App-BloomUtils
view release on metacpan or search on metacpan
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.
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.
lib/App/BloomUtils.pm view on Meta::CPAN
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
lib/App/BloomUtils.pm view on Meta::CPAN
0.1% - 14.4 bits per item
0.01% - 19.2 bits per item
=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.
=item * 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.
=back
Ref: https://corte.si/posts/code/bloom-filter-rules-of-thumb/index.html
B<FAQ>
=over
lib/App/BloomUtils.pm view on Meta::CPAN
0.1% - 14.4 bits per item
0.01% - 19.2 bits per item
=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.
=item * 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.
=back
Ref: https://corte.si/posts/code/bloom-filter-rules-of-thumb/index.html
B<FAQ>
=over
script/bloom-filter-calculator view on Meta::CPAN
0.1% - 14.4 bits per item
0.01% - 19.2 bits per item
=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.
=item * 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.
=back
Ref: https://corte.si/posts/code/bloom-filter-rules-of-thumb/index.html
B<FAQ>
=over
script/bloomcalc view on Meta::CPAN
0.1% - 14.4 bits per item
0.01% - 19.2 bits per item
=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.
=item * 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.
=back
Ref: https://corte.si/posts/code/bloom-filter-rules-of-thumb/index.html
B<FAQ>
=over
script/bloomgen view on Meta::CPAN
0.1% - 14.4 bits per item
0.01% - 19.2 bits per item
=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.
=item * 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.
=back
Ref: https://corte.si/posts/code/bloom-filter-rules-of-thumb/index.html
B<FAQ>
=over
script/gen-bloom-filter view on Meta::CPAN
0.1% - 14.4 bits per item
0.01% - 19.2 bits per item
=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.
=item * 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.
=back
Ref: https://corte.si/posts/code/bloom-filter-rules-of-thumb/index.html
B<FAQ>
=over
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