10+ top LeetCode patterns (2026) to ace FAANG coding interviews

Coding interviews are structured problem-solving challenges. Designed to evaluate how you think, communicate, and write code under pressure, these interviews simulate real-world engineering challenges in a time-constrained environment. It’s no wonder that many candidates fall into the trap of trying to power through their preparation.

Anyone aiming for a top-tier tech interview has probably heard the classic advice: 'grind LeetCode.' Most developers think the more problems they solve, the better their chances. As someone who has coached many engineers through this process, I can confidently tell you that this approach is ineffective.

Stop solving 2,500+ LeetCode problems.

Instead master these 10+ patterns.

Trusted by over 4,200 engineers on our platform who landed FAANG+ offers in 2026.

The FAANG interviews are structured around a core set of repeating patterns. Recent data from hundreds of real interviews at Google, Meta, Apple, Netflix, and Amazon shows that about 87% of questions are built around only 10 to 12 core problem‑solving patterns. Interviewers are not testing your memory of specific problems. They want to see how well you recognize underlying patterns and apply the correct algorithm quickly.

What to expect in this blog:

You’ll first see why patterns matter, then a quick reference cheat sheet of the most important patterns. After that, you’ll see a heatmap of where these patterns apply across categories, then dive into detailed explanations, examples, and live code examples for each pattern. Finally, you’ll find common pitfalls, additional patterns, and recommended resources.

Learn how to:

• Understand each core pattern with clear examples.

• Apply each pattern with clear explanations on when to use it.

• Reinforce your intuition with practical examples.

• Illustrate each pattern with variations of LeetCode problems.

Why patterns beat problem-grinding#

You’ve seen how overwhelming LeetCode can be. Jumping between random problems might feel productive, but it rarely builds the deep intuition you need. Many developers fall into randomly grinding these problems, hoping they’ll encounter similar problems in the interview room.

This shift to a pattern-based approach isn’t just a study hack; it aligns with how modern interviews and search algorithms evaluate expertise. For instance, the Google core algorithm update in March 2025 significantly elevated content demonstrating practical knowledge and fresh insights. Similarly, top-tier companies aren’t seeking candidates who can recall memorized solutions. They want engineers who demonstrate a transparent, structured problem-solving process. So, the more innovative way is to group problems by the patterns they share. Once you learn a pattern deeply, you can apply it to dozens of similar problems without starting from scratch every time.

Benefits of focusing on patterns:

1. You cut prep time dramatically by targeting high‑leverage ideas.

2. You get faster at identifying the right approach under interview pressure.

3. You build a reusable mental model that works across many problem types.

LeetCode tag frequency (July 2025)#

To put this into perspective, look at the distribution of problem categories on LeetCode right now.

The chart above shows the raw counts of problems tagged under each category on LeetCode as of July 2025. Some tags, like Array and Dynamic Programming, dominate. Others, like Union‑Find or Topological sort, appear less frequently but still appear in specialized interview questions. There’s no way to solve them all individually, and you don’t need to. So, when you see the sheer scale of these numbers and know how a pattern applies, you can pick representative problems for each pattern instead of grinding through everything.

Cheat-sheet of LeetCode patterns#

Before we discuss the patterns in detail, here’s a quick-reference summary of the key LeetCode patterns commonly tested in coding interviews at MAANG companies. Use this table for rapid review or as a convenient reference during your interview prep.

How each pattern maps to LeetCode categories#

Not all patterns are equally common across all types of problems. So, to help you focus your practice, it’s helpful to see which patterns appear most often across common LeetCode categories. The pattern-to-category relevance heatmap below shows the strength of each high‑leverage pattern (rows) across LeetCode problem categories where it most often applies, like Arrays, Strings, Graphs, and Trees (columns), based on trends from real FAANG/MAANG interview questions.

Legend:

• 🟩🟩🟩🟩🟩 Very frequent

• 🟩🟩🟩🟩 Common

• 🟩🟩🟩 Moderate

• ⬜️ Rare or uncommon

LeetCode Pattern playbook#

In this section, you’ll learn the top LeetCode patterns with visual intuitions, classic examples, runnable code, and real-world case studies from FAANG interviews.

1. Two Pointers#

When to use

Use the Two Pointers pattern when you’re dealing with sorted arrays or linked lists, and you need to find pairs, detect duplicates, or solve problems involving subarray boundaries.

Visual intuition

Imagine standing at the two ends of a sorted array. You have one pointer starting from the left, and the other from the right. Depending on your goal, such as finding a specific sum, you strategically move the pointers toward each other. If the combined values at your pointers are larger than your target, you shift the right pointer to a smaller number. If they’re smaller, you move the left pointer to a larger number. You converge on your solution by systematically adjusting your pointers without unnecessary checks.

Canonical problem: 3Sum—Find all unique triplets in an array that sum to zero.

MAANG mini-case study: A recent Google interview question asked candidates to find pairs in a sorted array whose sum was closest to a target number. Candidates who recognized it as a Two Pointers problem solved it in minutes, while others struggled with less efficient brute-force approaches.

2. Sliding Window#

When to use

The Sliding Window technique is ideal for problems involving contiguous sequences like arrays or strings, especially when finding the longest or shortest substring, subarray, or sequence that meets specific conditions.

Visual intuition

Think of a sliding window like a virtual “frame” that moves over an array or string. Start by setting the window boundaries (usually two pointers at the beginning), then slide these boundaries forward step-by-step. Depending on your problem constraints, you either expand the window, shrink it, or maintain a fixed window size.

Canonical problem: Longest Substring Without Repeating Characters—Given a string, find the length of the longest substring without repeating characters.

MAANG mini-case study: A recent Meta interview question involved finding the smallest subarray whose sum exceeded a given number. Candidates who identified this as a Sliding Window problem solved it quickly, while others struggled with more complex solutions.

3. Fast and Slow Pointers (Cycle detection)#

When to use

Use the Fast and Slow Pointers technique to detect cycles or loops, typically in linked lists or circular data structures. It can also help identify midpoints or specific positions without needing extra space.

Visual intuition

Imagine two runners on a circular track: one runner moves slowly, steadily, and the other moves twice as fast. If the track forms a closed loop, eventually the fast runner will lap the slower one. Similarly, using two pointers moving at different speeds through a linked list will reveal whether or not a loop exists. If the two pointers ever meet, you’ve detected a cycle.

Canonical Problem: Linked List Cycle—Given a linked list, determine if it contains a cycle. Return true if there’s a cycle; otherwise, return false.

MAANG mini-case study: Apple often asks candidates to detect or remove cycles in linked lists during coding interviews. Candidates recognizing the Fast & Slow Pointers approach can confidently deliver an efficient solution without relying on additional data structures, impressing interviewers by demonstrating clear, optimized thinking.

4. Depth-First Search (DFS)#

When to use

Depth-first search (DFS) is ideal when exploring deep paths before considering alternative options. It is intuitive and straightforward to implement and is commonly used in scenarios where exhaustive exploration or discovery of all possible solutions is required. It works particularly well for problems involving backtracking, pathfinding, connected components, or detecting cycles.

Visual intuition

Imagine exploring a graph or tree: you start at one node and follow a single branch as far as it goes before backtracking to explore other branches. Depth‑First Search is precisely that. In trees and graphs, it dives deep along one path, using recursion or a stack, before returning to handle the remaining neighbors.

Canonical problem: Diameter of Binary Tree—Given a binary tree, you need to compute the length of the tree’s diameter.

MAANG mini-case study: Meta regularly asks candidates to solve graph and tree traversal problems that require DFS. For instance, a typical interview problem is counting connected groups of friends (similar to islands) in a social network graph. Candidates recognizing DFS can quickly identify connected components, deliver efficient implementations, and impress interviewers with clear and concise solutions.

5. Breadth-First Search (BFS)#

When to use

Use Breadth-First Search (BFS) when you need the shortest path or the minimum steps to reach a goal, especially in graph or tree structures. BFS explores nodes level-by-level and is ideal for problems involving minimum depth, shortest paths, or closest distances.

Visual intuition

Picture a tree or graph where you want to visit all nodes in order of their distance from the root or start node. Breadth‑First Search explores level by level, visiting all neighbors of a node before moving on to the next layer. It typically uses a queue to keep track of the traversal.

Canonical problem: Binary Tree Level Order Traversal—Given a binary tree, return the level order traversal of its nodes’ values (i.e., from left to right, level by level).

MAANG mini-case study: Amazon frequently tests candidates with graph problems involving shortest paths or network latency. Candidates familiar with BFS can quickly implement efficient, queue-based solutions to find the minimum number of hops or shortest routes between two points. This pattern recognition significantly boosts their performance and the interviewer’s perception of their expertise.

6. Binary Search#

When to use

Use Binary Search when working with sorted data or when a problem’s search space can be reasoned about in a monotonic way (for example, “is it possible with X?” leading to yes/no answers). In interviews, you often don’t use the vanilla version; instead, you modify the classic binary search algorithm to fit conditions like finding the first/last occurrence, a boundary value, or even searching over an answer space rather than an array.

Visual intuition

Imagine trying to find a specific word in a dictionary. Instead of flipping through pages from the start, you naturally open near the middle. Depending on whether the word you’re looking for comes before or after, you discard half of the remaining pages immediately. Binary Search applies this logic: repeatedly dividing your search interval in half until your target is found.

Canonical problem: Binary Search—Given a sorted array of integers and a target value, return the index of the target if it exists. Otherwise, return -1.

MAANG mini-case study: Netflix commonly tests candidates on problems requiring efficient searches through large, sorted data sets, such as logs or media catalogs. Candidates who recognize the potential of Binary Search significantly outperform those who rely on linear or brute-force approaches, showcasing their ability to handle large-scale data efficiently.

7. Merge Intervals#

When to use
Use Merge Intervals when handling problems that involve overlapping ranges, such as time slots, calendar events, or number‑line segments. You want to merge these intervals so that no overlaps remain, producing a minimal set of non‑overlapping intervals.

Visual intuition
Imagine you’re managing meeting schedules. Each meeting is a block on a timeline. When two meetings overlap, you merge them into a longer meeting covering both. This pattern sorts intervals by start time and merges them greedily as you scan from left to right.

Canonical problem: Merge Intervals—Given an array of intervals, merge all overlapping intervals and return an array of non‑overlapping intervals that cover all input intervals.

MAANG mini-case study: Google and Amazon often ask scheduling or resource allocation questions, where recognizing the Merge Intervals pattern leads to an efficient solution.

8. Topological Sort#

When to use

Topological Sort is used when tasks or items have clear dependencies, and you must find an order of execution that respects these dependencies. It’s commonly applied in course scheduling, prerequisite resolution, or dependency graphs.

Visual intuition

Think of Topological Sort as planning a series of tasks, where some tasks depend on others. Imagine you need to take specific courses before graduating: “Algorithms” requires “Data Structures,” and “Data Structures” requires “Intro to Programming.” Topological sort helps you arrange these courses into a sequence that satisfies all prerequisites.

To achieve this, the algorithm repeatedly selects nodes with no incoming dependencies and places them in order. Each time you remove a node, dependencies change, making new nodes eligible. The result is a linear sequence that respects all constraints.

Canonical problem: Course Schedule—Given a number of courses and prerequisites, determine if it’s possible to complete all courses.

MAANG mini-case study: Google regularly asks interviewees questions involving dependency resolutions, such as build orders for software compilation or task scheduling with constraints. Candidates familiar with Topological Sort can quickly implement efficient solutions and clearly articulate the dependency structure, impressing interviewers with their structured thinking and practical experience.

9. Backtracking#

When to use

Backtracking is perfect for problems where you need to systematically explore all possible solutions or combinations, typically for permutations, combinations, puzzles (like Sudoku), or constraint-satisfaction problems. It’s beneficial when the problem space is ample, but it can be reduced by pruning invalid options early.

Visual intuition

Think of Backtracking as navigating through a decision tree. At every step, you make a choice, proceed down that path, and see where it takes you. If you encounter a path that leads to no valid solution, you “backtrack,” reverse that choice, and explore another possibility.

Backtracking is a brute-force approach enhanced by intelligently pruning paths that clearly won’t lead to solutions. It ensures that you exhaustively explore valid possibilities without wasting time on clearly invalid scenarios.

Canonical problem: Word Search—Given a 2D grid of characters and a word, we need to determine if the word can be constructed from letters of sequentially adjacent cells.

MAANG mini-case study: Meta frequently asks backtracking-based interview questions, such as finding combinations or subsets that meet specific constraints. Candidates skilled at backtracking demonstrate their ability to navigate complex problem spaces, make efficient decisions about paths worth exploring, and impress interviewers with systematic thinking.

10. Union-Find (Disjoint Set)#

When to use

Use Union‑Find (also called Disjoint Set Union) to manage and query dynamic connectivity between elements. It’s especially useful in graph problems where you repeatedly check whether two nodes are in the same connected component or need to merge components, such as detecting cycles, counting isolated groups, or managing network connections.

Visual intuition

Think of Union-Find as a way to track friend groups at a party. Initially, everyone is in their group. When two people meet and become friends, their separate groups merge into one larger group. Union-Find efficiently manages this grouping and merging operation, letting you quickly determine whether two people are already part of the same friend group.

The algorithm typically consists of two primary operations:

1. Find: Determine the group (or parent) to which an element belongs.

2. Union: Combine two groups into one larger group.

Optimizations like “Path Compression” and “Union by Rank” make this process highly efficient, ensuring near constant time complexity for each operation.

Canonical problem: Number of Provinces—Given an adjacency matrix representing connections between cities, find the number of disconnected groups of cities (provinces).

MAANG mini-case study: Meta frequently asks Union-Find related interview questions, especially handling social network graphs or determining user groups based on connections. Candidates who recognize and apply Union-Find quickly produce optimized solutions, demonstrating algorithmic proficiency to interviewers.

11. Monotonic Stack#

When to use

Use a Monotonic Stack or Queue when dealing with problems finding the next greater or smaller element in an array. These problems usually require maintaining elements in strictly increasing or decreasing order.

Visual intuition

Imagine you are processing stock prices day by day. You want to know how far ahead you have to look to find a higher price each day. As you scan, you keep a stack of prices from previous days in decreasing order. When you encounter a higher price, you pop from the stack until the order is restored, instantly finding answers for multiple days.

Canonical problem: Daily Temperatures—Given an array of daily temperatures, return an array indicating how many days it takes to get a warmer temperature.

MAANG mini-case study: Google frequently tests candidates with problems involving monotonic stacks, such as stock price fluctuations or weather pattern analysis. Recognizing and applying monotonic structures immediately improves your solution’s efficiency, often making the difference between an average solution and one that demonstrates algorithmic proficiency.

12. Dynamic Programming (DP) - Advanced#

When to use

Use Dynamic Programming (DP) when a problem can be broken into smaller overlapping subproblems, and you can reuse solutions to those subproblems. Typical signs include problems that ask you to count the number of ways to do something, find an optimal result (like minimum cost or maximum value), or work with sequences where results depend on previous states.

Visual intuition

Imagine solving a complex puzzle that involves solving smaller puzzles within it. Rather than repeatedly solving these smaller puzzles from scratch, you store their solutions the first time you solve them. Later, when the same smaller puzzle reappears, you reuse the stored solution instead of recalculating it.

Dynamic Programming follows this logic exactly. It uses memoization or tabulation to store solutions of overlapping subproblems, thus reducing redundant calculations. The result is faster performance compared to brute-force approaches.

Canonical problem: Longest Increasing Subsequence—Given an integer array, find the length of the longest strictly increasing subsequence.

MAANG mini-case study: MAANG coding interviews frequently have Dynamic Programming questions due to their complexity and importance. Amazon often asks candidates to solve optimization problems involving profit maximization or resource allocation, and candidates who recognize and implement DP approaches consistently perform better, delivering more optimal and efficient solutions.

Common coding interview mistakes#

Even experienced engineers hit snags during live interviews. The challenge is not just solving problems but doing so under time pressure while thinking out loud. Many candidates get stuck because they apply the wrong pattern or overlook a more straightforward approach. Recognizing these traps early can save valuable minutes and show interviewers you know how to course‑correct.

Below is a quick reference table of common symptoms, what they usually mean, and how to adjust your approach:

Next steps#

Now that you have a clear picture, it’s time to practice with intent. Don’t just solve random problems—focus on representative problems for each common coding pattern. The Blind 75 list is a great starting point: it’s a curated set of high-yield problems that reflect what you’re likely to face in real interviews.

Work through them until you can solve each one from scratch. Build intuition, not just memory.

More patterns worth exploring:

Once you’ve mastered the top core patterns, other patterns can also be valuable, especially in senior‑level interviews or domain‑specific roles. Consider adding these to your practice plan over time:

• K‑way Merge: Useful for merging multiple sorted lists or streams, often with a heap.

• Bitwise Manipulation: Great for problems involving parity, unique elements, or low‑level optimizations.

• Math and Geometry: Patterns around number theory, modular arithmetic, convex hulls, and distance calculations; these show up in optimization and computational geometry problems.

Recommended resources#

Here are a few resources that can make your prep far more effective:

• Educative PAL (Personalized Adaptive Learning) tool: PAL analyzes your progress and adapts problem sets to your weak areas. It’s like having a coach who knows exactly where you need more practice. (It is highly recommended if you want a structured path rather than figuring it out alone.)

• AI Mock Interviews: Practice is one thing, performing under interview pressure is another. Educative’s AI‑powered mock interviews simulate real technical interviews and provide you with on‑the‑spot hints and detailed feedback to help you improve. It’s a safe way to build confidence before the actual day.

• Grokking the Coding Interview Patterns: A completely updated flagship course, covering all these patterns with interactive examples, step‑by‑step breakdowns, and interview‑style walkthroughs.

• Data Structures and DP Course: Build strong fundamentals that power these patterns.