Start by describing a systematic process: instrument the client to log each HTTP request with a correlation ID, use curl to manually verify and capture baseline traffic, then analyze logs to identify redundant calls. Next, apply optimizations like batching, caching, or debouncing, and finally validate correctness and measure improvements with metrics and tests.
Pro tip: Emphasize the importance of establishing a baseline before optimizing, and use correlation IDs to trace requests across services—this shows you understand real-world debugging and performance measurement.
Add lightweight logging to the client to record each HTTP request (method, URL, timestamp, and a generated correlation ID). Use curl to manually reproduce the workflow and capture the sequence of requests.
Parse the logs to count requests per endpoint and identify patterns like repeated identical calls, sequential calls that could be batched, or calls that could be cached. Use correlation IDs to group requests by workflow instance.
Modify the client to batch multiple requests into one, cache responses for repeated calls, or debounce rapid-fire requests. Ensure changes preserve correctness and handle edge cases like cache invalidation.
Write tests to confirm the optimized client behaves correctly (e.g., same final state, no lost data). Measure key metrics like total requests, latency, and error rates before and after to quantify improvement.
Add metrics (e.g., request count per endpoint) and alerts to catch regressions. Include integration tests that assert expected request patterns to prevent future redundant calls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.