DSA Mastery

Hash Tables: Handling Collisions like a Pro

1 Views Updated 5/4/2026

Hash Tables & Dictionaries

A Hash Table is a data structure that maps "Keys" to "Values." It is the most powerful tool for performance because it provides O(1) average search time. In .NET, this is implemented as Dictionary<K, V>.

1. The Hashing Process

When you add a key (e.g., "Sandeep"), the table runs a Hash Function that converts the string into an integer (e.g., index 4). It then stores the value at that specific array index. This is why lookup is instant—you don't search; you jump directly to the index.

2. Handling Collisions

What if two different strings (e.g., "Sandeep" and "Admin") produce the same hash index? This is a Collision.

  • Chaining: Each array index contains a small Linked List. If multiple keys hit the same index, they are added to the list. (.NET uses this).
  • Open Addressing: If index 4 is full, find the next empty spot (index 5) and put it there.

4. Interview Mastery

Q: "Why should you NEVER use a mutable object (like a List) as a Dictionary Key?"

Architect Answer: "Because if the object changes while it is in the dictionary, its **Hash Code** will also change. When you try to look up the key later, the dictionary will look at the *new* hash index, find nothing (or someone else), and tell you the key doesn't exist. You have effectively 'lost' the data in memory. ALWAYS use **Immutable** types like Strings or Records as dictionary keys."

DSA Mastery
1. Algorithmic Foundations
Big O Notation: Analyzing Time and Space Complexity Memory Management: Stack vs Heap in C# Recursion: The foundation of modern algorithms
2. Linear Data Structures
Arrays Deep Dive: Static vs Dynamic (List<T> Internals) Linked Lists: Singly, Doubly, and Circular Stacks and Queues: Implementing Undo/Redo & Message Buffers Hash Tables: Handling Collisions like a Pro
3. Non-Linear Data Structures
Binary Trees & BST: Searching at Log(N) speed Balanced Trees: AVL and Red-Black Trees Internals Heaps: Implementing a Priority Queue Tries (Prefix Trees): Optimizing Auto-complete features Graphs (Part 1): Representation (Matrix vs List) Graphs (Part 2): BFS vs DFS Traversal
4. Searching & Sorting
Binary Search: The power of Divide & Conquer Elementary Sorts: Bubble, Selection, and Insertion Merge Sort: Stable sorting for massive datasets Quick Sort: In-place sorting and Pivot selection Heap Sort: Leveraging the Priority Queue
5. Algorithmic Patterns
Sliding Window Pattern: Optimizing array performance Two Pointers Pattern: Reversing and Finding cycles Fast & Slow Pointers (Hare & Tortoise) Backtracking: Solving Sudoku and N-Queens
6. Dynamic Programming (DP)
Memoization vs Tabulation: Top-down vs Bottom-up The Knapsack Problem: 0/1 DP optimization Longest Common Subsequence (LCS) Matrix Chain Multiplication
7. Advanced Graphs & Interview
Dijkstra's Algorithm: Shortest path in weighted graphs Prim's and Kruskal's: Minimum Spanning Trees Disjoint Set Union (DSU) / Union-Find DSA Interview: FAANG Style Coding Challenges