Ever been stumped by a coding interview question that seems like it came from another dimension? You’re not alone—and with thousands (really) of Leetcode questions out there, it can feel overwhelming to know where to start.
The good news is many coding problems follow a handful of common patterns—and mastering them can be a game-changer for cracking MAANG interviews.
Coding patterns are flexible blueprints that help you solve related problems efficiently. Rather than memorizing each solution or trying to tackle every single problem on LeetCode, learning patterns allows you to apply optimized approaches across a wide range of challenges. To succeed, you need to understand:
- How each pattern works
- When to use each one
By mastering these patterns, you’ll improve your problem-solving skills and build a strong foundation for tackling diverse challenges. In this blog, we’ll explore the top 5 coding patterns behind most Leetcode questions and frequently featured in MAANG interviews, complete with examples and resources to help you succeed.
We’ll cover these key patterns:
- Two pointers
- Sliding window
- Modified binary search
- Merge intervals
- Dynamic programming
Let’s get started.
Pattern 1: Two pointers
The two-pointer technique is a powerful problem-solving approach that uses two indexes to traverse and manipulate data structures like arrays and linked lists.
The pointers move through the structure in a coordinated way, often starting from different positions or moving in opposite directions, until the problem’s requirements are met or the input is exhausted.
This technique enables efficient data exploration with optimal time and space complexity. When you need to find two elements in an array that satisfy a condition, the two-pointer pattern should be one of your first go-to strategies.
Key concepts
- Two pointers: This technique uses two pointers to traverse the data structure simultaneously.
- Pointer movement: The pointers can move independently or in tandem, depending on the problem’s requirements.
- Conditional checks: The algorithm often involves making decisions based on the values pointed to by the pointers.
Importance in interviews
This pattern is frequently applied in technical interviews because it helps candidates solve problems related to arrays and lists efficiently. This pattern provides a clear method for reducing time complexity and often simplifies problem-solving strategies.
Examples
These sample questions demonstrate how you can use this pattern to help you solve problems.
- Reversing an array: Given an array of integers, reverse it in place.
- Valid palindrome: Given a string, determine whether it is a palindrome.
With the two-pointers technique in your toolkit, let’s dive into another crucial coding pattern frequently seen in interviews: the sliding window.
Pattern 2: Sliding window
The sliding window technique is great for solving sequence or subarray problems, especially when finding optimal solutions in a contiguous block. It involves sliding a window across the data and adjusting its size or position to meet specific criteria, making it useful for evaluating subarrays or subsequences of a given size or range.
Key concepts
- Sliding window: This technique involves maintaining a window that expands or contracts as you traverse the data structure. The window can be of fixed or variable size.
- Window movement: The window slides across the data structure, often moving one element at a time. This movement helps to explore all possible subarrays or subsequences efficiently.
- Conditional checks: As the window slides, you perform checks or calculations based on its elements. This can involve summing values, finding maximums or minimums, or performing other operations.
Importance in interviews
The sliding window pattern is popular in interviews because it efficiently solves sequence and subarray problems. It’s faster than brute-force methods and helps optimize solutions. You’ll often see it used for finding maximum or minimum subarrays, longest subsequences, and similar challenges.
Examples
Here are some problems you can solve effectively using the sliding window technique:
- Maximum sum subarray of size k: Given an array of integers and a positive integer k, find the maximum sum of any contiguous subarray of size k.
- Longest substring without repeating characters: Given a string, find the length of the longest substring without repeating characters.
Now we’re ready to move on to our next pattern: modified binary search.
Pattern 3: Modified binary search
In many coding interviews, candidates encounter problems where binary search comes in handy—but it only works on sorted data.
The modified binary search pattern builds on this, handling more complex cases like rotated arrays or finding boundaries. By halving the search space, it reduces time complexity to O(logn), making it ideal for large datasets. This approach incorporates extra logic to handle irregularities, making it a powerful tool for retrieving data efficiently when standard sorting doesn’t apply. It also helps streamline data management and performance.
Key concepts
- Modified binary search: An adaptation of the traditional binary search, this pattern is designed to handle data that might not be perfectly sorted or ordered in the usual way.
- Handling irregularities: The algorithm incorporates additional logic to manage special cases such as rotated arrays or boundary conditions.
Importance in interviews
The modified binary search pattern is a game-changer in coding interviews because it’s both versatile and efficient. It steps in when regular binary search isn’t enough, helping you tackle tougher problems like rotated arrays, finding boundaries, and other advanced search challenges.
Examples
The following examples illustrate some problems this pattern can help you solve:
- First and last position of an element in a sorted array: For a given integer array sorted in increasing order, find a given target value’s starting and ending position.
- Sqrt(x): Find the integer square root of a positive number, x.
Now that we’ve explored the modified binary search pattern—which is great at efficiently locating elements or conditions in complex datasets—let’s switch focus to merge intervals.
Pattern 4: Merge intervals
The merge intervals pattern is a powerful technique for solving problems involving meeting times or intervals, like merging overlaps or finding intersections.
It starts by sorting intervals by start or end times, making it easy to identify overlaps. This pattern is useful in various scenarios, such as scheduling, resource allocation, and calendar management. Whether you’re analyzing time-based data or consolidating meeting schedules, it offers an efficient solution for handling interval-related tasks
Key concepts
- Sorting intervals: The first step in this technique involves sorting the intervals based on their start or end times. This sorting is important for efficiently identifying overlapping intervals and simplifying the merging process.
- Merging intervals: After sorting, the algorithm iterates through the sorted intervals and merges any overlapping. The merging typically involves comparing the current interval with the previous one to determine if they intersect, and then combining them if they do.
- Conditional checks: During the merging process, conditional checks determine if two intervals overlap. If they do, they are merged into a single interval. If not, the current interval is added to the result list as a non-overlapping interval.
Importance in interviews
The merge intervals pattern is highly useful in technical interviews because it addresses common real-world problems related to time management and scheduling. It provides a clear method for efficiently managing overlapping data and applies to many different scenarios, like calendar management and resource allocation.
Examples
Here are some examples of problems that can be solved using this approach:
- Merge intervals: Given a sorted list of intervals, merge all overlapping intervals.
- Meeting rooms: Given an array of meeting time intervals consisting of start and end times, determine if a person could attend all meetings.
Next up—our last pattern, dynamic programming.
Pattern 5: Dynamic programming
Dynamic programming (DP) solves complex problems by breaking them into simpler, overlapping subproblems. It’s useful when smaller subproblems can be solved independently and reused for larger solutions, improving time and space efficiency.
DP can be challenging, but with practice, it becomes easier. To master DP, check out these specialized courses from Educative in your preferred language:
Key concepts
- Optimal substructure: A problem exhibits optimal substructure if an optimal solution to the problem contains optimal solutions to its subproblems. In other words, solving the problem involves solving subproblems that can be solved independently.
- Overlapping subproblems: Dynamic programming is suitable when the same subproblems are solved multiple times. Instead of solving these subproblems repeatedly, DP stores the results of solved subproblems to avoid redundant computations.
- Memoization vs. tabulation:
- Memoization: A top-down approach where problems are solved recursively, and results are cached to avoid recomputation.
- Tabulation: A bottom-up approach where problems are solved iteratively, and results are stored in a table, typically starting with the smallest subproblems and building up to the solution.
Importance in interviews
Dynamic programming (DP) is key in technical interviews for efficiently solving optimization problems. It’s often needed for sequence-related challenges like finding the longest increasing subsequence or shortest path in graphs. Mastering DP boosts problem-solving skills and interview performance.
Examples
Let’s explore how the dynamic programming pattern can be applied to a common problem:
- 0/1 Knapsack: You are given
nitems whose weights and values are known and a knapsack to carry these items. The knapsack cannot carry more than a certain maximum weight, known as itscapacity. You need to maximize the total value of the items in your knapsack while ensuring that the sum of the weights of the selected items does not exceed the capacity of the knapsack. If there is no combination of weights whose sum is within the capacity constraint, return 0.
- Coin change: Given an integer
totalrepresenting the target amount of money and a list of integerscoinsrepresenting different coin denominations, find the minimum number of coins required to make up the total amount. If it’s impossible to achieve the target amount using the given coins, return-1. If the target amount is 0, return0.
Ready to put these patterns to the test?
Mastering these coding patterns is key to cracking MAANG interviews and tackling complex problems efficiently. Each pattern, from two pointers to dynamic programming, equips you with practical strategies to solve challenges with confidence.
By focusing on these foundational patterns, you’ll sharpen your problem-solving skills and set yourself up for success in technical interviews and beyond.
For more in-depth guidance, check out the Grokking Coding Interview Patterns course. It covers 26 essential patterns across six programming languages with real-world examples to boost your interview performance.
And if you’re hoping to tailor your interview preparation to a particular company, there are more resources to explore. Educative offers company-specific paths designed to help you prepare for your target top-tier tech company in the programming language of your choice. These curated paths provide problems frequently asked at your chosen company, including:
Best of luck with your interview preparation!
