- Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
- Models and Issues in Data Stream Systems
- Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
- Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
- [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t
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RSpec.configure do |config| | |
config.before(:suite) do | |
DatabaseCleaner.clean_with(:truncation) | |
end | |
config.before(:each) do | |
DatabaseCleaner.strategy = :transaction | |
end | |
config.before(:each, js: true) do |
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" copy all this into a vim buffer, save it, then... | |
" source the file by typing :so % | |
" Now the vim buffer acts like a specialized application for mastering vim | |
" There are two queues, Study and Known. Depending how confident you feel | |
" about the item you are currently learning, you can move it down several | |
" positions, all the way to the end of the Study queue, or to the Known | |
" queue. | |
" type ,, (that's comma comma) |
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# A simple Makefile alternative to using Grunt for your static asset compilation | |
# | |
## Usage | |
# | |
# $ npm install | |
# | |
# And then you can run various commands: | |
# | |
# $ make # compile files that need compiling | |
# $ make clean all # remove target files and recompile from scratch |
Let's have some command-line fun with curl, [jq][1], and the [new GitHub Search API][2].
Today we're looking for:
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs
Most active GitHub users (git.io/top)
The count of contributions (summary of Pull Requests, opened issues and commits) to public repos at GitHub.com from Wed, 21 Sep 2022 till Thu, 21 Sep 2023.
Only first 1000 GitHub users according to the count of followers are taken. This is because of limitations of GitHub search. Sorting algo in pseudocode:
githubUsers
.filter(user => user.followers > 1000)
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var user = { | |
validateCredentials: function (username, password) { | |
return ( | |
(!(username += '') || username === '') ? { error: "No Username Given.", field: 'name' } | |
: (!(username += '') || password === '') ? { error: "No Password Given.", field: 'pass' } | |
: (username.length < 3) ? { error: "Username is less than 3 Characters.", field: 'name' } | |
: (password.length < 4) ? { error: "Password is less than 4 Characters.", field: 'pass' } | |
: (!/^([a-z0-9_-]+)$/i.test(username)) ? { error: "Username contains invalid characters.", field: 'name' } | |
: false | |
); |