Mastering Coding Interviews: 15 Essential Patterns to Solve Any Problem
Coding interviews are a cornerstone of tech hiring, but their unpredictability can be daunting. The key to success lies in recognizing recurring problem-solving patterns. By mastering these patterns, you can efficiently tackle a wide range of questions, from array manipulations to graph traversals. In this article, we’ll break down 15 essential coding patterns, their use cases, and examples to help you ace your next interview.
1. Sliding Window
Use Case: Subarrays, substrings, or sequences where you need to find averages, sums, or optimize contiguous data.
Example Problem: Find the maximum sum of a subarray of size k.
Approach:
- Maintain a window (start and end pointers).
- Slide the window by adjusting pointers while computing results incrementally.
Why It Works: Reduces time complexity from O(n²) to O(n).
Easy examples:
- Maximum Sum Subarray of Size K
- Smallest Subarray with a given sum Educative.io
2. Two Pointers
Use Case: Sorted arrays, linked lists, or paired elements.
Example Problem: Find a pair of numbers in a sorted array that sum to a target.
Approach:
- Use left/right pointers to traverse from both ends.
- Adjust pointers based on comparisons (e.g., move left if the sum is too small).
Variants: Fast & Slow Pointers (for cycle detection in linked lists).
Easy examples:
3. Fast & Slow Pointers (Tortoise and Hare)
Use Case: Detect cycles, find middle nodes, or palindrome checks.
Example Problem: Detect a cycle in a linked list.
Approach:
- Advance the fast pointer by 2 steps and the slow by 1.
- If they meet, a cycle exists.
Easy examples:
1. LinkedList Cycle Leetcode
2. Middle of the LinkedList Leetcode
4. Merge Intervals
Use Case: Overlapping intervals or conflicting appointments.
Example Problem: Merge overlapping intervals.
Approach:
- Sort intervals by start time.
- Iterate and merge if the current interval overlaps with the previous.
5. Cyclic Sort
Use Case: Arrays with elements in a known range (e.g., 1 to n).
Example Problem: Find the missing number in an array of 1 to n.
Approach:
- Place each number in its correct index (e.g., 3 at index 2).
- Traverse to find mismatches indicating missing/duplicate numbers.
Easy examples:
- Find the Duplicate Number Leetcode
- Find the Missing Number Leetcode
- Cyclic Sort Geeksforgeeks
- Find all Missing Numbers Leetcode
- Problem Challenge 1: Find the Corrupt Pair TheCodingSimplified
- Find all Duplicate Numbers Leetcode
6. In-Place Reversal
Use Case: Reverse linked lists or sublists without extra memory.
Example Problem: Reverse a linked list in groups of k.
Approach:
- Use pointers to track previous, current, and next nodes.
- Reverse links iteratively.
Easy example: Reverse a LinkedList Leetcode
7. Tree BFS (Breadth-First Search)
Use Case: Level-order traversal, shortest path in unweighted graphs.
Example Problem: Zigzag traversal of a binary tree.
Approach:
- Use a queue to process nodes level by level.
Easy examples:
8. Tree DFS (Depth-First Search)
Use Case: Path sum, subtree checks, or permutations.
Example Problem: Validate a binary search tree.
Approach:
- Recursive or stack-based traversal with bounds checking.
Easy example: Binary Tree Path Sum Leetcode
9. Two Heaps
Use Case: Streams of data requiring median tracking or partitioning.
Example Problem: Find the median of a data stream.
Approach:
- Use a max-heap for the lower half and a min-heap for the upper half.
- Balance heaps to quickly compute medians.
10. Subsets/Backtracking
Use Case: Combinations, permutations, or exhaustive searches.
Example Problem: Generate all subsets of a set.
Approach:
- Recursively build subsets, adding/removing elements (backtrack).
Easy examples:
- Subsets With Duplicates Educative.io
- Subsets Educative.io
11. Modified Binary Search
Use Case: Rotated sorted arrays, unknown-order searches.
Example Problem: Search in a rotated sorted array.
Approach:
- Compare mid with start/end to determine the sorted half.
- Adjust search space based on target position.
Easy examples:
- Bitonic Array Maximum Geeksforgeeks
- Order-agnostic Binary Search Geeksforgeeks
12. Top K Elements
Use Case: Frequent elements, largest/smallest values.
Example Problem: Find the top k frequent numbers.
Approach:
- Use a min-heap to track the top k elements (O(n log k)).
- Alternatively, QuickSelect for O(n) average time.
Easy examples:
13. K-way Merge
Use Case: Merge k sorted lists or arrays.
Example Problem: Merge k sorted linked lists.
Approach:
- Use a heap to track the smallest element from each list.
14. Topological Sort
Use Case: Task scheduling, dependency resolution.
Example Problem: Course schedule with prerequisites.
Approach:
- Kahn’s algorithm (in-degree tracking with BFS).
- DFS-based post-order traversal.
15. Dynamic Programming (0/1 Knapsack)
Use Case: Optimization problems with constraints.
Example Problem: Maximize value in a knapsack with weight limits.
Approach:
- Build a DP table where dp[i][w] = max value for first i items and weight w.
Bonus Patterns
- Bitwise XOR: Find missing numbers or unique elements.
- Union-Find: Detect cycles in undirected graphs.
- Monotonic Stack: Next greater element or histogram areas.
How to Practice Effectively
- Pattern-First Approach: Categorize problems by pattern.
- Repetition: Solve similar problems to reinforce recognition.
- Mock Interviews: Simulate real conditions to build speed and accuracy.
Resources
- https://dev.to/arslan_ah/20-essential-coding-patterns-to-ace-your-next-coding-interview-32a3
- https://github.com/dipjul/Grokking-the-Coding-Interview-Patterns-for-Coding-Questions?tab=readme-ov-file
- https://hackernoon.com/14-patterns-to-ace-any-coding-interview-question-c5bb3357f6ed
- https://dvpr.gitbook.io/coding-interview-patterns
- LeetCode, HackerRank, or AlgoExpert for practice.
By internalizing these patterns, you’ll transform coding interviews from chaotic challenges into systematic exercises. Remember, practice and pattern recognition are your greatest allies — start today! 🚀
