How to Install and Uninstall golang-github-seiflotfy-cuckoofilter-dev Package on Ubuntu 20.10 (Groovy Gorilla)
Last updated: November 22,2024
1. Install "golang-github-seiflotfy-cuckoofilter-dev" package
This guide let you learn how to install golang-github-seiflotfy-cuckoofilter-dev on Ubuntu 20.10 (Groovy Gorilla)
$
sudo apt update
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$
sudo apt install
golang-github-seiflotfy-cuckoofilter-dev
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2. Uninstall "golang-github-seiflotfy-cuckoofilter-dev" package
This guide let you learn how to uninstall golang-github-seiflotfy-cuckoofilter-dev on Ubuntu 20.10 (Groovy Gorilla):
$
sudo apt remove
golang-github-seiflotfy-cuckoofilter-dev
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$
sudo apt autoclean && sudo apt autoremove
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3. Information about the golang-github-seiflotfy-cuckoofilter-dev package on Ubuntu 20.10 (Groovy Gorilla)
Package: golang-github-seiflotfy-cuckoofilter-dev
Architecture: all
Version: 0.0~git20170413.0.5bd91bc-5
Priority: extra
Section: universe/devel
Source: golang-github-seiflotfy-cuckoofilter
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Go Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 31
Depends: golang-github-leemcloughlin-gofarmhash-dev
Filename: pool/universe/g/golang-github-seiflotfy-cuckoofilter/golang-github-seiflotfy-cuckoofilter-dev_0.0~git20170413.0.5bd91bc-5_all.deb
Size: 6300
MD5sum: e16cd80ac4c4eb5fe01663f3a3d6165f
SHA1: acde1d5bbda67cc75c0a2235335362d88b210dfe
SHA256: d467c16422dad2cd38de84de17c381137ecf1a35d55f04215e82626e32781fd5
SHA512: eb8fdf50adaa68b2941806e66ee75cb1985c690b6d31e62ce5904ab9497fa143120d76b59b0ac183f4ec1f623f93bf80814750ef862f353759480b7a30aaac1b
Homepage: https://github.com/seiflotfy/cuckoofilter
Description-en: Bloom filter replacement using cuckoo hashing
Cuckoo filter is a Bloom filter replacement for approximated
set-membership queries. While Bloom filters are well-known space-efficient
data structures to serve queries like "if item x is in a set?", they do
not support deletion. Their variances to enable deletion (like counting
Bloom filters) usually require much more space.
.
Cuckoo filters provide the flexibility to add and remove items
dynamically. A cuckoo filter is based on cuckoo hashing (and therefore
named as cuckoo filter). It is essentially a cuckoo hash table storing
each key's fingerprint. Cuckoo hash tables can be highly compact, thus
a cuckoo filter could use less space than conventional Bloom filters,
for applications that require low false positive rates (< 3%).
Description-md5: c369cdf03f2925dfdad6beae35e80377
Architecture: all
Version: 0.0~git20170413.0.5bd91bc-5
Priority: extra
Section: universe/devel
Source: golang-github-seiflotfy-cuckoofilter
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Go Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 31
Depends: golang-github-leemcloughlin-gofarmhash-dev
Filename: pool/universe/g/golang-github-seiflotfy-cuckoofilter/golang-github-seiflotfy-cuckoofilter-dev_0.0~git20170413.0.5bd91bc-5_all.deb
Size: 6300
MD5sum: e16cd80ac4c4eb5fe01663f3a3d6165f
SHA1: acde1d5bbda67cc75c0a2235335362d88b210dfe
SHA256: d467c16422dad2cd38de84de17c381137ecf1a35d55f04215e82626e32781fd5
SHA512: eb8fdf50adaa68b2941806e66ee75cb1985c690b6d31e62ce5904ab9497fa143120d76b59b0ac183f4ec1f623f93bf80814750ef862f353759480b7a30aaac1b
Homepage: https://github.com/seiflotfy/cuckoofilter
Description-en: Bloom filter replacement using cuckoo hashing
Cuckoo filter is a Bloom filter replacement for approximated
set-membership queries. While Bloom filters are well-known space-efficient
data structures to serve queries like "if item x is in a set?", they do
not support deletion. Their variances to enable deletion (like counting
Bloom filters) usually require much more space.
.
Cuckoo filters provide the flexibility to add and remove items
dynamically. A cuckoo filter is based on cuckoo hashing (and therefore
named as cuckoo filter). It is essentially a cuckoo hash table storing
each key's fingerprint. Cuckoo hash tables can be highly compact, thus
a cuckoo filter could use less space than conventional Bloom filters,
for applications that require low false positive rates (< 3%).
Description-md5: c369cdf03f2925dfdad6beae35e80377