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.

MediumTechnical

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An S3-backed service exhibits occasional 10s tail latencies correlated with JVM GC pauses. Describe steps and experiments to determine whether S3 I/O or GC is the primary root cause and propose architectural changes to decouple S3 I/O from the request path to reduce tail latency. Include monitoring signals to prove improvement.

MediumSystem Design

0 practiced

Design an incremental caching and invalidation strategy for a read-heavy product detail API that must reduce DB reads by 80% while guaranteeing data staleness no greater than 5 seconds. Discuss cache placement options (edge, app-in-memory, distributed), invalidation options (event-driven, TTL, write-through), cache-warming, and the metrics you will use to measure success and detect regressions.

MediumTechnical

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You suspect database write throughput is the bottleneck. Compare vertical scaling (bigger instance), horizontal scaling (sharding/partitioning), write batching, asynchronous writes, and moving to a write-optimized store. For each mitigation list expected impact on latency and throughput, implementation complexity, typical failure modes, and rollback considerations.

HardSystem Design

0 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

0 practiced

Given a MySQL table 'orders(order_id PK, user_id, amount, created_at)' receiving 50k writes/sec and exhibiting hot partitions by user_id, propose partitioning and sharding options such as range partition by date, hash partition by order_id, and user-based sharding. For each option explain the effect on write throughput, common query patterns, migration strategy with minimal downtime, and implications for cross-shard transactions.

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