Enhanced Learned Bloom Filter

A comprehensive implementation of Enhanced Learned Bloom Filters that addresses three critical problems in traditional Learned Bloom Filters: poor cache locality, expensive retraining, and unstable false positive rates.

🎯 Overview

This project implements an enhanced version of Learned Bloom Filters (LBF) that combines machine learning with traditional probabilistic data structures to achieve:

• Lowest FPR - 0.2% average (5x better than Standard Bloom Filter)
• O(1) update complexity - 0.007ms per update with incremental learning
• ±10% FPR variance - Stable performance through adaptive control
• Cache-optimized architecture - 64-byte alignment, SIMD vectorization

📋 Documentation

• Results - Performance metrics and analysis
• Comparative Analysis - Comparison with 6 other filters
• Methodology - Testing approach and validation
• Testing Fix Summary - Data leakage fix details

📊 Key Features

Three Core Enhancements

1. Cache-Aligned Memory Layout

   • 64-byte aligned blocks matching CPU cache lines
   • SIMD vectorization for batch operations
   • Reduces cache misses from 70% to ~25%

2. Incremental Online Learning

   • Passive-Aggressive classifier for O(1) updates
   • Sliding window and reservoir sampling
   • Eliminates expensive O(n) retraining

3. Adaptive Threshold Control

   • PID controller for FPR stabilization
   • Real-time monitoring and adjustment
   • Maintains stable performance under varying workloads

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/yourusername/BloomFilter.git
cd BloomFilter

# Create virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Basic Usage

from src.enhanced_lbf.combined import CombinedEnhancedLBF

# Create enhanced filter with all optimizations
filter = CombinedEnhancedLBF(
    initial_positive_set=positive_items,
    initial_negative_set=negative_items,
    target_fpr=0.01,
    enable_cache_opt=True,
    enable_incremental=True,
    enable_adaptive=True
)

# Add items (O(1) complexity)
filter.add("new_item", label=1)

# Query items
if filter.query("test_item"):
    print("Item might be in the set")

# Batch operations (SIMD optimized)
results = filter.batch_query(["item1", "item2", "item3"])

# Get statistics
stats = filter.get_stats()
print(f"Cache hit rate: {stats['cache_hit_rate']:.1f}%")

📁 Project Structure

BloomFilter/
├── src/
│   ├── bloom_filter/
│   │   └── standard.py          # Traditional Bloom Filter
│   ├── learned_bloom_filter/
│   │   └── basic_lbf.py         # Basic Learned Bloom Filter
│   ├── enhanced_lbf/
│   │   ├── cache_aligned.py     # Cache optimization
│   │   ├── incremental.py       # Online learning
│   │   ├── adaptive.py          # Adaptive threshold
│   │   └── combined.py          # All enhancements
│   └── utils/
├── benchmarks/
│   └── comprehensive_benchmark.py # Performance evaluation
├── experiments/
│   └── problem_demonstration.py  # Problem demonstrations
├── tests/
│   └── test_standard_bf.py      # Unit tests
└── requirements.txt

🧪 Running Tests

# Run unit tests
pytest tests/

# Run comprehensive benchmarks (with real-world data)
python benchmarks/comparative_analysis_realworld.py

# Validate testing methodology
python docs/testing/verify_testing_methodology.py

# Problem demonstrations
python experiments/problem_demonstration.py

# Test individual enhancements
python src/enhanced_lbf/cache_aligned.py    # Cache solution
python src/enhanced_lbf/incremental.py      # Incremental learning
python src/enhanced_lbf/adaptive.py         # Adaptive threshold
python src/enhanced_lbf/combined.py         # All enhancements

📈 Performance Results

False Positive Rate (Lower is Better)

Filter	Average FPR	vs Standard BF
Enhanced LBF	0.20%	5x better 🏆
Counting BF	0.65%	1.6x better
Standard BF	1.03%	Baseline
Cuckoo Filter	2.33%	2.3x worse

Query Throughput

Filter	Throughput	vs Standard BF
Standard BF	3.4M ops/sec	Baseline 🏆
Cuckoo Filter	2.5M ops/sec	0.74x
Counting BF	2.3M ops/sec	0.68x
Enhanced LBF	270K ops/sec	0.08x ⚠️

Enhanced LBF trades throughput for superior accuracy

Update Performance

Implementation	Update Time	Complexity
Standard BF	N/A	Rebuild required
Enhanced LBF	0.007ms	O(1) ✅
Counting BF	<0.001ms	O(1)

FPR Stability

Implementation	FPR Variance
Basic LBF	±800%
Enhanced LBF	±10% ✅

See docs/RESULTS.md for detailed performance analysis.

🔬 Algorithms Used

Problem 1: Cache Misses

• Cache-line aligned blocks (64 bytes)
• SIMD vectorization (AVX2, 8-wide)
• Prefetching strategies
• Blocked memory layout

Problem 2: Expensive Retraining

• Passive-Aggressive classifier (online learning)
• Sliding window (bounded memory)
• Reservoir sampling (representative history)
• Dynamic backup filters

Problem 3: FPR Instability

• PID controller (Kp=2.0, Ki=0.5, Kd=0.1)
• Count-Min Sketch (frequency tracking)
• Exponential moving average
• Adaptive threshold adjustment

🎓 Academic Context

This implementation is based on the paper:

Kraska, T., et al. (2018). "The Case for Learned Index Structures." SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data.

Our enhancements address critical limitations identified in production deployments of Learned Bloom Filters.

🤝 Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues.

Development Setup

# Install development dependencies
pip install -r requirements-dev.txt

# Run linters
flake8 src/
black src/

# Run type checking
mypy src/

📊 Benchmark Results

Run the comprehensive benchmark suite to reproduce results:

python benchmarks/comprehensive_benchmark.py

Results will be saved to data/results/ with detailed performance metrics.

🔍 Key Findings

1. Lowest FPR achieved - 0.2% average across real-world datasets (5x better than Standard BF)
2. O(1) incremental updates - Eliminates expensive retraining bottleneck
3. Adaptive control stabilizes FPR - ±10% variance vs ±800% in basic LBF
4. Trade-offs are real - 12x throughput penalty for superior accuracy
5. Methodology matters - Fixed data leakage issue in testing (see docs/METHODOLOGY.md)

⚠️ Important Note on Testing

October 25, 2025 Update: We discovered and fixed a data leakage issue in the original testing methodology where training and test sets had 100% overlap. The corrected methodology uses proper 80/20 train/test split with no overlap. FPR results improved with the fix (0.1-0.3% vs 0.6-0.9% previously). See docs/METHODOLOGY.md for details.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Authors

• Miguel - Implementation and enhancements
• Based on research by Tim Kraska et al. (MIT CSAIL)

🙏 Acknowledgments

• Original Learned Bloom Filter paper authors
• scikit-learn for ML implementations
• NumPy for efficient numerical operations

📚 Further Reading

• The Case for Learned Index Structures
• Bloom Filter Wikipedia
• Cache-Oblivious Algorithms
• Online Learning Algorithms

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Note: This is a research implementation optimized for clarity and correctness. Production deployments should consider additional optimizations and security considerations.