What Is Distributed Tracing?
Distributed tracing is vital to manage the performance of applications that use microservices and containerization
What Is Distributed Tracing?
Distributed Tracing Definition
Organizations are increasingly developing and deploying their applications using service-oriented architecture (SOA) and are leveraging microservices, containerization, and distributed deployments for increased agility and flexibility in development, testing, and production phases. However, this leads to an increased number of components in an application, and it becomes chaotic to pinpoint performance issues and troubleshoot incidents.
Distributed tracing is a method to helps engineering teams with application monitoring, especially applications architected using microservices. It helps pinpoint issues and identify root causes to address failures and suboptimal performance.
In an application consisting of several microservices, a request may require invoking several services. Accordingly, a failure in one service may trigger failures in other services. To understand clearly how each service is performing with respect to servicing a request, distributed tracing tracks each request end-to-end and assigns a unique trace ID to identify each request and associated trace data. In general, this is achieved by adding instrumentation to the application code or by deploying auto-instrumentation in the application environment.
What Is a Trace?
A trace represents how a request spans across various services of an application. A request can result from an end user’s action or due to an internal trigger such as a scheduled job.
A trace is a collection of one or more spans. A span represents a unit of work done between two services and includes request and response data, timespan, and metadata such as logs and tags. Spans within a trace also have parent-child relationships representing how various services contributed to serving a request.
Distributed tracing also helps identify common paths in serving a request and services most critical to the business. Using this information, an organization can deploy more resources to encapsulate essential services from scenarios that can result in disruption.
Distributed Tracing vs. Logging
While distributed tracing tracks each request and its interaction with services and components in the application environment, logging continually captures the state of a service, component, or host machine. However, logging is specific to each service or host machine and can generate substantial amounts of data. Generally, log management tools gather logs from various sources and use structured logging to make it easier to sift through the data. On the other hand, distributed tracing identifies where the issue is, but it might not necessarily provide enough insights to understand the problem deeply. In such cases, log data can help you dig deeper into the problem as it provides more granular data. This is also why some application performance monitoring (APM) tools attach relevant log data to traces.
Other Core Concepts and Terminology
Distributed tracing is a method used to monitor and understand the performance and behavior of complex, distributed systems, especially those built using microservices.
1. Spans
A span is a fundamental unit of work in a trace. It represents a single operation or a segment of the request's journey. Each span has a name, a start time, a duration, and metadata (such as tags and logs) that provide context about the operation. Spans can be nested within each other to show the hierarchical relationship between operations. For example, a span for a database query might be nested within a span for an API call.
2. Transactions
A transaction is a higher-level concept that represents a complete business operation or user action. It can consist of multiple traces and spans. For instance, a transaction might be a user placing an order, which involves multiple API calls, database queries, and other operations. Transactions help in understanding the end-to-end performance and behavior of a specific user action.
3. Trace Context
Trace context is a set of data that is propagated through the system to maintain the continuity of a trace. It includes identifiers such as the trace ID, span ID, and parent span ID. These identifiers are passed along with the request as it moves from one service to another, allowing the tracing system to correlate and reconstruct the entire trace. Trace context is crucial for ensuring that all spans within a trace are linked correctly.
How They Work Together
- Traces provide a comprehensive view of a request's journey.
- Spans break down the trace into individual operations, each with its own start and end time.
- Transactions group related traces and spans to represent a complete user action.
- Trace context ensures that all spans are correctly linked and can be reconstructed into a coherent trace.
Understanding these concepts is essential for effectively using distributed tracing to diagnose and optimize the performance of modern, distributed applications.
Distributed Tracing Benefits
Distributed tracing is a common feature among some of the best application performance monitoring (APM) tools. APM tools drive the following benefits using distributed tracing:
Visibility: Distributed tracing provides end-to-end visibility into the application environment. Some APM tools leverage distributed tracing to represent service dependencies and the overall application environment visually. This is especially beneficial if an application comprises hundreds of microservices running across multiple data centers and availability regions. As the number of services and the infrastructure components increases, it becomes chaotic to manage, maintain, and track their contribution to the application environment. Visualizing application environments brings clarity to this chaos and aids in quickly pointing out services responsible for problems.
Performance: As each service’s request and response times are tracked, it becomes easier to understand performance and then scale or troubleshoot only the individual services required to improve overall performance and system health. APM tools also provide in-depth visualization of performance metrics to help identify performance variance and response times in different circumstances and determine a baseline performance. For example, when a change is applied to the application environment, the performance impact of the change can be benchmarked to analyze the systemic effects of changes on the overall performance in the future.
Root Cause Analysis: A distributed application could be serving hundreds of thousands of requests per day; for example, consider a distributed e-commerce application serving hundreds of thousands of customers a day. Similarly, this produces vast amounts of trace data, and unless the traces are correlated, distributed tracing can provide little value. Because an error or issue in one service or component may trigger subsequent failures in other services, analyzing and correlating traces is critical to identify root causes and fix problems early on. Some APM tools continuously correlate traces and related events to report performance issues and bottlenecks proactively.
Distributed Tracing Tools and Standards
Distributed tracing is a critical tool for monitoring and optimizing the performance of modern, distributed systems, particularly those built with microservices. Popular tools offer comprehensive tracing capabilities that support large-scale, cloud-native applications or provide a simpler yet robust solution for both small and large-scale systems.
OpenTelemetry a CNCF project, aims to standardize telemetry data collection by providing a single set of APIs and libraries that can integrate with various backends, including Jaeger and Zipkin.
Advanced concepts like eBPF-based tracing provide low-overhead, high-performance monitoring at the kernel level, ideal for high-performance environments. Middleware solutions simplify the implementation of distributed tracing by handling trace context propagation and other cross-cutting concerns, reducing the need for manual instrumentation in each service. Together, these tools and standards help organizations gain deep insights into their application's behavior, diagnose issues, and optimize performance in complex, distributed architectures.
Distributed Tracing vs. Other Observability Methods
Several observability methods are commonly used to monitor and understand the behavior of modern, distributed systems: distributed tracing, traditional tracing, logging, and metrics. Each method has its own strengths and is best suited for different aspects of system observability.
Distributed Tracing
Distributed tracing is a method for monitoring and analyzing the flow of requests through a distributed system. It provides a detailed, end-to-end view of how a request travels through various services, helping to identify performance bottlenecks and errors.
- Custom Distributed Trace Instrumentation: This involves adding specific code to your application to generate trace data. This can be done using open-source tools like OpenTelemetry, which provide a standardized way to collect and export trace data.
- Probabilistic Sampling: To manage the volume of trace data, probabilistic sampling is often used. This technique involves capturing only a subset of traces, which can help reduce the overhead and storage costs while still providing valuable insights.
- Root Cause Analysis: Distributed tracing is particularly useful for root cause analysis because it can pinpoint where a request is failing or slowing down. By following the path of a request, you can identify the exact service or component causing the issue.
Logging
Logging involves recording events and messages that occur during a system's execution. Logs are typically used for debugging, auditing, and compliance purposes.
- Logs: Logs provide a detailed record of what happened in the system, including errors, warnings, and informational messages. They are essential for understanding the system's state at a specific point in time.
- Logging: The process of generating and managing logs. Logs can be structured (e.g., JSON) or unstructured (e.g., plain text), and they can be aggregated and analyzed using tools like ELK (Elasticsearch, Logstash, Kibana) or Splunk.
Metrics
Metrics are numerical values that represent a system's state over time. They are used to monitor a system's performance and health.
- Metrics: Metrics can include things like request latency, error rates, and resource utilization. They are often visualized using dashboards and can be used to set up alerts for when certain thresholds are exceeded.
- Observability Strategy: An observability strategy should include a mix of metrics, logs, and traces to provide a comprehensive view of the system. Metrics are particularly useful for monitoring high-level system performance and for setting up alerts.
Complementary Roles in Observability
- Distributed Tracing and Metrics: While metrics provide a high-level overview of system performance, distributed tracing can drill down into specific requests to understand why certain metrics behave the way they do. For example, if you notice a spike in request latency, distributed tracing can help you identify which service or component is causing the delay.
- Distributed Tracing and Logging: Logs provide detailed, low-level information about what is happening in the system, while distributed traces provide a high-level, end-to-end view. By correlating trace IDs with log entries, you can get a more complete picture of a request's journey and the specific events that occurred along the way.
- Metrics and Logging: Metrics can help you identify when something is wrong, and logs can help you understand why. For example, if a metric shows an increase in error rates, you can use logs to find the specific error messages and stack traces that are causing the issue.
What Is Distributed Tracing?
Distributed Tracing Definition
Organizations are increasingly developing and deploying their applications using service-oriented architecture (SOA) and are leveraging microservices, containerization, and distributed deployments for increased agility and flexibility in development, testing, and production phases. However, this leads to an increased number of components in an application, and it becomes chaotic to pinpoint performance issues and troubleshoot incidents.
Distributed tracing is a method to helps engineering teams with application monitoring, especially applications architected using microservices. It helps pinpoint issues and identify root causes to address failures and suboptimal performance.
In an application consisting of several microservices, a request may require invoking several services. Accordingly, a failure in one service may trigger failures in other services. To understand clearly how each service is performing with respect to servicing a request, distributed tracing tracks each request end-to-end and assigns a unique trace ID to identify each request and associated trace data. In general, this is achieved by adding instrumentation to the application code or by deploying auto-instrumentation in the application environment.
What Is a Trace?
A trace represents how a request spans across various services of an application. A request can result from an end user’s action or due to an internal trigger such as a scheduled job.
A trace is a collection of one or more spans. A span represents a unit of work done between two services and includes request and response data, timespan, and metadata such as logs and tags. Spans within a trace also have parent-child relationships representing how various services contributed to serving a request.
Distributed tracing also helps identify common paths in serving a request and services most critical to the business. Using this information, an organization can deploy more resources to encapsulate essential services from scenarios that can result in disruption.
Distributed Tracing vs. Logging
While distributed tracing tracks each request and its interaction with services and components in the application environment, logging continually captures the state of a service, component, or host machine. However, logging is specific to each service or host machine and can generate substantial amounts of data. Generally, log management tools gather logs from various sources and use structured logging to make it easier to sift through the data. On the other hand, distributed tracing identifies where the issue is, but it might not necessarily provide enough insights to understand the problem deeply. In such cases, log data can help you dig deeper into the problem as it provides more granular data. This is also why some application performance monitoring (APM) tools attach relevant log data to traces.
Other Core Concepts and Terminology
Distributed tracing is a method used to monitor and understand the performance and behavior of complex, distributed systems, especially those built using microservices.
1. Spans
A span is a fundamental unit of work in a trace. It represents a single operation or a segment of the request's journey. Each span has a name, a start time, a duration, and metadata (such as tags and logs) that provide context about the operation. Spans can be nested within each other to show the hierarchical relationship between operations. For example, a span for a database query might be nested within a span for an API call.
2. Transactions
A transaction is a higher-level concept that represents a complete business operation or user action. It can consist of multiple traces and spans. For instance, a transaction might be a user placing an order, which involves multiple API calls, database queries, and other operations. Transactions help in understanding the end-to-end performance and behavior of a specific user action.
3. Trace Context
Trace context is a set of data that is propagated through the system to maintain the continuity of a trace. It includes identifiers such as the trace ID, span ID, and parent span ID. These identifiers are passed along with the request as it moves from one service to another, allowing the tracing system to correlate and reconstruct the entire trace. Trace context is crucial for ensuring that all spans within a trace are linked correctly.
How They Work Together
- Traces provide a comprehensive view of a request's journey.
- Spans break down the trace into individual operations, each with its own start and end time.
- Transactions group related traces and spans to represent a complete user action.
- Trace context ensures that all spans are correctly linked and can be reconstructed into a coherent trace.
Understanding these concepts is essential for effectively using distributed tracing to diagnose and optimize the performance of modern, distributed applications.
Distributed Tracing Benefits
Distributed tracing is a common feature among some of the best application performance monitoring (APM) tools. APM tools drive the following benefits using distributed tracing:
Visibility: Distributed tracing provides end-to-end visibility into the application environment. Some APM tools leverage distributed tracing to represent service dependencies and the overall application environment visually. This is especially beneficial if an application comprises hundreds of microservices running across multiple data centers and availability regions. As the number of services and the infrastructure components increases, it becomes chaotic to manage, maintain, and track their contribution to the application environment. Visualizing application environments brings clarity to this chaos and aids in quickly pointing out services responsible for problems.
Performance: As each service’s request and response times are tracked, it becomes easier to understand performance and then scale or troubleshoot only the individual services required to improve overall performance and system health. APM tools also provide in-depth visualization of performance metrics to help identify performance variance and response times in different circumstances and determine a baseline performance. For example, when a change is applied to the application environment, the performance impact of the change can be benchmarked to analyze the systemic effects of changes on the overall performance in the future.
Root Cause Analysis: A distributed application could be serving hundreds of thousands of requests per day; for example, consider a distributed e-commerce application serving hundreds of thousands of customers a day. Similarly, this produces vast amounts of trace data, and unless the traces are correlated, distributed tracing can provide little value. Because an error or issue in one service or component may trigger subsequent failures in other services, analyzing and correlating traces is critical to identify root causes and fix problems early on. Some APM tools continuously correlate traces and related events to report performance issues and bottlenecks proactively.
Distributed Tracing Tools and Standards
Distributed tracing is a critical tool for monitoring and optimizing the performance of modern, distributed systems, particularly those built with microservices. Popular tools offer comprehensive tracing capabilities that support large-scale, cloud-native applications or provide a simpler yet robust solution for both small and large-scale systems.
OpenTelemetry a CNCF project, aims to standardize telemetry data collection by providing a single set of APIs and libraries that can integrate with various backends, including Jaeger and Zipkin.
Advanced concepts like eBPF-based tracing provide low-overhead, high-performance monitoring at the kernel level, ideal for high-performance environments. Middleware solutions simplify the implementation of distributed tracing by handling trace context propagation and other cross-cutting concerns, reducing the need for manual instrumentation in each service. Together, these tools and standards help organizations gain deep insights into their application's behavior, diagnose issues, and optimize performance in complex, distributed architectures.
Distributed Tracing vs. Other Observability Methods
Several observability methods are commonly used to monitor and understand the behavior of modern, distributed systems: distributed tracing, traditional tracing, logging, and metrics. Each method has its own strengths and is best suited for different aspects of system observability.
Distributed Tracing
Distributed tracing is a method for monitoring and analyzing the flow of requests through a distributed system. It provides a detailed, end-to-end view of how a request travels through various services, helping to identify performance bottlenecks and errors.
- Custom Distributed Trace Instrumentation: This involves adding specific code to your application to generate trace data. This can be done using open-source tools like OpenTelemetry, which provide a standardized way to collect and export trace data.
- Probabilistic Sampling: To manage the volume of trace data, probabilistic sampling is often used. This technique involves capturing only a subset of traces, which can help reduce the overhead and storage costs while still providing valuable insights.
- Root Cause Analysis: Distributed tracing is particularly useful for root cause analysis because it can pinpoint where a request is failing or slowing down. By following the path of a request, you can identify the exact service or component causing the issue.
Logging
Logging involves recording events and messages that occur during a system's execution. Logs are typically used for debugging, auditing, and compliance purposes.
- Logs: Logs provide a detailed record of what happened in the system, including errors, warnings, and informational messages. They are essential for understanding the system's state at a specific point in time.
- Logging: The process of generating and managing logs. Logs can be structured (e.g., JSON) or unstructured (e.g., plain text), and they can be aggregated and analyzed using tools like ELK (Elasticsearch, Logstash, Kibana) or Splunk.
Metrics
Metrics are numerical values that represent a system's state over time. They are used to monitor a system's performance and health.
- Metrics: Metrics can include things like request latency, error rates, and resource utilization. They are often visualized using dashboards and can be used to set up alerts for when certain thresholds are exceeded.
- Observability Strategy: An observability strategy should include a mix of metrics, logs, and traces to provide a comprehensive view of the system. Metrics are particularly useful for monitoring high-level system performance and for setting up alerts.
Complementary Roles in Observability
- Distributed Tracing and Metrics: While metrics provide a high-level overview of system performance, distributed tracing can drill down into specific requests to understand why certain metrics behave the way they do. For example, if you notice a spike in request latency, distributed tracing can help you identify which service or component is causing the delay.
- Distributed Tracing and Logging: Logs provide detailed, low-level information about what is happening in the system, while distributed traces provide a high-level, end-to-end view. By correlating trace IDs with log entries, you can get a more complete picture of a request's journey and the specific events that occurred along the way.
- Metrics and Logging: Metrics can help you identify when something is wrong, and logs can help you understand why. For example, if a metric shows an increase in error rates, you can use logs to find the specific error messages and stack traces that are causing the issue.
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