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OpenTelemetry Distributed Tracing: Tutorial & Best Practices

Learn about OpenTelemetry distributed tracing, how it helps troubleshoot performance issues, optimize system performance, and improve collaboration. Explore best practices and advanced techniques.

Zan Faruqui
May 16, 2023

OpenTelemetry is an open-source observability framework that provides a standardized, vendor-neutral approach to collecting and analyzing telemetry data for distributed systems. It simplifies distributed tracing, allowing you to:

  • Understand the flow of requests across services
  • Identify performance bottlenecks and issues
  • Troubleshoot errors and exceptions
  • Optimize system performance

Key Benefits of OpenTelemetry Tracing


Benefit Description
Vendor-Neutral Works with various observability tools
Standardized Follows industry standards for data collection
Open-Source Community-driven and freely available
Comprehensive Supports tracing, metrics, and logging

Getting Started with OpenTelemetry Tracing

  1. Set up requirements (programming language, backend, dependencies)
  2. Initialize the SDK and create a Tracer
  3. Create and manage Spans for operations
  4. Add Span details (attributes, events, links)
  5. Propagate context across services

Instrumenting Applications with OpenTelemetry

  • Automatic instrumentation for popular frameworks and libraries
  • Manual instrumentation for custom scenarios and legacy systems
  • Best practices: follow naming conventions, prioritize critical components, keep it simple, test and validate

Visualizing and Analyzing Traces

  • Export traces to backends like Jaeger, Zipkin, or Honeycomb
  • Visualize trace data to identify performance issues and troubleshoot
  • Combine traces with logs and metrics for complete observability

Advanced Tracing Techniques

  • Trace sampling strategies (head-based, tail-based)
  • Correlating and propagating traces across systems
  • Integrating traces, logs, and metrics
  • Handling sensitive data (anonymization, redaction, encryption)

Best Practices

  • Follow naming conventions and handle errors properly
  • Minimize performance impact with sampling and optimizations
  • Set up monitoring and alerting based on trace data

Understanding Distributed Tracing

Distributed tracing helps developers understand how requests flow through complex, distributed systems. By tracking a request's path, developers can:

  • See the sequence of operations
  • Find performance issues and bottlenecks
  • Debug errors and exceptions
  • Optimize system performance

What is Distributed Tracing?

Distributed tracing monitors requests as they move through different system components. This technique is useful for microservices-based applications, where multiple services handle a single user request.

Key Tracing Components

Distributed tracing involves:

  • Traces: A trace represents a single user request. It's a collection of linked spans.
  • Spans: A span records a single operation within a trace, including its duration, name, start/end times, and metadata.
  • Context Propagation: Tracing information is passed from one service to another as the request flows through the system.

Challenges in Distributed Systems

Implementing distributed tracing can be difficult due to:

  • High Latency: Distributed systems often introduce delays, making real-time request tracking challenging.
  • Large Data Volumes: Tracing generates a lot of data, which can be hard to store, process, and analyze.
  • System Complexity: Distributed systems are inherently complex, making issue identification and troubleshooting difficult.

OpenTelemetry's Tracing Solutions

OpenTelemetry addresses distributed tracing challenges with:

  • Standardized Approach: A vendor-neutral way to collect and analyze telemetry data.
  • Automatic Instrumentation: Simplifies tracing setup and configuration.
  • Context Propagation: Ensures complete traces across multiple services.
  • Multiple Data Export Formats: Supports various observability tools and platforms.

OpenTelemetry Architecture: A Simple Overview

OpenTelemetry provides a standardized way to collect and analyze data from your applications. It consists of several key parts:

Architecture Components

  • API: Interfaces for adding instrumentation to your applications and collecting data. Works across programming languages.
  • SDK: Libraries that implement the API, providing tools to instrument apps and gather data.
  • Collector: A service that receives, processes, and exports data to different backends.
  • Exporters: Send data to specific observability tools like Prometheus, Jaeger, or Zipkin.

How the Components Work Together

Component Function
API Provides a standard way to instrument apps and collect data
SDK Implements the API, offering tools to instrument and gather data
Collector Receives data, processes it, and exports it to multiple backends
Exporters Send data to specific observability tools of your choice

The API and SDK allow you to instrument your applications consistently. The Collector receives data from your apps, processes it, and sends it to Exporters. Exporters then forward the data to your preferred observability tools.

Flexibility and Integration

OpenTelemetry is designed to work with various tools and platforms. Its modular architecture lets you integrate with multiple backends without being locked into a single vendor. The standardized API and SDK ensure consistent instrumentation across languages and frameworks, making it easy to switch between different tools as needed.

Getting Started with OpenTelemetry Tracing

Setup and Requirements

Before you begin, ensure you have:

  • A compatible programming language (e.g., Java, Python, Go) and its OpenTelemetry SDK
  • A chosen backend or observability platform (e.g., Jaeger, Prometheus, Zipkin) for data export
  • The necessary dependencies and libraries installed in your project

Initializing the SDK and Tracer

To initialize the OpenTelemetry SDK and create a tracer:

  1. Import the OpenTelemetry SDK for your chosen language.
  2. Create a TracerProvider instance to manage the tracer and span processors.
  3. Configure the TracerProvider with settings like service name and environment.
  4. Create a Tracer instance from the TracerProvider to create spans.

Creating and Managing Spans

A span represents a single operation or request. To create and manage spans:

  1. Use the Tracer to create a new span, specifying the operation name and details.
  2. Set the start time for the span.
  3. Perform the operation or request, then set the end time.
  4. Use the Span instance to add attributes, events, and context.

Adding Span Details

Enrich your spans with:

  • Attributes: Key-value pairs providing additional information (e.g., user ID, request parameters).
  • Events: Timestamped events occurring during the span (e.g., database queries, errors).
  • Links: Relationships between spans, tracing causality between operations.

Propagating Context

To maintain trace continuity across services, propagate the context using headers or metadata in communication protocols. OpenTelemetry provides mechanisms like the W3C Trace Context HTTP headers.

Step Description
1. Setup Install required dependencies and choose a backend
2. Initialize Create a TracerProvider and Tracer instance
3. Create Spans Use the Tracer to create spans for operations
4. Add Details Enrich spans with attributes, events, and links
5. Propagate Pass context between services using headers or metadata

Instrumenting Applications with OpenTelemetry

Instrumenting your application with OpenTelemetry is key to gaining visibility into its performance and behavior. There are two main approaches: automatic and manual instrumentation.

Automatic vs. Manual Instrumentation

Approach Description When to Use
Automatic Libraries and frameworks automatically generate spans and telemetry data, requiring minimal configuration. - For popular frameworks and libraries with built-in OpenTelemetry support
- For simple instrumentation needs
- To reduce development effort
Manual Developers write custom code to create spans and telemetry data, providing more control and flexibility. - For custom or proprietary frameworks and libraries
- For complex instrumentation needs
- To capture custom metrics

Instrumenting Common Libraries

Instrumenting popular libraries and frameworks is straightforward:

  • HTTP Clients: Use OpenTelemetry's HTTP client instrumentation to capture spans for HTTP requests and responses.
  • Databases: Use database instrumentation to capture spans for database queries and transactions.
  • Message Queues: Use message queue instrumentation to capture spans for message production and consumption.

Custom Instrumentation Scenarios

For custom scenarios, developers need to write custom code:

  • Custom Business Logic: Instrument specific operations or transactions.
  • Third-Party Libraries: Instrument libraries without built-in OpenTelemetry support.
  • Legacy Systems: Instrument legacy systems to integrate with modern observability tools.

Best Practices

To ensure effective instrumentation:

1. Follow Naming Conventions: Use OpenTelemetry's semantic conventions for naming spans, attributes, and metrics.

2. Prioritize Critical Components: Focus on instrumenting critical components like APIs, databases, and message queues.

3. Keep It Simple: Avoid complex instrumentation logic that can impact performance or introduce errors.

4. Test and Validate: Verify that instrumentation is working correctly and capturing expected telemetry data.

Visualizing and Analyzing Traces

After exporting trace data to a backend, you can use visualization tools to analyze and understand your application's performance and behavior.

Exporting Traces

Exporting traces to backends is straightforward. You configure the OpenTelemetry SDK to send trace data to your chosen backend, such as Jaeger, Zipkin, or Honeycomb. For example, to export to Jaeger:

import { tracer } from 'opentelemetry';

tracer.export(new JaegerExporter({
  endpoint: 'http://jaeger:14250',
  serviceName: 'my-service',

Visualizing Trace Data

Once exported, you can use visualization tools to analyze traces. For example, Jaeger provides a web UI for:

  • Viewing trace timelines and spans
  • Filtering traces by service, operation, or tag
  • Identifying performance bottlenecks and latency issues

Other backends like Zipkin and Honeycomb offer similar visualization capabilities.

Identifying Performance Issues

Analyzing trace data helps identify performance issues and latency bottlenecks, such as:

  • Slow operations or services
  • Errors and exceptions
  • High request latency and response times

By analyzing traces, you gain insights into your application's performance and can make data-driven decisions to optimize and improve it.

Troubleshooting with Traces

Distributed traces are also useful for troubleshooting and debugging application issues:

  • Identifying the root cause of errors and exceptions
  • Debugging complex issues spanning multiple services
  • Verifying that fixes and optimizations are effective
Trace Analysis Benefits
Visualize Traces View timelines, spans, and filter traces
Identify Performance Issues Detect slow operations, errors, and high latency
Troubleshoot Issues Find root causes, debug across services, verify fixes

Advanced Tracing Techniques

Here are some advanced techniques to get the most out of OpenTelemetry tracing:

Trace Sampling

Managing trace data volume is crucial. There are two main sampling strategies:

Head-Based Sampling

  • Selects a subset of traces at the start
  • Useful for analyzing a representative sample
  • May miss rare or unusual events

Tail-Based Sampling

  • Selects traces based on characteristics like errors or latency
  • Focuses on specific issues or anomalies

Correlating and Propagating Traces

Correlating traces across systems helps understand request flow and find bottlenecks. OpenTelemetry provides:

  • Trace IDs: Link related traces across services
  • Span IDs: Link related spans within a trace
  • Context Propagation: Pass context like user IDs or headers across services

Integrating Traces, Logs, and Metrics

Combining traces, logs, and metrics gives a complete observability picture:

Signal Provides
Traces Detailed view of request flow and latency
Logs Detailed view of system events and errors
Metrics Quantitative view of system performance and health

Handling Sensitive Data

Sensitive data like user IDs or credit cards must be handled carefully. OpenTelemetry offers:

  • Anonymization: Removing or obscuring sensitive data
  • Redaction: Removing sensitive data from traces
  • Encryption: Encrypting sensitive data in transit and at rest

OpenTelemetry Tracing Best Practices

Naming Conventions

When creating spans and attributes, use clear and descriptive names. Avoid abbreviations or acronyms unless widely recognized. Follow a consistent naming style, like camelCase or underscore notation. Avoid special characters or whitespace in names.

Handling Errors and Exceptions

Properly handle errors and exceptions to ensure accurate trace data:

  • Catch and record exceptions on a span using recordException
  • Set the span status to error when an exception occurs using setStatus
  • Use try-catch blocks to catch exceptions in critical code sections
  • Utilize OpenTelemetry's built-in error handling mechanisms

Performance Considerations

Minimize the performance impact of OpenTelemetry tracing:

Technique Description
Sampling Strategies Control the volume of trace data collected
Optimize Instrumentation Minimize overhead from instrumentation
Built-in Optimizations Use OpenTelemetry's adaptive sampling
Separate Thread/Process Run tracing in a separate thread or process

Monitoring and Alerting

Set up monitoring and alerting based on trace data:

  • Create alerts for critical errors or performance issues
  • Use OpenTelemetry's alerting mechanisms (error rates, latency thresholds)
  • Integrate with existing monitoring and alerting tools
  • Use a separate dashboard or visualization tool for trace data and alerts


OpenTelemetry distributed tracing offers a standardized way to monitor and understand complex distributed systems. By providing a vendor-neutral framework for collecting and analyzing telemetry data, it empowers developers to build more reliable and efficient applications.

In this tutorial, we explored the core concepts, components, and best practices of OpenTelemetry tracing. We saw how it helps teams:

  • Troubleshoot issues across services
  • Optimize performance
  • Improve collaboration

As applications grow more complex, observability becomes increasingly important. OpenTelemetry is well-positioned to play a vital role, providing an open-source platform for collecting and analyzing telemetry data from diverse sources.

As you begin using OpenTelemetry, remember to:

  • Follow best practices
  • Instrument your code thoughtfully
  • Leverage distributed tracing to gain insights into your application's behavior

With OpenTelemetry, the future of observability is promising, offering new possibilities for understanding and improving your systems.

Key Takeaways
- OpenTelemetry provides a standardized approach to distributed tracing
- It helps troubleshoot issues, optimize performance, and improve collaboration
- Follow best practices and instrument your code carefully
- Leverage distributed tracing to gain insights into your application
- OpenTelemetry offers new possibilities for observability


How does OpenTelemetry tracing work?

OpenTelemetry tracing helps you understand how your distributed system works. It does this by adding code to your application that collects data, including:

  • Spans: A span records a single operation, like a database query or API call. It includes details like the operation name, start and end times, and any additional information.
  • Traces: A trace is a collection of related spans, showing the path of a request as it moves through different parts of your system.
  • Metrics: OpenTelemetry also collects metrics about your application's performance and health.

By analyzing these traces, you can:

Benefit Description
Identify Bottlenecks Find slow operations or services that are causing delays.
Troubleshoot Issues Trace the root cause of errors or exceptions across multiple services.
Optimize Performance Pinpoint areas for improvement and make data-driven optimizations.

OpenTelemetry provides a standardized way to collect and analyze this data, making it easier to understand and improve your distributed system.

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