ssh root@YOURDOMAIN
adduser deploy
visudo # Add deploy ALL=(ALL) ALL
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DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE | |
Version 2, December 2004 | |
Copyright (C) 2011 Jed Schmidt <http://jed.is> | |
Everyone is permitted to copy and distribute verbatim or modified | |
copies of this license document, and changing it is allowed as long | |
as the name is changed. | |
DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSE |
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#!/usr/bin/env sh | |
## | |
# This is script with usefull tips taken from: | |
# https://github.com/mathiasbynens/dotfiles/blob/master/.osx | |
# | |
# install it: | |
# curl -sL https://raw.github.com/gist/2108403/hack.sh | sh | |
# |
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var parser = document.createElement('a'); | |
parser.href = "http://example.com:3000/pathname/?search=test#hash"; | |
parser.protocol; // => "http:" | |
parser.hostname; // => "example.com" | |
parser.port; // => "3000" | |
parser.pathname; // => "/pathname/" | |
parser.search; // => "?search=test" | |
parser.hash; // => "#hash" | |
parser.host; // => "example.com:3000" |
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for i in $HOME/local/*; do | |
[ -d $i/bin ] && PATH="${i}/bin:${PATH}" | |
[ -d $i/sbin ] && PATH="${i}/sbin:${PATH}" | |
[ -d $i/include ] && CPATH="${i}/include:${CPATH}" | |
[ -d $i/lib ] && LD_LIBRARY_PATH="${i}/lib:${LD_LIBRARY_PATH}" | |
[ -d $i/lib ] && LD_RUN_PATH="${i}/lib:${LD_RUN_PATH}" | |
# uncomment the following if you use macintosh | |
# [ -d $i/lib ] && DYLD_LIBRARY_PATH="${i}/lib:${DYLD_LIBRARY_PATH}" | |
[ -d $i/lib/pkgconfig ] && PKG_CONFIG_PATH="${i}/lib/pkgconfig:${PKG_CONFIG_PATH}" | |
[ -d $i/share/man ] && MANPATH="${i}/share/man:${MANPATH}" |
A friend asked me for a few pointers to interesting, mostly recent papers on data warehousing and "big data" database systems, with an eye towards real-world deployments. I figured I'd share the list. It's biased and rather incomplete but maybe of interest to someone. While many are obvious choices (I've omitted several, like MapReduce), I think there are a few underappreciated gems.
###Dataflow Engines:
Dryad--general-purpose distributed parallel dataflow engine
http://research.microsoft.com/en-us/projects/dryad/eurosys07.pdf
Spark--in memory dataflow
http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf
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// data comes from here http://stat-computing.org/dataexpo/2009/the-data.html | |
// download 1994.csv.bz2 and unpack by running: cat 1994.csv.bz2 | bzip2 -d > 1994.csv | |
// 1994.csv should be ~5.2 million lines and 500MB | |
// importing all rows into leveldb took ~50 seconds on my machine | |
// there are two main techniques at work here: | |
// 1: never create JS objects, leave the data as binary the entire time (binary-split does this) | |
// 2: group lines into 16 MB batches, to take advantage of leveldbs batch API (byte-stream does this) | |
var level = require('level') |
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