19 articles
Master data structures and algorithms for tech interviews in 2026 by learning 14 core patterns that solve 90% of LeetCode problems, with a structured 90-day study plan. Built for engineers targeting FAANG and top-tier coding rounds.
Master mathematical algorithms for FAANG DSA interviews: primes, GCD, modular arithmetic, fast exponentiation, combinatorics, and number theory with complexity reference and full problem index.
The Z algorithm computes Z[i] equals the length of the longest substring starting at index i that matches a prefix of the string in linear O(n) time. Cleaner than KMP for many problems and the foundation of competitive programming string toolkits.
Rabin Karp uses a polynomial rolling hash to fingerprint each window of the text and matches against the pattern hash in expected O(n + m) time. Master this and you unlock substring search, plagiarism detection, and the entire family of hash-based string problems.
Manacher finds the longest palindromic substring in O(n) by exploiting palindrome symmetry to skip redundant character comparisons. The same mirror trick that powers the Z algorithm, applied to the palindrome radius array.
Precompute polynomial prefix hashes once in O(n) and answer substring equality queries in O(1) for the lifetime of the string. The Swiss army knife behind longest duplicate substring, longest common substring binary search, and dozens of competitive programming techniques.
A suffix array sorts all suffixes of a string lexicographically. Built in O(n log n) with prefix doubling and paired with the Kasai LCP array, it answers substring search, distinct substring count, and longest repeated substring queries in optimal time.
Find the longest contiguous substring shared by two strings. The DP solution is O(n*m), binary search plus rolling hash gives O((n+m) log min(n,m)) expected, and suffix array plus LCP achieves O((n+m) log(n+m)).
The four canonical string DP problems — edit distance, longest common subsequence, interleaving strings, and regular expression matching — share a common 2D state structure. Master the family and you cover 80 percent of FAANG string DP questions.
Group anagrams, find anagram occurrences, and detect anagrams under constraints. Master the three classical encodings — sorted key, 26-bucket frequency vector, and prime-product hash — with rolling-window extensions used at Meta and Google.
Longest Palindromic Substring, Palindrome Partitioning, and Palindromic Substrings form a tight family of FAANG questions. Master expand-around-center, 2D DP, and the link to Manacher and you can adapt to any variant on the spot.
Word Search II (LC 212) is the canonical FAANG hard combining trie data structures with grid backtracking. Build a trie from the dictionary, DFS each cell, and prune aggressively to convert an exponential brute force into a fast practical algorithm.
Encode and Decode Strings (LC 271) is a deceptively simple FAANG question that mirrors how real protocols frame variable-length payloads. Master length-prefix encoding and you have the foundation for HTTP chunked transfer, Protobuf, and most binary wire formats.
Shortest Palindrome (LC 214) is a hard FAANG problem that hides a classic KMP application. Concatenate s with reversed(s) and the failure function reveals the longest palindromic prefix in linear time. Master this trick and you unlock half a dozen related KMP applications.
Counting distinct substrings of a string is the gateway problem to suffix arrays and suffix automata. Three classical solutions — n^2 hash set, suffix array plus LCP, and suffix automaton — span the full toolbox of competitive programming and FAANG hard interviews.
String Compression (LC 443) is a deceptively careful two-pointer problem with strict in-place memory requirements. Master the read-write pointer pattern and you have the blueprint for in-place array transformations across many FAANG questions.
Beyond the basic trie there is a rich landscape of advanced applications — XOR tries for maximum-XOR queries, prefix-and-suffix tries, ternary tries, compressed tries (PATRICIA), and persistent tries. Master these and you have the toolkit for half a dozen FAANG hards.
A curated and battle-tested set of the string problems that show up most often in Meta and Google onsite loops, with the canonical pattern for each. Cover this list and you cover roughly 80 percent of string-heavy FAANG screens.
Understand approximate nearest neighbor algorithms: HNSW internals, IVFFlat trade-offs, quantization impact, and benchmarking strategies.