What is distributed tracing?

The practice of observing data requests as they travel through a distributed system forms the foundation of distributed tracing. Contemporary software architectures typically consist of multiple services—small, independent components that continuously interact and share data via APIs to accomplish complex tasks. Distributed tracing allows developers to trace and visually follow a request's path as it moves across different processes, services, or components. This visibility proves invaluable for identifying errors, resolving bugs, and addressing performance bottlenecks.

What are the benefits of distributed tracing?

Distributed tracing systems can be deployed in virtually any environment, and applications can generate traces whether they run on-premises, in cloud-native settings, or across hybrid infrastructures. The ability to process data across distributed infrastructure makes these tools highly flexible. Tracing tools accommodate numerous programming languages and software stacks, enabling teams to monitor and gather performance metrics for multiple applications within a single environment.

Development teams leverage distributed tracing to enhance observability and resolve performance challenges that traditional debugging and monitoring solutions cannot address. Operations teams and systems administrators use distributed tracing to track application performance, identify optimization opportunities, and respond to incidents.

Accelerate software troubleshooting

Modern applications depend on numerous processes exchanging data and fulfilling requests across distributed systems. Diagnosing performance problems in such intricate architectures presents far greater difficulty than troubleshooting monolithic applications. In distributed systems, the underlying cause of a specific issue may not be immediately obvious. The intricate and overlapping interactions among multiple software modules can obscure diagnosis.

Distributed tracing permits software teams to examine data traveling through complex pathways connecting various processes, applications, data storage, and other infrastructure components. Using distributed tracing tools, teams can track requests and map data propagation routes with high precision. This capability allows teams to resolve performance issues swiftly and reduce service interruptions.

Improve developer collaboration

Building applications typically involves multiple developers, each responsible for one or more modules. When developers cannot trace data exchanged between systems, the development process becomes slower. Distributed tracing enables developers to work together more effectively by sharing telemetry data—including logs and traces—for each service request their module generates. This approach allows developers to respond accurately to bugs and other issues discovered during testing and production phases.

Reduce time to market

Organizations that implement distributed tracing platforms can expedite and streamline the process of releasing software applications to users. By examining distributed traces, software teams gain insights that accelerate development, lower costs, clarify user behaviors, and enhance readiness for market launch.

How does end-to-end distributed tracing work in a microservices architecture?

When users interact with applications, they initiate service requests that various application components process. Consider a practical example: a user booking a movie ticket through an online application. The user provides contact details, movie information, and payment data, then selects Book Now.

  • Microservice A validates the data entered by the user
  • Microservice B takes data from A and creates a customer database record
  • Microservice C takes data from B and verifies the payment
  • Microservice D takes data from C, reserves a seat, and generates ticket information
  • Microservice E takes data from D and produces a formatted ticket PDF

The response containing the ticket PDF then travels back through the microservice chain from E to D to C to B to A before reaching the user. This example represents a simplified scenario. In production environments, requests frequently pass through dozens of microservices and chains of third-party software components external to the application, or form directed acyclic graphs. This complexity increases substantially.

Distributed tracing systems monitor these interactions between service requests and other microservices and software components in the distributed computing environment. A distributed trace captures the timeline and all actions occurring between request generation and response receipt. Software teams use traces to follow data movement through multiple microservices that the initial request engages with.

Span

When an application processes a service request, it may perform several actions. Distributed tracing represents these actions as spans. A span might represent an API call, user authentication, or storage access activation. When a single request triggers multiple actions, the initial (or parent) span may divide into several child spans. These hierarchical layers of parent and child spans create a continuous logical representation of the steps required to complete the service request.

Trace ID

The distributed tracing system assigns a unique identifier to every request to enable tracking. Each span inherits the same trace ID from the original request to which it belongs. Spans receive additional tagging with a unique span ID that helps the tracing system consolidate the metadata, logs, and metrics it gathers.

Metric collection

As each span moves through different microservices, it accumulates metrics that furnish developers with detailed and precise understanding of software behavior. Distributed tracing depends on gathering error rate, timestamp, response time, and other metadata alongside spans. Once the trace completes its full cycle, the distributed tracing tool aggregates all collected data.

For instance, an API call receives evaluation based on response time, error status, and breakdown of secondary functions delivered by multiple third-party services. The tracing tool converts the data into visual representations, emphasizing key indicators and performance summaries, to illustrate how services interact. This capability allows site reliability engineers to quickly pinpoint errors, examine critical data elements, and work with development teams to address performance issues and maintain alignment with Service Level Objectives (SLOs). Distributed tracing monitors metrics that correspond with these performance objectives and can feed back into metrics to uphold Service Level Agreements (SLAs).

What are the different types of distributed tracing?

Teams employ distributed tracing tools to monitor, examine, and enhance applications.

Span-level tracing

Span-level tracing, also referred to as code tracing, represents a software process that examines source code flow in an application during a specific function's execution. It assists developers in comprehending the logical progression of code and discovering unfamiliar issues through trace data examination. For example, developers apply code tracing to confirm that the service request has triggered steps to execute a database query. Should certain software functions fail to respond, the tracing system collects the appropriate error status and highlights the response time.

End-to-end tracing

End-to-end tracing permits development teams to monitor data transformation along the service request path, crossing service boundaries. When an application sends a request, it transmits data to other distributed software components for additional processing. Developers apply tracing tools to track and document changes that critical data undergoes from beginning to end. This approach delivers an application-focused perspective of requests moving through the application.

What are distributed tracing standards?

Distributed tracing standards establish a common framework and software tools for developers. These standards facilitate monitoring, visualization, and analysis of service requests in contemporary application environments. By standardizing distributed tracing workflows, software teams can implement request-tracing while avoiding vendor lock-in.

OpenTracing

OpenTracing represents a historical, deprecated open source distributed tracing specification maintained by the Cloud Native Computing Foundation (CNCF). OpenTracing concentrated on enabling developers to produce traces using an instrumentation API. This capability allowed developers to generate distributed traces from various sections of the code base, library, or other dependencies.

OpenCensus

OpenCensus constituted a historical project featuring multi-language libraries capable of extracting software metrics and transmitting them to backend systems for examination. Developers utilized the provided API to control how traces were generated and collected. Differing from OpenTracing, developers engaged with OpenCensus from a single project repository rather than individual code bases and libraries.

OpenTelemetry

OpenTelemetry merges OpenTracing and OpenCensus and has become the predominant standard for observability instrumentation. It incorporates the strongest aspects of both standards to deliver a thorough distributed tracing framework. OpenTelemetry furnishes comprehensive software development kits, APIs, libraries, and other instrumentation tools that facilitate distributed tracing implementation.

What's the difference between distributed tracing and logging?

Logging involves documenting specific events that happen when an application executes. Logging tools capture timestamped events—such as system errors, user interactions, and communication statuses—to assist development teams in identifying system irregularities. Two primary logging approaches exist:

  • Centralized logging gathers all recorded activities and stores them in one location
  • Distributed logging combines log files from multiple locations

Both logging approaches offer a static snapshot of incidents that demonstrate what occurred in the application. Distributed tracing, by contrast, furnishes an audit trail that explains why an incident happened by connecting various telemetry data gathered throughout a service request's lifecycle. Distributed tracing may incorporate logging and other data collection methods for tracking a specific service request.

What's the difference between program tracing and distributed tracing?

Program tracing constitutes a method allowing developers to examine the addresses of instructions and variables invoked by a single process in an active application. When a software application runs, it processes each line of code residing in a specific allocated memory space. The application also processes variables kept in machine memory. Examining changes in program and data memories becomes challenging without automated tools. Program tracing enables software teams to identify deep-seated performance issues like memory overflow, excessive resource consumption, and blocking logic operations.

Distributed tracing, conversely, inspects various services across a distributed application, emphasizing dependencies, spans, and request flow between components.

What are the challenges of distributed tracing?

Although distributed tracing has made it easier for developers to diagnose, debug, and fix software issues, several challenges persist that software teams should consider when selecting tracing tools.

Manual instrumentation

Certain tracing tools demand that software teams manually instrument their code to produce the required traces. When developers modify code for tracing purposes, risks emerge including coding errors that compromise production releases. Additionally, the absence of automatic instrumentation complicates tracing, creating delays and potentially generating inaccurate data collection.

Limited frontend coverage

Developers may lack comprehensive visibility into performance issues if their tracing tools restrict analysis to backend systems. In certain situations, the distributed tracing system only begins collecting data when the first backend service receives the request. Consequently, developers cannot identify and examine issues originating from frontend services during the corresponding user session.

Random sampling

Some tools prevent software teams from prioritizing tracing, restricting observability to randomly sampled traces. When sample data remains limited, organizations require supplementary software troubleshooting methods to identify major issues that the tracing tool misses.

How can AWS help with your distributed tracing requirements?

Amazon CloudWatch functions as an intelligent observability service providing a unified perspective of your generative AI agents, applications, workloads, and supporting infrastructure. CloudWatch integrates metrics, logs, and traces to enable rapid comprehension of relationships, identification of performance bottlenecks, and discovery of hidden dependencies—without requiring navigation between separate tools.

Source: AWS News Blog