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.

HardSystem Design

82 practiced

Design an in-memory cache layer for a data pipeline that must serve both heavy-read dashboard queries and heavy-write streaming updates. Specify eviction policies, data structures for fast reads (hash maps, LRU lists, segmented caches), consistency models with the source of truth, and how to handle concurrency and cache invalidation at scale.

EasyTechnical

74 practiced

As a data engineer, explain the practical differences between arrays and linked lists in terms of memory layout, cache locality, insertion/deletion costs, iteration performance, and typical space overhead. Give concrete examples of when you would pick an array (or dynamic array) vs a linked list in batch ETL, streaming buffers, and in-memory transformation tasks, and estimate time/space complexity for each example.

EasyTechnical

66 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.

HardTechnical

84 practiced

For a large join between two very large tables in Spark, describe strategies to minimize shuffle and memory usage: partitioning by join key, broadcast smaller table, bucketed tables, and Bloom join (filter pushdown). For each approach, explain the underlying data structure and when it is appropriate based on table sizes and cluster resources.

EasyTechnical

82 practiced

You need to implement a lookup table for metadata in a data pipeline that stores millions of keys and must support both fast point lookups and occasional range queries over lexicographic keys. Compare using a hash map (unordered) vs a tree-based ordered map. Discuss typical time complexity, memory overhead, iteration order guarantees, and when each is appropriate in a data engineering context.

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