- 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
A list of the most common functionalities in Jekyll (Liquid). You can use Jekyll with GitHub Pages, just make sure you are using the proper version.
Running a local server for testing purposes:
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| // Remember to @import WebKit at the top of the class | |
| // Javascript that disables pinch-to-zoom by inserting the HTML viewport meta tag into <head> | |
| let source: NSString = "var meta = document.createElement('meta');" + | |
| "meta.name = 'viewport';" + | |
| "meta.content = 'width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no';" + | |
| "var head = document.getElementsByTagName('head')[0];" + | |
| "head.appendChild(meta);"; | |
| let script: WKUserScript = WKUserScript(source: source, injectionTime: .AtDocumentEnd, forMainFrameOnly: true) |
A running example of the code from:
- http://marcio.io/2015/07/handling-1-million-requests-per-minute-with-golang
- http://nesv.github.io/golang/2014/02/25/worker-queues-in-go.html
This gist creates a working example from blog post, and a alternate example using simple worker pool.
TLDR: if you want simple and controlled concurrency use a worker pool.
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| // Implementation in ES6 | |
| function pagination(c, m) { | |
| var current = c, | |
| last = m, | |
| delta = 2, | |
| left = current - delta, | |
| right = current + delta + 1, | |
| range = [], | |
| rangeWithDots = [], | |
| l; |
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| var Comment = {} | |
| Comment.store = Store('Comment') | |
| Comment.fetchForPost = function (postId) { | |
| return m.request({ method: 'GET', url: '/api/post/' + postId + '/comments' }) | |
| .then(Comment.store.syncAll) | |
| } |
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| // poller.go | |
| package main | |
| import ( | |
| "fmt" | |
| "os" | |
| "os/signal" | |
| "time" |
Picking the right architecture = Picking the right battles + Managing trade-offs
- Clarify and agree on the scope of the system
- User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?
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