String Algorithms and Pattern Matching Questions

Covers algorithmic techniques and practical skills for solving string problems and pattern matching tasks. Core algorithm knowledge includes substring search and pattern matching algorithms such as Knuth Morris Pratt, Rabin Karp, Boyer Moore, Z algorithm, Aho Corasick for multiple pattern matching, and rolling hash methods. Data structures and suffix structures are important, including tries, suffix arrays, suffix trees, and suffix automata, together with longest common prefix arrays and related construction techniques. Also includes dynamic programming approaches for string problems such as edit distance and longest common subsequence, palindrome and anagram detection methods, and regular expression concepts and engine behavior. Emphasizes algorithmic complexity analysis, time and space trade offs, memory and streaming constraints, and optimization strategies for very long inputs and high throughput text processing. Practical considerations include parsing and string manipulation idioms in common languages, Unicode and character encoding issues, edge case handling, test case design for strings, and real world applications such as log analysis, text search, and data transformation.

HardTechnical

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You need to compute Levenshtein distance between very long strings but expect the true distance k to be small. Describe Ukkonen's algorithm (or banded DP) to compute distance in O(k * n) time and O(k) space. Provide pseudocode outline, correctness intuition, and how to abort early when distance exceeds k.

MediumTechnical

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Implement Levenshtein (edit) distance between two strings in Python using O(min(n,m)) space for just the distance value: edit_distance(a: str, b: str) -> int. Explain how rolling arrays reduce memory and when you'd choose Ukkonen's algorithm for bounded edit distance.

EasyTechnical

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Explain how immutable string implementations (Python str, Java String) influence memory usage and performance when performing many concatenations or substrings. Recommend efficient patterns for building large strings in Python and Java. Discuss when ropes or bytearrays might be preferable.

MediumTechnical

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Implement Rabin-Karp in Python: rabin_karp_search(text: str, pattern: str, base: int = 256, mod: int = 2**61-1) -> List[int]. Use a rolling hash and demonstrate how you update the hash in O(1). Explain how you handle hash collisions and the impact of modulus choice on false positives.

EasyTechnical

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You're reviewing a teammate's function that extracts email addresses from logs. Describe a comprehensive set of unit and edge-case tests you would write: valid vs invalid emails, max-length boundaries, internationalized addresses, inputs designed to cause catastrophic regex backtracking, and fuzz tests. Include performance and regression-test ideas.

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