What is OpenTelemetry? A Comprehensive Guide

In today's world of distributed systems and microservices, understanding how your applications behave in production is more challenging—and more critical—than ever.

OpenTelemetry (OTel) has emerged as the industry's answer to this challenge by offering a unified, open-source framework for collecting and managing observability data.

It takes a vendor-neutral approach solves a fundamental problem in modern observability: the fragmentation of telemetry collection methods. Instead of maintaining different instrumentation code for each monitoring tool, you can instrument once with OpenTelemetry and send your data anywhere.

In this comprehensive guide, we'll explore how OpenTelemetry works, its key components, and how it can transform your observability strategy.

Whether you're just starting your observability journey or looking to modernize your existing approach, this article will help you understand why OpenTelemetry is becoming the backbone of modern observability.

What is telemetry data?

Telemetry data is the foundation of observability. It consists of detailed measurements and records automatically collected from your applications, infrastructure, and services that help you understand their behavior, performance, and health.

The most common types of telemetry data include:

• Metrics: These include request rates and latencies, error counts, resource utilization like CPU and memory usage, and business metrics such as active users or transaction volumes. These are often plotted on dashboards and used for alerting.

• Traces: A detailed record of a request's journey through your distributed system. Traces show the exact path and timing of service calls, reveal dependencies between services, highlight performance bottlenecks, and provide context about each step in a request's lifecycle.

• Logs: Time-stamped records of discrete events that provide rich context about system behavior. This encompasses application logs showing user actions and system responses, error logs with stack traces, security and audit logs tracking access patterns, and infrastructure logs from servers and network devices.

• Events: Structured records of significant occurrences in your system. Events capture the complete context of important transactions, state changes, business processes, and deployment or configuration changes.

• Profiles: Deep analysis of resource consumption and code execution patterns. Profiling data reveals CPU usage across function calls, memory allocation patterns, lock contention issues, and I/O or network usage hotspots to help optimize application performance.

The importance of telemetry has grown exponentially with the rise of cloud-native architectures. Modern applications often span multiple services, cloud providers, and data centers, making traditional monitoring approaches insufficient.

By collecting and correlating these different types of telemetry data, you can build a complete picture of your system's behavior and quickly respond to issues when they arise.

What is OpenTelemetry?

OpenTelemetry represents a significant milestone in observability tooling. It is an open-source, vendor-neutral framework that standardizes how we collect and handle telemetry data across distributed systems.

It was born in 2019 through the merger of OpenTracing and OpenCensus

Each project had unique strengths but also limitations that hindered broader adoption. By combining their best features under the Cloud Native Computing Foundation (CNCF), OpenTelemetry provides a unified, standardized framework for collecting all kinds of observability signals, addressing the shortcomings of its predecessors.

Today, OpenTelemetry stands as the second most active CNCF project after Kubernetes, reflecting its critical role in modern observability.

Key problems OpenTelemetry solves

OpenTelemetry addresses several critical challenges in modern observability. Before its emergence, teams struggled with fragmented tooling and multiple proprietary agents, leading to vendor lock-in and high maintenance overhead.

Different monitoring tools required different instrumentation methods, and data consistency suffered as tools used varying formats and semantics.

To solve these challenges, OpenTelemetry operates as a vendor-neutral, language-agnostic platform that maintains backwards compatibility with existing implementations.

It's designed for high performance with minimal overhead in production environments, while remaining extensible enough to accommodate specific use cases.

It excels at collecting telemetry data from various sources, establishing standardized formats and semantics, processing and enriching this data, and ensuring reliable export to analysis tools.

Rather than trying to do everything, it deliberately leaves data analysis and visualization to specialized tools, concentrating instead on being the most efficient and reliable telemetry collection and processing pipeline possible.

By maintaining this focused scope while adhering to its core principles, OpenTelemetry has emerged as the de facto standard for instrumenting cloud-native applications.

This approach reduces implementation complexity, lowers maintenance costs, and provides organizations with future-proof instrumentation that enables seamless backend switching and comprehensive observability practices.

Components of OpenTelemetry

The OpenTelemetry framework comprises of several components that work together to capture and process telemetry data, which are outlined below:

Specification and standards

At its heart lies the OpenTelemetry specification, which defines how the framework should be implemented across different environments. This specification encompasses three crucial elements:

The API specification establishes the fundamental interfaces for creating and handling telemetry data consistently across languages. The SDK specification implements these interfaces, handling essential tasks like sampling and data export. The Data specification defines the OpenTelemetry Protocol (OTLP), ensuring standardized data transmission throughout the ecosystem.

Supporting these specifications are semantic conventions which provide consistent naming and structure for telemetry data. For instance, HTTP status codes, database systems, and error types all follow standardized naming patterns, making data correlation and analysis more straightforward.

The data pipeline

The OpenTelemetry Collector serves as the central nervous system for telemetry data. As a standalone service, it receives, processes, and routes telemetry data between your applications and analysis tools. Its general mode of operation is as follows:

First, it receives data through various receivers that support multiple formats, from OTLP to Prometheus and Jaeger. Next, processors handle tasks like filtering, enrichment, and transformations. Finally, exporters send the processed data to your chosen analysis platforms.

The Collector comes in two major flavors: a streamlined core version (otelcol) for basic OTLP handling, and a contrib version (otelcol-contrib) packed with the whole kitchen sink.

For production use, its recommended that you build a custom flavour that includes only the components you need.

Two key technologies enhance the Collector's capabilities. The Open Agent Management Protocol (OpAMP) enables centralized management of collector instances across large deployments, while the OpenTelemetry Transformation Language (OTTL) provides powerful data transformation capabilities within the collection pipeline.

Protocol and data exchange

The OpenTelemetry Protocol provides the universal language that all OpenTelemetry components use to communicate. It is a complete vendor-neutral system for encoding, transporting, and delivering telemetry data between different components within the observability pipeline.

While OTLP is OpenTelemetry's native protocol, the framework maintains broad compatibility through its collector components, which can ingest data from familiar formats like Zipkin, Prometheus, and Jaeger.

This flexibility allows you to gradually adopt OpenTelemetry without throwing out your existing observability tools and workflows entirely.

Learning through practice

The OpenTelemetry Demo Application brings these components together in a practical context. It is a microservice-based shopping application that demonstrates real-world instrumentation and observability practices, making it an invaluable learning tool for anyone interested in adopting or integrating with OpenTelemetry.

The current state of OpenTelemetry

OpenTelemetry is a collaborative effort spanning multiple working groups, each focused on different aspects of the framework. From telemetry signals, to language-specific implementations, each component evolves at its own pace through distinct maturity stages:

Components in OpenTelemetry progress through several stages:

• Stable (GA): Production-ready with guaranteed long-term support and backward compatibility.
• Experimental (Alpha/Beta): Suitable for evaluation and proof-of-concept. testing
• Draft: Early development stage.
• Deprecated/Unmaintained: End-of-life stages.

OpenTelemetry provides official support for major programming languages through dedicated APIs and SDKs:

• Java
• JavaScript
• Python
• Go
• .NET
• C++
• Ruby
• PHP
• Erlang/Elixir
• Rust
• Swift

Beyond these officially supported languages, the OpenTelemetry Registry hosts numerous community-developed SDKs and instrumentation libraries.

When evaluating OpenTelemetry for your project, consider these key factors:

1. Technology stack: Identify your programming languages and frameworks to determine which client libraries and instrumentation agents you'll need.

2. Signal requirements: Determine which telemetry signals you need and their sources (applications, infrastructure, third-party services) to note which receivers you'll need in your collector pipeline.

3. Observability backend: Your chosen analysis tools (like Jaeger, Prometheus, or Better Stack will guide your collector exporter configuration.

Ensure to always check the current stability status of required components in the official documentation before implementation.

OpenTelemetry signals and stability explained

OpenTelemetry's framework is built around three core telemetry signals - traces, metrics, and logs - with continuous profiling in early development as the fourth.

Each signal serves a distinct purpose in understanding system behavior, and their implementations have reached different levels of maturity across languages and platforms.

In OpenTelemetry, stability indicates a component's maturity and production-readiness. A stable component offers a well-defined API, schema, and behavior that organizations can rely on without fear of disruptive changes. However, stability can vary across specifications, semantic conventions, protocols, and language implementations.

Current signal status

Distributed Tracing was the first signal to reach General Availability (GA) in September 2021. Tracing is now production-ready across most supported languages, with comprehensive APIs for tracking request flows through distributed systems. Only the Rust implementation remains in beta, while all other languages offer stable tracing support.

Metrics achieved general availability in 2021, signifying that its API, SDK, and Protocol specifications are production-ready for various programming languages. That said, development for full SDK stability is still ongoing across the board.

Logs reached stability in late 2023, completing OpenTelemetry's core observability trifecta. This milestone enables seamless integration of logging with traces and metrics, though language support is still evolving. Java, C++, .NET, and PHP lead with stable implementations, while other languages are progressing through experimental and alpha stages.

For the most current stability information, always consult the OpenTelemetry documentation, as the project maintains detailed status tracking for each component and language implementation.

Criticisms of OpenTelemetry

While OpenTelemetry has emerged as the de facto standard for observability instrumentation, it's important to also understand its limitations and challenges.

Like any ambitious open-source project, it has faced several criticisms that you should consider before choosing to adopt it for your observability strategy.

Let's look at a few of this criticisms below:

Too many moving parts

One of the primary criticisms of OpenTelemetry is its broad scope and complexity. What began as as a simple idea around standardized observability instrumentation has grown into a vast ecosystem with numerous concepts, configurations, and dependencies.

The sheer number of SDKs, exporters, processors, and pipelines that need to be configured can make it difficult to implement and scale efficiently, and it's sure to require significant effort to maintain on an ongoing basis.

Lack of stability

Despite being promoted as a standard, OpenTelemetry still lacks a cohesive, stable implementation across different languages and frameworks. Features are often in different stages of development or missing entirely in some SDKs.

This inconsistency creates frustration. Developers who expect a seamless experience across languages are met with partially implemented APIs, evolving specifications, and breaking changes between versions. The promise of standardization feels hollow when you constantly have to work around compatibility issues.

Enterprise influence

Large tech companies significantly influence the project's direction, sometimes prioritizing features that align with their commercial interests. This can lead to feature bloat and complexity that may not serve the broader community's needs.

Documentation Gaps

While extensive, OpenTelemetry's documentation often lacks practical guidance for common scenarios and troubleshooting. Critical information is scattered across multiple repositories and websites, making it difficult to find authoritative answers.

Operational overhead

Managing OpenTelemetry at scale presents significant challenges. Configuration management, capacity planning, and troubleshooting become increasingly complex as deployments grow, requiring dedicated expertise and resources.

While many of the criticisms of OpenTelemetry are valid, they should be viewed in the context of what the project is trying to achieve.

The project's extensibility and broad scope stems from its fundamental mission: creating a universal standard for observability that works across diverse technology stacks and organizational needs.

Features that may seem unnecessary for some organizations are often essential for others, and this flexibility is what allows teams across different technical environments to adopt a common observability framework.

Ultimately, the advantages of having a single, widely-adopted standard for observability outweigh the challenges of its complexity. A common foundation enables innovation in observability tooling and practices that would be impossible in a fragmented ecosystem.

While OpenTelemetry may not be perfect, it is successfully delivering on its core promise: providing a standard way to instrument, collect, and export telemetry data across the industry.

How to adopt OpenTelemetry in five steps

The journey to effective observability through OpenTelemetry requires careful planning and a structured approach.

While the framework is powerful and flexible, successful adoption depends on understanding your current environment and implementing changes methodically.

In essence, you need to:

• Define clear observability objectives.
• Assess your current technology landscape and component stability.
• Plan your migration path from existing solutions.
• Design a robust collection pipeline.
• Implement changes incrementally.

Let's explore how each of these steps in more detail.

1. Define your observability goals

Establish clear objectives that align with your business needs. This ensures you're not collecting data simply because you can, but because it serves a well-defined purpose. Identify which service interactions need tracing, what metrics indicate system health, and which logs are essential for operations.

These decisions will influence your instrumentation strategy and help optimize storage costs. Consider compliance requirements and Service Level Objectives (SLOs) that your observability solution needs to support.

2. Assessing your current telemetry

Begin your OpenTelemetry journey with a thorough evaluation of your technology stack, including languages, frameworks, and existing monitoring tools.

Take inventory of your current telemetry sources, whether they're applications, infrastructure components, or external services.

This is necessary because OpenTelemetry's stability varies by language and signal type. You can also checkout the OpenTelemetry Demo to see how the various components of OpenTelemetry comes together in a near real-world scenario.

3. Evaluate migration paths

Map your transition strategy based on your current tooling. If you're using OpenCensus or OpenTracing, you can leverage OpenTelemetry's backward compatibility to migrate gradually.

For vendor-specific implementations, assess the effort required for re-instrumentation against the benefits of a vendor-neutral solution. This evaluation helps you understand the resource requirements and potential risks of migration, allowing you to plan accordingly.

4. Design your collection pipeline

Your telemetry pipeline design should account for both current needs and future scale. Consider factors like data volume, retention requirements, and performance impact when choosing between different collector deployment patterns (agent, gateway, or both).

Select receivers based on your data sources and exporters that match your analysis tools. This architecture planning ensures your observability solution can grow with your system without becoming a bottleneck.

5. Implementation strategy

Start with automatic instrumentation where available, as it provides immediate visibility with minimal effort. Then gradually expand to custom instrumentation for business-critical paths where deeper insights are needed.

Validate that the collected data answers important questions about system behavior and performance. Set clear success criteria for each phase of the rollout to ensure your implementation delivers value incrementally.

This phased approach helps manage risks while building expertise in your team and proving the value of OpenTelemetry to stakeholders. Each step builds on the previous one, creating a solid foundation for comprehensive observability.

Final thoughts

OpenTelemetry represents a significant shift in how we approach observability. By providing a standardized, vendor-neutral framework for telemetry collection, it offers a future-proof foundation for your observability needs.

While the journey to implementing OpenTelemetry may seem daunting, the benefits of standardized instrumentation and vendor independence make it a compelling choice for modern observability strategies.

Remember that successful adoption is a gradual process. Start small, focus on your immediate observability needs, and expand your implementation as your expertise grows.

For more insights into OpenTelemetry's capabilities or to contribute to the project, explore the official website and GitHub Repository. You can also see our observability guides for more in-depth articles on various OpenTelemetry components.

Thanks for reading!

Article by

Ayooluwa Isaiah

Ayo is a technical content manager at Better Stack. His passion is simplifying and communicating complex technical ideas effectively. His work was featured on several esteemed publications including LWN.net, Digital Ocean, and CSS-Tricks. When he's not writing or coding, he loves to travel, bike, and play tennis.

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