Focuses on evaluating and improving solutions with attention to trade offs between performance, resource usage, simplicity, and reliability. Topics include analyzing time complexity and space complexity, choosing algorithms and data structures with appropriate trade offs, profiling and measuring real bottlenecks, deciding when micro optimizations are worthwhile versus algorithmic changes, and explaining why a less optimal brute force approach may be acceptable in certain contexts. Also cover maintainability versus performance, concurrency and latency trade offs, and cost implications of optimization decisions. Candidates should justify choices with empirical evidence and consider incremental and safe optimization strategies.
MediumTechnical
55 practiced
For an analytics warehouse supporting dashboards, discuss the trade-offs between denormalized wide tables (single large flattened table) versus a normalized star schema that uses joins. Consider storage cost, refresh complexity, query latency, data freshness, and maintainability. Recommend when to use each approach.
MediumTechnical
53 practiced
Your company needs near-real-time metrics but has a tight budget. Describe how you would evaluate and decide between a streaming architecture (Kafka + Flink or Structured Streaming) and a micro-batching approach (e.g., minute-level Airflow jobs processing micro-batches). Consider latency requirements, operational complexity, developer productivity, cost, and correctness guarantees.
MediumSystem Design
62 practiced
Design a caching strategy for a heavily-read analytics dataset that backs a dashboard requiring sub-second responses for 95% of requests. Data refreshes hourly. Compare options (in-memory caches like Redis, materialized views, pre-computation, OLAP engines like Druid/ClickHouse) and describe cache invalidation, TTL, warm-up, cost trade-offs, and an architecture for ensuring freshness and reliability.
MediumTechnical
48 practiced
You must design partitioning and storage layout for a time-series events table expected to grow to ~500 TB over 3 years. Typical queries: (A) time-range + device_id lookup, (B) recent aggregated metrics for all devices, (C) ad-hoc scans for anomaly detection. Propose partitioning/bucketing strategy, target Parquet file sizes, compaction approach, and a hot/warm/cold lifecycle policy. Explain trade-offs between query performance, write cost, and maintainability.
EasyTechnical
60 practiced
Describe the step-by-step process you would follow to profile and identify performance bottlenecks in a failing ETL pipeline running on Apache Spark. Include which metrics and tools you would use (for example Spark UI, Ganglia/Prometheus, JVM GC logs, Java Flight Recorder, flame graphs) and how you would decide whether to optimize configuration, change code, or scale resources.
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