The main benefit of distributed tracing is visibility into real user transactions in one place regardless of the complexity of your underlying application or infrastructure. What are the benefits of distributed tracing? But distributed request tracing makes it possible. Keeping the game running smoothly would be unthinkable with traditional tracing methods. Now think of a microservice-based popular online video game with millions of users, which will need to keep track of each player’s location, every interaction they have with each other, the items they pick up in the game and a variety of other data generated during play.
Detailed stack traces and error messages in the event of a failure.Ī distributed tracing tool like Zipkin or Jaeger (both of which we will explore in more detail in a bit) correlates the data from all the spans and formats them into visualizations that are available on request through a web interface or provided automatically through alerting or AIOps tools.Tags to query and filter requests by session ID, database host, HTTP method, and other identifiers.Logs and events that provide context about the process’s activity.The name and address of the process handling the request.
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As the request moves through the host system, every operation performed on it (called a “span” or a “child span”) is tagged with that first request’s trace ID, as well as its own unique ID, plus the ID of the operation that originally generated the current request (called the “parent span”).Įach span represents one segment of the request’s path and includes important information related to the service performing the operation. When the user sends an initial request - adding an item to their cart, for example - it is assigned a unique trace ID. Tracing starts the moment an end user interacts with an application. To understand the process of distributed tracing, it helps to understand first how a single request is handled. In the pages that follow, we’ll take a deep dive into distributed tracing and the technologies used to make it possible in your enterprise.
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Anyone wishing to monitor the request (analysts, software reliability engineers, developers and others) can observe each iteration of a function and conduct performance monitoring by noting which instance of a function is causing the issue. Requests that come from an environment like this are nearly impossible to monitor using traditional, single-service methods.ĭistributed tracing solves this problem by tracking end-user requests through each service or module and providing a holistic view of the request. Therefore, it’s normal to have multiple instances of a single service running at the same time on different servers, in different locations and different environments. Microservices, because of the way they are constructed, scale independently from one another. Tracing is a basic and important process in software engineering to gather more information about an application’s performance, but it can be less effective when used with applications built on a distributed software architecture, such as microservices. (Visit the Splunk Observability page to learn more about the only full-stack, analytics-powered and OpenTelemetry-native observability solution.) This lets them identify any issues, including bottlenecks and bugs, that could be having a negative impact on the application’s performance and affect user experience. Distributed tracing is used by IT and DevOps teams to track requests or transactions through the application they are monitoring - gaining vital end-to-end observability into that journey. Distributed tracing, also known as distributed request tracing, is a method of monitoring and observing service requests in applications built on a microservices architecture.
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