The Ultimate List of Coding Patterns for Tech Interviews

Let’s face it—coding interviews are tough, and prepping for them can feel like an endless grind.

Spending weeks poring over random coding questions or practicing thousands of LeetCode problems isn’t exactly the most efficient way to prepare, but it’s what a lot of us end up doing.

What if there was a smarter approach? Instead of diving into hundreds of unrelated problems, what if you could focus on patterns?

Think of it like learning a handful of strategies that you can apply to dozens of problems. Rather than memorizing solutions, you learn to recognize the underlying structure of the problem and apply a known approach.

In this blog, we’ll explore the top coding patterns that can save you time and make your coding interviews less exhausting.

Why Are Coding Patterns Important

With thousands of problems available on famous platforms like LeetCode and HackerRank, it’s easy to feel overwhelmed. Candidates get confused about where and how to start.

Focusing on coding patterns allows you to practice in a more targeted manner.

For instance, instead of randomly solving problems, you can concentrate on mastering patterns like "Dynamic Programming" or "Divide and Conquer." This approach ensures that you cover a broader range of problems without wasting time on irrelevant ones.

It's not just about finding the right answer in the interview; it's also about explaining your thought process. Coding patterns help you articulate your solution clearly and logically.

For example, when you're applying the "Greedy" pattern, you can explain why you're making certain choices step-by-step.

Moreover, coding patterns are essential because they provide a blueprint for solving common problems in software development.

Rather than figuring out every problem from scratch, patterns help you recognize familiar structures and apply tested solutions. This not only makes coding faster but also ensures you avoid common pitfalls, promotes code reuse, and improves code readability.

For instance, if you’re dealing with problems involving backtracking, knowing patterns like "Depth-First Search" can save hours of trial and error. These coding patterns are flexible and can be adapted to different problems with small tweaks.

Top 43 Coding Patterns for Interviews

If you want to prepare efficient problem-solving techniques, you need to familiarize certain patterns.

After extensive research, we have compiled a list of the top 43 patterns that can help you grok any coding interview.

Let us discuss them quickly:

Pattern 1: Two Pointers

The two pointers pattern uses two indices, usually starting at different positions, to traverse a data structure like an array.

It helps solve problems efficiently by reducing the need for nested loops, making it faster for tasks like finding pairs or reversing elements.

Problems

1. Pair with Target Sum

2. Remove Duplicates

3. Squaring a Sorted Array

Pattern 2: Fast and slow pointers

The fast and slow pointers pattern uses two pointers that move at different speeds to detect cycles or find key positions in linked lists or arrays. It’s often used to identify the middle of a list or check for loops efficiently.

Problems

1. LinkedList Cycle

2. Middle of the LinkedList

3. Palindrome LinkedList

Pattern 3: Sliding Window

The sliding window pattern solves problems by creating a fixed or variable window that moves over a data structure like an array or string.

It’s useful for tasks like finding subarrays with a certain sum or the longest substring with unique characters.

Problems

1. Maximum Sum Subarray of Size K

2. Smallest Subarray with a given sum

3. Longest Substring with K Distinct Characters

Pattern 4: Merge Intervals

Merge intervals pattern involves combining overlapping intervals into one, simplifying a collection of time ranges or events.

It helps in scheduling tasks, managing ranges, or finding gaps between intervals.

Problems

1. Merge Intervals

2. Insert Interval

3. Intervals Intersection

Pattern 5: Cyclic Sort

Cyclic sort pattern arranges numbers by placing each element in its correct position through swapping.

It’s commonly used for problems involving sequences of consecutive numbers where each element should match its index.

Problems

1. Cyclic Sort

2. Find the Missing Number

3. Find all Duplicates

Pattern 6: In-place reversal of a Linked List

The in-place reversal of a linked list pattern reverses the order of nodes in a linked list by adjusting their pointers without using extra memory.

This method efficiently flips the list in a single pass while maintaining constant space usage.

Problems

1. Reverse a LinkedList

2. Reverse a Sub-list

3. Reverse Every K-element Sub-list

Pattern 7: Stacks

Stacks follow a last-in, first-out order, where the most recent element added is the first to be removed.

It helps solve coding problems like evaluating expressions, undo operations, or tracking nested structures like parentheses.

Problems

1. Balanced Parentheses

2. Decimal to Binary Conversion

3. Sorting a Stack

Learn the technique to efficiently solve the coding problems.

Pattern 8: Monotonic Stack

The monotonic stack pattern maintains a stack where all the elements are sorted either in increasing or decreasing order.

It's useful for solving problems like finding the next greater or smaller element in a sequence efficiently.

Problems

1. Remove Nodes from Linked List

2. Next Greater Element

3. Daily Temperatures

Pattern 9: Hash Maps

The hash maps pattern stores key-value pairs, allowing fast lookups, insertions, and deletions.

It’s commonly used for counting occurrences, checking for duplicates, or mapping elements for quick access.

Problems

1. Largest Unique Number

2. Longest Palindrome

3. First non-repeating Character

Pattern 10: Breadth First Search

The breadth-first search (BFS) pattern explores nodes level by level, starting from a root node and visiting all its neighbors before moving to the next level.

This tree structure is ideal for finding the shortest path in unweighted graphs or solving problems like maze navigation.

Problems

1. Binary Tree Level Order Traversal

2. Zigzag Traversal

3. Level Order Successor

Pattern 11: Depth First Search

The depth-first search (DFS) pattern explores as far as possible along one path before backtracking. It’s used in graphs or trees to traverse nodes deeply using the tree depth and is great for tasks like finding connected components or solving puzzles.

Problems

1. Binary Tree Path Sum

2. All Paths for a Sum

3. Count Paths for a Sum

Pattern 12: Graphs

Graphs represent relationships between nodes using edges, making them useful for modeling networks, paths, or dependencies.

They help solve problems like route finding, social connections, or task scheduling.

Problems

1. Number of Provinces

2. Find if Path Exists in Graph

3. Minimum Number of Vertices to Reach All Nodes

Pattern 13: Island

The island pattern counts connected regions in a grid, like clusters of land in a map. It's typically solved using DFS or BFS to explore all neighboring cells.

Problems

1. Number of Islands

2. Biggest Island

3. Flood Fill

Pattern 14: Two Heaps

The two heaps pattern uses a min-heap and max-heap to efficiently track the median or balance data, especially in streaming or dynamic scenarios where you need to keep track of the middle values.

This pattern describes the implementation of heaps in software development.

Problems

1. Find the Median of a Number Stream

2. Sliding Window Median

3. Maximize Capital

Discover the secrets of LeetCode coding patterns.

Pattern 15: Subsets

The subsets pattern generates all possible combinations of a set’s elements.

It’s commonly used in combinatorial problems, helping to explore different selections or groupings.

Problems

1. Subsets

2. Subsets With Duplicates

3. Permutations

Pattern 16: Modified Binary Search

Modified binary search adjusts the traditional binary search algorithm to work on rotated or unsorted arrays, still providing efficient search results in logarithmic time.

Problems

1. Order-agnostic Binary Search

2. Ceiling of a Number

3. Next Letter

Pattern 17: Bitwise XOR

Bitwise XOR compares bits and helps find unique or missing numbers in sets where duplicates exist, as identical numbers cancel each other out.

It is used for finding missing or unique elements in sets of numbers.

Problems

1. Single Number

2. Two Single Numbers

3. Complement of Base 10 Number

Pattern 18: Top K Elements

The Top K elements pattern finds the largest or smallest K elements in a collection, often using a heap for efficient retrieval.

Problems

1. Top K Frequent Numbers

2. Kth Largest Number in a Stream

3. Rearrange String

Pattern 19: K-Way Merge

The K-way merge pattern combines multiple sorted lists into a single sorted list, typically using a heap to track the smallest elements from each list.

Problems

1. Merge K Sorted Lists

2. Kth Smallest Number in M Sorted Lists

3. Smallest Number Range

Pattern 20: Greedy Algorithms

Greedy algorithms build solutions step-by-step by always choosing the best option at each moment, aiming for an optimal result.

Problems

1. Valid Palindrome

2. Maximum Length of Pair Chain

3. Largest Palindromic Number

Pattern 21: Backtracking

Backtracking explores all possible solutions by trying options and undoing them if they don't lead to a valid solution, often used in constraint-satisfaction problems or puzzles.

Problems

1. Sudoku Solver

2. Factor Combinations

3. Word Search

Pattern 22: Trie

A Trie (prefix tree) organizes strings character by character, making it easy to search for prefixes or auto-complete words efficiently.

Problems

1. Index Pairs of a String

2. Search Suggestions System

3. Extra Characters in a String

Pattern 23: Topological Sort

Topological sort orders nodes in a directed acyclic graph based on dependencies, used for tasks like scheduling.

Problems

1. Task Scheduling Order

2. All Tasks Scheduling Orders

3. Alien Dictionary

Pattern 24: Union Find

Union Find tracks connected components in graphs, efficiently determining if two elements are in the same set and merging them if needed.

It helps solve coding problems related to connectivity and cycles.

Problems

1. Graph Redundant Connection

2. Number of Provinces

3. Is Graph Bipartite

Pattern 25: Ordered Set

An ordered set keeps elements sorted and provides fast access for tasks requiring both sorting and fast lookups.

Problems

1. Merge Similar Items

2. 123 Pattern

3. My Calendar

Pattern 26: Prefix Sum

Prefix sum precomputes cumulative sums in an array to answer range sum queries efficiently without recalculating sums from scratch.

Problems

1. Find the Middle Index in Array

2. Left and Right Sum Differences

3. Subarray Sum Equals K

Pattern 27: Multi-threaded

The multithreaded pattern splits tasks into smaller parts that run simultaneously on multiple processors or threads, speeding up large computations.

Problems

1. Invert Binary Tree

2. Binary Search Tree Iterator

3. Same Tree

Find more information about these patterns in Grokking the Coding Interview Patterns course by DesignGurus.

Pattern 28: 0/1 Knapsack

The 0/1 knapsack problem involves selecting items with maximum value under a weight limit, where each item can be chosen only once, solved with dynamic programming.

Problems

1. 0/1 Knapsack

2. Equal Subset Sum Partition

3. Subset Sum

Pattern 29: Unbounded Knapsack

The unbounded knapsack is a variation where items can be chosen multiple times, requiring dynamic programming to maximize value under a weight constraint.

Problems

1. Rod Cutting

2. Coin Change

3. Maximum Ribbon Cut

Pattern 30: Fibonacci Numbers

This pattern solves problems related to the Fibonacci sequence, often using dynamic programming to optimize recursive calculations.

Problems

1. Fibonacci Numbers

2. Staircase

3. House thief

Pattern 31: Palindromic Subsequence

Finds the longest subsequence in a string that reads the same forwards and backwards, typically solved with dynamic programming.

Problems

1. Longest Palindromic Subsequence

2. Longest Palindromic Substring

3. Palindromic Partitions

Pattern 32: Longest Common Substring

This pattern finds the longest shared substring between two strings, using dynamic programming for efficient comparison.

Problems

1. Longest Common Substring

2. Longest Common Subsequence

3. Shortest Common Supersequence

Check out our course on Dynamic Programming.

Pattern 33: Counting Pattern

The counting pattern uses arrays or hash maps to track occurrences of elements, solving problems like frequency counts or duplicate detection.

Problems

1. Count Elements With Maximum Frequency

2. Maximum Population Year

3. Least Number of Unique Integers after K Removals

Pattern 34: Monotonic Queue Pattern

A monotonic queue maintains a sorted order of elements and is used to optimize sliding window problems for maximum or minimum values.

Problems

1. Minimum Number of Coins for Fruits

2. Continuous Subarrays

3. Sum of Subarray Minimums

Pattern 35: Simulation Pattern

The simulation pattern mimics the steps of a system or process directly to observe how it behaves over time.

Problems

1. Array Transformation

2. Water Bottles

3. Spiral Matrix III

Pattern 36: Linear Sorting Algorithm Pattern

Sorting algorithms like counting sort or radix sort, which can sort elements in linear time, improving efficiency over comparison-based sorts.

Problems

1. Relative Sort Array

2. Height Checker

3. Array Partition

Pattern 37: Meet in the Middle Pattern

This pattern splits a problem into two parts, solving each separately and then combining results for improved performance in large searches.

Problems

1. Subset Sum Equal to Target

2. Subsets having Sum between A and B

3. Closest Subsequence Sum

Pattern 38: MO's Algorithm Pattern

MO's algorithm efficiently answers range queries by sorting and processing them in a specific order to minimize redundant calculations.

Problems

1. XOR Queries of a Subarray

2. Distinct Elements in Subarray

3. Minimum Absolute Difference Queries

Pattern 39: Serialize and Deserialize Pattern

This pattern converts complex data structures like trees into a string format (serialization) and back into the original form (deserialization).

Problems

1. Serialize and Deserialize Binary Tree

2. Serialize and Deserialize N-ary Tree

Pattern 40: Clone Pattern

The clone pattern creates deep copies of data structures like graphs or linked lists, ensuring independent copies without shared references.

Problems

1. Clone Graph

2. Copy List with Random Pointer

Pattern 41: Articulation Points and Bridges Pattern

This pattern identifies critical nodes or edges in a graph whose removal would disrupt connectivity, often used in network analysis.

Problems

1. Minimum Number of Days to Disconnect Island

2. Minimize Malware Spread

3. Minimize Malware Spread II

Pattern 42: Segment Tree Pattern

Segment trees efficiently perform range queries and updates on an array, useful for problems involving range sums or minimum values.

Problems

1. Rectangle Area II

2. Range Minimum Query

3. Queue Reconstruction by Height

Pattern 43: Binary Indexed Tree Pattern

Also known as a Fenwick Tree, this pattern efficiently handles updates and prefix queries on arrays, commonly used in frequency counts.

Problems

1. Number of Longest Increasing Subsequence

2. Maximum Profitable Triplets With Increasing Prices I

3. Count of Range Sum

Read about the top 20 coding patterns or check out our course Grokking Advanced Coding Patterns for Interviews.

Wrapping Up

Whether it's optimizing your algorithmic thinking or preparing for coding interviews, these patterns offer a structured approach that saves time.

By mastering these patterns, you’re not only preparing yourself for coding interviews but also building a solid foundation for tackling everyday challenges in software development, complementing software design patterns, which focus on scalability and maintainability.

For those looking to dive deeper into these patterns and practice them with real-world examples, I highly recommend checking out Grokking Advanced Coding Patterns at designgurus.io. It has been tailored to improve problem-solving skills, especially for top tech interviews.

Start practicing smarter, not harder, and watch your coding abilities grow!