OpenTelemetry Overview: Unifying Traces, Metrics, and Logs

The IT landscape has evolved rapidly, transitioning from monolithic applications to complex, distributed system architectures comprising microservices that run on platforms like Kubernetes. With this added complexity, simply checking if a server is running is no longer sufficient. As IT professionals, we need insight into what’s really happening inside these systems. That’s where observability comes in.

This is where OpenTelemetry (OTel) steps in—a powerful, open-source framework that provides a unified approach to gathering the necessary information. OTel goes beyond basic data collection; it is evolving from the traditional three pillars of telemetry data—Traces, Metrics, and Logs—into a four-pillar model by officially adding Continuous Profiling. Let’s explore these signals further and see how OTel can help us bring order to complexity.

Key Takeaways

• Observability is now defined by four key signals: Traces (the journey of a request), Metrics (performance indicators), Logs (records of events), and Continuous Profiling (deep analysis of resource usage).
• OpenTelemetry unifies the instrumentation, collection, and export of all three signals through the OTEL SDK, the OpenTelemetry eBPF auto-instrumentation, and OTLP.
• Semantic conventions establish a common terminology for attribute names, enabling easy correlation across diverse microservices.
• Beyond performance, OTel provides critical security insights; by using Resource Attributes to identify library versions and runtimes, security teams can identify microservices running vulnerable packages in real-time.
• The main advantage lies in correlation: OTel automatically connects logs and metrics to a distributed tracing path using the traceid and span id.
• You can deploy the OpenTelemetry Collector in a hybrid Agent/Gateway setup to reduce data volume via sampling and filtering before delivering it to the observability backend.

The Dynamic Duo: Comparative Analysis of Traces vs. Metrics

The core differences between traces and metrics come down to three key areas: granularity, aggregation, and cost.

1. Granularity and Context

• Traces (High Granularity): A distributed tracing trace provides a complete, end-to-end record of a single request. It consists of spans, each representing a specific operation, such as an HTTP request or a database query, including exact timestamps and extensive metadata. This level of detail makes traces essential for in-depth troubleshooting and debugging. Traces are especially effective with high-cardinality data—meaning data with many unique identifiers (like a particular userID or a full URL path)—because their goal is to track a single event. For example, if a request fails, the trace reveals every action taken, the duration of each microservice call (latency), and identifies exactly which endpoint failed. It answers, “where did this specific thing go wrong?”
• Metrics (Low Granularity): Metrics are numerical representations (aggregations) of events over time, such as average CPU usage or the number of error requests per minute. Aggregating data removes the context of individual events (for instance, which user or request caused a particular error). For example,a metric might show that the average latency for your login service is 500ms, making it useful for overall monitoring and alerting. It answers, “what is the general health trend?”

2. Primary Use Case

• Metrics are for Monitoring Trends and Sending Alerts: Since they are lightweight and designed for efficient storage in time-series databases, metrics are perfect for building real-time dashboards and configuring automated alerts. They are well-suited for analyzing long-term trends and detecting anomalies—an increase in the error count metric is often the first indication that something is wrong.
• Traces are for Identifying Root Causes and Optimizing: After a metric notifies you of an issue, you turn to traces. Traces allow you to pinpoint exact bottlenecks and map the journey of a request through a complex distributed system. They are crucial for fine-tuning performance and understanding how services interact.

3. Data Volume and Cost

• Metrics are Cost-Efficient: Because metrics are simply numerical values collected over time, they are much more affordable to store and analyze compared to logs or traces. With a limited set of dimensions (labels), their storage remains efficient, allowing them to be kept for long-term historical analysis.
• Traces Generate Volume: Traces naturally produce large amounts of data, as they capture detailed information about every instrumented action. To manage this, tracing often uses sampling, which means only a sample of the total requests is saved. Storing every trace at high volume would lead to high costs.

In summary, you use metrics to identify that an issue has occurred, and you use traces (linking them to logs through the trace ID and span ID) to pinpoint exactly where and how the issue occurred. Both are essential for a comprehensive observability strategy.

In-Depth Analysis of Traces and Logs

1. Traces

As discussed, a trace consists of a series of spans that represent the flow of a request throughout your distributed system. OpenTelemetry excels at standardizing how this data is collected.

• The Anatomy: Each span records the operation’s start and end times, its name, and its context (trace context). This context is essential because the OpenTelemetry SDK manages context propagation, ensuring the trace ID and other identifiers are passed across network boundaries (for example, between two microservices communicating over HTTP requests or gRPC).
• Instrumentation: OpenTelemetry offers robust automatic instrumentation libraries for languages such as Java, Python, and JavaScript. This enables distributed tracing with little or no code modification. For deeper visibility into your business logic, the OpenTelemetry API lets you manually create custom spans and add metadata as needed.

2. Logs

Logs are the most adaptable signal, offering a detailed textual account of an event at a specific timestamp.

• The Role: Logs reveal what occurred within a particular service at a given moment. Whereas traditional logs were just plain text, OTel promotes structured logging.
• The Unification: The main point here is correlation. OpenTelemetry logging is designed to embed the current trace ID and span ID directly into the log entry, along with other resource attributes related to the host or Kubernetes pod. This seemingly minor update is significant, transforming a basic text file into a contextual link that connects straight to the span that created it.

3. Metrics

Metrics are the most efficient type of telemetry data for large-scale analysis and alerting purposes.

• The Instruments: OTel’s API provides several instruments designed to measure various types of data:
  • Counter: Ideal for counting occurrences of events (such as total requests or errors).
  • Gauge: Captures values at a specific moment that may change over time (for example, CPU usage or queue depth).
  • Histogram: Important for recording value distributions, such as endpoint call latency, enabling the monitoring of percentiles like p95 and p99.
• The Pipeline: The OpenTelemetry SDK collects and aggregates these metrics according to its specification, then exports them. This enables OTel to maintain compatibility, structuring its data for export and use by tools like Prometheus and Grafana, which excel at visualizing metrics.

4. Continuous Profiling:

Now a stable component of the OTel ecosystem, profiling allows for a deeper look at code-level performance, such as CPU and memory consumption per function, helping identify “hot paths” in your application.

The Shared Language: OpenTelemetry Semantic Conventions

How can OTel data from both Python and Java services be immediately recognized by the same observability backend? The answer lies in semantic conventions.

This crucial, yet sometimes overlooked, aspect of OTel consists of a formal specification that defines how attributes, span names, and metric names should be structured and named across all telemetry data. These conventions guarantee consistency and clarity, which are vital for effective correlation.

• Standardized Attributes: Rather than having one service refer to the HTTP status code as status_code and another as http.status, the conventions require standardized names like http.request.method and db.system. This standardization enables your traces, metrics, and logs to be automatically connected.
• Resource Conventions: Every piece of telemetry data is linked to a Resource. Resource conventions define how to consistently describe the entity generating the data, such as the microservice name (service.name), library version, Kubernetes namespace, and more. This uniform metadata allows your backend to construct meaningful filters and views.
• A Universal Vocabulary: Following this common vocabulary significantly minimizes confusion when troubleshooting complex distributed systems. You no longer need to decipher what a team named an attribute; you immediately know what to look for.
• Security Benefit: These conventions allow security teams to use OTel data as a live “Software Bill of Materials” (SBOM), quickly locating services running vulnerable library versions like Log4j.

How Telemetry Signals Work Together (And Why It Matters)

OTel is becoming the industry standard because it unifies these three signals into one clear, integrated view.

The investigation process naturally flows between the signals:

1. Identifying the ‘What’: A metric dashboard indicates a spike in your average response time—this is the anomaly (What is occurring?).
2. Locating the ‘Where’: By clicking on the spike, you access an exemplar (which links a relevant traceid to the metric data). This leads you to the distributed tracing view, where you see a service’s database call span consuming 99% of the time (Where is the issue?).
3. Understanding the ‘Why’: In that slow span, the related log shows an “Out of memory” error just before the database query (Why did it happen?).

This smooth transition between all three signals exemplifies a robust observability approach, speeding up troubleshooting and reducing your Mean Time to Resolution (MTTR).

Transforming and Deriving Metrics from Traces

Here’s a cool trick: sometimes you have excellent trace instrumentation but lack sufficient metrics. OpenTelemetry allows you to produce metrics using your trace data.

This is typically accomplished in the OpenTelemetry Collector with a processor such as the Span Metrics Connector. Here’s how it works:

• The collector ingests a steady flow of spans from your application.
• The processor examines span attributes (like the service name, http request method, or status code).
• It compiles this span information into time-series metrics. For instance, it can use the span duration to build a histogram metric for latency distribution, broken down by service and endpoint.

The Advantage: This technique offers perfect consistency, as both metrics and traces originate from the same data. It’s an effective way to create high-quality, comprehensive aggregate data for alerts and dashboards—without requiring duplicate metric instrumentation in your application code.

OTel Implementation Best Practices

Maximizing the benefits of OTel requires some expert strategies, particularly when it comes to deploying the OpenTelemetry Collector.

Collector Deployment Patterns

How you deploy the Collector dramatically impacts scale and performance. There are two primary patterns:

1. Agent (Sidecar/DaemonSet): The Collector runs alongside your application code, either as a sidecar container in the same pod or as a DaemonSet on every node in Kubernetes.
   • Pros: Minimal network latency for data transmission, great for gathering host-level metrics (CPU, memory), and perfect for local pre-processing and buffering.
   • Cons: Increases resource consumption on the worker nodes.
2. Gateway (Centralized Deployment): The Collector runs as a separate, horizontally scalable service (Deployment) that acts as a central ingestion point. Your Agents or applications send data to this Gateway.
   • Pros: Centralized control for heavy lifting like tail-based sampling, filtering, and routing to multiple observability backends.
   • Cons: Introduces an extra network hop and can become a bottleneck if not scaled correctly.

Best Practice: The most common and effective pattern is a hybrid approach: Agents collect the data locally and apply basic pre-processing (like resource enrichment and batching), then forward that data to the central Gateway collectors for complex processing, filtering, and final export via OTLP.

Data Optimization and Integrity

• Manage Cardinality: This is crucial for metrics. Refrain from attaching high-cardinality attributes (such as unique user IDs) to every metric, as this can severely impact backend performance. Leverage the processors in the Collector to remove these high-volume attributes before exporting.
• Apply Selective Sampling: Complete traces offer the most comprehensive details, but they also produce a huge amount of data. Implement probabilistic sampling for traces to minimize unnecessary data, or, even better, utilize tail-based sampling in your Gateway collector to ensure you keep all traces that resulted in errors or high latency.
• Uphold Semantic Conventions: Ensure your teams adhere to the official semantic conventions in all manual instrumentation and when creating attributes. This ensures your correlation features function consistently across teams and programming languages.

Comprehensive Monitoring Solutions: The OTel Backend

OTel collects the data, but the main tasks of storing, connecting, and visualizing that data are handled by an observability backend.

A genuine OTel monitoring solution should:

1. Directly support the OTLP (OpenTelemetry Protocol) data model for traces, metrics, and logs.
2. Enable automatic and strong correlation among traces, metrics, and logs by leveraging shared trace context and resource metadata.
3. Let you query and visualize all three signals together in contextual dashboards.

Vendors that fully support OTel provide a significant benefit: you gain the flexibility of an open-source framework combined with the reliability, scalability, and extensive features of a commercial solution—all without being locked into a single vendor.

SolarWinds Observability Supercharges Your OpenTelemetry Data

We recognize that your primary goal is to modernize your technology stack, and you shouldn’t have to sacrifice your monitoring tools to achieve this. That’s why SolarWinds® Observability SaaS is designed as an OpenTelemetry-native platform, serving as the ideal backend for all the valuable telemetry data you collect.

Here’s how we help you turn OTel data into actionable insights:

• Native OTel Ingestion: We accept OTLP data for traces, metrics, and logs directly. You can send your OTel exporters straight to our endpoint for immediate visibility in our Explorers, but we suggest using our verified integrations to automatically generate and map entities in your environment, providing a comprehensive health overview.
• Trace-Context Correlation: Our platform connects different signals by applying semantic conventions and inserting Trace IDs into your application logs. With SolarWinds Observability SaaS, you can easily follow a metric spike to the exact trace span and related logs, enabling faster root cause analysis (MTTR).
• Verified SolarWinds OTel Collector: To streamline your data pipeline, we offer a customized SolarWinds OpenTelemetry Collector distribution. It features specialized processors that refine telemetry data before it reaches the backend, supporting accurate service mapping and effective data management for complex, high-cardinality setups.

Using SolarWinds Observability SaaS gives you the flexibility of the open-source OTel standard, combined with enterprise-level analytics and integrated correlation within the SolarWinds ecosystem.

FAQs

What Is Observability?

Observability refers to understanding a system’s internal state by examining the external telemetry data it generates. It measures how effectively you can ask any question about your system’s behavior without adding new instrumentation or modifying code.

What is telemetry data?

Telemetry data refers to the unprocessed information produced by a software system for the purposes of monitoring, troubleshooting, and optimization. The primary categories of telemetry data include metrics, traces, and logs.