Simple Bloom Filter implmentation in Python
Project description
Bloom Filter
Implemented in Python.
- The price we pay for efficiency through bloom filters is that it is probabilistic in nature that means, there might be some False Positive results. False positive means, it might tell that given username is already taken but actually it’s not.
- Not being False Negative such that telling that username doesn't exist while it is there, i.e., if exists it reports it's existenece in terms of maybe, else if not present it is 100% confident to report the same.
- Deleting elements from filter is not possible because, if we delete a single element by clearing bits at indices generated by k hash functions, it might cause deletion of few other elements.
Distributed as a PyPi Package.
Dependencies
- Twine
- bitarray
- setuptools
- wheel
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
| Filename, size | File type | Python version | Upload date | Hashes |
|---|---|---|---|---|
| Filename, size bloomf-0.2-py3-none-any.whl (3.2 kB) | File type Wheel | Python version py3 | Upload date | Hashes View |
| Filename, size bloomf-0.2.tar.gz (2.5 kB) | File type Source | Python version None | Upload date | Hashes View |