Common LeetCode Patterns by Top Tech Companies: Essential Algorithms for FAANG Interviews

Top tech companies including Google, Amazon, Meta, and Netflix consistently interview candidates on ten core algorithmic patterns—Sliding Window, Binary Search, Depth-First Search, Dynamic Programming, and Union-Find—which are comprehensively documented with template code in the azl397985856/leetcode repository.

Technical interviews at FAANG-style companies rely on recurring algorithmic patterns rather than obscure tricks. The open-source repository azl397985856/leetcode organizes these common LeetCode patterns by top tech companies in the thinkings/ directory, providing architectural overviews and production-ready templates. Mastering these patterns is the most efficient path to interview success.

Why These Patterns Dominate Technical Interviews

Interviewers at leading technology firms use these patterns because they offer predictability and scalability. Each pattern provides clear time-complexity guarantees—such as O(n) for sliding window or O(log n) for binary search—which are key evaluation metrics. Real-world systems at these companies handle massive data streams, making these algorithmic techniques directly applicable to production-grade engineering challenges.

The 10 Essential LeetCode Patterns for Technical Interviews

The following sections detail the highest-frequency patterns encountered in interviews at Google, Amazon, Meta, Apple, and Netflix. Each includes the specific source file path from the repository where the pattern is documented.

1. Two-Pointer and Sliding Window

Core Concept: Maintain two indices that move in tandem to represent a "window" of elements satisfying a specific condition. This approach reduces nested loops to linear time.

Why It Matters: Google, Amazon, and Meta frequently use subarray and substring problems to test a candidate's ability to optimize brute-force solutions to O(n) time.

Repository Source: [thinkings/slide-window.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/slide-window.md)

Template Implementation (LeetCode 209 – Minimum Size Subarray Sum):

class Solution:
    def minSubArrayLen(self, s: int, nums: List[int]) -> int:
        l = total = 0
        ans = len(nums) + 1
        for r, val in enumerate(nums):
            total += val
            while total >= s:
                ans = min(ans, r - l + 1)
                total -= nums[l]
                l += 1
        return 0 if ans == len(nums) + 1 else ans

2. Binary Search

Core Concept: Repeatedly halve a sorted search space to locate a target or determine an insertion point. Variants include finding the left-most or right-most occurrence and "counting" binary search for answers that can be verified in O(log n).

Why It Matters: Google, Apple, and Netflix emphasize this pattern for problems where the answer space is monotonic, testing a candidate's ability to avoid off-by-one errors.

Repository Source: [thinkings/binary-search-1.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/binary-search-1.md)

Template Implementation (LeetCode 278 – First Bad Version):

class Solution:
    def firstBadVersion(self, n: int) -> int:
        lo, hi = 1, n
        while lo < hi:
            mid = lo + (hi - lo) // 2
            if isBadVersion(mid):
                hi = mid
            else:
                lo = mid + 1
        return lo

3. Depth-First Search and Backtracking

Core Concept: Explore all possible solution paths via recursion, pruning invalid states early to reduce the search space. This pattern suits combinatorial search, permutation generation, and subset problems.

Why It Matters: Meta and Amazon use backtracking problems to assess a candidate's comfort with recursion and their ability to implement efficient pruning strategies.

Repository Source: [thinkings/backtrack.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/backtrack.md)

4. Dynamic Programming

Core Concept: Break problems into overlapping sub-problems and store intermediate results to avoid redundant computation. This approach works for optimal substructure problems including knapsack, path-finding, and sequence alignment.

Why It Matters: Google, Meta, and Microsoft rely heavily on DP to test a candidate's ability to define state transitions and optimize both time and space complexity.

Repository Source: [thinkings/dynamic-programming.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/dynamic-programming.md)

5. Greedy Algorithms

Core Concept: Build a solution step-by-step, always selecting the locally optimal choice. This pattern is effective when the greedy-choice property and optimal substructure can be mathematically proven.

Why It Matters: Amazon and Apple use greedy problems to evaluate whether candidates can recognize when local optima lead to global optima versus when dynamic programming is required.

Repository Source: [thinkings/greedy.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/greedy.md)

6. Tree Traversals and Binary Tree Patterns

Core Concept: Systematic exploration of tree structures using pre-order, in-order, post-order, and level-order traversals. This pattern is frequently applied to BST validation, lowest common ancestor (LCA), and tree serialization problems.

Why It Matters: Google and Meta consistently use tree problems to assess fundamental data structure manipulation and recursive thinking.

Repository Source: [thinkings/binary-tree-traversal.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/binary-tree-traversal.md)

7. Graph Algorithms and Union-Find

Core Concept: Model problems as nodes and edges, utilizing BFS, DFS, topological sort, Dijkstra's algorithm, and Union-Find (Disjoint Set Union) for connectivity and cycle detection.

Why It Matters: Google and Amazon use graph problems to test a candidate's ability to handle complex relationships and optimize connectivity queries.

Repository Source: [thinkings/union-find.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/union-find.md) and [thinkings/graph.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/graph.md)

Template Implementation (Union-Find Cycle Detection):

class UnionFind:
    def __init__(self, n):
        self.parent = list(range(n))

    def find(self, x):
        while self.parent[x] != x:
            self.parent[x] = self.parent[self.parent[x]]
            x = self.parent[x]
        return x

    def union(self, a, b):
        pa, pb = self.find(a), self.find(b)
        if pa == pb:
            return False          # cycle detected

        self.parent[pa] = pb
        return True

def hasCycle(n, edges):
    uf = UnionFind(n)
    for u, v in edges:
        if not uf.union(u, v):
            return True
    return False

8. Heap and Priority Queue

Core Concept: Maintain a dynamic ordering of elements using min-heaps or max-heaps to efficiently retrieve k-th largest elements, merge sorted sequences, or process events by priority.

Why It Matters: Netflix and Meta use heap problems to evaluate a candidate's understanding of partial ordering and efficient data retrieval under constraints.

Repository Source: [thinkings/heap.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/heap.md)

9. Monotonic Stack

Core Concept: Maintain a stack where elements are monotonic (strictly increasing or decreasing) to solve "next greater element" problems, largest rectangle in histogram, and similar range-query optimizations.

Why It Matters: Google and Amazon use monotonic stack problems to test optimization skills for problems that appear to require O(n²) time but can be solved in O(n).

Repository Source: [thinkings/monotone-stack.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/monotone-stack.md)

Template Implementation (LeetCode 84 – Largest Rectangle in Histogram):

def largestRectangleArea(heights):
    stack = [-1]
    max_area = 0
    for i, h in enumerate(heights):
        while stack[-1] != -1 and heights[stack[-1]] >= h:
            height = heights[stack.pop()]
            width = i - stack[-1] - 1
            max_area = max(max_area, height * width)
        stack.append(i)
    while stack[-1] != -1:
        height = heights[stack.pop()]
        width = len(heights) - stack[-1] - 1
        max_area = max(max_area, height * width)
    return max_area

10. Trie and String Manipulation

Core Concept: Store strings in a prefix tree (Trie) for efficient retrieval of words with common prefixes, enabling fast autocomplete, spell-checking, and word search implementations.

Why It Matters: Meta and Apple use Trie problems to assess string handling efficiency and prefix-based search optimizations.

Repository Source: [thinkings/trie.md](https://github.com/azl397985856/leetcode/blob/master/thinkings/trie.md)

Template Implementation (LeetCode 212 – Word Search II):

class TrieNode:
    def __init__(self):
        self.children = {}
        self.word = None   # stores complete word at leaf

class Solution:
    def findWords(self, board, words):
        root = TrieNode()
        for w in words:
            node = root
            for ch in w:
                node = node.children.setdefault(ch, TrieNode())
            node.word = w

        rows, cols = len(board), len(board[0])
        result = set()

        def dfs(r, c, node):
            ch = board[r][c]
            if ch not in node.children:
                return
            nxt = node.children[ch]
            if nxt.word:
                result.add(nxt.word)
            board[r][c] = '#'
            for dr, dc in [(1,0),(-1,0),(0,1),(0,-1)]:
                nr, nc = r+dr, c+dc
                if 0 <= nr < rows and 0 <= nc < cols and board[nr][nc] != '#':
                    dfs(nr, nc, nxt)
            board[r][c] = ch

        for i in range(rows):
            for j in range(cols):
                dfs(i, j, root)
        return list(result)

How to Navigate the Repository for Interview Preparation

The azl397985856/leetcode repository structures its knowledge base to mirror the interview-focused pattern catalogue used by top tech firms. The thinkings/ directory contains architectural explanations for each pattern, while the collections/ directory provides curated lists of Easy, Medium, and Hard problems for targeted practice.

To maximize preparation efficiency, study the template code in each thinkings/ file first, then apply the patterns to the corresponding problem sets in collections/. This method ensures you recognize the underlying structure rather than memorizing individual solutions, which is exactly what interviewers at Google, Amazon, and Meta evaluate.

Summary

• Ten core patterns—Sliding Window, Binary Search, DFS/Backtracking, Dynamic Programming, Greedy, Tree Traversals, Graph Algorithms/Union-Find, Heaps, Monotonic Stacks, and Tries—constitute the majority of LeetCode problems asked at FAANG companies.
• The azl397985856/leetcode repository documents each pattern in the thinkings/ directory with architectural overviews and production-ready templates.
• Mastering these patterns allows candidates to reduce time complexity from O(n²) to O(n) or from O(n) to O(log n), which is a primary evaluation metric in technical screens.

Frequently Asked Questions

Which LeetCode pattern is most frequently asked at Google?

Google interviews heavily emphasize Binary Search and Sliding Window patterns, often combining them with Tree Traversals or Graph Algorithms. Candidates should focus on mastering the "left-most/right-most" binary search variants and variable-size sliding windows documented in thinkings/binary-search-1.md and thinkings/slide-window.md.

How do I choose between Dynamic Programming and Greedy approaches?

Use Greedy when you can prove that a locally optimal choice leads to a globally optimal solution, typically in interval scheduling or Huffman coding problems. Use Dynamic Programming when the problem exhibits overlapping sub-problems and optimal substructure but lacks the greedy-choice property, such as in knapsack or edit distance problems.

Where can I find practice problems for these specific patterns?

The collections/ directory in the azl397985856/leetcode repository contains curated lists of Easy, Medium, and Hard problems organized by pattern type. Additionally, each thinkings/ file includes a problem list section linking to specific LeetCode questions that exemplify the pattern.

Are these patterns sufficient for senior engineer interviews at FAANG companies?

These patterns form the foundational algorithmic knowledge required for senior roles, but senior interviews also emphasize system design, distributed systems, and the ability to optimize for specific constraints like memory or throughput. However, fluency in these ten patterns is a prerequisite for passing the coding screens at all levels.

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