Arrays and Hash Map Operations Questions

Covers algorithmic patterns that use arrays together with hash based maps or dictionaries to achieve efficient lookup and counting. Topics include frequency counting, duplicate detection, two sum and k sum variants, sliding window with counts, index mapping, grouping by keys, and using hash maps to reduce time complexity from quadratic to linear. Emphasize insertion deletion and lookup costs, collision and memory considerations, trade offs between using hash maps versus sorting or two pointer techniques, and typical interview problem families that rely on combining arrays with associative containers.

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Implement an LRU cache in Python with methods get(key) and put(key, value) with capacity N, ensuring O(1) expected time for both. Explain how a hash map and a doubly-linked list work together and why each is necessary. Mention thread-safety considerations if used in a multi-threaded prediction server caching model artifacts.

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Given two equal-length arrays 'ids' and 'values' (length n), implement a Python function 'build_index(ids, values)' that returns two hash maps: id_to_indices mapping id -> list of indices where it appears, and id_to_sum mapping id -> sum of its values. Example: ids=[101,102,101], values=[5,7,3] -> id_to_indices={101:[0,2],102:[1]}, id_to_sum={101:8,102:7}. Discuss memory/time trade-offs and how to adapt to streaming updates.

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Implement 'group_anagrams(strs)' in Python to group anagrams together using hash maps. For very large datasets (hundreds of millions of short strings), discuss design choices for keys (sorted string vs character-count tuple vs fingerprint) to balance collision risk, memory usage, and performance. Explain when a secondary verification is required.

MediumTechnical

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You receive unsorted log entries as tuples (user_id, timestamp, event_type). Design an algorithm using arrays and hash maps to compute, for each user, the number of unique event_types they performed in the last 24 hours. Include a single-machine approach, memory bounds, and how to scale to distributed processing when logs are very large or arriving in real time.

MediumTechnical

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Describe and implement an approach to solve the 4-sum problem: find all unique quadruplets in array 'nums' that sum to target. Explain how hashing pair-sums into a map reduces complexity versus naive O(n^4), and discuss deduplication and memory trade-offs for n up to ~2000.

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