12 Popular Patterns To Ace Coding Interviews

If you’re preparing for coding interviews, you know it can feel overwhelming at times. But the good news is most coding problems fall into these 12 patterns. If you can master these, you’ll be better equipped to tackle even the trickiest questions.

Here, we’ll break down 12 essential patterns, complete with practical tips and plenty of examples to help you practice. Let’s get started!

1. Arrays

Arrays are everywhere in coding interviews. Whether it’s traversals, sorting, or searching, knowing how to work with arrays is foundational.

Key Techniques:

• Sort the array and apply two-pointer techniques for efficiency.
• Use hash maps to track counts or manage duplicates.
• Use prefix sums for range-based queries.

Example Problems:

1. Find the maximum subarray sum (Kadane’s Algorithm).
2. Merge two sorted arrays.
3. Rotate an array by k steps.
4. Find all pairs with a given sum.
5. Move all zeroes to the end of the array.
6. Check if an array contains duplicates.
7. Find the product of all elements except self.

2. Backtracking

Backtracking is about exploring all possibilities and rolling back when a path doesn’t work. It’s perfect for problems with constraints and multiple solutions.

Key Techniques:

• Use recursion to explore potential solutions.
• Prune paths that don’t meet the conditions to save time.
• Track visited states with sets or arrays.

Example Problems:

1. Solve the N-Queens problem.
2. Find all subsets of a set.
3. Generate all permutations of a string or array.
4. Solve a Sudoku puzzle.
5. Word search in a grid.
6. Find all unique combinations that sum to a target.
7. Generate valid parentheses.

3. Dynamic Programming (DP)

DP is a powerhouse for optimization problems. It’s all about solving smaller subproblems and combining them to find the overall solution.

Key Techniques:

• Use memoization for top-down approaches.
• Use tabulation for bottom-up approaches.
• Clearly define states and transitions.

Example Problems:

1. Find the longest increasing subsequence.
2. Solve the 0/1 Knapsack problem.
3. Compute the nth Fibonacci number.
4. Find the minimum path sum in a grid.
5. Calculate the number of unique paths in a matrix.
6. Partition an array into subsets with equal sums.
7. Determine if a string matches a pattern (regex matching).

4. Fast & Slow Pointers

This pattern is a simple yet effective way to detect cycles and find critical points in linked lists and arrays.

Key Techniques:

• Use two pointers moving at different speeds.
• Fast pointer usually moves twice as quickly as the slow pointer.
• Use the meeting point to derive additional information.

Example Problems:

1. Detect a cycle in a linked list.
2. Find the starting node of a cycle.
3. Find the middle of a linked list.
4. Check if a linked list is a palindrome.
5. Determine if a number is a happy number.
6. Find the intersection point of two linked lists.
7. Rearrange a linked list around a pivot.

5. Graph Traversal

Graphs can represent everything from social networks to mazes. Traversal algorithms like BFS and DFS help you explore them efficiently.

Key Techniques:

• Represent graphs using adjacency lists or matrices.
• Use BFS for shortest paths and level-by-level exploration.
• Use DFS for connected components and pathfinding.

Example Problems:

1. Find all paths between two nodes in a graph.
2. Detect cycles in a directed graph.
3. Solve a maze using DFS.
4. Implement a social network friend suggestion algorithm.
5. Count the number of islands in a grid.
6. Find the shortest path in an unweighted graph.
7. Topologically sort a directed acyclic graph (DAG).

6. K-Way Merge

When working with multiple sorted arrays or lists, merging them efficiently is key. This pattern makes use of heaps for optimal performance.

Key Techniques:

• Use a min-heap to keep track of the smallest elements.
• Merge two lists at a time or use divide-and-conquer.
• Keep track of indices for sorted arrays.

Example Problems:

1. Merge k sorted arrays.
2. Find the smallest range in k sorted lists.
3. Sort a nearly sorted array.
4. Find the k-th smallest element in a matrix.
5. Merge k sorted linked lists.
6. Find the median of two sorted arrays.
7. Find common elements in k sorted arrays.

7. Binary Search

Binary Search excels in scenarios with sorted or monotonic data. It reduces the search space logarithmically, making it highly efficient.

Key Techniques:

• Identify the search space and pivot logic.
• Handle edge cases like duplicates or empty ranges.
• Adapt binary search to solve optimization problems.

Example Problems:

1. Search for an element in a rotated sorted array.
2. Find the square root of a number.
3. Find the peak element in an array.
4. Allocate minimum pages in a book allocation problem.
5. Determine the position to insert a number in a sorted array.
6. Find the smallest missing positive integer.
7. Count occurrences of a number in a sorted array.

8. Sliding Window

The sliding window is perfect for problems involving contiguous subarrays or substrings. It keeps track of the current window and adjusts it dynamically.

Key Techniques:

• Expand and shrink the window based on conditions.
• Use hash maps for tracking elements within the window.
• Optimize by maintaining only relevant elements in the window.

Example Problems:

1. Find the maximum sum of a subarray of size k.
2. Longest substring without repeating characters.
3. Smallest substring containing all characters of a target string.
4. Maximum number of fruits in baskets.
5. Find all anagrams of a pattern in a string.
6. Longest substring with at most k distinct characters.
7. Minimum window substring.

9. Top K Elements

This pattern is useful for finding the most significant elements in a dataset. Heaps are often the go-to data structure here.

Key Techniques:

• Use a min-heap or max-heap to maintain the top k elements.
• Use bucket sort for frequency-based problems.
• Apply partitioning (Quickselect) for optimized solutions.

Example Problems:

1. Find the k largest elements in an array.
2. Find the k most frequent elements in a dataset.
3. Sort characters by frequency.
4. Find k closest points to the origin.
5. Find the top k frequent words in a document.
6. Find the k smallest elements in a matrix.
7. Reorganize a string with character frequency constraints.

10. Topological Sort

This pattern is critical for problems involving dependency resolution. It’s typically used in Directed Acyclic Graphs (DAGs).

Key Techniques:

• Use in-degrees to track dependencies.
• Apply BFS-based Kahn’s Algorithm or DFS for ordering.
• Use stacks to store the sorted order.

Example Problems:

1. Find the order of courses to take (Course Schedule problem).
2. Task scheduling with prerequisites.
3. Determine the build order in a dependency graph.
4. Alien dictionary problem.
5. Find if a cycle exists in a dependency graph.
6. Reconstruct a sequence from a topological order.
7. Find all topological orders of a DAG.

11. BFS & DFS

Breadth-First Search (BFS) and Depth-First Search (DFS) are indispensable tools for exploring graphs.

Key Techniques:

• BFS uses a queue for level-by-level traversal.
• DFS uses a stack or recursion to explore depth-first.
• Mark visited nodes to avoid infinite loops.

Example Problems:

1. Find the shortest path in an unweighted graph.
2. Determine if there is a path between two nodes.
3. Count the number of islands in a grid.
4. Solve a maze using BFS.
5. Find all connected components in a graph.
6. Clone a graph.
7. Detect cycles in a graph using DFS.

12. Two Pointers

The two-pointer technique shines in problems involving sorted arrays or linked lists, often optimizing time complexity.

Key Techniques:

• Place one pointer at the start and another at the end.
• Move pointers inward based on conditions.
• Skip duplicates or unnecessary elements dynamically.

Example Problems:

1. Find a pair with a target sum.
2. Find a triplet that sums to zero.
3. Remove duplicates from a sorted array.
4. Merge two sorted arrays in-place.
5. Partition an array around a pivot.
6. Move zeroes to the end of an array.
7. Check if a string is a palindrome.

Conclusion

Coding interviews are challenging, but mastering these patterns can make a world of difference. Practice these techniques with real problems, and you’ll start to see these patterns everywhere. Consistency and effort are your best allies. Good luck—you’ve got this!