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Scalability Analysis and Bottleneck Identification Questions

Techniques for analyzing existing systems to find and prioritize bottlenecks and to validate scaling hypotheses. Topics include profiling and benchmarking strategies instrumentation and monitoring of latency throughput error rates and resource utilization; identification of common bottlenecks such as database write throughput central processing unit saturation memory pressure disk input output limits and network bandwidth constraints; designing experiments and load tests to reproduce issues and validate mitigations; proposing incremental fixes such as caching partitioning asynchronous processing or connection pooling; and measuring impact with clear metrics and iteration. Interviewers will probe the candidate on moving from observations to root cause and on designing low risk experiments to validate improvements.

HardSystem Design
61 practiced
An OLTP database has write-heavy workloads with hot partitions due to user_id skew. Propose architectural patterns to remove write hot spots while preserving transactional integrity: options may include request-level queuing with per-shard workers, id pre-allocation, multi-master conflict resolution, and moving certain operations to event-sourced asynchronous flows. Discuss trade-offs, complexity, reconciliation strategies, and expected performance improvements.
MediumSystem Design
76 practiced
Design a load test plan to reproduce a write-throughput bottleneck for an order ingestion pipeline that must handle 10,000 writes/sec with 100ms average latency. Include test dataset characteristics, ramp-up strategy, failure modes to detect, specific system and application metrics to capture, and how to run these tests safely against staging and progressively in production.
EasyBehavioral
62 practiced
You can only fix one of three issues right now: a bug impacting 5% of revenue, a performance bug that increases p99 latency, or a developer-flakiness slowing deployments. Explain how you would prioritize the fixes and justify your decision to engineering, product, and sales stakeholders. Include metrics and risk considerations in your reasoning.
HardTechnical
71 practiced
You must decide between a managed database service and a self-hosted database for a high-throughput write analytics platform. Create a decision matrix covering performance, scalability, operational effort, cost, compliance, backup/restore time, and vendor lock-in. Describe the benchmarks and chaos tests you would run and the acceptance thresholds that would justify choosing the managed option.
MediumTechnical
75 practiced
Design a low-risk canary experiment to validate that switching to a pooled DB driver reduces latency under production traffic. Specify canary rollout percentage, metrics to compare (latency percentiles, DB connection counts, error rates), monitoring windows, automatic rollback criteria, and how to control for traffic differences between canary and baseline.

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