heavykeeper 0.2.3

Python bindings for the HeavyKeeper algorithm implemented in Rust

heavykeeper

Python bindings for the HeavyKeeper algorithm - a fast, memory-efficient sketch-based algorithm for finding the top-K most frequent items in data streams.

Overview

HeavyKeeper is a probabilistic data structure that identifies the most frequent items in a data stream using minimal memory. This implementation provides Python bindings for a high-performance Rust implementation of the algorithm.

Key Features

• 🚀 High Performance: Rust-based implementation with Python bindings via PyO3
• 💾 Memory Efficient: Uses probabilistic sketching to track millions of items with minimal memory
• 🎯 Top-K Tracking: Efficiently maintains the K most frequent items
• 🔄 Stream Processing: Designed for continuous data streams
• 📊 Approximate Counts: Provides estimated frequencies with high accuracy
• 🧪 Battle Tested: Includes comprehensive benchmarks and tests

Use Cases

• Log Analysis: Find the most frequent IP addresses, user agents, or error messages
• Text Processing: Identify the most common words in large documents
• Network Monitoring: Track heavy hitters in network traffic
• Clickstream Analysis: Find the most popular pages or user actions
• Time Series Data: Monitor frequently occurring events or anomalies

Installation

From Source (Development)

# Clone the repository
git clone https://github.com/pmcgleen/heavykeeper-py.git
cd heavykeeper-py

# Install Rust (if not already installed)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Build and install the Python package
maturin develop

# Or build a wheel
maturin build --release

Requirements

• Python 3.11+
• Rust toolchain (for building from source)

Quick Start

from heavykeeper import HeavyKeeper

# Create a HeavyKeeper instance
# k=100: track top 100 items
# width=2048: sketch width (affects accuracy)
# depth=8: number of hash functions (affects accuracy)
# decay=0.9: aging factor for old items
hk = HeavyKeeper(k=100, width=2048, depth=8, decay=0.9)

# Add items to the stream
items = ["apple", "banana", "apple", "cherry", "apple", "banana"]
for item in items:
    hk.add(item)

# Query individual items
print(f"Is 'apple' in top-K? {hk.query('apple')}")
print(f"Estimated count for 'apple': {hk.count('apple')}")

# Get all top-K items
top_items = hk.list()  # Returns list of (item, count) tuples
print("Top items:", top_items)

# Get as dictionary
top_dict = hk.get_topk()  # Returns {item: count} dictionary
print("Top items dict:", top_dict)

API Reference

HeavyKeeper(k, width, depth, decay)

Creates a new HeavyKeeper instance.

Parameters:

• k (int): Number of top items to track
• width (int): Width of the sketch (number of buckets)
• depth (int): Depth of the sketch (number of hash functions)
• decay (float): Decay factor for aging items (between 0.0 and 1.0)

Methods

add(item: str) -> None

Add an item to the sketch.

query(item: str) -> bool

Check if an item is being tracked in the top-K list.

count(item: str) -> int

Get the estimated count for an item (returns 0 if not tracked).

list() -> List[Tuple[str, int]]

Get the top-K items as a list of (item, count) tuples, sorted by count.

get_topk() -> Dict[str, int]

Get the top-K items as a dictionary mapping items to counts.

len() -> int

Get the current number of items being tracked.

is_empty() -> bool

Check if the sketch is empty.

Benchmarking

The repository includes a simnple script for performance testing:

Word Count Benchmark

# Basic benchmark with a text file
python benchmark_wordcount.py -k 10 -f data/war_and_peace.txt --time

Parameter Tuning

Choosing Parameters

• k: Set to the number of top items you need
• width: Larger values improve accuracy but use more memory (try 1024-8192)
• depth: More hash functions improve accuracy (try 4-16)
• decay: Controls how quickly old items are forgotten (0.8-0.99)

Memory Usage

Approximate memory usage: width × depth × 16 bytes + k × (item_size + 16 bytes)

For typical usage (width=2048, depth=8, k=100):

• Sketch: ~262 KB
• Top-K storage: ~depends on item sizes

Accuracy vs Performance

• Higher width and depth → better accuracy, more memory
• Lower decay → faster adaptation to changes, less stability
• Higher k → more items tracked, slightly more overhead

Development

Building

# Development build
maturin develop

# Release build  
maturin build --release

# Build with debugging
maturin develop --debug

Testing

# Run the test suite
python test_heavykeeper.py

# Run benchmarks
python benchmark_wordcount.py -k 10 -f test_file.txt

Project Structure

heavykeeper-py/
├── src/
│   └── lib.rs          # Rust implementation and Python bindings
├── benchmark_*.py      # Performance benchmarks
├── test_heavykeeper.py # Test suite
├── Cargo.toml          # Rust dependencies
├── pyproject.toml      # Python package configuration
└── README.md           # This file

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

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

Acknowledgments

• Based on the HeavyKeeper algorithm
• Built with PyO3 for Rust-Python interoperability
• Uses Maturin for building Python extensions