spans are correlated with logs using OpenTelemetry, it will be easy to spot the associated logs.

You quickly find the evidence: multiple log entries indicating timeouts and retries when calling an external service. Some WARNCopy logs show the initial struggle, followed by a definitive ERRORCopy:

Finally, logs have revealed precisely WHY the operation failed: timeouts while attempting to communicate with an external dependency.

This seamless transition from the “what” (metrics) to the “where” (traces) and finally the “why” (logs) allows for a swift and accurate resolution, perfectly demonstrating the combined power of properly correlated telemetry data.

Final thoughts

The path to observability is paved with (at least) three signals.

A successful strategy doesn’t treat these signals as separate pillars standing on their own. Instead, it focuses on correlating them, which is what allows you to move seamlessly from a high-level view of your system’s health to the on-the-ground reality of a single transaction.

This philosophy is at the core of Dash0. As an OpenTelemetry-native platform, we automatically connect these signals to provide a seamless troubleshooting experience from a single UI.

If you’re ready to see what a truly unified observability platform can do, try Dash0 for free today.