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Distributed Trace Visualization: Guide & Tools

Learn about distributed trace visualization, setting up tools, analyzing traces, and best practices. Explore Jaeger, Zipkin, Honeycomb, and Grafana Tempo. Get insights on instrumenting services and integrating with other tools.

Zan Faruqui
September 18, 2024

Distributed tracing tracks requests as they move through different microservices, providing a detailed view of the request journey. This guide covers:

  • What is Distributed Tracing? How it works and why visualizing traces is beneficial for debugging, performance monitoring, and user experience.

  • Setting Up Distributed Tracing: Requirements, instrumenting services, collecting data, and open standards like OpenTelemetry and OpenTracing.

  • Choosing a Visualization Tool: Popular options like Jaeger, Zipkin, Honeycomb, and Grafana Tempo, with a comparison of their key features.

  • Setting Up the Tool: Step-by-step guide for installing, configuring, integrating, and troubleshooting Jaeger.

  • Understanding the Interface: Interface components, reading trace visuals, navigation, and filtering.

  • Analyzing Traces: Strategies for finding performance issues and correlating with logs and metrics.

  • Advanced Visualization: Visualizing dependencies, service interactions, and customization options.

  • Integrating with Other Tools: Methods, benefits, and examples of integrating with logging and metrics platforms.

  • Best Practices and Tips: Clear visualization, optimizing performance, and collaborating effectively.

Quick Comparison of Visualization Tools:

Tool Instrumentation Data Collection Visualization Scalability Cost
Jaeger OpenTelemetry, OpenTracing In-memory, Cassandra, Elasticsearch Service maps, trace graphs High Free (open-source)
Zipkin OpenZipkin, Brave In-memory, Cassandra, MySQL Service maps, latency graphs Medium Free (open-source)
Honeycomb Automated SaaS-based Customizable dashboards, service maps High Paid (commercial)
Grafana Tempo OpenTelemetry, OpenTracing In-memory, Cassandra, Elasticsearch Service maps, trace graphs High Free (open-source)

Setting Up Distributed Tracing

To visualize distributed traces, you need to set up distributed tracing in your environment. This section explains the requirements, how to instrument your services, and collect trace data, as well as popular open-source standards.

Requirements

To implement distributed tracing, you'll need:

  • Services or applications that generate requests and responses
  • A tracing backend to collect, process, and store trace data
  • Instrumentation libraries or agents to capture trace data
  • A compatible programming language and framework
  • A service mesh or API gateway to manage service interactions (optional)

Instrumenting Services and Collecting Data

Instrumentation involves adding code to your services to capture trace data, such as request and response headers, timestamps, and error messages. This data is then sent to a tracing backend.

Supported languages and frameworks include Java, Python, Node.js, and .NET. Popular instrumentation libraries include:

Library Description
OpenTelemetry Provides SDKs, data collection software, and vendor-neutral APIs and tools for instrumentation.
OpenTracing A vendor-agnostic API that assists developers in instrumenting code for distributed tracing.
Jaeger An open-source distributed tracing system for monitoring and troubleshooting microservices-based applications.

Open Standards

OpenTelemetry and OpenTracing are key standards for implementing distributed tracing. These standards provide a vendor-neutral approach, allowing you to switch between tracing backends without modifying your application code.

OpenTelemetry combines OpenCensus and OpenTracing, offering:

  • Software development kits (SDKs)
  • Data collection software
  • Vendor-neutral APIs and tools for instrumentation

OpenTracing is a vendor-agnostic API that helps developers instrument code for distributed tracing.

Choosing a Visualization Tool

Selecting the right tool for visualizing distributed traces is crucial. Here, we'll explore popular options, compare their features, and provide guidance on choosing the best fit for your needs.

Several tools are available for distributed trace visualization, each with its strengths:

  • Jaeger: An open-source, end-to-end tracing system. It provides real-time monitoring, root cause analysis, and service dependency visualization.
  • Zipkin: An open-source tool that gathers timing data to troubleshoot latency issues in microservices. It offers detailed tracing information and service dependency visualization.
  • Honeycomb: A commercial tool with an intuitive interface for exploring and analyzing traces. It offers automated instrumentation, customizable dashboards, and collaboration tools.
  • Grafana Tempo: An open-source, scalable, and cost-effective solution for large-scale environments. It provides real-time tracing, service maps, and alerting.

Tool Comparison

Here's a comparison of the tools' key features:

Tool Instrumentation Data Collection Visualization Scalability Cost
Jaeger OpenTelemetry, OpenTracing In-memory, Cassandra, Elasticsearch Service maps, trace graphs High Free (open-source)
Zipkin OpenZipkin, Brave In-memory, Cassandra, MySQL Service maps, latency graphs Medium Free (open-source)
Honeycomb Automated SaaS-based Customizable dashboards, service maps High Paid (commercial)
Grafana Tempo OpenTelemetry, OpenTracing In-memory, Cassandra, Elasticsearch Service maps, trace graphs High Free (open-source)

Selection Criteria

When choosing a tool, consider the following:

  • Scalability: Can it handle your environment's size and complexity?
  • Ease of use: How easy is it to instrument services, collect data, and visualize traces?
  • Integration: Does it support your existing tech stack and integrate with other tools?
  • Cost: What are the costs for licensing, maintenance, and support?
  • Customization: Can the tool be tailored to your specific needs?

Setting Up the Tool

Setting up a distributed tracing tool involves several steps, including installation, configuration, integration, and troubleshooting. Here, we'll guide you through the process of setting up Jaeger, an open-source visualization tool.

Installation

To install Jaeger, you can use Docker with this command:

docker run -d --name jaeger \
  -e COLLECTOR_ZIPKIN_HOST_PORT=:9412 \
  -p 16686:16686 \
  -p 9412:9412 \
  jaegertracing/all-in-one:1.22

This command starts a Jaeger instance with the all-in-one image, which includes the collector, query, and agent components.

Configuration

After installation, configure Jaeger to connect with your tracing infrastructure. You can use a configuration file or environment variables.

For example, create a jaeger.yaml file with this content:

collector:
  zipkin:
    host_port: ":9412"

This configuration sets up the collector to listen on port 9412 for Zipkin traces.

Integration

To integrate Jaeger with your existing tracing setup, configure your application to send traces to Jaeger. This involves instrumenting your application with a tracing library, such as OpenTelemetry or OpenTracing.

For example, you can use the OpenTelemetry Java agent to instrument your Java application:

  1. Add this dependency to your pom.xml file:
<dependency>
  <groupId>io.opentelemetry</groupId>
  <artifactId>opentelemetry-javaagent</artifactId>
  <version>1.22.0</version>
</dependency>
  1. Configure the agent to send traces to Jaeger:
import io.opentelemetry.javaagent.OpenTelemetryJavaAgent;

public class MyApplication {
  public static void main(String[] args) {
    OpenTelemetryJavaAgent.init("jaeger", "http://localhost:9412/api/v2/spans");
    //...
  }
}

Troubleshooting

During setup, you may encounter issues such as connection errors, data loss, or performance problems. To troubleshoot these issues, you can use Jaeger's built-in debugging tools, such as the query component's debug endpoint.

For example, use this command to check the query component's debug endpoint:

curl http://localhost:16686/debug/vars

This command returns a list of debug variables, including the number of traces received and the number of errors encountered.

Understanding the Interface

Interface Components

A typical distributed tracing interface has these main parts:

  • Timeline view: A visual timeline showing the order and duration of each step (span) in the trace.
  • Span details: Detailed information about a specific step, like metadata, tags, and logs.
  • Dependency graph: A diagram showing how different services are connected and depend on each other.

Reading Trace Visuals

When looking at traces, pay attention to:

  • Long spans: These could mean performance issues or delays.
  • Error spans: These show errors or exceptions that need to be fixed.
  • Parallel spans: Multiple spans happening at the same time, which can impact overall performance.

To find specific issues or events in a trace, you can:

  • Zoom and pan: Use the timeline to zoom in on certain time ranges or move across the trace.
  • Filter by service: Focus on just one service or component to see how it behaves.
  • Filter by errors: Look only at spans with errors to quickly find and fix issues.
Navigation Technique Description
Zooming and Panning Use the timeline view to zoom in on specific time ranges or pan across the trace to explore different segments.
Filtering by Service Isolate specific services or components to analyze their behavior and dependencies.
Filtering by Error Focus on error spans to identify and troubleshoot issues quickly.
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Analyzing Traces

Analyzing distributed traces helps you understand how your distributed systems perform and identify issues. Here are some strategies for effective trace analysis:

Analysis Strategies

  • Filtering: Use filters to narrow down trace data by service, operation, or error codes.
  • Aggregation: Group trace data by attributes like service, endpoint, or user ID to spot trends.
  • Drill-down: Focus on specific spans or trace segments to understand system behavior in detail.

Finding Performance Issues

To optimize system performance, identify bottlenecks and latency issues:

  • Long Spans: Look for spans with long durations or high latency to pinpoint bottlenecks.
  • Errors: Analyze error spans to find issues that need fixing.
  • Parallel Spans: Examine parallel spans for optimization opportunities.

Correlating with Other Data

Correlating trace data with logs and metrics provides a comprehensive view of system performance:

Technique Description
Log Correlation Correlate trace data with log data for deeper insights into system behavior.
Metric Correlation Correlate trace data with metric data to identify performance trends and patterns.
Service Dependency Analysis Analyze how services interact and impact system performance.

Advanced Visualization

Visualizing Dependencies

Distributed tracing tools allow you to see how different parts of your system interact through visual dependency maps or service maps. These maps show the relationships and connections between services and components, helping you identify potential bottlenecks or performance issues.

For example, Lightstep's Service Diagram provides a live visual representation of your services and their dependencies. This helps you understand the complexity of your system, spot areas for improvement, and investigate the root causes of problems.

Service Interactions

In addition to dependency maps, many tools offer ways to analyze and visualize how services interact. This can help you identify potential points of failure or performance bottlenecks caused by specific services or components.

Datadog's Service Map, for instance, shows a visual representation of your services and their relationships. You can drill down into individual services to see detailed metrics and trace data.

Customization

Most distributed tracing tools allow you to customize the visualization experience to suit your needs. This might include creating custom dashboards, defining alert conditions, or configuring the level of detail displayed.

For example, Site24x7's APM allows you to create custom transaction monitors and set thresholds for performance metrics. You can then generate detailed reports and set up alerts when thresholds are breached.

Many tools also provide APIs or SDKs that allow you to integrate the visualization capabilities into your own applications or build custom visualizations tailored to your specific requirements.

Customization Option Description
Custom Dashboards Create dashboards tailored to your specific monitoring needs.
Alert Conditions Define conditions to trigger alerts for performance issues.
Detail Level Configure the level of detail displayed in visualizations.
APIs and SDKs Integrate visualization capabilities into your own applications.

Integrating with Other Tools

Distributed tracing provides insights into how your complex systems perform. However, to get the most out of it, you need to integrate it with other monitoring tools. This section explores ways to combine distributed trace visualization with other tools, correlate traces with logs and metrics for comprehensive monitoring, and how this integration can enhance troubleshooting and optimization.

Integration Methods

You can integrate distributed tracing tools with other monitoring tools in several ways:

  • API Integration: Use the tracing tool's APIs to send trace data to logging or metrics platforms.
  • SDK Integration: Use SDKs to instrument applications and send trace data to other tools.
  • Open Standards: Use open standards like OpenTelemetry to integrate with a wide range of tools and platforms.

Benefits of Integration

Integrating distributed tracing with other monitoring tools offers these advantages:

  • Complete Monitoring: By correlating trace data with logs and metrics, you get a full picture of your system's performance and behavior.
  • Improved Troubleshooting: With integrated data, you can quickly identify and resolve issues.
  • Enhanced Optimization: By analyzing correlated data, you can identify areas for optimization and improve system performance.

Examples

Here are examples of how integrated monitoring setups have improved troubleshooting and system performance:

Example Description
Example 1 A company integrated their distributed tracing tool with their logging platform. By correlating trace data with log data, they identified the root cause of a performance issue and resolved it quickly.
Example 2 A team integrated their tracing tool with their metrics platform. By correlating trace data with metrics data, they identified areas for optimization and improved system performance by 30%.

Best Practices and Tips

Clear Visualization

To get the most value from distributed trace visualization:

  • Use consistent naming: Follow a standard naming convention for services, spans, and tags to easily identify and filter them.
  • Customize your view: Tailor the visualization to your needs by selecting relevant data and filtering out noise.
  • Group related items: Aggregate and group related spans and services to spot patterns and trends.

Optimize Performance

When working with large trace data volumes, optimize performance:

  • Use sampling: Sample your trace data to reduce volume and improve speed.
  • Streamline instrumentation: Ensure your instrumentation is efficient and doesn't slow things down.
  • Cache data: Cache frequently accessed data to reduce load on your visualization tool.

Collaborate Effectively

Share trace data and insights across teams:

  • Create a central dashboard: Provide a unified view of system performance and behavior.
  • Control access: Use role-based access control so each team member sees relevant data.
  • Get feedback: Encourage feedback and collaboration between teams for continuous improvement.
Tip Description
Consistent Naming Follow a standard naming convention for easy identification and filtering.
Customized View Tailor the visualization to your needs by selecting relevant data.
Group Related Items Aggregate and group related spans and services to spot patterns.
Use Sampling Sample trace data to reduce volume and improve speed.
Streamline Instrumentation Ensure efficient instrumentation without significant overhead.
Cache Data Cache frequently accessed data to reduce load on the visualization tool.
Central Dashboard Provide a unified view of system performance and behavior.
Control Access Use role-based access control for relevant data visibility.
Get Feedback Encourage feedback and collaboration for continuous improvement.

Conclusion

Visualizing distributed traces is a powerful tool for understanding complex systems, finding performance issues, and optimizing applications. By following the guidelines in this guide, you can implement distributed tracing effectively and unlock its full potential.

Remember, distributed tracing is not just about collecting data; it's about gaining insights that drive improvements. By visualizing traces, you can:

  • Identify bottlenecks and latency issues
  • Understand system interactions and dependencies
  • Optimize application performance and resource usage
  • Improve collaboration across teams

As you begin your distributed tracing journey, keep these key points in mind:

Clear Visualization

  • Use consistent naming conventions for easy identification and filtering
  • Customize your view to show relevant data
  • Group related spans and services to spot patterns

Optimize Performance

Tip Description
Use Sampling Sample trace data to reduce volume and improve speed
Streamline Instrumentation Ensure efficient instrumentation without significant overhead
Cache Data Cache frequently accessed data to reduce load on the visualization tool

Collaborate Effectively

Tip Description
Central Dashboard Provide a unified view of system performance and behavior
Control Access Use role-based access control for relevant data visibility
Get Feedback Encourage feedback and collaboration for continuous improvement

We hope this guide has provided you with a clear understanding of distributed trace visualization and its applications. Happy tracing!

FAQs

How to View Distributed Traces in Dynatrace?

Dynatrace

To view distributed traces in Dynatrace, follow these simple steps:

  1. Open Distributed Traces: In your Dynatrace dashboard, navigate to the Distributed Traces section.

  2. Configure View: Set filters to customize your view:

    • Select PurePaths to view traces captured by OneAgent.
    • Select Ingested traces to view traces from other instrumentation libraries.
  3. Choose a Service: Pick a service to see its distributed traces. Analyze the trace data to identify:

    • Bottlenecks
    • Latency issues
    • Other performance problems
Step Action
1 Open Distributed Traces section
2 Configure view filters
3 Select a service to analyze traces

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