14 Essential Coding Interview Patterns Every Developer Should Master

1. Sliding Window

The sliding window pattern processes a contiguous sub‑range (window) of an array, string, or linked list. The window can be fixed‑size or variable‑size and moves forward one element at a time, expanding or shrinking based on problem constraints. It is ideal when the task asks for the longest/shortest sub‑array or substring that satisfies a condition.

Identify: input is a linear structure (array, string, list) and the problem requires a longest/shortest contiguous segment.

Example problems:

Maximum sum of a subarray of size K (easy)

Longest substring with K distinct characters (medium)

Find all anagrams of a pattern in a string (hard)

2. Two Pointers (Iterators)

Two pointers start at opposite ends (or at a fixed offset) of a sorted array or linked list and move toward each other until a condition is met. This reduces a naïve O(n²) scan to O(n) time and uses O(1) extra space.

Identify: the input is sorted and you need to locate pairs, triples, or compare elements.

Example problems:

Square each element of a sorted array (easy)

3‑sum problem (medium)

Compare strings with backspace characters (medium)

3. Fast and Slow Pointers (Hare & Tortoise)

Two pointers traverse the same structure at different speeds (typically 1× and 2×). The fast pointer eventually meets the slow pointer in a cycle, enabling detection of loops, finding the middle of a list, or checking palindromes.

Identify: the problem involves a cyclic structure or requires the middle element of a list/array.

Example problems:

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. Interval Merging

When intervals overlap, merge them into a set of disjoint intervals. The algorithm sorts intervals by start time and iteratively combines overlapping ones.

Identify: the statement mentions “overlapping intervals” or asks for a list of non‑overlapping intervals.

Example problems:

Interval intersection (medium)

Maximum CPU load (hard)

5. Cyclic Sort

For arrays containing numbers in the range 1..n (or a known range), place each element at its correct index by swapping. This runs in O(n) time and O(1) space.

Identify: the array consists of values within a known contiguous range and the task asks for missing, duplicate, or minimum values.

Example problems:

Find the missing number (easy)

Find the smallest missing positive integer (medium)

6. In‑Place List Reversal

Reverse a linked list (or a sub‑list) by re‑linking nodes using only existing pointers. Iterate through the list, redirect each node’s next to the previous node, and advance.

Identify: the problem requires reversing a list without extra memory.

Example problems:

Reverse a sub‑list (medium)

Reverse every K nodes (medium)

7. Tree Breadth‑First Search (BFS)

BFS traverses a tree level by level using a queue. Enqueue the root, then repeatedly dequeue a node, process it, and enqueue its children.

Identify: the task asks for level‑order processing of a tree.

Example problems:

Binary tree level order traversal (easy)

Zigzag (spiral) traversal (medium)

8. Tree Depth‑First Search (DFS)

DFS explores a tree using recursion or an explicit stack. It can be performed in pre‑order, in‑order, or post‑order depending on when the node is visited relative to its children.

Identify: the problem requires a specific ordering of node visits or searching deeper nodes first.

Example problems:

Path sum (medium)

All root‑to‑leaf paths that sum to a target (medium)

9. Two Heaps

Maintain a max‑heap for the lower half of a data stream and a min‑heap for the upper half. The median is either the top of one heap or the average of both tops, allowing O(log n) insertion and O(1) median retrieval.

Identify: the problem asks for the median, or the smallest/largest element of a dynamic set.

Example problem:

Find the median of a number stream (medium)

10. Subsets (Power Set)

Generate all subsets of a set by iteratively adding each element to existing subsets (BFS style). Starting from [[]], for each element create new subsets that include it.

Identify: the task involves enumerating all combinations or permutations of a collection.

Example problems:

Subsets with duplicate elements (easy)

String permutations with case changes (medium)

11. Modified Binary Search

Standard binary search on a sorted array, list, or matrix with careful midpoint calculation to avoid overflow: mid = left + (right‑left)/2. Adjust left or right based on comparison, repeat until left > right.

Identify: the input is sorted and the problem asks for a target value.

Example problems:

Order‑agnostic binary search (easy)

Search in an infinite sorted array (medium)

12. Top K Elements

Use a heap of size K to keep the smallest (min‑heap) or largest (max‑heap) K elements while scanning the data set. When a new element outranks the heap’s root, replace the root and heapify.

Identify: the problem asks for the first K numbers, the most frequent K, or similar.

Example problems:

First K numbers (easy)

Most frequent K numbers (medium)

13. K‑Way Merge

Merge K sorted arrays by inserting the first element of each array into a min‑heap. Repeatedly extract the smallest element, append it to the result, and push the next element from the same array. Continue until the heap is empty.

Identify: the input consists of multiple sorted lists that need to be merged into a single sorted sequence.

Example problems:

Merge K sorted lists (medium)

Find K pairs with the largest sums (hard)

14. Topological Sort

Topological sorting orders vertices of a directed acyclic graph (DAG). Compute in‑degree for each vertex, enqueue all vertices with in‑degree 0, then repeatedly dequeue a vertex, add it to the ordering, and decrement the in‑degree of its neighbors, enqueuing any that become 0.

Identify: the problem involves ordering tasks with dependencies, or finding a linear order of elements where some must precede others.

Example problems:

Task scheduling (medium)

Find the minimum height of a tree (medium)