Stop Grinding LeetCode: Master These 20 Coding Patterns Instead

If you are tired of practicing hundreds of random coding questions before an interview, there is a better way. Whether you are building complex backend logic or optimizing full-stack applications, memorizing solutions isn't scalable. Instead, you should focus on mastering the underlying foundational patterns.

By recognizing these 20 specific coding patterns, you can efficiently tackle almost any algorithmic challenge and distinguish yourself from the pack.

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The 20 Essential Coding Patterns

• 1. Sliding Window
  • Usage: Ideal for handling input data within a specific window size.
  • Data Structures: Array, String, HashTable.
  • Sample Problems: Longest Substring with 'K' Distinct Characters, Fruits into Baskets.


• 2. Islands (Matrix Traversal)
  • Usage: Describes efficient ways to traverse a matrix or 2D array.
  • Data Structures: Matrix, Queue.
  • Sample Problems: Number of Islands, Flood Fill, Cycle in a Matrix.


• 3. Two Pointers
  • Usage: Uses two pointers to iterate through input data, often moving in opposite directions at a constant interval.
  • Data Structures: Array, String, LinkedList.
  • Sample Problems: Squaring a Sorted Array, Dutch National Flag Problem.


• 4. Fast & Slow Pointers
  • Usage: Also known as the Hare & Tortoise algorithm, this uses two pointers moving at different speeds.
  • Data Structures: Array, String, LinkedList.
  • Sample Problems: Middle of the LinkedList, Happy Number.


• 5. Merge Intervals
  • Usage: The go-to technique for dealing with overlapping intervals.
  • Data Structures: Array, Heap.
  • Sample Problems: Conflicting Appointments, Minimum Meeting Rooms.


• 6. Cyclic Sort
  • Usage: Solves array problems where the input data lies strictly within a fixed range.
  • Data Structures: Array.
  • Sample Problems: Find all Missing Numbers, Find all Duplicate Numbers.


• 7. In-place Reversal of a LinkedList
  • Usage: Efficiently reverses links between a set of nodes in a LinkedList in-place, without using extra memory.
  • Data Structures: LinkedList.
  • Sample Problems: Reverse every K-element Sub-list, Rotate a LinkedList.


• 8. Breadth-First Search (BFS)
  • Usage: Traverses trees or graphs in a breadth-first manner.
  • Data Structures: Tree, Graph, Matrix, Queue.
  • Sample Problems: Binary Tree Level Order Traversal, Minimum Depth of a Binary Tree.


• 9. Depth First Search (DFS)
  • Usage: Traverses trees or graphs in a depth-first manner.
  • Data Structures: Tree, Graph, Matrix.
  • Sample Problems: Path With Given Sequence, Count Paths for a Sum.


• 10. Two Heaps
  • Usage: Used when elements can be divided into two parts, utilizing a Min-Heap for the smallest elements and a Max-Heap for the largest.
  • Data Structures: Heap, Array.
  • Sample Problems: Find the Median of a Number Stream, Next Interval.


• 11. Subsets
  • Usage: Best for problems involving permutations or combinations of a set of elements.
  • Data Structures: Queue, Array, String.
  • Sample Problems: String Permutations by changing case, Unique Generalized Abbreviations.


• 12. Modified Binary Search
  • Usage: Efficiently searches a sorted set of elements.
  • Data Structures: Array.
  • Sample Problems: Ceiling of a Number, Bitonic Array Maximum.


• 13. Bitwise XOR
  • Usage: Manipulates bits using the XOR operator to solve algorithmic problems.
  • Data Structures: Array, Bits.
  • Sample Problems: Two Single Numbers, Flip and Invert an Image.


• 14. Top 'K' Elements
  • Usage: Finds the top, smallest, or most frequently occurring 'K' elements in a set.
  • Data Structures: Array, Heap, Queue.
  • Sample Problems: 'K' Closest Points to the Origin, Maximum Distinct Elements.


• 15. K-way Merge
  • Usage: Helps solve problems that involve a list of multiple sorted arrays.
  • Data Structures: Array, Queue, Heap.
  • Sample Problems: Kth Smallest Number in M Sorted Lists.


• 16. Topological Sort
  • Usage: Finds a linear ordering of elements that have dependencies on each other.
  • Data Structures: Array, HashTable, Queue, Graph.
  • Sample Problems: Tasks Scheduling, Alien Dictionary.


• 17. 0/1 Knapsack
  • Usage: An optimization technique to select elements for maximum profit from a set with capacity limitations, where each element is picked only once.
  • Data Structures: Array, HashTable.
  • Sample Problems: Equal Subset Sum Partition, Minimum Subset Sum Difference.


• 18. Fibonacci Numbers
  • Usage: Solves sequence problems where every subsequent number is calculated from the last few numbers.
  • Data Structures: Array, HashTable.
  • Sample Problems: Staircase, House Thief.


• 19. Palindromic Subsequence
  • Usage: Optimization problems specifically related to palindromic sequences or strings.
  • Data Structures: Array, HashTable.
  • Sample Problems: Longest Palindromic Subsequence.


• 20. Longest Common Substring
  • Usage: Finds the optimal part of a sequence or a set of strings.
  • Data Structures: Array, HashTable.
  • Sample Problems: Maximum Sum Increasing Subsequence, Edit Distance.

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Conclusion

Stop getting lost in the endless sea of practice questions. By mapping problems to these 20 foundational patterns, you'll be able to design optimal solutions much faster.

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