NAME App::BloomUtils - Utilities related to bloom filters VERSION This document describes version 0.003 of App::BloomUtils (from Perl distribution App-BloomUtils), released on 2020-05-24. DESCRIPTION This distributions provides the following command-line utilities: * bloom-filter-calculator * check-with-bloom-filter * gen-bloom-filter FUNCTIONS bloom_filter_calculator Usage: bloom_filter_calculator(%args) -> [status, msg, payload, meta] Help calculate num_bits (m) and num_hashes (k). Bloom filter is setup using two parameters: "num_bits" ("m") which is the size of the bloom filter (in bits) and "num_hashes" ("k") which is the number of hash functions to use which will determine the write and lookup speed. Some rules of thumb: * One byte per item in the input set gives about a 2% false positive rate. So if you expect two have 1024 elements, create a 1KB bloom filter with about 2% false positive rate. For other false positive rates: 1% - 9.6 bits per item 0.1% - 14.4 bits per item 0.01% - 19.2 bits per item * Optimal number of hash functions is 0.7 times number of bits per item. * What is an acceptable false positive rate? This depends on your needs. Ref: https://corte.si/posts/code/bloom-filter-rules-of-thumb/index.html This function is not exported. Arguments ('*' denotes required arguments): * false_positive_rate => *num* (default: 0.02) * num_hashes => *num* * num_hashes_to_bits_per_item_ratio => *num* (default: 0.7) 0.7 (the default) is optimal. * num_items* => *posint* Expected number of items to add to bloom filter. Returns an enveloped result (an array). First element (status) is an integer containing HTTP status code (200 means OK, 4xx caller error, 5xx function error). Second element (msg) is a string containing error message, or 'OK' if status is 200. Third element (payload) is optional, the actual result. Fourth element (meta) is called result metadata and is optional, a hash that contains extra information. Return value: (any) check_with_bloom_filter Usage: check_with_bloom_filter(%args) -> [status, msg, payload, meta] Check with bloom filter. You supply the bloom filter in STDIN, items to check as arguments, and this utility will print lines containing 0 or 1 depending on whether items in the arguments are tested to be, respectively, not in the set (0) or probably in the set (1). This function is not exported. Arguments ('*' denotes required arguments): * items* => *array[str]* Items to check. Returns an enveloped result (an array). First element (status) is an integer containing HTTP status code (200 means OK, 4xx caller error, 5xx function error). Second element (msg) is a string containing error message, or 'OK' if status is 200. Third element (payload) is optional, the actual result. Fourth element (meta) is called result metadata and is optional, a hash that contains extra information. Return value: (any) gen_bloom_filter Usage: gen_bloom_filter(%args) -> [status, msg, payload, meta] Generate bloom filter. Examples: * Create a bloom filter for 100k items and 0.1% false-positive rate: gen_bloom_filter( false_positive_rate => "0.1%", num_items => 100000); You supply lines of text from STDIN and it will output the bloom filter bits on STDOUT. You can also customize "num_bits" ("m") and "num_hashes" ("k"), or, more easily, "num_items" and "fp_rate". Some rules of thumb to remember: * One byte per item in the input set gives about a 2% false positive rate. So if you expect two have 1024 elements, create a 1KB bloom filter with about 2% false positive rate. For other false positive rates: 10% - 4.8 bits per item 1% - 9.6 bits per item 0.1% - 14.4 bits per item 0.01% - 19.2 bits per item * Optimal number of hash functions is 0.7 times number of bits per item. Note that the number of hashes dominate performance. If you want higher performance, pick a smaller number of hashes. But for most cases, use the the optimal number of hash functions. * What is an acceptable false positive rate? This depends on your needs. 1% (1 in 100) or 0.1% (1 in 1,000) is a good start. If you want to make sure that user's chosen password is not in a known wordlist, a higher false positive rates will annoy your user more by rejecting her password more often, while lower false positive rates will require a higher memory usage. Ref: https://corte.si/posts/code/bloom-filter-rules-of-thumb/index.html This function is not exported. Arguments ('*' denotes required arguments): * false_positive_rate => *percent* * num_bits => *uint* The default is 80000 (generates a ~10KB bloom filter). If you supply 10,000 items (meaning 1 byte per 1 item) then the false positive rate will be ~2%. If you supply fewer items the false positive rate is smaller and if you supply more than 10,000 items the false positive rate will be higher. * num_hashes => *num* * num_items => *uint* Returns an enveloped result (an array). First element (status) is an integer containing HTTP status code (200 means OK, 4xx caller error, 5xx function error). Second element (msg) is a string containing error message, or 'OK' if status is 200. Third element (payload) is optional, the actual result. Fourth element (meta) is called result metadata and is optional, a hash that contains extra information. Return value: (any) HOMEPAGE Please visit the project's homepage at . SOURCE Source repository is at . BUGS Please report any bugs or feature requests on the bugtracker website When submitting a bug or request, please include a test-file or a patch to an existing test-file that illustrates the bug or desired feature. AUTHOR perlancar COPYRIGHT AND LICENSE This software is copyright (c) 2020, 2018 by perlancar@cpan.org. This is free software; you can redistribute it and/or modify it under the same terms as the Perl 5 programming language system itself.