[[["易于理解","easyToUnderstand","thumb-up"],["解决了我的问题","solvedMyProblem","thumb-up"],["其他","otherUp","thumb-up"]],[["很难理解","hardToUnderstand","thumb-down"],["信息或示例代码不正确","incorrectInformationOrSampleCode","thumb-down"],["没有我需要的信息/示例","missingTheInformationSamplesINeed","thumb-down"],["翻译问题","translationIssue","thumb-down"],["其他","otherDown","thumb-down"]],["最后更新时间 (UTC):2024-12-30。"],[[["\u003cp\u003eThis principle focuses on assessing user experience to establish reliability goals and metrics within the Google Cloud Well-Architected Framework's reliability pillar.\u003c/p\u003e\n"],["\u003cp\u003eDistinguishing between internal system behavior and user-facing issues is crucial, as only the latter truly impacts service reliability.\u003c/p\u003e\n"],["\u003cp\u003ePrioritizing metrics that directly reflect user experience, such as query success ratio, application latency, and error rates, is more effective than solely relying on server-centric metrics like CPU usage.\u003c/p\u003e\n"],["\u003cp\u003eMeasuring user experience data directly from user devices or browsers is ideal; however, if not feasible, data collection should occur as close to the user as possible, like at a load balancer or frontend service.\u003c/p\u003e\n"],["\u003cp\u003eAnalyzing user journeys with tracing tools, like Cloud Trace, helps identify bottlenecks and latency issues that negatively impact user experience, thus improving overall service reliability.\u003c/p\u003e\n"]]],[],null,["# Define reliability based on user-experience goals\n\nThis principle in the reliability pillar of the\n[Google Cloud Well-Architected Framework](/architecture/framework)\nhelps you to assess your users' experience, and then map the findings to\nreliability goals and metrics.\n\nThis principle is relevant to the *scoping*\n[focus area](/architecture/framework/reliability#focus-areas)\nof reliability.\n\nPrinciple overview\n------------------\n\nObservability tools provide large amounts of data, but not all of the data\ndirectly relates to the impacts on the users. For example, you might observe\nhigh CPU usage, slow server operations, or even crashed tasks. However, if these\nissues don't affect the user experience, then they don't constitute an outage.\n\nTo measure the user experience, you need to distinguish between internal system\nbehavior and user-facing problems. Focus on metrics like the success ratio of\nuser requests. Don't rely solely on server-centric metrics, like CPU usage,\nwhich can lead to misleading conclusions about your service's reliability. True\nreliability means that users can consistently and effectively use your\napplication or service.\n\nRecommendations\n---------------\n\nTo help you measure user experience effectively, consider the recommendations\nin the following sections.\n\n### Measure user experience\n\nTo truly understand your service's reliability, prioritize metrics that reflect\nyour users' actual experience. For example, measure the users' query success\nratio, application latency, and error rates.\n\nIdeally, collect this data directly from the user's device or browser. If this\ndirect data collection isn't feasible, shift your measurement point\nprogressively further away from the user in the system. For example, you can use\nthe load balancer or frontend service as the measurement point. This approach\nhelps you identify and address issues before those issues can significantly\nimpact your users.\n\n### Analyze user journeys\n\nTo understand how users interact with your system, you can use tracing tools\nlike\n[Cloud Trace](/trace/docs/overview).\nBy following a user's journey through your application, you can find bottlenecks\nand latency issues that might degrade the user's experience. Cloud Trace\ncaptures detailed performance data for each *hop* in your service architecture.\nThis data helps you identify and address performance issues more efficiently,\nwhich can lead to a more reliable and satisfying user experience."]]