Understanding Hash Tables: Implementation and Collision Resolution Techniques

Arrays and Buckets

The array holds the actual data, while each slot (or bucket) can store multiple entries. For example, a hash table with 10 slots might index keys 0-9.

Also Read: Google DSA: Qs & Prep

Collision Resolution Techniques

What Are Collisions?

Collisions occur when two keys hash to the same index. For example, “apple” and “orange” might both map to index 3.

Separate Chaining

This method uses linked lists to handle collisions. Each bucket points to a list of entries sharing the same index.

Pros:

• Simple to implement.
• Handles unlimited collisions.

Cons:

• Overhead from pointers in linked lists.

Open Addressing

All entries are stored in the array itself. If a collision occurs, the algorithm probes for the next available slot.

Linear Probing

Searches for the next empty slot linearly (e.g., index+1, index+2).

Quadratic Probing

Uses a quadratic function (e.g., index + 1², index + 2²) to reduce clustering.

Double Hashing

Applies a second hash function to calculate the probe interval.

Table 2: Collision Methods Comparison

Step-by-Step Hash Table Implementation

1. Choose a Data Structure

Use an array of linked lists (for chaining) or a dynamic array (for open addressing).

2. Define the Hash Function

For strings, convert characters to ASCII and apply modulo:

def hash_function(key, size):

    return sum(ord(char) for char in key) % size

3. Handle Collisions

Implement chaining or probing based on your use case.

4. Test and Optimize

Check performance under high load and adjust the hash function or table size.