The Patterns Behind Most Coding Interview Questions

20 recurring algorithmic techniques that appear again and again in product company interviews.

Coding interview problems often look very different on the surface.

But if you solve enough problems, you start noticing something interesting:

Most questions are built on top of a small set of recurring algorithmic patterns.

Once you learn to recognize these patterns, solving new problems becomes much easier because you’re no longer starting from scratch. Instead, you’re identifying the technique behind the question.

Below are 20 important DSA patterns that cover a large portion of coding interview questions asked in product companies.

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1. Prefix Sum

Used for range queries and subarray sum problems.

Examples:

• Subarray Sum Equals K

• Continuous Subarray Sum

• Range Sum Query

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2. Kadane’s Algorithm

Used for maximum subarray type optimization problems.

Examples:

• Maximum Subarray

• Maximum Circular Subarray

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3. Two Pointers (Opposite Direction)

Typically used when working with sorted arrays or scanning from both ends.

Examples:

• Two Sum (sorted array)

• Container With Most Water

• 3Sum

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4. Two Pointers (Slow–Fast)

Often used in linked lists or cycle detection problems.

Examples:

• Detect Cycle in Linked List

• Find Middle of Linked List

• Happy Number

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5. Sliding Window (Fixed Size)

Used when the window size remains constant.

Examples:

• Maximum Sum Subarray of Size K

• Average of Subarray of Size K

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6. Sliding Window (Variable Size)

Used when the window expands and shrinks based on a condition.

Examples:

• Longest Substring Without Repeating Characters

• Minimum Window Substring

• Longest Repeating Character Replacement

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7. HashMap / Frequency Counting

Extremely common in interview problems involving counts and lookups.

Examples:

• Two Sum

• Group Anagrams

• Top K Frequent Elements

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8. Monotonic Stack

Used for next greater/smaller element problems.

Examples:

• Next Greater Element

• Daily Temperatures

• Largest Rectangle in Histogram

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9. Binary Search (Classic)

Used for searching elements in sorted arrays.

Examples:

• Binary Search

• First / Last Occurrence

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10. Binary Search on Answer

Binary search can also be applied on the answer space.

Examples:

• Koko Eating Bananas

• Capacity to Ship Packages

• Minimum Eating Speed

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11. Merge Intervals

Used when dealing with overlapping ranges.

Examples:

• Merge Intervals

• Insert Interval

• Meeting Rooms

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12. Heap / Priority Queue

Useful for Top-K problems and streaming data.

Examples:

• Kth Largest Element

• Top K Frequent Elements

• Merge K Sorted Lists

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13. BFS (Level Order Traversal)

Commonly used in tree traversal problems.

Examples:

• Binary Tree Level Order Traversal

• Minimum Depth of Binary Tree

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14. DFS (Recursive Traversal)

Used for exploring tree structures and recursive computations.

Examples:

• Path Sum

• Diameter of Binary Tree

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15. Graph BFS / DFS

Used in graph traversal and connected component problems.

Examples:

• Number of Islands

• Clone Graph

• Flood Fill

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16. Topological Sort (Kahn’s Algorithm)

Used for dependency ordering problems.

Examples:

• Course Schedule

• Course Schedule II

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17. Backtracking

Used when generating all possible combinations or permutations.

Examples:

• Subsets

• Permutations

• Combination Sum

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18. Union Find (Disjoint Set)

Useful for connected components and grouping problems.

Examples:

• Number of Connected Components

• Accounts Merge

• Redundant Connection

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19. Dynamic Programming (1D)

Used for sequential optimization problems.

Examples:

• Climbing Stairs

• House Robber

• Coin Change

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20. Dynamic Programming (2D / String)

Used for string comparison and subsequence problems.

Examples:

• Longest Common Subsequence

• Edit Distance

• Longest Palindromic Substring

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Final Thought

Many interview questions may appear different, but they often rely on a small number of fundamental patterns.

Once these patterns become familiar, new problems start looking like variations of something you have already solved.

And that’s when coding interviews start feeling much more manageable.

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