10 Top LeetCode Patterns to Crack FAANG Coding Interviews

The top topic is Array with 1142 problems, followed by String with 549 problems, and so on.

Let’s take a closer look at each category of topics, namely Data Structures, Algorithms, and Coding Patterns.

Top Data Structures with the Best ROI

Here are the top Data Structures with the highest return on investment:

1. Array (1142 problems)
2. String (549)
3. Hash Table (392)
4. Tree (191)
5. Matrix (171)
6. Stack (128)
7. Heap or Priority Queue (107)
8. Graph (102)
9. Linked List (69)
10. Trie (44)

Top Algorithmic Techniques with the Best ROI

Here are the top common algorithms techniques with the highest return on investment:

1. Dynamic Programming (383)
2. Sorting (253)
3. Greedy (248)
4. Binary Search (186)
5. Backtracking (91)
6. Recursion (44)
7. Divide and Conquer (38)

Top Coding Patterns with the Best ROI

Here are the top coding patterns with the highest return on investment:

1. Depth First Search (250)
2. Breadth First Search(198)
3. Binary Search (186)
4. Two Pointers (147)
5. Sliding Window (72)
6. Monotonic Stack (44)
7. Union Find (63)
8. Memoization (32)
9. Topological Sort (28)
10. Segment Tree (27)

Best Coding Patterns with Highest ROI

Combining all categories from the above data, here is the list of best coding interview patterns or problem-solving techniques with the highest ROI:

1. Two Pointers (Arrays, Strings, Fast & Slow Pointer)

The Two Pointers technique uses two indices to traverse a data structure, often from opposite ends or at different speeds.

This pattern covers a huge set of questions related to Arrays and Strings, which are the highest tagged data structures.

For example, to check if a string is a palindrome (reads the same forward and backward), you can put one pointer at the start and one at the end, moving them toward the center and comparing characters.

This approach shines in problems like Container With Most Water or Two Sum II (Input Array Is Sorted), where using two pointers from each end quickly zeroes in on the optimal pair instead of brute-forcing all combinations.

2. Sliding Window (Arrays, Strings, Hash Tables)

A Sliding Window is an ingenious pattern for tackling problems involving subarrays or substrings.

You slide a window (which can expand or shrink) across the data to maintain a subset that satisfies a given condition.

It’s especially handy for finding longest/shortest sequences or checking sums in an array without restarting the count from scratch each time.

Imagine trying to find the longest substring without repeating characters – instead of checking every possible substring, you move a window along the string and adjust it when a repeat is found. This way, you efficiently track the current valid substring.

A classic example is Longest Substring Without Repeating Characters, where the sliding window lets you use a hash set (or map) to keep track of seen characters and achieve an optimal solution.

3. Tree and Graph Depth First Search (Matrix Traversal)

Depth-First Search (DFS) is like an explorer who dives deep into one path before backtracking and trying another. It’s a fundamental pattern for tree and graph problems, allowing you to traverse all the way down one branch of a structure before exploring a different branch.

This is great for scenarios where you need to exhaustively search paths or connected components.

For instance, consider the Number of Islands problem – using DFS, you start at one land cell and recursively spread out in all directions (depth-wise) to mark the entire island before moving on to the next unvisited land.

DFS is also behind the scenes in tasks like computing a binary tree’s maximum depth or checking all root-to-leaf paths for a target sum. If you systematically explore and backtrack, you’ll ensure no possible path is left unchecked.

4. Tree and Graph Breadth First Search (Queue, Subsets, Matrix Traversal, Topological Sort)

Breadth-First Search (BFS) takes a layer-by-layer approach.

Instead of diving deep, it explores neighbors level by level, which makes it perfect for finding the shortest path or the closest relationship in a graph.

BFS uses a queue to traverse a tree or graph in waves: first all nodes one step away, then two steps away, and so on.

A common use-case is Binary Tree Level Order Traversal, where BFS cleanly retrieves nodes level-by-level (just like reading a book one line at a time).

Another real-world example: imagine you want to find the fewest word transformations to get from “hit” to “cog” (as in the Word Ladder problem) – BFS will find the solution by exploring all one-letter transformations first, ensuring the first time you reach the target, it was via the shortest route.

5. Binary Search (Arrays)

The Binary Search pattern is a classic technique to pinpoint an element’s position (or a condition’s boundary) in sorted data in logarithmic time.

By repeatedly dividing the search range in half, binary search eliminates large chunks of possibilities in each step. You might already know the simple case: finding a target number in a sorted array.

But binary search goes beyond that – it appears in many guises, like finding the rotation point in a rotated array or determining an optimal value under certain constraints by binary-searching the answer space.

For example, Search in Rotated Sorted Array uses a modified binary search to account for an array that’s been rotated (split and swapped).

Another example is First Bad Version, where binary search quickly narrows down the first defective product in a sorted sequence of good-to-bad versions.

The key is recognizing when a problem’s structure is sorted or monotonic enough that binary chopping makes sense.

6. Interval Merge

The Merge Intervals pattern revolves around dealing with overlapping intervals – think of time ranges or number ranges that might intersect.

The strategy typically involves sorting intervals by start time and then merging any that overlap. This pattern doesn’t have as many different LeetCode problems as some others, but it shows up frequently in interviews because it’s so practical.

Interviewers love to see if you can handle scheduling or calendar-like problems efficiently.

A prime example is the Merge Intervals problem itself: given a set of time intervals, you merge all overlapping intervals to produce a cleaner schedule with no overlaps.

Another scenario is Meeting Rooms (and its variants) where you determine if a person can attend all given meetings or how many conference rooms are needed – by merging or sorting intervals, you can solve these elegantly.

The pattern teaches you to think in terms of sorting by start times and then deciding if intervals intersect (and need merging) or not.

7. Recursion/Backtracking

Recursion and Backtracking are all about exploring possibilities in a structured way – and undoing choices when you hit a dead end.

In a recursive solution, the function calls itself on subproblems, and backtracking adds the strategy of reverting a choice to try a new one, which is invaluable for exhaustive search problems.

This pattern is behind many puzzle-like interview questions where you need to try different configurations, such as generating all permutations of a list, solving Sudoku, or the classic N-Queens problem (placing queens on a chessboard such that none can attack each other).

For instance, permutations of an array: you pick a starting element, recursively permute the rest of the list, and backtrack to swap and try a new starting element until all permutations are explored.

Similarly, Combination Sum uses backtracking to build combinations of numbers that add up to a target.

The key insight here is that by systematically trying options and undoing them when they don’t work out, you ensure you cover all possible solutions without getting stuck in one path.

Mastering recursion/backtracking will greatly expand the kinds of problems you can solve, since many complex problems boil down to searching through possibilities in a smart way.

8. Union-Find (Disjoint Set Union)

Union-Find, also known as Disjoint Set Union (DSU), is a pattern centered around efficiently connecting and grouping elements.

Think of it as a way to keep track of a bunch of items split into some number of groups where you can quickly union (join) groups and find if two items belong to the same group. This is extremely useful for connectivity problems – for example, figuring out how many isolated networks or communities exist in a graph.

A classic case is the Number of Provinces problem (formerly known as “Friend Circles”), where you determine how many groups of friends are connected directly or indirectly.

With Union-Find, you can union any two people that are direct friends, and ultimately count how many separate friend groups remain.

Another scenario is in networking or clustering problems: if you have a list of connections between nodes (like roads between cities or hyperlinks between web pages), Union-Find helps you quickly tell if adding a new connection links two previously separate groups or if they were already connected.

The beauty of this common algorithm pattern is that it gives near constant-time operations for these group queries, making seemingly complex connectivity questions quite manageable.

9. Monotonic Stack Pattern

A Monotonic Stack is a specialized stack data structure that maintains its elements in sorted order – either always increasing or always decreasing from top to bottom.

This coding pattern is a lifesaver for many “next greater” or “next smaller” element problems, where you need to find, for each element in a list, the nearest subsequent element that’s larger or smaller than it.

The stack structure allows you to keep track of candidates for the next greater element in a way that you can discard those that are no longer useful.

For example, in Next Greater Element you iterate through the array, and a monotonically decreasing stack (where the top is always the largest so far) helps you efficiently find the next bigger number for each item.

As you go, you pop from the stack until the current number is smaller than the stack’s top, which means you’ve found the next greater for all the popped ones.

This pattern also appears in problems like Daily Temperatures (finding the next warmer day for each day) and Largest Rectangle in Histogram (finding how far an increasing sequence extends).

Once you spot a question asking for “next greater” or dealing with spans/ranges in an array, a monotonic stack is often the secret sauce to solve it in linear time.

10. Dynamic Programming (DP)

Last but certainly not least is Dynamic Programming, an approach so prevalent that it tags along to hundreds of LeetCode problems on its own.

Dynamic programming isn’t a single problem pattern but rather a general technique: you break a problem into smaller overlapping subproblems, solve each subproblem just once, and store those solutions (often using a table or memoization) to build up an answer.

It’s a bit like solving a puzzle by filling in pieces you know and using those to deduce the ones you don’t.

DP is the key to tackling optimization and counting problems – anywhere you see a problem asking for the maximum, minimum, or number of ways to do something under certain constraints, there’s a good chance DP can be applied.

A gentle example is Climbing Stairs: instead of naively computing every way to climb to the Nth step (which blows up exponentially), you realize that to reach step N, you must have come from step N-1 or N-2.

By knowing the number of ways to get to those two earlier steps, you can add them up for N. This yields a simple recurrence (essentially the Fibonacci sequence) that you can solve iteratively or recursively with memoization – classic DP in action.

Another well-known DP problem is House Robber, where you maximize money stolen without robbing adjacent houses. You solve it by deciding for each house whether to rob it or skip it, based on subproblem solutions for earlier houses.

While dynamic programming can be tricky to master at first (it’s all about finding the subproblem relation), it’s undeniably high ROI: once the pattern “clicks”, you unlock a huge array of problems from knapsack-style optimizations to path-finding in grids.

Practice recognizing DP scenarios by the presence of overlapping sub-tasks and you'll start seeing the matrix, so to speak, for these problems.

For an extensive list, here are other common patterns that you should look into if you are preparing for software engineering interviews:

1. K-way Merge

2. Bitwise XOR

3. Binary Search Tree

 4. Tree Breadth-First Search & Tree Depth-Frist Search

5. AVL Tree or self-balancing binary search tree

6. Monotonic Queue

Some of the questions related to these common patterns need Leetcode premium; alternately, you can access the most important question sets from Grokking the Coding Interview.

Conclusion

Most technical interviews include LeetCode-type questions. Software engineers practice such coding problems before interviews. The highest return on investment is achieved by preparing smartly and focusing on the top LeetCode interview patterns.

We have covered the most common algorithm patterns in this guide.

By focusing on these coding interview patterns, candidates can solve new problems by recognizing similarities to known problems – a strategy far more efficient than random LeetCode grinding.

You can learn more about popular coding interview patterns and related problems in Grokking the Coding Interview and Grokking Dynamic Programming for Coding Interviews.

Here are some more interview prep sources:

• Don’t Just LeetCode; Follow the Coding Patterns Instead
• System Design Interview Survival Guide (2023)
• The Complete Guide to Ace the System Design Interview
• System Design Interviews: What distinguishes you from others?
• Grokking LeetCode: A Smarter Way to Prepare for Coding Interviews
• 14 Most Popular Amazon Coding Interview Questions
• Do We Really Need LeetCode Premium
• How to Solve Dynamic Programming Problems in Coding Interviews