Understanding OpenTelemetry

In today's world of distributed systems and microservices, understanding the performance and behavior of applications is more critical than ever. OpenTelemetry, an open-source observability framework, has emerged as a powerful solution for collecting and analyzing telemetry data - metrics, logs, and traces. OpenTelemetry is fast becoming the dominant observability telemetry standard in cloud-native applications. OpenTelemetry was the second fastest-growing project within the CNCF in 2024. Adopting OpenTelemetry is considered critical for organizations that want to be prepared for the data demands of the future without being tied to a specific vendor or the limitations of their existing technologies.

What is OpenTelemetry?

OpenTelemetry (OTel) is a set of APIs, libraries, tools, and instrumentation designed to capture and export telemetry data from applications and infrastructure in a single, unified format. It is a vendor-neutral, open-source project under the Cloud Native Computing Foundation (CNCF), formed by merging two earlier projects: OpenTracing and OpenCensus. Its primary goal is to provide a standardized way to instrument applications, enabling developers to monitor, troubleshoot, and optimize systems effectively.

OpenTelemetry supports multiple programming languages, including Python, Java, Go, JavaScript, and C++, and integrates with popular observability backends like Jaeger, Prometheus, Zipkin, and Elasticsearch. By offering a unified approach, it eliminates the need for proprietary or fragmented observability solutions.

So, what is telemetry data?

OpenTelemetry is built around three main types of telemetry data: traces, metrics, and logs. Known as the “pillars of observability,” these three categories of data helps, developers, DevOps and IT teams understand the behavior and performance of their systems.

These types of telemtry data is referred as “signals“ in OTel specification. Every signal is developed as a standalone component (but there are ways to connect data streams to one another). Signals are defined inside OpenTelemetry’s language-agnostic specification, which lies at the very heart of the project. The end-user probably won’t come into direct contact with the specification, but it plays a vital role in ensuring consistency and interoperability within the OpenTelemetry ecosystem.

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1. Traces

Tracing tracks the journey of a request as it travels through various services in a distributed system. A trace is a collection of spans that shows the path of a request through your system, where each span represents a named, timed single unit of work one in a system (for example, an HTTP request, a database query, or a function call).

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OpenTelemetry’s tracing capabilities allow developers to:

• Identify bottlenecks and latency issues.

• Understand dependencies between services.

• Debug errors in complex, distributed workflows.

For example, in a microservices architecture, a user request might trigger multiple service calls. OpenTelemetry traces the entire path, providing a detailed view of each service's contribution to the request's lifecycle.

2. Metrics

Metrics are numerical measurements collected over time, such as request counts, error rates, or CPU usage. OpenTelemetry provides tools to instrument applications for metrics collection, enabling developers to:

• Monitor system health and performance.

• Set alerts for anomalies (e.g., spikes in error rates).

• Analyze trends for capacity planning.

Unlike traditional metrics systems, OpenTelemetry’s metrics API is designed to be extensible, supporting both push-based (e.g., Prometheus) and pull-based observability tools.

3. Logs

Logs are textual records of events or errors in an application. Log entries are produced every time a block of code gets executed. They usually include a timestamp that shows when the event occurred along with a context payload. OpenTelemetry enhances log collection by correlating logs with traces and metrics, providing context for debugging. For instance, a log entry for a failed API call can be linked to the corresponding trace, helping developers pinpoint the root cause.

Core Components of OpenTelemetry

1. OpenTelemetry Collector

The OpenTelemetry Collector is a standalone service that receives, processes, and exports telemetry data. It acts as a flexible pipeline, allowing developers to:

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• Aggregate data from multiple sources.

• Transform or filter telemetry data before export.

• Send data to multiple observability backends simultaneously (e.g., Jaeger for traces and Prometheus for metrics).

The Collector supports a variety of protocols, such as OTLP (OpenTelemetry Protocol), gRPC, and HTTP/1.1, HTTP/2 making it highly interoperable.

2. Instrumentation Libraries

OpenTelemetry provides language-specific libraries to instrument applications. These libraries allow developers to add telemetry data collection to their code manually or automatically.

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For example:

• Automatic instrumentation: Libraries like those for Java or Python can inject telemetry collection into frameworks (e.g., Spring or Flask) without code changes.

• Manual instrumentation: Developers can use OpenTelemetry APIs to add custom spans, metrics, or logs for specific use cases.

3. Context Propagation

In distributed systems, maintaining context across services is crucial. OpenTelemetry uses context propagation to carry metadata (e.g., trace IDs, span IDs) across service boundaries. This ensures that traces and logs remain correlated, even when requests span multiple services or protocols (e.g., HTTP, gRPC).

How OpenTelemetry Works

OpenTelemetry operates in three main phases:

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1. Instrumentation: Developers instrument their applications using OpenTelemetry APIs or libraries. This involves adding code to generate traces, metrics, or logs. Automatic instrumentation can reduce the effort required for popular frameworks.

2. Data Collection: The instrumented application sends telemetry data to the OpenTelemetry Collector or directly to a backend. The Collector can process and enrich the data as needed.

3. Data Export: Telemetry data is exported to observability platforms for visualization and analysis. OpenTelemetry supports a wide range of backends, ensuring flexibility.

Benefits of OpenTelemetry

• Instrument once, use everywhere: A key promise of OpenTelemetry is that you instrument code once and never again, giving you the ability to use that instrumentation everywhere. OpenTelemetry recognizes that, should its efforts be successful, it will be a core dependency for many software projects. Therefore, it follows strict processes to provide long-term stability guarantees. Once a signal is declared stable, the promise is that clients will never experience a breaking API change.

• Separate telemetry generation from analysis: Another core idea of OpenTelemetry is to separate the mechanisms that produce telemetry from the systems that analyze it. Open and vendor-agnostic instrumentation marks a fundamental change in the observability business. Instead of pouring resources into building proprietary instrumentation and keeping it up to date, vendors must differentiate themselves through feature-rich analysis platforms with great usability. OpenTelemetry fosters competition, because users are no longer stuck with the observability solution they chose during development. After switching to OpenTelemetry, you can move platforms without having to re-instrument your entire system.

• Make software observable by default: With OpenTelemetry, open source developers are able to add native instrumentation to their projects without introducing vendor-specific code that burdens their users. The idea is to make observability a first-class citizen during development. By having software ship with built-in instrumentation, we no longer need elaborate mechanisms to capture and integrate it after the fact.

• Improve how we use telemetry: Last (and definitely not least), OpenTelemetry tries to change how we think about and use telemetry. Instead of having three separate silos for logs, metrics, and traces, OpenTelemetry follows a paradigm of linking telemetry signals together. With context creating touch points between signals, the overall value and usability of telemetry increase drastically. For instance, imagine the ability to jump from conspicuous statistics in a dashboard straight to the related logs. Correlated telemetry data helps to reduce the cognitive load on humans operating complex systems. Being able to take advantage of linked data will mark a new generation of observability tools.

Use Cases

OpenTelemetry is widely used across industries for various purposes, including:

• Performance Monitoring: Identifying slow API endpoints or database queries in microservices.

• Error Tracking: Correlating logs and traces to debug failures in distributed systems.

• Capacity Planning: Using metrics to predict resource needs and optimize infrastructure costs.

• Compliance and Auditing: Capturing detailed telemetry data to meet regulatory requirements.

For example, an e-commerce platform might use OpenTelemetry to trace a checkout process, monitor cart abandonment rates, and log payment failures, all within a unified observability pipeline.

Challenges and Considerations

While OpenTelemetry is powerful, it comes with some challenges:

• Learning Curve: Instrumenting applications and configuring the Collector can be complex, especially for teams new to observability.

• Performance Overhead: Adding telemetry collection may introduce latency, particularly if not optimized.

• Data Volume: Telemetry data can grow rapidly, requiring careful management to avoid high storage or processing costs.

To mitigate these, teams should start with automatic instrumentation, use sampling to reduce data volume, and leverage the Collector’s filtering capabilities.

Getting Started with OpenTelemetry

To begin using OpenTelemetry, follow these steps:

1. Choose a Backend: Select an observability platform (e.g., Jaeger, Prometheus, or a commercial solution like Datadog).

2. Install Language-Specific Libraries: Add OpenTelemetry SDKs to your application. For example, in Python, install opentelemetry-api and opentelemetry-sdk via pip.

3. Instrument Your Code: Use automatic instrumentation for supported frameworks or manually add traces, metrics, and logs.

4. Set Up the Collector: Deploy the OpenTelemetry Collector to process and export telemetry data.

5. Visualize Data: Configure your backend to display traces, metrics, and logs for analysis.

The official OpenTelemetry documentation provides detailed guides and examples.

Future of OpenTelemetry

As a CNCF incubating project, OpenTelemetry is rapidly evolving. Future developments include:

• Enhanced support for serverless and edge computing.

• Improved integration with AI-driven observability tools for automated anomaly detection.

• Broader adoption of the OpenTelemetry Protocol (OTLP) as a standard for telemetry data exchange.

With its growing ecosystem and community, OpenTelemetry is poised to become the de facto standard for observability in cloud-native environments.

References

• Free Getting Started with OpenTelemetry (LFS148) Course