Complexity Analysis and Performance Modeling Questions

Analyze algorithmic and system complexity including time and space complexity in asymptotic terms and real world performance modeling. Candidates should be fluent with Big O, Big Theta, and Big Omega notation and common complexity classes, and able to reason about average case versus worst case and trade offs between different algorithmic approaches. Extend algorithmic analysis into system performance considerations: estimate execution time, memory usage, I O and network costs, cache behavior, instruction and cycle counts, and power or latency budgets. Include methods for profiling, benchmarking, modeling throughput and latency, and translating asymptotic complexity into practical performance expectations for real systems.

EasyTechnical

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You're responsible for a Java-based ETL job that has become noticeably slower. Describe a step-by-step profiling and benchmarking approach to identify where time is spent. Include which tools you'd use (e.g., JFR, jstack, perf), metrics to collect, how to create representative benchmarks, and how to ensure reproducible measurements.

MediumSystem Design

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Design a throughput vs latency model for a streaming ingestion pipeline that must process 100k events/sec with 99th percentile processing latency < 500 ms. Cover message broker, consumers, and downstream DB: describe queuing models, how to size the number of consumers, how to compute required service rates, and heuristics for headroom to absorb spikes.

MediumSystem Design

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Estimate memory and shuffle I/O cost for a Spark join where dataset A has 500M rows (key 10 bytes, payload 90 bytes) and dataset B has 50M rows (key 10 bytes, payload 30 bytes). Executors are 50 machines with 64 GB RAM each. Assume default partitioning and no broadcast. Provide back-of-envelope calculations for total shuffle bytes and per-executor memory pressure.

HardTechnical

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A Flink streaming job exhibits rare long-tail latency spikes caused by GC on the JVM. Propose a detailed plan to profile GC activity, tune JVM flags, and consider alternatives like off-heap state or RocksDB state backend. Explain trade-offs between GC tuning, off-heap memory, I/O introduced by RocksDB, and system throughput.

EasyTechnical

0 practiced

Explain Big O notation and its practical significance for data engineers designing ETL pipelines. Include concrete examples comparing O(n), O(n log n), and O(n^2) as input size grows, and describe how asymptotic growth should influence algorithm and infrastructure choices (e.g., single-node vs distributed execution) for large datasets.

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