This principle in the reliability pillar of the Google Cloud Architecture Framework helps you to assess your users' experience, and then map the findings to reliability goals and metrics.
This principle is relevant to the scoping focus area of reliability.
Principle overview
Observability tools provide large amounts of data, but not all of the data directly relates to the impacts on the users. For example, you might observe high CPU usage, slow server operations, or even crashed tasks. However, if these issues don't affect the user experience, then they don't constitute an outage.
To measure the user experience, you need to distinguish between internal system behavior and user-facing problems. Focus on metrics like the success ratio of user requests. Don't rely solely on server-centric metrics, like CPU usage, which can lead to misleading conclusions about your service's reliability. True reliability means that users can consistently and effectively use your application or service.
Recommendations
To help you measure user experience effectively, consider the recommendations in the following sections.
Measure user experience
To truly understand your service's reliability, prioritize metrics that reflect your users' actual experience. For example, measure the users' query success ratio, application latency, and error rates.
Ideally, collect this data directly from the user's device or browser. If this direct data collection isn't feasible, shift your measurement point progressively further away from the user in the system. For example, you can use the load balancer or frontend service as the measurement point. This approach helps you identify and address issues before those issues can significantly impact your users.
Analyze user journeys
To understand how users interact with your system, you can use tracing tools like Cloud Trace. By following a user's journey through your application, you can find bottlenecks and latency issues that might degrade the user's experience. Cloud Trace captures detailed performance data for each hop in your service architecture. This data helps you identify and address performance issues more efficiently, which can lead to a more reliable and satisfying user experience.