Data Structure Selection and Trade Offs Questions

Skill in selecting appropriate data structures and algorithmic approaches for practical problems and performance constraints. Candidates should demonstrate how to choose between arrays lists maps sets trees heaps and specialized structures based on access patterns memory and CPU requirements and concurrency considerations. Coverage includes case based selection for domain specific systems such as games inventory or spatial indexing where structures like quadtrees or spatial hashing are appropriate, and language specific considerations such as value versus reference types or object pooling. Emphasis is on explaining rationale trade offs and expected performance implications in concrete scenarios.

EasyTechnical

0 practiced

Explain what an LRU cache is and describe its typical implementation that supports O(1) get and put. For a data ingestion microservice that caches recent schema lookups, discuss concurrency implications and how you might scale the cache across multiple service instances.

MediumTechnical

0 practiced

Implement reservoir sampling (Algorithm R) that maintains a uniform random sample of size k from a single-pass stream. Provide Python function def reservoir_sample(stream: Iterable[Any], k: int) -> List[Any]. Explain how you would adapt the algorithm to weighted sampling and how to merge samples from parallel partitions.

HardTechnical

0 practiced

A multi-tenant analytics platform must support tenants ranging from tiny to very large. Discuss sharding and index strategies: global indexes, per-tenant indexes, and hybrid approaches. Consider storage efficiency, query isolation, tenant rebalancing, and how different data structures affect cross-tenant operations and operational complexity.

HardTechnical

0 practiced

You must decide between ingesting semi-structured event JSON into a document store (nested documents) or normalizing into relational tables for downstream analytics. Describe the tradeoffs in storage layout, indexing for analytics, schema evolution handling, query performance, and developer productivity. Give specific recommendations for a data engineer building the pipeline.

EasyTechnical

0 practiced

Compare compression codecs commonly used in big data (Snappy, LZ4, Gzip, Zstd). For a nightly ETL job that writes compressed Parquet files, discuss tradeoffs between compression ratio, CPU usage, decompression speed for downstream queries, and how you would choose a codec when I/O is the bottleneck.

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