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.

MediumTechnical

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You need an in-memory index for fast text search over 50M short product titles to support prefix search (autocomplete). Compare using a trie (prefix tree) versus an inverted index with tokenization. Discuss memory, retrieval latency, multi-language support, and practicality for a data science prototype vs. production service.

MediumTechnical

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For a recommender system, you need to maintain user-item interactions and compute user histories efficiently. Compare storing histories as arrays per user, fixed-size circular buffers, or linked lists in terms of memory, cold-start handling, and ease of computing time-decayed features in batch and streaming contexts.

HardSystem Design

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You are to implement an on-disk key-value store used by a feature retrieval service that needs fast writes and occasional reads. Compare append-only logs + compaction, B-trees, and LSM-tree (Log-Structured Merge) data structures for this workload. Highlight trade-offs in write amplification, read latency, and compaction overhead.

MediumTechnical

0 practiced

A service must provide per-user sampling of high-cardinality events for analytics with bounded memory. Compare options: maintaining reservoir samples per user, using a global sample with per-user post-filtering, or probabilistic sampling keyed by hashed user ID. Explain biases, memory cost, and downstream analysis implications.

HardTechnical

0 practiced

Describe an appropriate data structure and access pattern to store embeddings for 100 million items where you need fast approximate nearest neighbor (ANN) retrieval under memory constraints. Compare FAISS indices (IVF, HNSW), locality-sensitive hashing (LSH), and flat indexes, focusing on recall vs. latency and memory trade-offs.

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