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A performance test is only useful when its workload resembles real usage. Start by studying traffic patterns: peak hours, request mix, data size, session length, and the paths that matter to users. Capture baseline measurements from production or staging, then define targets for response time, throughput, error rate, and resource use. Decide whether you are testing capacity, stability, latency spikes, or recovery after a failure, because each goal needs a different shape of load. Build a data set that is large enough to trigger caching, indexing, and queueing behavior without exposing private information. Warm up the environment, monitor application and infrastructure metrics together, and record deployments or network changes during the run. When results are surprising, repeat the test before drawing conclusions and compare distributions instead of single averages. Share the report with clear graphs, assumptions, and limits. The outcome should guide practical decisions about scaling, bottlenecks, and priorities for the next release.
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