14 Essential Coding Interview Patterns Every Developer Should Master

When preparing for a programming interview, many candidates spend weeks solving hundreds of LeetCode problems that often have little relevance to day‑to‑day development. Fahim ul Haq, founder of Educative.io, identified fourteen recurring patterns that underlie most interview questions, allowing candidates to use a template‑based approach instead of memorizing countless isolated problems.

1. Sliding Window

The sliding window pattern operates on a subarray or sub‑list of a fixed or variable size, moving the window across the data structure to maintain a desired property (e.g., longest subarray containing only 1s). It is useful when the input is a linear structure and the task involves finding the longest/shortest substring, subarray, or a specific value.

Maximum sum of a subarray of size K (easy)

Longest substring with K distinct characters (medium)

Find anagrams of a string (hard)

2. Two Pointers / Iterators

Two pointers traverse a data structure from opposite ends (or with a fast‑slow relationship) until a condition is met. This technique is ideal for sorted arrays or linked lists when searching for pairs or comparing elements, offering better time/space complexity than brute‑force O(n²) scans.

Square of a sorted array (easy)

Three‑sum to zero (medium)

Compare strings with backspaces (medium)

3. Fast and Slow Pointers (Hare & Tortoise)

Two pointers move at different speeds through a sequence, guaranteeing they meet if a cycle exists. This method is used to detect cycles in linked lists or arrays and to find the middle of a structure.

Detect a cycle in a linked list (easy)

Check if a linked list is a palindrome (medium)

Find a cycle in a circular array (hard)

4. Merge Intervals

When intervals overlap, this pattern merges them into a list of disjoint intervals. Recognize the need when the problem asks for a list of non‑overlapping intervals or mentions “overlapping intervals”.

Interval intersection (medium)

Maximum CPU load (hard)

5. Cycle Sort

Cycle sort places each element at its correct index by swapping, useful for problems that require sorting numbers within a known range without extra space. It runs in O(n) time with O(1) extra memory.

Find the missing number (easy)

Find the smallest missing positive integer (medium)

6. In‑Place Reversal of a Linked List

This pattern reverses a linked list using only existing node objects. It iterates through the list, redirecting each node’s next pointer to the previous node.

Reverse a sub‑list (medium)

Reverse every K‑node segment (medium)

7. Tree Breadth‑First Search (BFS)

BFS traverses a tree level by level using a queue. It is applicable whenever a problem requires processing nodes in order of depth.

Binary tree level order traversal (easy)

Zigzag traversal (medium)

8. Tree Depth‑First Search (DFS)

DFS explores a tree using recursion or an explicit stack, supporting pre‑order, in‑order, and post‑order traversals. It is useful when the problem needs to search deeper nodes first.

Path sum (medium)

All paths that sum to a value (medium)

9. Two Heaps

Maintain a min‑heap for the larger half and a max‑heap for the smaller half of a data stream to quickly retrieve median or other order statistics.

Find the median of a data stream (medium)

10. Subsets

Generate all subsets of a set using BFS. Start with an empty set and iteratively add each element to existing subsets.

Subsets with duplicates (easy)

String permutations with case changes (medium)

11. Modified Binary Search

Binary search on sorted arrays, linked lists, or matrices to locate a target value, with careful mid‑point calculation to avoid overflow.

Order‑agnostic binary search (easy)

Search in an infinite sorted array (medium)

12. Top K Elements

Use a heap to keep track of the K largest or smallest elements while scanning the input once.

First K numbers (easy)

Most frequent K numbers (medium)

13. K‑Way Merge

Merge K sorted lists by inserting the smallest element of each list into a min‑heap, repeatedly extracting the minimum and pushing the next element from the same list.

Merge K sorted lists (medium)

Find the K largest pair sums (hard)

14. Topological Sort

Order dependent tasks linearly by repeatedly removing nodes with zero in‑degree and updating neighbors, useful for DAGs and scheduling problems.

Task scheduling (medium)

Minimum height tree (medium)

Understanding these patterns lets developers approach interview problems methodically, recognize the underlying structure, and apply a reusable solution template rather than solving each question from scratch.