This article is now published on my website: Prefer Subshells for Context.
| # Install from Source - at this time ubuntu's universe only install version 0.8 using sudo aptitude install couchdb so we have to install from source version 0.9* | |
| # IMPORTANT: Couchdb is a pain in the ass to install on ubuntu hardy. Hopefully, you used Jaunty. There is an erlang package issue. | |
| # Install dependencies | |
| sudo apt-get install automake autoconf libtool subversion-tools help2man | |
| sudo apt-get install build-essential erlang libicu38 libicu-dev | |
| sudo apt-get install libreadline5-dev checkinstall libmozjs-dev wget | |
| sudo apt-get install libcurl4-openssl-dev | |
| # Get Couchdb |
Previously, in The Architecture of Open Source Applications, I described SocialCalc, an in-browser spreadsheet system that replaced the server-centric WikiCalc architecture. SocialCalc performs all of its computations in the browser; it uses the server only for loading and saving spreadsheets.
For the Socialtext team, performance was the primary goal behind SocialCalc's design in 2006. The key observation was this: Client-side computation in JavaScript, while an order of magnitude slower than server-side computation in Perl, was still much faster than the network latency incurred during AJAX roundtrips:
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| // pre-render d3 charts at server side | |
| var d3 = require('d3') | |
| , jsdom = require('jsdom') | |
| , fs = require('fs') | |
| , htmlStub = '<html><head></head><body><div id="dataviz-container"></div><script src="js/d3.v3.min.js"></script></body></html>' | |
| jsdom.env({ | |
| features : { QuerySelector : true } | |
| , html : htmlStub | |
| , done : function(errors, window) { |
- 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
| """ | |
| Simple web server that listens for Github webhooks to implement push-to-deploy | |
| with Pelican static sites | |
| Settings are loaded from a json file except for SECRET which should be an | |
| environment variable | |
| Example `deployer.json` | |
| { |
| # Demo: | |
| # | |
| # Aggregate population per independence type for every year | |
| # Sources: Population and Country Codes datasets | |
| # | |
| from bubbles import Pipeline | |
| # List of stores with datasets. In this example we are using the "datapackage" | |
| # store |
| import asyncio | |
| import aiohttp | |
| import bs4 | |
| import tqdm | |
| @asyncio.coroutine | |
| def get(*args, **kwargs): | |
| response = yield from aiohttp.request('GET', *args, **kwargs) | |
| return (yield from response.read_and_close(decode=True)) |
