Hashing and Hash Based Data Structures Questions

Comprehensive coverage of hashing and hash based associative data structures including hash tables, hash maps, dictionaries and hash sets. Candidates should explain hashing fundamentals and the role and properties of hash functions, causes of collisions, and common collision resolution strategies such as chaining and open addressing. Discuss load factor, resizing behavior and how these influence amortized performance and memory usage. Describe average case constant time behavior for lookup insertion and deletion and worst case linear time under pathological collision scenarios, and contrast trade offs with alternatives such as balanced search trees and sorting based approaches. Expect practical problem solving using hash based structures for frequency counting, duplicate detection, grouping, membership testing, two sum and pair problems, anagram detection, sliding window frequency problems and cache or memoization designs including least recently used eviction concepts. Be familiar with common language level implementations such as HashMap and HashSet in Java and dictionary and set in Python and be able to reason about implementation pitfalls including unhashable or mutable keys, custom hash and equality semantics, resizing costs, collision attacks and memory overhead. Interviewers will probe time and space trade offs, when a hash based approach is preferable, and optimization strategies when facing pathological inputs.

EasyTechnical

28 practiced

Describe the key properties of a good non-cryptographic hash function used for in-memory hash tables in AI systems (e.g., determinism, uniformity, speed, low collision rate, and avalanche behavior). For common key types in AI (strings, integer IDs, binary embedding fingerprints), which hash families or implementations would you pick and why?

MediumTechnical

22 practiced

Discuss hash table resizing strategies: doubling capacity, growing by 1.5x, and using prime-sized buckets. Explain performance impact (number of resizes, amortized cost), memory fragmentation, and practical considerations for large hash tables used in embedding lookups. When would you consider shrinking a table?

HardSystem Design

24 practiced

Design a system to deduplicate near-duplicate text embeddings at scale for 100M documents. Goals: high recall for cosine similarity > 0.95, limited memory per worker, and reasonable compute. Compare using LSH, HNSW (graph ANN), and brute-force partitioning. Explain trade-offs, batching, and verification steps.

MediumTechnical

22 practiced

Implement a simple BloomFilter class in Python with add(item) and might_contain(item). Use a bytearray (or bitarray if available) for bits and simulate multiple hash functions via double hashing or hashlib. Discuss choices for hash functions and how to compute multiple indices efficiently.

MediumTechnical

30 practiced

Describe Java's HashMap implementation (post-Java 8): internal table of Node<K,V>, how load factor and threshold work (default loadFactor=0.75), when chains get converted into balanced trees, and how hashCode() and equals() are used. Explain pitfalls such as mutable keys and the effect of bad hashCode implementations.

Unlock Full Question Bank

Get access to hundreds of Hashing and Hash Based Data Structures interview questions and detailed answers.

Join thousands of developers preparing for their dream job.