Most Efficient Data Structures for Coding Interviews

Mastering data structures is key to cracking coding interviews at top tech companies like Google, Amazon, Meta, and Microsoft. Here’s a breakdown of the most efficient ones, their use cases, and why they matter.

1️⃣ Arrays 📊

✅ Best for: Fast lookups, storing elements in contiguous memory
✅ Operations:

• Access: O(1)
• Search: O(n) (unsorted), O(log n) (sorted with binary search)
• Insertion/Deletion: O(n) (worst case)

💡 Common Interview Problems:
🔹 Two Sum (Leetcode 1)
🔹 Merge Intervals
🔹 Sliding Window problems

2️⃣ Linked Lists 🔗

✅ Best for: Dynamic memory allocation, efficient insertions/deletions
✅ Operations:

• Access: O(n)
• Search: O(n)
• Insertion/Deletion: O(1) (at head or tail)

💡 Common Interview Problems:
🔹 Reverse a Linked List (Leetcode 206)
🔹 Detect Cycle (Floyd’s Cycle Detection)
🔹 Merge Two Sorted Lists

3️⃣ Stacks 📚

✅ Best for: LIFO (Last In, First Out) operations, backtracking, function calls
✅ Operations:

• Push: O(1)
• Pop: O(1)
• Peek: O(1)

💡 Common Interview Problems:
🔹 Valid Parentheses (Leetcode 20)
🔹 Next Greater Element
🔹 Min Stack (Leetcode 155)

4️⃣ Queues & Deques 🚎

✅ Best for: FIFO (First In, First Out) processing, BFS traversal
✅ Operations:

• Enqueue: O(1)
• Dequeue: O(1)
• Peek: O(1)

Deque (Double-Ended Queue) 🔁

✅ Best for: Sliding window problems, efficient insertions/removals from both ends
✅ Operations:

• Insert at front/back: O(1)
• Remove from front/back: O(1)

💡 Common Interview Problems:
🔹 Implement Queue using Stacks (Leetcode 232)
🔹 Sliding Window Maximum (Leetcode 239)
🔹 BFS Traversal

5️⃣ Hash Maps & Hash Sets 🗄️

✅ Best for: Fast lookups, removing duplicates, counting frequencies
✅ Operations:

• Insert/Delete/Search: O(1) (average), O(n) (worst case with collisions)

💡 Common Interview Problems:
🔹 Two Sum (Leetcode 1)
🔹 Longest Consecutive Sequence (Leetcode 128)
🔹 Group Anagrams

6️⃣ Trees 🌳

✅ Best for: Hierarchical data, fast search, range queries
✅ Types:

• Binary Search Tree (BST) – Sorted order operations
• AVL Tree / Red-Black Tree – Self-balancing BSTs
• Trie (Prefix Tree) – Used for searching words efficiently

✅ Operations (BST):

• Search: O(log n) (balanced), O(n) (unbalanced)
• Insert/Delete: O(log n) (balanced)

💡 Common Interview Problems:
🔹 Lowest Common Ancestor (LCA)
🔹 Binary Tree Level Order Traversal (Leetcode 102)
🔹 Serialize & Deserialize a Binary Tree

7️⃣ Heaps (Priority Queues) ⛰️

✅ Best for: Finding min/max elements efficiently
✅ Operations:

• Insert: O(log n)
• Delete (extract min/max): O(log n)
• Peek: O(1)

💡 Common Interview Problems:
🔹 Kth Largest Element (Leetcode 215)
🔹 Merge K Sorted Lists (Leetcode 23)
🔹 Top K Frequent Elements (Leetcode 347)

8️⃣ Graphs & Adjacency Lists 🌍

✅ Best for: Representing networks (social, road maps, etc.), pathfinding algorithms
✅ Common Representations:

• Adjacency Matrix: O(1) edge lookup, O(V²) space
• Adjacency List: O(V+E) space (better for sparse graphs)

💡 Common Interview Problems:
🔹 BFS & DFS Traversals
🔹 Dijkstra’s Algorithm (Shortest Path)
🔹 Detect Cycle in Graph

🚀 Final Tips for Interviews:

✔ Know Time & Space Complexities 📊
✔ Practice with Leetcode, CodeSignal, or HackerRank 🏆
✔ Use the right data structure for the problem 🛠️
✔ Explain your approach before coding 🗣️