Bloom filter: A Probabilistic data structure
Project description
This bloom filter is forked from pybloom, and its tightening ratio is changed to 0.9, and this ration is consistently used. Choosing r around 0.8 - 0.9 will result in better average space usage for wide range of growth, therefore the default value of model is set to LARGE_SET_GROWTH. This is a Python implementation of the bloom filter probabilistic data structure. The module also provides a Scalable Bloom Filter that allows a bloom filter to grow without knowing the original set size.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pybloom_live-4.0.0.tar.gz
(10.1 kB
view details)
File details
Details for the file pybloom_live-4.0.0.tar.gz.
File metadata
- Download URL: pybloom_live-4.0.0.tar.gz
- Upload date:
- Size: 10.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/3.4.1 importlib_metadata/4.5.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.9.15
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 99545c5d3b05bd388b5491e36b823b706830a686ba18b4c19063d08de5321110 |
|
| MD5 | 4271be52d5ee6c57e24c8bcc564cf6a5 |
|
| BLAKE2b-256 | 8c06868053bdca7afcc22905d6fa5f515880c31cbb12437aea1814c26cdd1c92 |
