NAME

App::BloomUtils - Utilities related to bloom filters

VERSION

This document describes version 0.007 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

• bloomcalc

• bloomchk

• bloomgen

• 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).

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

FAQ

• Why does two different false positive rates (e.g. 1% and 0.1%) give the same bloom filter size?

  The parameter m is rounded upwards to the nearest power of 2 (e.g. 1024*8 bits becomes 1024*8 bits but 1025*8 becomes 2048*8 bits), so sometimes two false positive rates with different m get rounded to the same value of m. Use the bloom_filter_calculator routine to see the actual_m and actual_p (actual false-positive rate).

This function is not exported.

Arguments ('*' denotes required arguments):

• false_positive_rate => float (default: 0.02)

• num_bits => posint

  Number of bits to set for the bloom filter.

• num_hashes => posint

• num_hashes_to_bits_per_item_ratio => num

  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% maximum false-positive rate (actual bloom size and false-positive rate will be shown on stderr):

  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

FAQ

• Why does two different false positive rates (e.g. 1% and 0.1%) give the same bloom filter size?

  The parameter m is rounded upwards to the nearest power of 2 (e.g. 1024*8 bits becomes 1024*8 bits but 1025*8 becomes 2048*8 bits), so sometimes two false positive rates with different m get rounded to the same value of m. Use the bloom_filter_calculator routine to see the actual_m and actual_p (actual false-positive rate).

This function is not exported.

Arguments ('*' denotes required arguments):

• false_positive_rate => float

• num_bits => posint

  The default is 16384*8 bits (generates a ~16KB bloom filter). If you supply 16k 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 16k items the false positive rate will be higher.

• num_hashes => posint

• num_items => posint

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 https://metacpan.org/release/App-BloomUtils.

SOURCE

Source repository is at https://github.com/perlancar/perl-App-BloomUtils.

BUGS

Please report any bugs or feature requests on the bugtracker website https://rt.cpan.org/Public/Dist/Display.html?Name=App-BloomUtils

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 <perlancar@cpan.org>

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.

cpanm App::BloomUtils

perl -MCPAN -e shell
install App::BloomUtils