Distributed Tracing: A Guide for 2023
Explore the basics of distributed tracing, how it works, the major components, key benefits, challenges, and best practices.
Distributed tracing is becoming popular with the growth of distributed systems. As applications become increasingly complex and distributed, traditional monitoring methods like metrics and logs are no longer enough to understand the behavior and performance of different components and services.
The newest way for monitoring and troubleshooting microservices-based applications is tracing. Distributed tracing allows tracking of request flows across various components and services. Thus enabling developers and operators to identify performance bottlenecks, errors, and other issues that could impact the user experience.
In this article, we will explore the basics of distributed tracing, including how it works, the major components, key benefits, challenges, and best practices for distributed tracing systems.
What is distributed tracing?
When a request is received in a distributed system, it can go through several microservices and infrastructure components before returning a response. Distributed tracing provides a way to visualize and track the path of requests flowing through distributed applications.
What are the components of distributed tracing?
The components of a distributed tracing system might vary based on the implementation, but a typical system is built up with the below components:
1. Trace
A trace refers to a complete end-to-end path of a request or transaction as it flows through a distributed system. It represents the journey of a specific operation as it traverses various components and services in a distributed architecture.
2. Span
A span represents a single operation or unit of work within a distributed system. It captures the timing and metadata associated with a specific operation and provides a way to track and understand the behavior of individual components and services.
3. Context propagation
Context propagation refers to passing contextual information between different components or services within a distributed system. In distributed tracing, context propagation is crucial for connecting and correlating spans to construct a complete trace of a request or transaction as it flows through various services.
4. Instrumentation libraries
Instrumentation libraries are software components that developers integrate into their applications to collect tracing data. They are specifically designed to generate, collect, and manage trace data, which includes information about spans and the overall trace.
Instrumentation libraries can automatically capture useful information such as start time, end time, and metadata about each operation (span). They are also responsible for context propagation, ensuring that trace context is passed along with requests as they move through different services in a distributed system.
Examples of popular instrumentation libraries include OpenTelemetry, Jaeger, Zipkin, New Relic and Datadog.
5. Tracing data collectors
These are the components that receive and store trace data, usually in a distributed datastore such as Elasticsearch or Cassandra. Some of the available tracing data collectors are,
Jaeger, Zipkin, OpenTelemetry Collector, AWS X-Ray, Google Cloud Trace
6. Visualization and analysis tools
These are the tools that provide a graphical representation of the trace data, allowing developers to visualize the flow of requests through the system and identify performance issues. Some of the available trace visualizers are,
Helios, Jaeger UI, Zipkin UI, LightStep, AWS X-Ray, Google Cloud Trace
7. Trace analysis tools
These are tools that provide detailed analysis of trace data, allowing developers to identify bottlenecks and optimize system performance. Some of the available trace analysis tools are,
Helios, Trace Compass, amegraphs, Performance Co-Pilot (PCP), Grafana, Apache SkyWalking, Dynatrace.
How does distributed tracing work?
Now let’s discuss on a high-level how distributed tracing work with its components.
1. A unique identifier is assigned to each incoming request
When a request enters your system, a unique identifier is assigned to it. This identifier tracks the request as it moves through the system. This is called a “Trace Id”. Bellow image shows how a trace id is assigned to a request as it flows down the downstream components.
2. Instrumentation libraries capture trace data
As the request moves through the system, various components and services capture trace data and add it to the request’s trace context. This includes timestamps, service and endpoint names, and any relevant metadata. The below image depicts the trace context.
3. Trace data is propagated through the system
As the request moves from one component to another, the trace context is also propagated. This allows all the components involved in the request to add their trace data to the request’s trace context.
4. Trace data is collected and stored
A tracing data collector receives the trace data from each component and service involved in the request and stores it in a distributed datastore such as Elasticsearch or Cassandra.
5. Trace data is visualized and analyzed
Developers can use trace visualizers and analysis tools to view the trace data and identify any performance issues or bottlenecks in the system. Below is a screen from Helios dataflow visualization.
By following these steps, distributed tracing provides a way to monitor and debug complex, distributed systems.
What are the key benefits of using distributed tracing?
1. Debugging
Distributed tracing can help identify the root cause of errors or performance issues in a distributed system. By tracing the path of a request through the system.
2. Performance optimization
Distributed tracing can help identify bottlenecks and areas of poor performance in a system. By enabling the trace data, developers can optimize the performance of individual services and the system as a whole.
3. Monitoring
Distributed tracing provides a real-time view of the behavior of a distributed system. This can monitor system health, detect anomalies, and trigger alerts when issues arise.
4. Service dependency mapping
By tracing requests through a distributed system, developers can create a map of the dependencies between services; this can be used to identify potential issues and optimize the system’s architecture.
5. Collaboration
Distributed tracing can facilitate collaboration between teams by providing a shared view of the behavior of a system. This can help to improve communication, increase efficiency, and reduce the time required to diagnose and fix issues.
Learn more:
Choosing a distributed tracing tool
There are both open-source and commercial tools available for distributed tracing. You should review their features and compatibility with the tech stack and select one that makes sense to your system.
Open-source tools
Here are several popular open-source distributed tracing tools available for developers to use. Some of the most widely used tools are Jaeger, Zipkin, Appdash, SkyWalking and Tracee.
Commercial tools
There are several commercial distributed tracing tools available for organizations that require more advanced features or support.
Some of the most widely used commercial tools are Helios, Datadog APM, New Relic, Dynatrace, Splunk, AppDynamics and Lightstep. These are just a few examples of the many commercial distributed tracing tools available. You can read more about them here.
💡 Before you decide on a distributed tracing tool, consider reading this guide.
Also, this article lists some of the best distributed tracing tools around, be sure to check it out.
Integrating distributed tracing into your application
Integrating distributed tracing into your application involves a few key steps:
1. Choose a tracing system
There are several tracing systems available, both open-source and commercial. Hence, choosing a tracing system compatible with your technology stack and organization decisions is important.
2. Instrument your application
To start tracing requests in your application, you need to add tracing code to your application’s codebase. This usually involves adding tracing libraries to your application’s dependencies and using them to wrap your code’s entry and exit points. This will allow the tracing system to record the flow of requests through your application.
3. Set up tracing spans
Spans are the basic unit of work in a distributed tracing system. A span represents a single unit of work, such as a database query, an HTTP request, or a function call. Each span has a start time and a duration and can be annotated with metadata such as the name of the operation and any relevant tags or attributes.
4. Propagate trace context
To ensure tracing information is properly correlated across services and systems, you must propagate trace context between requests. This typically involves adding trace IDs and span IDs to the headers of outgoing requests and extracting them from incoming requests to continue the trace.
5. Analyze tracing data
Once your application is instrumented and tracing data is being collected, you can use the tracing system’s UI or API to analyze the data and gain insights into your application’s behavior. This can include identifying performance bottlenecks, understanding how requests flow through your system, and troubleshooting errors and exceptions.
Overall, integrating distributed tracing into your application can provide valuable insights into the behavior of your distributed systems and help you identify and resolve issues more quickly.
Analyzing distributed tracing data
Analyzing distributed tracing data can provide valuable insights into the performance and behavior of your distributed systems. Here are some steps you can follow to analyze distributed tracing data:
Understand the structure of a trace
A trace consists of a sequence of spans representing the different work units involved in processing a single request. Each span has a start and end time and may have additional metadata such as tags, logs, and annotations.
Identify performance bottlenecks
Look for spans with long durations or high error rates, as these may indicate performance issues or errors in your application. You can also use tools such as histograms and heat maps to visualize the distribution of span durations and identify outliers.
Trace request flows
Use the trace data to understand how requests flow through your system, including which services and endpoints are involved in processing each request. This can help you identify your system’s dependencies and potential points of failure.
Correlate traces
Correlate traces across different services and systems to understand the end-to-end performance of your application. This can help you identify performance issues that span multiple services or systems.
Use context to investigate issues
Use the context the trace data provides, such as logs and annotations, to investigate issues and understand the root cause of performance problems.
Use aggregation to identify trends
Use aggregation to identify trends and patterns in your trace data over time. This can help you identify long-term performance issues or changes in application behavior.
Share insights with your team
Share your insights with your team to drive application performance and reliability improvements. Use the trace data to inform decisions about architecture, infrastructure, and application design.
Overall, analyzing distributed tracing data requires technical expertise and domain knowledge.
Scaling Distributed Tracing — Challenges
Scaling distributed tracing can present several challenges, including:
The volume of data
As the number of services and requests in your application grows, so does the volume of tracing data that needs to be collected and stored. This can put weight on your storage systems and increase costs, especially if you are using a commercial tracing solution.
Sampling
Most tracing systems rely on sampling to reduce the volume of tracing data. Sampling techniques selectively capture a subset of requests for tracing. However, choosing the correct sampling rate can be challenging, as too low a rate may miss critical issues, while too high a rate can overwhelm your tracing system and increase costs.
Compatibility with different technologies
Your application may use a mix of technologies and frameworks, each with its own instrumentation requirements and tracing formats. Ensuring that your tracing system is compatible with these technologies can be challenging.
Scalability of tracing infrastructure
As the volume of tracing data increases, so does the load on your tracing infrastructure. You may need to invest in additional hardware or a cloud-based tracing solution to ensure your tracing system can scale to handle this load.
Managing distributed traces
Distributed tracing involves collecting data from multiple sources, each with its own set of metadata and context. Managing and correlating this data across different services and systems can be challenging, especially as the complexity of your application grows.
Overall, scaling distributed tracing requires careful planning and attention to detail and may require significant hardware, infrastructure, and expertise investments.
Best practices for scaling distributed tracing
Here are some best practices for scaling distributed tracing:
1. Start with a clear understanding of your tracing needs
Before implementing a distributed tracing system, start with a clear understanding of your application’s tracing needs, including the level of detail required, the types of requests that need to be traced, and the volume of data that will be generated.
2. Use sampling
Sampling is a technique used to reduce the volume of tracing data by selectively capturing a subset of requests for tracing. Use sampling to ensure that your tracing system can handle the volume of data generated by your application without overwhelming your storage and processing systems.
3. Choose an appropriate sampling rate
Choose an appropriate sampling rate that captures enough data to identify issues while controlling storage and processing costs. The sampling rate should be based on the expected traffic volume, the resources available for tracing, and the business needs of your application.
4. Use distributed storage
Use distributed storage systems such as Cassandra, Elasticsearch, or HBase to store tracing data across multiple nodes or clusters. This can improve scalability and fault tolerance and reduce network latency.
5. Use a scalable backend
Ensure that your tracing backend can handle the volume of data generated by your application. This may involve investing in more powerful hardware or using cloud-based tracing solutions such as AWS X-Ray or Google Cloud Trace.
6. Optimize data storage
Optimize the storage of tracing data using data compression, partitioning, and retention policies. This can help reduce storage costs and improve the performance of your tracing system.
7. Use caching
Use caching to store frequently accessed tracing data in memory, reducing the need to retrieve it from a disk or a remote server. This can improve the performance of your tracing system and reduce latency.
8. Use distributed tracing libraries
Use distributed tracing libraries such as OpenTelemetry or Zipkin to standardize the tracing of requests across different technologies and frameworks. This can simplify the management of tracing data across your application and improve the accuracy of your tracing data.
9. Monitor and optimize
Continuously monitor and optimize your tracing system to meet your application’s needs. This may involve adjusting sampling rates, optimizing storage and processing, or investing in additional hardware or infrastructure.
Overall, scaling distributed tracing requires a comprehensive approach that includes careful planning, appropriate tools and techniques, and ongoing monitoring and optimization. By following these best practices, you can build a scalable, reliable, and effective tracing system that meets the needs of your application.
Conclusion
Distributed tracing is a crucial tool for understanding the performance and behavior of complex, distributed applications. It enables developers and operators to trace the flow of requests across multiple components and services, providing valuable insights into performance bottlenecks, errors, and other issues that can impact the user experience. Overall, distributed tracing is a critical tool for building, managing, and optimizing complex, distributed applications. By providing detailed insights into application behavior and performance, distributed tracing enables developers and operators to deliver better user experiences, improve scalability, and enhance collaboration between development and operations teams.
