What Is Distributed Tracing?

Software developers can implement distributed tracing systems in almost any cloud-native environment, as well as record distributed traces that the cloud applications generate. Moreover, tracing tools support numerous programming languages and software stacks, allowing software teams to monitor and collect performance data for different applications on the same platform. 

Development teams use distributed tracing to improve observability, as well as solve performance issues that conventional software debugging and monitoring tools can’t help with. 

The following are more benefits of distributed tracing.

Accelerate software troubleshooting

Modern applications rely on numerous microservices to exchange data and fulfill service requests across distributed systems. Troubleshooting performance issues in a microservice-based architecture is significantly more challenging than in a monolithic software application. Unlike a monolithic application, the root cause of a specific software problem might not be apparent—the overlapping and complex interactions between multiple software modules can make it difficult to diagnose issues. 

With distributed tracing, software teams can monitor data that passes through complex paths connecting various microservices and data storage. Using distributed tracing tools, software teams track requests and visualize data propagation paths with precision. Software teams can solve performance issues promptly and minimize service disruptions. 

Improve developer collaboration

Several developers are often involved in building a cloud application, with each responsible for one or several microservices. The software development process slows down if developers cannot trace data exchanged by microservices. With distributed tracing systems developers can collaborate by providing telemetry data, such as logs and traces, for every service request the microservice makes. Developers can accurately respond to bugs and other software issues discovered during testing and production. 

Reduce time to market

Organizations deploying distributed tracing platforms can streamline and accelerate efforts to release software applications for end users. Software teams review distributed traces to gain insight that speeds up software development, minimizes development costs, understands user behaviors, and improves market readiness. 

When using applications, users initiate service requests and different application components process the request. 

Consider a user making a ticket booking in an online movie booking application. The user enters their contact details, movie details, and payment information and chooses Book Now. A request is created that goes to:

• Microservice A that validates the user-entered data.
• Microservice B that takes the data from A and creates a record in the customer database.
• Microservice C that takes the data from B and validates payment.
• Microservice D that takes the data from C, allocates a seat, and generates movie ticket data.
• Microservice E that takes the data from D and creates a formatted ticket PDF file.

A response containing the ticket PDF is then returned back up the chain of microservices from E to D to C to B to A, until it eventually reaches the user. The above example is simple—a request often passes through several dozen microservices and even chains of third-party software components outside the application. This makes the process increasingly complex.

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

Span

When processing a service request, an application might take several actions. These actions are represented as spans in distributed tracing. For example, a span might be an API call, user authentication, or enabling storage access. If a single request results in several actions, the initial (or parent) span may branch into several child spans. These nested layers of parent and child spans form a continuous logical representation of steps taken to accomplish the service request.

Trace ID

The distributed tracing system assigns a unique ID to every request in order to track it. Each span inherits the same trace ID from the original request it belongs to. Spans are also tagged with a unique span ID that helps the tracing system consolidate the metadata, logs, and metrics it collects. 

Metric collection

As each span passes through different microservices, it appends metrics that provide developers with deep and precise insights into the software behavior. You can collect error rate, timestamp, response time, and other metadata with the spans. After the trace completes an entire cycle, the distributed tracing tool consolidates all data collected. 

For example, an API call is evaluated with response time, error status, and breakdown of secondary functions fulfilled by multiple third-party services. The tracing tool turns the data into visual forms, highlighting key indicators and performance summaries. This way, site reliability engineers can rapidly identify errors, inspect critical data elements, and collaborate with development teams to remediate performance issues and ensure compliance with Service Level Agreements (SLAs).

Logging is a practice of recording specific events that occur when an application runs. Logging tools collect timestamped events—such as system errors, user interactions, communication statuses, and other metrics—to help development teams detect system anomalies. Generally, there are two types of logging: 

• Centralized logging collects all recorded activities and stores them in a single location.
• Distributed logging stores log files in separate locations on the cloud. 

Both logging methods provide a static overview of incidents that show developers what happened in the application. In contrast, distributed tracing provides an audit trail that clarifies why an incident occurred by correlating various telemetry data collected throughout a service request's period. Distributed tracing may use logging and other data collection methods for tracing a specific service request. 

AWS X-Ray is a distributed tracing platform that helps software developers trace user requests and identify bottlenecks in their cloud applications. Organizations use X-Ray to visualize application metrics and improve workload availability. With AWS X-Ray you can:

• Integrate with all applications running on Amazon Elastic Compute Cloud (EC2), Amazon EC2 Container Service (Amazon ECS), AWS Lambda, and AWS Elastic Beanstalk.
• Set the appropriate sampling rate to provide end-to-end visibility for the traces.
• Visualize aggregated data with a service map, displaying key metrics such as latency and failure rates. 

Get started with distributed tracing on AWS by creating an account today.