Performance Optimization and Latency Engineering Questions

Covers systematic approaches to measuring and improving system performance and latency at architecture and code levels. Topics include profiling and tracing to find where time is actually spent, forming and testing hypotheses, optimizing critical paths, and validating improvements with measurable metrics. Candidates should be able to distinguish central processing unit bound work from input output bound work, analyze latency versus throughput trade offs, evaluate where caching and content delivery networks help or hurt, recognize database and network constraints, and propose strategies such as query optimization, asynchronous processing patterns, resource pooling, and load balancing. Also includes performance testing methodologies, reasoning about trade offs and risks, and describing end to end optimisation projects and their business impact.

HardTechnical

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Design a storage access pattern for predictable low-latency reads (p99 < 5ms) for small objects at very high QPS. Compare using local SSDs, distributed KV stores on SSDs, in-memory caches, and memory-mapped files. Discuss warm-up, persistence, eviction, and cost trade-offs.

EasyTechnical

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You need to compare two implementations of the same function. Describe a benchmarking approach for reliable, repeatable results: environment controls, warmup, iterations, statistical analysis, avoiding JIT/artifact pitfalls, and how to present the findings to engineers and product stakeholders.

EasyTechnical

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List the key observability signals you would collect to diagnose latency issues in a microservice architecture. Include metrics, logs, distributed traces, resource-level statistics, synthetic checks, and describe how you would correlate these signals during triage.

HardSystem Design

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Design an observability strategy for latency engineering across polyglot microservices. Include choices for distributed tracing, sampling strategies (head vs tail sampling), span enrichment, correlating traces with metrics and logs, storage/retention, query performance, and balancing observability detail vs cost.

HardTechnical

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How would you implement low-overhead, production-safe profiling to capture CPU hotspots and allocation patterns across thousands of services written in multiple languages, while keeping data volume and cost manageable? Discuss sampling rates, aggregation, privacy concerns, and integration with alerting.

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