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

26 practiced

Compare separate chaining and open addressing as collision resolution strategies for hash tables. For each approach explain memory layout, average and typical worst-case performance, deletion semantics (including tombstones), cache behavior and which approach you would prefer for an SRE service that needs predictable low-latency lookups under memory constraints.

HardTechnical

40 practiced

Discuss design principles for a lock-free concurrent hash table suitable for low-latency SRE systems. Cover techniques such as atomic compare-and-swap for inserts, safe memory reclamation (hazard pointers, epoch-based reclamation), how to handle concurrent resizing, and trade-offs versus sharded lock-based approaches.

MediumTechnical

38 practiced

Explain hash-flooding (a collision-based DoS attack). How can it cause denial-of-service in web applications that use naive hash maps? Describe practical mitigation strategies at the application, runtime, and infrastructure levels, and what monitoring signals you would use to detect such attacks.

HardSystem Design

31 practiced

Design a monitoring backend that ingests 1M new unique metric series per minute and maintains per-series counters. A naive hash map will run out of memory. Propose an architecture using hash-based sketches (e.g., count-min, HyperLogLog), sharding and aggregation strategies to handle high-cardinality metrics while still supporting SRE needs like alerting and top-k queries.

EasyTechnical

27 practiced

In Python, implement two_sum(nums, target) that returns indices of two numbers that add to target. Aim for O(n) time and O(n) space using a hash map (dictionary). Provide an example: nums=[2,7,11,15], target=9 -> [0,1]. Explain any edge-cases and duplicate handling assumptions.

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