Start by defining clear SLIs and SLOs to measure performance, then describe a systematic approach to instrument, load test, identify bottlenecks, and apply optimizations with trade-offs. Emphasize a data-driven, iterative process that balances performance, cost, and complexity.
Pro tip: Tie your answer to TikTok's scale and real-time nature by mentioning specific techniques like tail latency optimization and graceful degradation, showing you understand high-stakes production environments.
Identify key metrics (e.g., latency, throughput, error rate) that reflect user experience and set target SLOs. Ensure they are measurable and aligned with business goals.
Implement comprehensive instrumentation (metrics, logs, traces) to collect data on SLIs. Use monitoring tools to visualize and alert on SLO violations.
Design realistic load tests that simulate peak traffic patterns, including spike and soak tests. Measure system behavior against SLOs to identify breaking points.
Analyze test results and production data to pinpoint bottlenecks across the stack (e.g., CPU, memory, I/O, network, database, dependencies). Use profiling and tracing.
Apply targeted optimizations (e.g., caching, sharding, async processing) and evaluate trade-offs like cost, complexity, and consistency. Iterate based on measurements.
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