Counter Bloom Filter Data Structure Visualizer

How to Use

Initialize: Set size (e.g., 16) and hash functions (e.g., 3) and click "Initialize"
Sample Data: Click Sample Data to populate with examples.
Add: Enter element (e.g., "apple") and click "Add"
Remove: Enter element to remove (decrements counters)
Test: Enter element to test if it might be in the set
Clear: Reset all counters to zero

Counter Bloom Filter Concept

Counter Bloom Filter extends standard Bloom Filter by using counters instead of bits, enabling deletions.

Key Characteristics:

Uses counter array instead of bit array
Supports both insertions and deletions
No false negatives (if says "no", definitely not in set)
Possible false positives (if says "yes", might be in set)
Counters can overflow (limitation)

How it Works: To add an element, hash it with k different hash functions to get k array positions, then increment those counters. To remove, decrement the counters. To test membership, check if all k counters are greater than 0.

Advantage over Standard Bloom Filter: Supports deletions without rebuilding the entire filter.

Applications

Network packet filtering with dynamic rules
Cache systems with item expiration
Distributed systems with membership changes
Real-time analytics with sliding windows
Database query optimization with updates

Time Complexity

Operation	Time
Add	O(k)
Remove	O(k)
Test	O(k)
Space	O(m × log c)

k = hash functions, m = array size, c = max counter value

Advantages

Supports deletions
No false negatives
Fast insertion and lookup
Dynamic membership changes

Limitations

Higher memory usage than standard Bloom Filter
Counter overflow possible
False positives still occur
Cannot retrieve stored elements

vs Standard Bloom Filter

Aspect	Standard	Counter
Storage	Bits	Counters
Deletions	No	Yes
Memory	Lower	Higher
Overflow	No	Possible

Counter Bloom Filter Data Structure Visualizer

Counter Bloom Filter Setup

Counter Bloom Filter Operations

Hash Functions

Statistics

Counter Bloom Filter Visualization

Operation Steps