Optimization and Technical Trade Offs Questions

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.

HardTechnical

43 practiced

Discuss the implications of the CAP theorem for a distributed stateful stream processing system (for example, Flink) that must tolerate network partitions. When designing for partition tolerance, how do you choose between consistency and availability? Give concrete trade-offs in checkpointing frequency, operator state replication, and sink semantics.

MediumTechnical

84 practiced

Implement in Python a streaming deduplicator class with the API below. It should drop duplicate event IDs seen within a sliding time window W seconds. Constraints: throughput ~100k events/sec, memory budget ~200MB, allow up to 1% false positives (dropping some unique events is acceptable) but no false negatives (do not emit duplicates). Explain your data structure choices and provide code sketch for the class:

class StreamingDeduplicator: def __init__(self, window_seconds: int, memory_mb: int, fp_rate: float): ... def process_event(self, event_id: str, timestamp: int) -> bool: """Return True if event_id should be emitted (not seen in last W seconds)."""

Include eviction strategy, how you tune Bloom filter parameters, and how to rotate filters to enforce the sliding window.

MediumTechnical

60 practiced

A Spark job processing a 1 TB dataset spends most of its time in shuffle write/read, shows heavy disk I/O, and experiences long GC pauses. The job uses many small partitions and performs aggregation across keys. Suggest concrete code-level, data-layout, and Spark configuration optimizations (for example reduceByKey vs groupByKey, repartitioning, serializer, shuffle manager settings, memory fractions) to reduce shuffle overhead and GC impact. Explain trade-offs and expected effects.

HardTechnical

51 practiced

Your platform runs monolithic ETL DAGs in Airflow. Evaluate the pros and cons of migrating parts of the pipeline to microservice-based stream processors (Kafka + stream processors). Cover operational complexity, testing, deployment, cross-team coordination, data consistency, and cost. Propose a migration plan that minimizes customer impact and provides rollback safety.

MediumTechnical

44 practiced

Given the following table stored in Parquet on S3:

Table: transactions- transaction_id BIGINT- user_id BIGINT- amount DECIMAL(12,2)- status VARCHAR(20)- transaction_time TIMESTAMP- merchant_id BIGINT

The table contains ~2 billion rows partitioned by transaction_date. The query below is slow:

SELECT user_id, SUM(amount) AS totalFROM transactionsWHERE status = 'complete' AND transaction_time BETWEEN '2025-01-01' AND '2025-01-31'GROUP BY user_idHAVING SUM(amount) > 1000ORDER BY total DESCLIMIT 100;

Explain why it's slow and propose a prioritized optimization plan (layout, partitioning, bucketing, indexes/materialized views, query rewrite). Estimate which changes give the largest improvements and discuss trade-offs.

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