14 problem solving patterns to crack any coding interview 2025

Coding interviews are intimidating, but they generally follow fairly predictable patterns. Knowing and mastering these patterns will greatly enhance your skills to solve problems and prepare you for a wide variety of questions. This article explores 14 basic patterns that commonly appear in coding interviews along with examples and practical application.

1. Sliding Window

Use case:

This pattern is best suited for problems that deal with contiguous subarrays or substrings.

Key Idea:

Keep a window of variable size and slide it over the data to find the result. The size of the window can expand or contract based on the requirements of the problem.

Example Problems:

Find the maximum subarray sum of size K.
Find the longest substring which contains at most two distinct characters.

Practical tips:

Fix-size window approach, optimize dynamically by shrinking or expanding the window.

2. Two Pointers

When to Use:

You should use this pattern for sorted array problems and scenarios where you have to find pairs or triplets.

Key Idea:

Use two pointers, which either traverse towards each other or traverse an array from both the ends.

Example Problems:

Pair with a Target Sum: Return two numbers from a sorted array adding up to the target.
Trapping Rain Water: Calculate amount of water caught after rain falls.

Practical Tips:

This pattern is efficient in reducing the time complexity from nested loops when it could appear in a solution.

3. Fast and Slow Pointers (Tortoise and Hare)

When to Apply:

This is used to solve cyclic patterns when finding a value in a linked list or even in an array.

Key Concept:

Use two pointers moving at different speeds to detect cycles or find the midpoint of a linked list.

Example Problems:

Cycle Detection in a Linked List: Determine if a linked list has a cycle.
Finding the Middle of a Linked List: Locate the midpoint of a linked list in one pass.

Practical Tips:

This is especially useful in problems where memory usage needs to be minimal.

4. Merge Intervals

When to Use:

Use this pattern when solving problems where the intervals overlap.

Important Idea:

Order intervals by their starting times and then merge overlapping intervals.

Example Problems:

Merge all the overlapping intervals into one.
Insert a new interval into an ordered list.

Tips for Practical Use:

To make the merge process easier, first order the intervals.

5. Cyclic Sort

When to Apply:

Apply this pattern in problems where arrays are used to hold numbers from some range; this is most commonly between 1 and N.

Important Idea:

Place each number at its correct index.

Example Problems:

Find the Missing Number: Identify the missing number in an array.
Find All Duplicate Numbers: Locate duplicate numbers in an array.

Practical Tips:

This pattern often leads to O(n) solutions.

6. In-Place Reversal of Linked List

When to Use:

Use this pattern for problems that involve reversing or rotating segments of a linked list.

Key Idea:

Reverse linked list segments iteratively without using extra space.

Example Problems:

Reverse a Sub list: Reverse a given range of a linked list.
Reverse Nodes in K-Groups: Reverse nodes in groups of K.

Practical Tips:

Divide the problem into smaller subproblems to deal with reversals systematically.

7. Tree Traversal

When to Use:

Apply this pattern when the problem involves binary or N-ary trees.

Key Idea:

Explore the tree using Depth First Search (DFS) or Breadth First Search (BFS).

Example Problems:

Find the height of the binary tree.
Traverse a tree level by level in a zigzag pattern.

Tips to Practice:

Familiarize yourself with preorder, inorder, and postorder traversals for DFS.

8. BFS and DFS

When to Use:

Use these patterns for graph traversal problems.

Key Idea:

BFS is suitable for shortest path problems and level-wise traversal.
DFS is suitable for exploring all possible paths or detecting cycles.

Example Problems:

Word Ladder: Find the shortest transformation sequence from start to end.
Number of Islands: Count the number of disconnected islands in a grid.

Practice using recursion for DFS and queues for BFS

9. Backtracking

When to Use:

Use this pattern for problems requiring all possible combinations or permutations.

Key Idea:

Explore each possibility recursively, backtracking when constraints are violated.

Example Problems

N Queens problem: Place N queens on a chessboard such that no two queens threaten each other.
Find all subsets of a given set.

Practice Tips:

Focus on pruning the search space as early as possible to improve efficiency.

10. Dynamic Programming (DP)

When to Use:

This pattern is used on problems that involve overlapping subproblems and optimal substructure.

Main Idea:

Find subproblems and reuse results using a table (memoization/tabulation).

Example Problems:

Knapsack Problem: Try to maximize value items that may fit in knapsack
Longest Increasing Subsequence: Find the size of the length of the subsequence in array

Practical Tips:

Start with recursive solutions, then optimize them with memoization or tabulation

11. Greedy Algorithm

When to Use:

Use this pattern when local optimization is actually global optimization

Key Idea:

Always take the best immediate step

Example Problems:

Activity Selection Problem: Select maximum number of activities that don’t overlap
Huffman Encoding: Construct optimal prefix codes for data compression

Practical Tips:

Be sure the problem has the greedy-choice property before applying this method

12. Subsets (Power Set)

Use this pattern for problems involving all subsets or subsequences of a set.

Key Idea:

Produce subsets iteratively or recursively.

Example Problems:

Power Sets: Produce all subsets of a set.
Subset Sum Problem: Find subsets that sum to a given value.

Practical Tips:

Use this with backtracking for more complicated variations.

13. Topological Sort

When to Use:

Use this pattern for Directed Acyclic Graph (DAG) problems with dependencies.

Key Idea:

Order nodes based on dependencies using DFS or Kahn’s Algorithm.

Example Problems:

Course Schedule: Check if you can complete all courses given the prerequisites.
Task Scheduling: Schedule tasks based on their dependencies.

Practical Tips:

Try both DFS-based and queue-based solutions.

14. Binary Search

When to Use:

Use this pattern when the solution space is ordered or can be constrained.

Key Idea:

Divide the problem space in half repeatedly.

Example Problems:

Find the First Bad Version: Find the first bad version of a product in a production line.
Search in a Rotated Sorted Array: Find an element in a rotated sorted array.

Practical tips:

Expand your understanding of binary search to apply it to non-standard problems involving real numbers or monotonic functions.

Pro Tip: Master patterns, not just problems

While solving problems, focus on recognizing these patterns rather than memorizing individual solutions. Coding interview questions are mostly variations or combinations of these patterns. Mastering them will not only help you solve problems faster but also give you the confidence to tackle unfamiliar questions.

For Top 5 coding patterns click here

Practice these patterns with diverse problems to build your intuition and solidify your understanding. Happy Pongal Mawa!