# Rensa: A novel high-performance MinHash Implementation in Rust
## Introduction
Rensa (Swedish for "clean") is a high-performance MinHash implementation written in Rust with Python bindings. It's designed for efficient similarity estimation and deduplication of large datasets.
Rensa implements a variant of the MinHash algorithm that combines ideas from traditional MinHash and the C-MinHash algorithm proposed in the paper [C-MinHash: Rigorously Reducing K Permutations to Two](https://arxiv.org/abs/2109.03337) to create a novel MinHash implementation that I call `R-MinHash`.
Rensa is particularly useful in scenarios where you need to:
- Quickly estimate the similarity between large sets of data
- Deduplicate large datasets
- Perform locality-sensitive hashing (LSH) for approximate nearest neighbor search
Use cases include:
- Content deduplication in large document collections
- Identifying similar items in recommendation systems
- Clustering of high-dimensional data
- Near-duplicate detection in web crawling
## Technical Implementation
Key aspects of Rensa's implementation include:
1. **Efficient permutation generation**: Instead of storing full permutations or using k independent hash functions, Rensa uses a pair of random numbers (a, b) to generate permutations on-the-fly. This approach significantly reduces memory usage while maintaining the algorithm's effectiveness.
2. **Simplified C-MinHash**: While inspired by C-MinHash, Rensa's implementation differs in a few key ways:
- It does not apply an initial independent permutation (σ) to the input data.
- Instead of using circulant permutations (π_k) for each hash value, Rensa uses the same pair of random numbers (a, b) for all permutations.
3. **Trade-off between memory and variance reduction**: Rensa's approach trades some of the variance reduction benefits of full C-MinHash for improved memory efficiency and simplicity. While it may not achieve the same level of variance reduction as C-MinHash, it still offers better performance than traditional MinHash in many scenarios.
4. **Fast hash function**: Rensa uses the [rustc-hash](https://github.com/rust-lang/rustc-hash) crate which implements the FxHash algorithm, a fast, non-cryptographic hash function, to further optimize performance.
5. **Vectorized operations**: The R-MinHash computation is optimized using vector operations, allowing for efficient parallel processing of multiple hash values.
6. **Memory-efficient data structures**: The implementation uses compact data structures to minimize memory usage while maintaining fast access times.
7. **Efficient LSH implementation**: The LSH index uses a band-based approach with optimized data structures for fast insertion and query operations.
These design choices result in a MinHash implementation that is fast, memory-efficient, and suitable for large-scale similarity estimation and deduplication tasks. While Rensa may not provide the same theoretical guarantees as full C-MinHash, our benchmarks show that it offers significant performance improvements over traditional MinHash implementations like `datasketch`.
## Installation
You can install Rensa using `pip`. It's available in all platforms:
```bash
pip install rensa
```
## Usage Example
Here's an example of how to use Rensa to deduplicate a dataset:
```python
from datasets import load_dataset
from rensa import RMinHash
from tqdm import tqdm
def rensa_minhash(text, num_perm=128):
m = RMinHash(num_perm=num_perm, seed=42)
m.update(text.split())
return m
def deduplicate_dataset(dataset, num_perm=128):
unique_hashes = set()
deduplicated_indices = []
for idx, example in tqdm(enumerate(dataset), total=len(dataset), desc="Deduplicating"):
minhash = rensa_minhash(example["sql"], num_perm)
hash_tuple = tuple(minhash.digest())
if hash_tuple not in unique_hashes:
unique_hashes.add(hash_tuple)
deduplicated_indices.append(idx)
return deduplicated_indices
def main():
print("Loading dataset...")
sql_dataset = load_dataset("gretelai/synthetic_text_to_sql", split="train")
print("Deduplicating dataset...")
deduplicated_indices = deduplicate_dataset(sql_dataset)
deduplicated_dataset = sql_dataset.select(deduplicated_indices)
print("\nDeduplication Results:")
print(f"Original dataset size: {len(sql_dataset)}")
print(f"Deduplicated dataset size: {len(deduplicated_dataset)}")
print(f"Rows removed: {len(sql_dataset) - len(deduplicated_dataset)}")
if __name__ == "__main__":
main()
```
## Benchmark Results
### Speed
Rensa significantly outperforms `datasketch` in terms of speed. The table below provides a detailed comparison of execution times for different numbers of permutations:
| Permutations | Datasketch Time (s) | Rensa Time (s) | Speedup |
|--------------|---------------------|----------------|--------------|
| 64 | 34.48 | 4.89 | 7.05x faster |
| 128 | 49.62 | 5.21 | 9.52x faster |
| 256 | 84.76 | 6.39 | 13.26x faster|
### Memory Usage
Memory usage is comparable between Rensa and `datasketch`, with Rensa showing slightly better performance for smaller numbers of permutations. The table below provides the details:
| Permutations | Datasketch Memory (MB) | Rensa Memory (MB) | Difference (MB) |
|--------------|-------------------------|-------------------|-----------------|
| 64 | 265.75 | 242.36 | 23.39 less |
| 128 | 487.02 | 472.97 | 14.05 less |
| 256 | 811.64 | 774.49 | 37.15 less |
### Accuracy
Despite the simplified implementation, Rensa achieves the same deduplication results as `datasketch`. The Jaccard similarity between the deduplicated sets produced by both libraries is 1.0000, indicating identical results.
## Running the Benchmarks
To run the benchmarks yourself, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/beowolx/rensa.git
cd rensa
```
2. Create a virtual environment:
```bash
python3 -m venv venv
source venv/bin/activate
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
4. Run the simple benchmark:
```bash
python benchmarks/simple_benchmark.py
```
5. Run the advanced benchmark:
```bash
python benchmarks/advanced_benchmark.py
```
The `simple_benchmark.py` script provides a basic comparison of deduplication performance between Rensa and `datasketch`. The `advanced_benchmark.py` script offers a more comprehensive analysis, including multiple runs with different numbers of permutations, memory usage tracking, and detailed profiling information.
## Limitations and Future Work
While Rensa offers significant performance improvements, it has some limitations compared to `datasketch`:
1. **Feature set**: Rensa currently implements only the core MinHash and LSH functionality. It doesn't include some of the advanced features found in `datasketch`.
2. **Customization**: `datasketch` offers more options for customizing the hash functions and other parameters, while Rensa currently has a more fixed implementation.
3. **Theoretical guarantees**: Due to the simplified C-MinHash implementation, Rensa may not provide the same level of variance reduction as the full C-MinHash algorithm in all scenarios.
Future work on Rensa may include:
- Adding more advanced features and customization options
- Further optimizing performance for specific use cases and data types
Despite these limitations, Rensa's performance benefits make it an excellent choice for applications where speed and efficiency are critical, especially when working with large datasets.
## Contributing
Contributions to Rensa are welcome! Please feel free to submit pull requests, report bugs, or suggest features through the GitHub issue tracker.
## License
Rensa is released under the MIT License. See the LICENSE file for details.
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"description": "# Rensa: A novel high-performance MinHash Implementation in Rust\n\n## Introduction\n\nRensa (Swedish for \"clean\") is a high-performance MinHash implementation written in Rust with Python bindings. It's designed for efficient similarity estimation and deduplication of large datasets.\n\nRensa implements a variant of the MinHash algorithm that combines ideas from traditional MinHash and the C-MinHash algorithm proposed in the paper [C-MinHash: Rigorously Reducing K Permutations to Two](https://arxiv.org/abs/2109.03337) to create a novel MinHash implementation that I call `R-MinHash`.\n\nRensa is particularly useful in scenarios where you need to:\n\n- Quickly estimate the similarity between large sets of data\n- Deduplicate large datasets\n- Perform locality-sensitive hashing (LSH) for approximate nearest neighbor search\n\nUse cases include:\n- Content deduplication in large document collections\n- Identifying similar items in recommendation systems\n- Clustering of high-dimensional data\n- Near-duplicate detection in web crawling\n\n## Technical Implementation\n\nKey aspects of Rensa's implementation include:\n\n1. **Efficient permutation generation**: Instead of storing full permutations or using k independent hash functions, Rensa uses a pair of random numbers (a, b) to generate permutations on-the-fly. This approach significantly reduces memory usage while maintaining the algorithm's effectiveness.\n\n2. **Simplified C-MinHash**: While inspired by C-MinHash, Rensa's implementation differs in a few key ways:\n - It does not apply an initial independent permutation (\u03c3) to the input data.\n - Instead of using circulant permutations (\u03c0_k) for each hash value, Rensa uses the same pair of random numbers (a, b) for all permutations.\n\n3. **Trade-off between memory and variance reduction**: Rensa's approach trades some of the variance reduction benefits of full C-MinHash for improved memory efficiency and simplicity. While it may not achieve the same level of variance reduction as C-MinHash, it still offers better performance than traditional MinHash in many scenarios.\n\n4. **Fast hash function**: Rensa uses the [rustc-hash](https://github.com/rust-lang/rustc-hash) crate which implements the FxHash algorithm, a fast, non-cryptographic hash function, to further optimize performance.\n\n5. **Vectorized operations**: The R-MinHash computation is optimized using vector operations, allowing for efficient parallel processing of multiple hash values.\n\n6. **Memory-efficient data structures**: The implementation uses compact data structures to minimize memory usage while maintaining fast access times.\n\n7. **Efficient LSH implementation**: The LSH index uses a band-based approach with optimized data structures for fast insertion and query operations.\n\nThese design choices result in a MinHash implementation that is fast, memory-efficient, and suitable for large-scale similarity estimation and deduplication tasks. While Rensa may not provide the same theoretical guarantees as full C-MinHash, our benchmarks show that it offers significant performance improvements over traditional MinHash implementations like `datasketch`.\n\n## Installation\n\nYou can install Rensa using `pip`. It's available in all platforms:\n\n```bash\npip install rensa\n```\n\n## Usage Example\n\nHere's an example of how to use Rensa to deduplicate a dataset:\n\n```python\nfrom datasets import load_dataset\nfrom rensa import RMinHash\nfrom tqdm import tqdm\n\ndef rensa_minhash(text, num_perm=128):\n m = RMinHash(num_perm=num_perm, seed=42)\n m.update(text.split())\n return m\n\ndef deduplicate_dataset(dataset, num_perm=128):\n unique_hashes = set()\n deduplicated_indices = []\n \n for idx, example in tqdm(enumerate(dataset), total=len(dataset), desc=\"Deduplicating\"):\n minhash = rensa_minhash(example[\"sql\"], num_perm)\n hash_tuple = tuple(minhash.digest())\n \n if hash_tuple not in unique_hashes:\n unique_hashes.add(hash_tuple)\n deduplicated_indices.append(idx)\n \n return deduplicated_indices\n\ndef main():\n print(\"Loading dataset...\")\n sql_dataset = load_dataset(\"gretelai/synthetic_text_to_sql\", split=\"train\")\n \n print(\"Deduplicating dataset...\")\n deduplicated_indices = deduplicate_dataset(sql_dataset)\n \n deduplicated_dataset = sql_dataset.select(deduplicated_indices)\n \n print(\"\\nDeduplication Results:\")\n print(f\"Original dataset size: {len(sql_dataset)}\")\n print(f\"Deduplicated dataset size: {len(deduplicated_dataset)}\")\n print(f\"Rows removed: {len(sql_dataset) - len(deduplicated_dataset)}\")\n \nif __name__ == \"__main__\":\n main()\n```\n\n## Benchmark Results\n\n\n\n### Speed\n\nRensa significantly outperforms `datasketch` in terms of speed. The table below provides a detailed comparison of execution times for different numbers of permutations:\n\n| Permutations | Datasketch Time (s) | Rensa Time (s) | Speedup |\n|--------------|---------------------|----------------|--------------|\n| 64 | 34.48 | 4.89 | 7.05x faster |\n| 128 | 49.62 | 5.21 | 9.52x faster |\n| 256 | 84.76 | 6.39 | 13.26x faster|\n\n### Memory Usage\n\nMemory usage is comparable between Rensa and `datasketch`, with Rensa showing slightly better performance for smaller numbers of permutations. The table below provides the details:\n\n| Permutations | Datasketch Memory (MB) | Rensa Memory (MB) | Difference (MB) |\n|--------------|-------------------------|-------------------|-----------------|\n| 64 | 265.75 | 242.36 | 23.39 less |\n| 128 | 487.02 | 472.97 | 14.05 less |\n| 256 | 811.64 | 774.49 | 37.15 less |\n\n\n### Accuracy\n\nDespite the simplified implementation, Rensa achieves the same deduplication results as `datasketch`. The Jaccard similarity between the deduplicated sets produced by both libraries is 1.0000, indicating identical results.\n\n\n## Running the Benchmarks\n\nTo run the benchmarks yourself, follow these steps:\n\n1. Clone the repository:\n ```bash\n git clone https://github.com/beowolx/rensa.git\n cd rensa\n ```\n\n2. Create a virtual environment:\n ```bash\n python3 -m venv venv\n source venv/bin/activate\n ```\n\n3. Install the required dependencies:\n ```bash\n pip install -r requirements.txt\n ```\n\n4. Run the simple benchmark:\n ```bash\n python benchmarks/simple_benchmark.py\n ```\n\n5. Run the advanced benchmark:\n ```bash\n python benchmarks/advanced_benchmark.py\n ```\n\nThe `simple_benchmark.py` script provides a basic comparison of deduplication performance between Rensa and `datasketch`. The `advanced_benchmark.py` script offers a more comprehensive analysis, including multiple runs with different numbers of permutations, memory usage tracking, and detailed profiling information.\n\n## Limitations and Future Work\n\nWhile Rensa offers significant performance improvements, it has some limitations compared to `datasketch`:\n\n1. **Feature set**: Rensa currently implements only the core MinHash and LSH functionality. It doesn't include some of the advanced features found in `datasketch`.\n\n2. **Customization**: `datasketch` offers more options for customizing the hash functions and other parameters, while Rensa currently has a more fixed implementation.\n\n3. **Theoretical guarantees**: Due to the simplified C-MinHash implementation, Rensa may not provide the same level of variance reduction as the full C-MinHash algorithm in all scenarios.\n\nFuture work on Rensa may include:\n\n- Adding more advanced features and customization options\n- Further optimizing performance for specific use cases and data types\n\nDespite these limitations, Rensa's performance benefits make it an excellent choice for applications where speed and efficiency are critical, especially when working with large datasets.\n\n## Contributing\n\nContributions to Rensa are welcome! Please feel free to submit pull requests, report bugs, or suggest features through the GitHub issue tracker.\n\n## License\n\nRensa is released under the MIT License. See the LICENSE file for details.\n\n",
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