{"id":190,"date":"2026-01-05T05:04:00","date_gmt":"2026-01-04T23:34:00","guid":{"rendered":"https:\/\/craqly.com\/?p=186"},"modified":"2026-07-22T12:19:47","modified_gmt":"2026-07-22T12:19:47","slug":"leetcode-patterns-cheat-sheet","status":"publish","type":"post","link":"https:\/\/craqly.com\/blog\/leetcode-patterns-cheat-sheet\/","title":{"rendered":"Algorithm Patterns for Coding Interviews: The Framework for Problem-Solving 2026"},"content":{"rendered":"

Here’s something I find genuinely useful to say out loud: there are roughly 15 patterns that cover most of what you’ll see in a coding interview. Not 150. Not “thousands of problems.” Fifteen patterns, applied in different combinations.<\/p>\n

The Stack Overflow Developer Survey 2024<\/a> found that 62% of developers actively preparing for jobs said algorithmic problem-solving was their biggest source of anxiety. My guess is that number is high partly because of how most people prepare: they grind solutions instead of learning to recognize what kind of problem they’re looking at. This cheat sheet is about the second thing.<\/p>\n

The recognition problem<\/h2>\n

The hard part of a coding interview isn’t usually the implementation. It’s the first 90 seconds: what am I even looking at here? If you can identify the pattern, the implementation tends to follow. If you can’t, you end up flailing at a whiteboard hoping your fingers remember something your brain doesn’t.<\/p>\n

So here’s the framing I’d suggest: for each pattern below, focus on the “when does this apply” column more than the code itself. That’s the muscle you actually need.<\/p>\n

The 15 patterns, with honest notes on each<\/h2>\n

Two Pointers.<\/strong> Use this when you have a sorted array and you’re looking for pairs or need to compare elements from both ends. Classic tells: “find two numbers that sum to X” or “remove duplicates in place.” Fast to implement once you see it.<\/p>\n

Sliding Window.<\/strong> Subarray problems. Substring problems. Any time “contiguous sequence” appears in the problem. The window expands and contracts; you track a running value instead of recomputing. Good practice: Longest Substring Without Repeating Characters (LeetCode 3).<\/p>\n

Fast and Slow Pointers (Floyd’s).<\/strong> Cycle detection. If someone asks whether a linked list has a cycle, or asks you to find the middle element in one pass, this is it. The slow pointer moves one step, the fast pointer moves two. They meet if there’s a cycle.<\/p>\n

Merge Intervals.<\/strong> Any problem involving overlapping ranges. Interview scheduling. Calendar conflicts. Sort by start time, then check if the current interval overlaps the last merged one. Straightforward once the pattern clicks.<\/p>\n

Binary Search.<\/strong> Not just “is this value in the sorted array.” Binary search applies to any monotonic function. “Find the minimum in a rotated sorted array” is binary search. “Find the smallest X such that condition holds” is binary search. I think people underuse this pattern because they associate it only with the textbook case.<\/p>\n

BFS (Breadth-First Search).<\/strong> Shortest path in an unweighted graph. Level-order traversal. “Minimum number of steps” problems. BFS guarantees you find the shortest path first. DFS doesn’t.<\/p>\n

DFS (Depth-First Search).<\/strong> Exploring all possibilities. Path existence. Tree traversals. Any problem where you need to visit every node or try every combination before backtracking.<\/p>\n

Backtracking.<\/strong> Subset generation, permutations, combinations, constraint satisfaction. The pattern: try an option, recurse, undo the option. N-Queens is the textbook example but you’ll see it in “generate all valid parentheses” and similar problems. (Side note: backtracking problems look scarier than they are. Once you write the template once, it’s mostly filling in the pruning condition.)<\/p>\n

Dynamic Programming.<\/strong> Overlapping subproblems, optimal substructure. The honest truth about DP is that it covers a huge range of problems and doesn’t have one clean template. What does have a template is the approach: define the state, write the recurrence, decide top-down or bottom-up. Coin change and longest common subsequence are the two I’d practice first.<\/p>\n

Topological Sort.<\/strong> Dependency ordering. Build systems. Course prerequisites. Whenever you have a directed acyclic graph and need to order nodes so that all dependencies come first. Kahn’s algorithm (BFS-based) is easier to implement in an interview than DFS-based toposort.<\/p>\n

Heap \/ Priority Queue.<\/strong> “K largest elements,” “merge K sorted lists,” “find the median of a data stream.” If the problem involves repeatedly pulling the smallest or largest element from a changing set, reach for a heap.<\/p>\n

Union Find (Disjoint Sets).<\/strong> Connected components. Network connectivity. “Are these two nodes in the same group?” Union Find answers that question in near-constant time after initialization. It comes up less often than heaps or DP, but when it does come up, you want to recognize it quickly.<\/p>\n

Trie.<\/strong> Prefix matching. Autocomplete. “Does any word start with this prefix.” A trie is a tree where each node is a character. You’ll see this mostly in string problems where suffix\/prefix operations repeat across many queries.<\/p>\n

Monotonic Stack.<\/strong> “Next greater element,” “daily temperatures,” problems involving “the next X that is bigger\/smaller.” The stack maintains a decreasing (or increasing) sequence. When you push an element that breaks the monotonicity, you process whatever gets popped.<\/p>\n

Bit Manipulation.<\/strong> XOR for finding unpaired elements. Bit shifting for fast multiplication\/division by powers of two. Checking whether a number is a power of two (n & (n-1) == 0<\/code>). These problems show up occasionally. They’re not the majority, but they reward having seen them before.<\/p>\n

Quick recognition table<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
If the problem says…<\/th>\nThink about…<\/th>\n<\/tr>\n<\/thead>\n
sorted array, pairs, duplicates<\/td>\nTwo Pointers<\/td>\n<\/tr>\n
contiguous subarray, substring<\/td>\nSliding Window<\/td>\n<\/tr>\n
linked list cycle, middle element<\/td>\nFast & Slow Pointers<\/td>\n<\/tr>\n
overlapping intervals, calendar<\/td>\nMerge Intervals<\/td>\n<\/tr>\n
find minimum X such that condition<\/td>\nBinary Search<\/td>\n<\/tr>\n
shortest path, minimum steps<\/td>\nBFS<\/td>\n<\/tr>\n
all paths, permutations, subsets<\/td>\nDFS \/ Backtracking<\/td>\n<\/tr>\n
dependencies, prerequisites<\/td>\nTopological Sort<\/td>\n<\/tr>\n
K largest \/ smallest, median<\/td>\nHeap<\/td>\n<\/tr>\n
connected components, groups<\/td>\nUnion Find<\/td>\n<\/tr>\n
prefix search, autocomplete<\/td>\nTrie<\/td>\n<\/tr>\n
next greater\/smaller element<\/td>\nMonotonic Stack<\/td>\n<\/tr>\n
find unique, XOR tricks<\/td>\nBit Manipulation<\/td>\n<\/tr>\n
repeated subproblems, optimal<\/td>\nDynamic Programming<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

How to actually use this<\/h2>\n

Print it out (or keep it open in another tab) while you practice. Before you look at a solution, write down which pattern you think applies and why. Getting this wrong isn’t a problem. Getting it wrong and then seeing the right answer teaches you more than solving problems where you already know the approach.<\/p>\n

Three to five problems per pattern is enough for most people preparing for mid-level roles. More for senior positions where the problems get combined (sliding window plus heap, for example). I wouldn’t try to grind all 15 in a week; pick the five or six you’re weakest on and focus there.<\/p>\n

If you’re doing mock interviews, having someone (or an AI interview tool) give you hints framed around pattern names rather than solutions is a much more effective way to practice than just reading the editorial. You want to build the recognition reflex, not the solution memory.<\/p>\n

One last thing: this list doesn’t cover system design. That’s a separate skill entirely.<\/p>\n","protected":false},"excerpt":{"rendered":"

Stop memorizing solutions. Learn the 15 LeetCode patterns that cover most coding interview problems, with when-to-use triggers and practice problem picks.<\/p>\n","protected":false},"author":25,"featured_media":519,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"fifu_image_url":"https:\/\/images.unsplash.com\/photo-1515879218367-8466d910aaa4?w=1200&h=600&fit=crop","fifu_image_alt":"Algorithm Patterns for Coding Interviews: The Framework for Problem-Solving 2026","footnotes":""},"categories":[4],"tags":[865,866,870,869,867,868],"class_list":["post-190","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technical-prep","tag-algorithm-interview-patterns","tag-coding-interview-techniques","tag-dynamic-programming","tag-graph-traversal-patterns","tag-problem-solving-frameworks","tag-sliding-window-algorithm"],"yoast_head":"\nLeetCode Patterns Cheat Sheet: 15 That Matter | Craqly<\/title>\n<meta name=\"description\" content=\"Stop memorizing solutions. 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