See significant changes in performance, such as a complete service failure that causes all of the sampled transactions to result in errors.Understand general trends associated with the most common types of user requests.For example, tracing with sampling would, at best, allow IT and SRE teams to: But because it’s only taking samples of transactions, not looking at all of them, you don’t have full visibility. Probabilistic sampling provides a little insight into what is happening. This captures only a small - and arbitrary - portion of all transactions. The tracing tools that did exist performed probabilistic sampling. In old-school applications that ran as monoliths, tracing was possible, but the need to understand what was happening was less important: There were fewer moving parts through which requests had to flow as the application processed them. Let’s first look at traditional tracing, how it used to work, and then we can start to understand why this isn’t a great solution today. It doesn’t work, though, when used with applications built on a distributed software architecture, such as microservices. The fundamental goal behind tracing - understanding transactions - is always the same. How to trace performance problems to specific lines in the application source code.Teams who develop and manage monolithic applications have long used traces to understand the performance of applications: Detailed stack traces and error messages in the event of a failure.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.So, each span includes important information related to the service performing the operation, such as: Some identifier or tag to add additional information about the request, like the particular version of microservice that generated the span.Įach span represents one segment of the request’s path.A trace ID to correlate them to the specific user transaction involved.The trace is made up of a collection of spans - each span a single operation, which contains: As the request moves through the host system, every operation performed on it (span) is tagged a few items: One trace represents one user interaction. You send an initial request - adding an item to your cart, for example - and that is assigned a unique trace ID. Tracing starts the moment a user interacts with an application. A trace is a collection of transactions (spans) that represent a unique user or API transaction that is handled by an application and its constituent services. It’s the third pillar- traces - that may be less familiar.
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