A Brief Overview of Distributed Tracing

2025/03/05

What is Distributed Tracing?

Developing and monitoring applications with traditional monolithic architecture is simple, but as applications scale, innovation and development in such platforms become challenging. For this reason, most modern applications and software platforms are developed using a microservices architecture. By leveraging modular implementation, microservices architecture addresses many of these challenges. Components, or services, are designed and developed independently in small units, each with its own interfaces implemented for a specific functionality.

Given this modular architecture, debugging such systems presents numerous challenges, as services operate in a distributed manner, and each request involves a sequence of API calls across multiple services. Distributed tracing is a method for comprehensively examining the path of a request as it travels from the frontend to the backend. For example, when a user clicks a button in the frontend, distributed tracing tracks the request until it reaches the backend and the database service.

Distributed tracing systems provide a detailed view of how multiple services work together to process a single request.

Key Components of Distributed Tracing

A distributed tracing system consists of three main components:

Trace: The complete end-to-end journey of a user request as it moves through different services.
Span: Each operation within a trace is called a span, which includes start time, end time, and associated metadata.
Context Propagation: The transfer of request-related information (trace and span identifiers) across different services during execution.

Distributed tracing platforms begin collecting data as soon as a request is sent, such as when a user submits a purchase order. This process generates a unique trace ID and an initial parent span. The trace represents the entire execution path of the request, while each span within the trace represents a specific unit of work, such as an API call, user authentication, or a database query. Each span contains a trace ID, a span ID, execution duration, error data, and additional metadata.

By analyzing the execution time of spans, it becomes possible to identify which service or span takes the longest and diagnose errors within each span. This process is typically visualized using a flame graph, where the horizontal axis represents the execution time of each span, and the vertical axis represents the call stack. Using this graph, slower services and factors affecting overall system performance can be identified.

Flame Graph

Difference Between Tracing and Monitoring

A common misconception in the tech industry today is using tracing tools interchangeably with monitoring tools. While monitoring focuses on collecting predefined metrics to assess the overall health of services and notify users when thresholds are exceeded, tracing is dedicated to detecting anomalies, measuring request execution times in each service, and analyzing how services interact with one another. This data helps improve service performance and facilitates debugging.

Distributed Tracing Standards

As the need for tracing grew, several open-source tools and methods emerged to integrate tracing capabilities into various services in a standardized manner:

OpenTracing: Provides an API that enables developers to generate traces within their services.
OpenCensus: Offers libraries for multiple programming languages to collect trace and metric data.
OpenTelemetry: A comprehensive and widely adopted standard that combines OpenTracing and OpenCensus. It provides APIs, libraries, and agents to collect trace, metric, and log data.

Advantages and Disadvantages of Distributed Tracing

Advantages

Speeds up software debugging

Provides better visibility into service dependencies by analyzing traces
Helps measure and reduce the time required to complete user operations
Improves collaboration between software teams by quickly identifying the root cause of issues

Challenges in Implementing Distributed Tracing

Despite its advantages, implementing distributed tracing presents several challenges:

Instrumenting services accurately: One of the biggest challenges is manually implementing distributed tracing. This requires modifying the code of each service to generate traces. While this method offers greater control over trace generation, it is time-consuming and prone to errors, especially in large and complex systems. Additionally, since different services may be developed by separate teams, inconsistencies in trace generation can arise. To overcome this, it is recommended to use tools that automate trace generation by injecting the necessary tracing code into the application.
Managing large volumes of data: High-traffic systems generate significant amounts of trace data, making data management a key challenge.
Ensuring data security: Trace data may contain sensitive information, requiring careful handling to maintain security and compliance.