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Distributed Tracing Guide: Trace Data Collection 2024

Learn about trace data collection in distributed systems, including components, types, sampling strategies, challenges, and best practices. Gain valuable insights into system performance and reliability.

Zan Faruqui
May 16, 2023

Distributed tracing tracks requests across services in microservices systems, providing visibility into system performance and aiding issue resolution. Collecting trace data is crucial for effective tracing, revealing:

  • How requests flow through the system
  • Where performance bottlenecks occur
  • Which services are slow or error-prone
Benefit Description
Analyze Performance Identify slow services and bottlenecks
Detect Anomalies Spot unusual behavior or errors
Optimize Resources Allocate resources based on usage patterns

Trace data collection involves capturing key components:

Component Description
Spans Track individual operations or tasks
Trace IDs Unique identifiers linking spans for a request
Parent-Child Relationships Hierarchical structure showing request flow

Data types collected include metadata, logs, and events. Approaches for collection range from manual instrumentation to automatic tools like OpenTelemetry.

Sampling strategies like head-based, tail-based, and adaptive sampling help manage data volume. Challenges include performance impact, data volume management, and data privacy concerns.

Best practices involve:

  • Identifying critical paths
  • Using consistent naming conventions
  • Integrating with logging and monitoring
  • Regularly reviewing and optimizing instrumentation
  • Managing data retention and purging

By following these guidelines, you can gain valuable insights into your distributed system's performance and reliability.

Trace Data Collection Basics

Components of a Trace

A distributed trace consists of key parts:

  • Spans: A span tracks a single operation or task within the system. It records timing and details about that operation.
  • Trace IDs: Each request gets a unique ID as it moves through the system. This ID links all the spans for that request.
  • Parent-child relationships: Spans are connected in a parent-child structure. This shows how requests flow through different services.

Passing Trace Context

To keep tracking requests across services, trace context must be passed along. Code libraries add trace IDs and other details to requests as they move through the system. This lets you connect all the spans for a request, even in complex systems.

Types of Trace Data

Distributed tracing collects different kinds of data:

  • Metadata: Details about the request, like headers and payloads.
  • Logs: Timestamped messages from services, showing what happened.
  • Events: Important occurrences, like errors or warnings.

This trace data helps you understand how the system works, find slow areas, and troubleshoot issues. By analyzing it, you can optimize performance and reliability.

Presenting Data in Tables

Trace Component Description
Spans Track individual operations or tasks within the system, capturing timing and metadata.
Trace IDs Unique identifiers assigned to each request, allowing correlation of spans across services.
Parent-Child Relationships Hierarchical structure representing the flow of requests through different services.
Trace Data Type Purpose
Metadata Provides context about requests, such as headers, query parameters, and payloads.
Logs Timestamped messages emitted by services, offering insights into system behavior.
Events Captures specific occurrences within the system, like errors, warnings, or notable events.

Collecting Trace Data

Gathering trace data is a crucial step in distributed tracing. It involves capturing information about requests as they move through your system. This section explores different ways to collect trace data, including manual, automatic, and combined approaches.

Manual Instrumentation

Manual instrumentation means adding code to your application to generate trace data. This approach gives you control over what data is collected and how it's formatted. However, it can be time-consuming and prone to errors.

Manual instrumentation is suitable for small to medium-sized applications or when you need to collect specific, custom data. It's important to follow best practices, such as:

  • Using consistent naming conventions
  • Implementing tracing for critical paths
  • Integrating with logging and monitoring tools

Automatic Instrumentation Tools

Automatic instrumentation tools, like OpenTelemetry, simplify the process of collecting trace data. These tools provide libraries and frameworks that automatically generate trace data for your application. They offer several advantages, including:

  • Reduced development time and effort
  • Improved accuracy and consistency
  • Support for multiple programming languages and frameworks

When choosing an automatic instrumentation tool, consider factors such as:

Factor Description
Compatibility Ensure the tool works with your application's technology stack
Ease of Use Look for tools that are simple to set up and configure
Customization Determine if the tool allows for customizing data collection

Combining Manual and Automatic Approaches

In some cases, a hybrid approach that combines manual and automatic instrumentation may be beneficial. This approach allows you to leverage the strengths of both methods. For example, you can use automatic instrumentation for most of your application and manual instrumentation for specific, custom requirements.

When combining manual and automatic approaches, ensure that you:

1. Use consistent naming conventions and formatting

Maintain consistency across both manual and automatic instrumentation.

2. Integrate both methods seamlessly

Ensure a smooth integration between the two approaches.

3. Monitor and analyze trace data from both sources effectively

Analyze trace data from both manual and automatic instrumentation sources.


Sampling Strategies for Trace Data

Sampling strategies help manage the volume of trace data and reduce storage costs. Here are three sampling strategies:

Head-based Sampling

Head-based sampling decides to sample a trace at the start of the request. This approach:

  • Works well for applications with lower transaction throughput
  • Is fast and simple to set up
  • Suits blended monolith and microservice environments
  • Provides a low-cost solution for sending tracing data to third-party tools


  • Traces are sampled randomly, potentially missing critical issues
  • Sampling happens before a trace has fully completed its path through many services
  • Statistical sampling provides limited transparency into the distributed system
Pros Cons
Lower transaction throughput Random sampling may miss issues
Fast and simple setup Samples before trace completion
Suitable for blended environments Limited system transparency
Low-cost third-party tool integration

Tail-based Sampling

Tail-based sampling delays the sampling decision until all spans in a trace have arrived. This approach:

  • Observes and analyzes 100% of traces
  • Samples traces after they are fully completed
  • Visualizes traces with errors or unusual slowness more quickly


  • It may require additional gateways, proxies, and satellites to run sampling software
  • It requires work to manage and scale third-party software in some cases
  • It incurs additional costs for transmitting and storing more data
Pros Cons
Observes 100% of traces Requires additional infrastructure
Samples after trace completion Requires third-party software management
Identifies errors and slowness quickly Higher data transmission and storage costs

Adaptive Sampling

Adaptive sampling is a technique applied to head-based sampling that adjusts the number of transactions collected based on changes in transaction throughput. This approach:

  • Adapts to changes in transaction throughput
  • Captures a representative sample of system activity


  • It requires additional complexity in sampling logic
  • It may not be suitable for applications with high variability in transaction throughput
Pros Cons
Adapts to throughput changes Requires complex sampling logic
Captures representative samples May not suit high throughput variability

Challenges in Trace Data Collection

Collecting trace data is crucial, but it comes with its own set of challenges. Here are some common issues and how to address them:

Performance Impact

Instrumenting code to collect traces can slow down your application, affecting user experience. To minimize this:

  • Optimize instrumentation code to reduce execution time
  • Use efficient data storage and retrieval methods
  • Implement sampling strategies to reduce trace data volume
  • Utilize async logging to prevent blocking calls

Managing Data Volume and Storage

Applications generate massive amounts of trace data, making data management a significant challenge. To tackle this:

Approach Description
Data Retention Policies Purge unnecessary data regularly
Data Compression Reduce storage needs through compression and encoding
Distributed Storage Spread data across multiple storage nodes for scalability
Cloud Storage Utilize cost-effective, scalable cloud storage solutions

Data Privacy and Security Concerns

Trace data often contains sensitive information, so proper handling and storage are essential:

  • Implement encryption and access controls for trace data storage
  • Use secure communication protocols for data transmission
  • Anonymize or mask sensitive data to prevent exposure
  • Regularly audit and monitor trace data access to detect potential breaches

Best Practices for Trace Data Collection

Collecting trace data effectively is crucial for understanding your distributed system's performance. Follow these guidelines to optimize the process and gain high-quality insights:

Identify Critical Paths

Start by instrumenting the most critical paths in your system, which have the biggest impact on performance and reliability. Focus on entry points like incoming user requests or external clients, then move inward. This will demonstrate value and allow you to expand from there.

Use Consistent Naming

Use a consistent naming convention for spans, services, operations, and tags. This makes it easier to search, filter, and analyze your trace data, helping you understand traces and diagnose issues more effectively.

Integrate with Logging and Monitoring

Correlate trace data with application logs to gain deeper insights into root causes and operation context within a trace. This helps identify trends, anomalies, and respond to performance issues proactively.

Review and Optimize Regularly

As your system evolves, your tracing needs may change. Regularly review your instrumentation, sampling strategies, and data retention policies to ensure they align with your organization's needs.

Manage Data Retention and Purging

Set up data retention policies to purge unnecessary data regularly. Implement data compression and encoding to reduce storage needs. Audit and monitor trace data access to detect potential breaches and ensure data privacy compliance.

Identify Critical Paths

Approach Description
Focus on Entry Points Instrument entry points like incoming user requests or external clients first
Move Inward Then instrument deeper into the system
Demonstrate Value This shows the value of tracing and allows expansion

Consistent Naming Conventions

Practice Benefit
Use consistent names for spans, services, operations, tags Easier to search, filter, and analyze trace data
Follow a standard convention Helps understand traces and diagnose issues

Integrate with Logging and Monitoring

Integration Purpose
Correlate trace data with application logs Gain deeper insights into root causes and operation context
Identify trends and anomalies Respond to performance issues proactively

Regular Review and Optimization

1. Review Instrumentation

As your system evolves, review and update your instrumentation to align with changing needs.

2. Optimize Sampling Strategies

Regularly evaluate and adjust your sampling strategies to ensure efficient data collection.

3. Manage Data Retention

Review and update data retention policies to manage storage usage and ensure data privacy compliance.

Trace Data Retention and Purging

1. Set Data Retention Policies

Establish policies to purge unnecessary data regularly.

2. Implement Data Compression

Use data compression and encoding to reduce storage needs.

3. Monitor Data Access

Audit and monitor trace data access to detect potential breaches and ensure data privacy compliance.


Key Takeaways

In this guide, we covered the essential aspects of collecting trace data in distributed systems. To summarize:

  • Identify the most critical paths in your system and instrument them first.
  • Use consistent naming conventions for spans, services, operations, and tags to make it easier to search, filter, and analyze trace data.
  • Integrate trace data with application logs to gain deeper insights into root causes and operation context.
  • Regularly review and optimize your instrumentation, sampling strategies, and data retention policies as your system evolves.
  • Manage data retention and purging by setting policies to remove unnecessary data, implementing data compression, and monitoring data access to ensure privacy compliance.

Following these best practices will help you gain valuable insights into your system's performance and reliability.

As distributed systems continue to evolve, collecting trace data will become even more crucial. Trends like AI-powered tracing, automated instrumentation, and cloud-native tracing solutions will shape the future. Staying up-to-date with these developments will enable you to optimize your tracing strategies.

Additional Resources

For further learning, we recommend:

Key Takeaways in a Table

Takeaway Description
Identify Critical Paths Instrument the most critical paths in your system first
Consistent Naming Use consistent naming conventions for spans, services, operations, and tags
Integrate with Logging Correlate trace data with application logs for deeper insights
Regular Review Review and optimize instrumentation, sampling, and data retention regularly
Data Retention and Purging Manage data retention, compression, and access monitoring
Trend Description
AI-powered Tracing Leveraging AI to enhance tracing capabilities
Automated Instrumentation Automating the process of instrumenting code for tracing
Cloud-native Tracing Tracing solutions designed for cloud-native environments

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