LeetCode Patterns

Master the top 10 algorithmic patterns that solve 80% of LeetCode problems. Learn the approaches, see the code, and dominate your interviews.

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LeetCode Patterns
Design Patterns

Top 10 Essential Patterns

Click any pattern below to see detailed explanations, code examples, and complexity analysis

Two Pointers

#1

Use two pointers moving towards each other or in the same direction to solve problems efficiently.

Time:O(n)
Space:O(1)

Problems: Two Sum II, 3Sum...

Real-world: Database Query Optimization...

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Sliding Window

#2

Maintain a window of elements and slide it across the array to find optimal solutions.

Time:O(n)
Space:O(1) to O(k)

Problems: Longest Substring, Minimum Window...

Real-world: Performance Monitoring...

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Fast & Slow Pointers

#3

Use two pointers moving at different speeds to detect cycles or find middle elements.

Time:O(n)
Space:O(1)

Problems: Linked List Cycle, Find Duplicate Number...

Real-world: Infinite Loop Detection...

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Merge Intervals

#4

Sort intervals and merge overlapping ones to solve scheduling and range problems.

Time:O(n log n)
Space:O(n)

Problems: Merge Intervals, Insert Interval...

Real-world: Calendar & Scheduling Systems...

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Cyclic Sort

#5

Place each number at its correct index when dealing with arrays containing numbers in a given range.

Time:O(n)
Space:O(1)

Problems: Missing Number, Find Duplicates...

Real-world: Data Validation & Integrity...

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In-place Reversal of LinkedList

#6

Reverse parts of a linked list without using extra memory.

Time:O(n)
Space:O(1)

Problems: Reverse Linked List, Reverse Nodes in k-Group...

Real-world: Undo/Redo Functionality...

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Tree BFS

#7

Use breadth-first search to traverse trees level by level.

Time:O(n)
Space:O(w) where w is max width

Problems: Level Order Traversal, Zigzag Traversal...

Real-world: UI Component Rendering...

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Tree DFS

#8

Use depth-first search to traverse trees and solve path-related problems.

Time:O(n)
Space:O(h) where h is height

Problems: Path Sum, Path Sum II...

Real-world: Decision Tree Analysis...

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Two Heaps

#9

Use two heaps (min and max) to efficiently find medians or solve optimization problems.

Time:O(log n) for insertion
Space:O(n)

Problems: Find Median, Sliding Window Median...

Real-world: Real-time Analytics...

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Subsets

#10

Generate all possible combinations, permutations, or subsets using backtracking.

Time:O(2^n) for subsets
Space:O(n) recursion depth

Problems: Subsets, Subsets II...

Real-world: Feature Flag Combinations...

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