← Back to Projects

Hash Function & Collision Analysis

Interactive visualization of hash functions, collision resolution strategies, and advanced hashing applications. Explore how different hash functions distribute data and handle collisions.

Basic Hashing
Collision Resolution
Bloom Filter
Consistent Hashing
Analysis
Hash: -

Hash Table Visualization

Statistics

Load Factor
0%
Collisions
0
Max Chain
0
Uniformity
-
Division Method: The simplest hash function that uses modulo operation (key % table_size). Fast to compute but can have poor distribution if table size is not chosen carefully (preferably prime).

Collision Resolution Visualization

Current Strategy: Separate Chaining
Links colliding elements in a chain at each table position. Simple to implement but requires additional memory for pointers.
Result: -

Bloom Filter Bit Array

Performance Metrics

Items Added
0
Bits Set
0
Fill Rate
0%
FP Rate
0%
Bloom Filter: A space-efficient probabilistic data structure that tests whether an element is a member of a set. False positive matches are possible, but false negatives are not. The more elements added, the higher the false positive rate.

Consistent Hash Ring

Consistent Hashing: Used in distributed systems to minimize reorganization when nodes are added or removed. Each node is mapped to multiple points on the ring (virtual nodes) for better load distribution.

Analysis Results

About Hash Functions

Hash functions are fundamental to computer science, enabling efficient data storage and retrieval. A good hash function distributes keys uniformly across the hash table, minimizing collisions while being fast to compute.

Key Concepts
  • Load Factor: The ratio of filled slots to total slots in the hash table
  • Collision: When two different keys hash to the same index
  • Clustering: The tendency of collisions to group together in certain resolution strategies
  • Universal Hashing: A randomized scheme that guarantees good average-case performance
  • Perfect Hashing: A hash function with no collisions for a known set of keys
Applications
  • Hash Tables: Fast key-value storage with O(1) average access time
  • Bloom Filters: Space-efficient probabilistic data structures for set membership
  • Consistent Hashing: Distributed caching and load balancing
  • Cryptographic Hashing: Data integrity, password storage, and digital signatures
  • Similarity Detection: MinHash for finding similar documents