Which coding pattern is best?

There is no single "best" coding pattern, as the choice of coding pattern depends on the problem you're trying to solve. However, some coding patterns are frequently used across various algorithms and data structures. Here are some of the most commonly recommended and effective coding patterns:

1. Sliding Window Pattern

• Use Case: This pattern is particularly useful when dealing with problems related to subarrays, strings, or sequential data.
• Example Problem: "Find the maximum sum subarray of size K" or "Longest substring without repeating characters."
• Why It’s Good: Sliding window allows you to avoid recalculating data by keeping track of a moving subset of the array. It helps reduce time complexity, often from O(n^2) to O(n).

2. Two Pointers Pattern

• Use Case: Two pointers are great for solving problems involving pairs in a sorted array or linked list, such as finding pairs with a certain sum or detecting cycles.
• Example Problem: "Given a sorted array, find two numbers that add up to a specific target."
• Why It’s Good: It reduces the time complexity by leveraging sorted data, typically from O(n^2) to O(n).

3. Fast and Slow Pointers (Tortoise and Hare)

• Use Case: This pattern is useful for problems involving cyclic structures, such as detecting cycles in linked lists or arrays.
• Example Problem: "Detect a cycle in a linked list."
• Why It’s Good: By moving one pointer faster than the other, you can detect cycles in linear time with constant space.

4. Merge Intervals

• Use Case: Merge intervals is a common pattern used in problems where intervals (or ranges) overlap, such as scheduling or range-based queries.
• Example Problem: "Given a list of intervals, merge all overlapping intervals."
• Why It’s Good: It simplifies problems that involve sorting and combining overlapping ranges, often reducing complexity by an efficient sorting-based approach.

5. Depth-First Search (DFS) / Breadth-First Search (BFS)

• Use Case: These are fundamental graph traversal algorithms used for exploring trees and graphs.
• Example Problem: "Find all paths from the root to leaf nodes in a binary tree."
• Why It’s Good: DFS is great for exploring all possible solutions (backtracking), while BFS is useful when finding the shortest path or level-by-level traversal.

6. Backtracking Pattern

• Use Case: Backtracking is used when solving constraint satisfaction problems, like combinations, permutations, and pathfinding problems.
• Example Problem: "Solve the N-Queens problem" or "Find all combinations of a set of numbers."
• Why It’s Good: It systematically explores all possible options, making it ideal for solving problems that require exploring every solution path.

7. Dynamic Programming (DP)

• Use Case: DP is used to solve optimization problems by breaking them down into smaller overlapping subproblems.
• Example Problem: "Fibonacci sequence," "Knapsack problem," or "Longest increasing subsequence."
• Why It’s Good: It reduces the time complexity of recursive algorithms by storing the results of overlapping subproblems and reusing them.

8. Greedy Pattern

• Use Case: Greedy algorithms are useful when you want to make local optimal choices to find a global optimum.
• Example Problem: "Find the minimum number of coins to make a specific amount."
• Why It’s Good: Greedy algorithms are often faster and simpler to implement, though they are only applicable when local optimality leads to global optimality.

9. Topological Sort

• Use Case: Used for ordering tasks or vertices in a Directed Acyclic Graph (DAG) based on their dependencies.
• Example Problem: "Course schedule" where you need to find if a schedule is possible given prerequisite courses.
• Why It’s Good: It helps in solving problems where a specific order is needed, such as resolving task dependencies.

Conclusion

The best coding pattern depends on the problem type. For example, use Sliding Window for subarrays, DFS/BFS for graph traversals, and Dynamic Programming for optimization problems. By mastering these coding patterns, you can efficiently solve a wide variety of coding challenges, both in interviews and real-world scenarios.

Resources like Grokking the Coding Interview are excellent for learning and practicing these patterns in-depth.