Advanced Data Structures and Implementation Questions

Deep and practical expertise in advanced data structures, their implementation details, performance characteristics, and selection for both algorithmic problems and production systems. Topics include arrays and dynamic arrays, strings, linked lists, stacks and queues, hash tables, heaps and priority queues, various tree forms including binary search trees and balanced trees, tries or prefix trees, segment trees and binary indexed trees or fenwick trees, union find or disjoint set union, suffix arrays, and advanced graph representations. Candidates should be able to implement core structures from first principles, demonstrate interfaces and invariants, reason about insertion deletion search traversal and iteration costs including worst case average case and amortized analysis, and discuss memory management and ownership in low level languages such as C and C plus plus as well as safe memory and reference use in managed languages. Evaluation also covers trade offs between contiguous and pointer based layouts, cache friendliness, concurrency considerations, selection of structures based on access patterns update frequency and memory constraints, handling of edge cases, testing and performance tuning for realistic inputs, and applying structures to problems such as top K queries prefix search connectivity range queries caches and union operations.

HardTechnical

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Explain and provide pseudocode for a lock-free multi-producer multi-consumer queue using atomic CAS (compare-and-swap). Discuss the Michael-Scott queue algorithm, the ABA problem, and memory reclamation approaches (hazard pointers, epoch-based reclamation). Describe fairness and starvation concerns for producers/consumers.

EasyTechnical

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Implement a FIFO queue using two LIFO stacks in Python. Provide methods enqueue(value), dequeue(), peek(), and is_empty() and ensure amortized O(1) time for operations. Briefly explain why dequeue is amortized O(1) and describe worst-case cost of a single dequeue call.

HardTechnical

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Design and describe a concurrent hash map for high-throughput data ingestion. Include ideas for sharding/segmentation, lock striping, optimistic reads, compare-and-swap-based resizing, and memory reclamation strategies for removed entries. Provide trade-offs for read-heavy vs write-heavy workloads and low-latency SLAs.

MediumTechnical

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Compare graph storage representations: adjacency list, adjacency matrix, and CSR (Compressed Sparse Row). For a very large sparse graph stored on disk, explain why CSR/adjacency arrays are often preferred, provide memory formulas for each, and discuss neighbor-iteration performance characteristics and suitability for parallel graph algorithms.

MediumTechnical

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Implement a min-priority queue in C++ that supports `insert(node, priority)`, `decrease_key(node, new_priority)`, `extract_min()`, and `contains(node)`. Describe how you maintain positions of nodes for O(log n) decrease_key and memory concerns when nodes are complex objects in a data processing graph.

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