Let’s think about this from the perspective of microservices. A single request to a microservices-based system may traverse a hundred or more services running across a distributed cloud infrastructure and log request information to a great number of locations, making it nearly impossible to correlate events and effectively trace problematic requests. In the instance of slowness or failure, where do development and IT teams begin when trying to determine the root causes? How can they comfortably evaluate the system’s health and performance? The answer to both questions is to make the systems as observable as possible.
The “How” of End-To-End Observability
When systems are highly observable, DevOps personnel can efficiently evaluate the state of the system, which enables them to monitor its health, rectify issues, and improve applications over time. This can be accomplished by maximizing visibility across all components of the system.
One way to increase visibility within a modern system is to centralize log management. This involves ingesting log data from all services using a modern log management platform. Bringing the data together allows DevOps teams to analyze data easily, as opposed to futilely trying to derive insights from disparately structured log data spread across dozens or even hundreds of sources. Through centralized log management, DevOps teams can gain a holistic view of critical system details from a single, centralized location.
Along the same lines, distributed tracing can help provide a better, more holistic view of a user’s journey through a service-oriented application. With distributed tracing, trace IDs are leveraged to enable DevOps teams to isolate individual requests as they traverse multiple services. This type of event correlation makes it significantly easier to identify the problematic component in the instance of slowness or failure.