Strings

Reading `arr[1000000]` takes the same amount of time as reading `arr[0]`, because an array's contiguous memory layout lets the runtime compute the address of any element with a single multiplication. That single property is what makes **Arrays & Strings** the workhorse data structure of nearly every program you will ever write. This lesson walks through array declaration and indexing in JavaScript and Python, the cost of inserts and deletes that force shifting, slicing and subarray patterns, and the basic string operations every interview problem assumes you know. You will also see why strings behave as immutable character arrays in both languages and what that means for performance when you build strings inside a loop. In **How to Read Code (JS & Python)**, you practiced tracing programs line by line; that habit is exactly how you will reason about loop indices and slice boundaries here. **Big-O Notation (Upper Bound)** gave you the language to express scaling behavior, and arrays are where you will first see `O(1)` access sit next to `O(n)` insertion in the same data structure. Once array fundamentals are second nature, **Matrix/Grid Fundamentals** extends the same indexing intuition into two dimensions, where row and column traversal patterns power BFS, DFS, and dynamic programming on grids.

Genome alignment tools like BWA and Bowtie find a 100-character read inside a 3-billion-base human reference in milliseconds, not by scanning the genome but by querying an index built once over all of its suffixes. **Suffix Array / Suffix Tree** is the family of data structures behind that workflow, and the same machinery powers full-text search, plagiarism detection, and the BWT-based compression in bzip2. This lesson covers the suffix array (a sorted array of starting positions of every suffix), the binary-search pattern match in `O(m log n)` for a pattern of length `m`, the LCP (Longest Common Prefix) array that captures shared prefixes between adjacent sorted suffixes, and the suite of substring problems the pair solves: longest repeated substring, number of distinct substrings, and longest common substring. You will also meet the suffix tree, a compressed trie of all suffixes that pushes pattern matching down to `O(m)` independent of text length, and weigh the suffix array's smaller memory footprint against the suffix tree's faster query. In **Arrays & Strings**, sorting and binary search worked over numbers; here the same primitives extend to strings, with a comparator that compares characters position by position. **Trie (Prefix Tree)** introduced the prefix-matching tree shape, and a suffix tree is a compressed trie built from every suffix of the input. Next, the curriculum explores data structures organized around a different axis entirely: preserving every historical version of themselves through time.

Type the letters `a-p-p` into a search box and the autocomplete dropdown produces `apple`, `application`, `apply`, and `appoint` before you finish the next keystroke. The data structure doing that work is a **Trie** (or prefix tree), where every stored word is a path from root to a marked node and every shared prefix is shared in memory exactly once. This lesson covers the trie node layout (a `children` map plus an `isEndOfWord` flag), `insert`, `search`, and `startsWith` operations that all run in `O(m)` for a query of length `m` (independent of how many words the trie holds), and a `delete` that has to be careful not to break paths used by other words. You will also analyze the space behavior, including when a trie wins over a hash set (prefix queries, lexicographic enumeration) and when it loses (memory-tight workloads with no prefix structure to exploit). In **Hash Map (Dictionary) Basics**, you used hashing to answer 'have I seen this exact key' in `O(1)`; a trie answers a strictly richer question (any prefix lookup) by trading `O(1)` exact-match for `O(m)` traversal. **Trees: Binary Tree Fundamentals** introduced the recursive child-pointer pattern, and a trie generalizes it from two children per node to one child per alphabet symbol. With a trie in your toolkit, the curriculum moves on to graph and hash extensions that solve a different family of problems built on the same node-and-edge thinking.

Look at almost any solved interview problem and you will see the same six or seven shapes of `for` loop reappearing: a single sweep that finds a max, a nested pair that compares every element to every other, a single sweep that fills a frequency table. Before you can recognize when two pointers or sliding window will help, you have to recognize these primitive shapes on sight. **Iteration Patterns on Arrays/Strings** catalogs those shapes. You will work through single-pass templates (running sum, find max/min, counting), nested-loop templates that consider all pairs in `O(n^2)` time, frequency counting with a hash map, early termination with `break`, sentinel values, and the choice between in-place modification and building a new array. Classic transformations like reverse, rotate, and partition appear as named patterns rather than puzzles solved from scratch each time. In **Arrays & Strings**, you saw that arrays give you constant-time indexed access and contiguous memory. This lesson turns that storage model into actual movement: how an index walks across an array, what it costs, and when one walk is enough. Next you will use these patterns directly in **Prefix Sum & Difference Array**, where a single preprocessing pass replaces many later range-sum loops.

Boyer-Moore is the algorithm `grep` actually uses, and it has a counterintuitive property: longer patterns make it _faster_, not slower, because the bad-character rule lets it skip whole chunks of the text without inspecting them. The best case is `O(n / m)`, sublinear in the text length. The same insight (start from the right, jump aggressively on mismatch) defines a different family of pattern-matching algorithms from the linear-scan family of KMP and Z. **Pattern Matching Algorithms** rounds out the string-search toolkit. You will implement Boyer-Moore with both the bad-character rule and the good-suffix rule, build a deterministic finite automaton (DFA) from a pattern and use the resulting state-transition table for `O(n)` matching after `O(m * |alphabet|)` preprocessing, and extend pattern search into two dimensions row by row. The lesson closes with a head-to-head comparison of every string-matching algorithm you have seen so far (KMP, Z, Rabin-Karp, Manacher, Aho-Corasick, Boyer-Moore, DFA), so you can pick the right tool for any matching task: long patterns, multi-pattern, streaming, or 2D. In **String Algorithms**, you mastered the failure-function family of linear-time matchers. This lesson adds the right-to-left family and the automaton-construction family. From here, **Advanced Greedy & Data Structures** turns to monotonic stacks.

To find the longest substring of `s` with no repeated characters, the brute-force approach checks every substring in `O(n^3)` time. The sliding-window solution touches each character at most twice and runs in `O(n)`: extend a right pointer until a duplicate appears, then advance a left pointer until the duplicate is gone, repeating until the right pointer falls off the end. **Sliding Window (Intro)** turns that idea into two reusable templates. The fixed-size window slides a range of length `k` across the array and updates the running sum or count by adding the new right element and removing the old left element, never recomputing from scratch. The variable-size window expands the right edge while a condition holds and shrinks the left edge to restore that condition, tracking the window's contents with a hash map or counter. You will apply both to maximum-sum subarray of size `k`, longest substring without repeating characters, minimum window substring, and longest substring with at most `k` distinct characters. In **Two Pointers (Intro)**, you used coordinated indices to walk an array linearly. **Hash Map (Dictionary) Basics** gave you the `O(1)` lookup and update that lets a window track its own contents efficiently. Sliding window combines both: two indices and one hash map. From here you turn to **Recursion Fundamentals**, which trades sequential index movement for self-similar subproblems.

Naive substring search compares the pattern against every position in the text, restarting from scratch on each mismatch, for `O(n * m)` worst-case time. KMP, by precomputing where to resume after a mismatch, never restarts. The text pointer moves forward strictly monotonically, the pattern pointer is corrected by a failure function, and the whole search runs in `O(n + m)`. That single insight defines a family of linear-time string algorithms that power editors, search engines, and bioinformatics pipelines. **String Algorithms** covers that family. KMP teaches you to build the failure function (prefix function) and use it to skip redundant comparisons. The Z algorithm offers an alternative `O(n)` framework based on the Z-array. Rabin-Karp introduces rolling hashes for multi-pattern search and is the backbone of plagiarism detection. Manacher's algorithm finds the longest palindromic substring in linear time using a clever transform with separators. Aho-Corasick generalizes KMP to multiple patterns simultaneously by adding failure links to a trie, producing a finite automaton that scans the text once. In **Arrays & Strings**, you treated strings as arrays of characters with `O(1)` indexed access. **Hash Map (Dictionary) Basics** gave you the `O(1)` lookup used by Rabin-Karp. This lesson layers algorithmic structure on top of those primitives. Next, **Pattern Matching Algorithms** extends the toolkit with Boyer-Moore, DFA-based matching, and 2D pattern search.