mahmoudimus

from fabric.api import env, run, sudo, local, put

def production():
    """Defines production environment"""
    env.user = "deploy"
    env.hosts = ['example.com',]
    env.base_dir = "/var/www"
    env.app_name = "app"
    env.domain_name = "app.example.com"
    env.domain_path = "%(base_dir)s/%(domain_name)s" % { 'base_dir':env.base_dir, 'domain_name':env.domain_name }

shnjp

"""
Fabric tunneling utilities
    by [email protected]

class ForwardServer and relates things are refere Robey Pointer's paramiko example.
(http://bazaar.launchpad.net/~robey/paramiko/trunk/annotate/head%3A/demos/forward.py)

usage::

    with make_tunnel('[email protected]:10022') as t:

t3hnar

package org.squeryl

import annotations.Transient
import java.lang.reflect.Field

/**
 * @author Yaroslav Klymko
 */
trait HasValues {
  protected def values: List[Any]

n0ts

UnixBench
http://code.google.com/p/byte-unixbench/

Xen vs KVM benchmark
OS: CentOS 5.8 x86_64
CPU: 4core
RAM: 8GB
on Hoge-Cloud

UnixBench 5.1.3 Command: ./Run -c 4

guillaumebort

# --- !Ups

CREATE TABLE users(
  email                     VARCHAR(255) NOT NULL PRIMARY KEY,
  name                      VARCHAR(255)
);

CREATE TABLE subjects(
  id                        INTEGER NOT NULL AUTO_INCREMENT PRIMARY KEY,
  title                     LONGTEXT NOT NULL,

ndarville

Business Models

Advertising

Models	Examples
Display ads	Yahoo!
Search ads	Google

ragingwind

Backend Architectures

Twitter

ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats

• Introduce Gizzard of Twitter - 트위터의 새로운 분산 관리 라이브러리 Gizzard를 소개합니다.
• The Architecture Twitter Uses to Deal with 150M Active Users, 300K QPS, a 22 MB/S Firehose, and Send Tweets in Under 5 Seconds
• Scaling Twitter: Making Twitter 10000 Percent Faster

temoto

def namedlist(typename, field_names):
    """Returns a new subclass of list with named fields.

    >>> Point = namedlist('Point', ('x', 'y'))
    >>> Point.__doc__                   # docstring for the new class
    'Point(x, y)'
    >>> p = Point(11, y=22)             # instantiate with positional args or keywords
    >>> p[0] + p[1]                     # indexable like a plain list
    33
    >>> x, y = p                        # unpack like a regular list

jbenet

a simple git branching model (written in 2013)

This is a very simple git workflow. It (and variants) is in use by many people. I settled on it after using it very effectively at Athena. GitHub does something similar; Zach Holman mentioned it in this talk.

Update: Woah, thanks for all the attention. Didn't expect this simple rant to get popular.

hgfischer

Benchmarking Nginx with Go

There are a lot of ways to serve a Go HTTP application. The best choices depend on each use case. Currently nginx looks to be the standard web server for every new project even though there are other great web servers as well. However, how much is the overhead of serving a Go application behind an nginx server? Do we need some nginx features (vhosts, load balancing, cache, etc) or can you serve directly from Go? If you need nginx, what is the fastest connection mechanism? This are the kind of questions I'm intended to answer here. The purpose of this benchmark is not to tell that Go is faster or slower than nginx. That would be stupid.

So, these are the different settings we are going to compare:

• Go HTTP standalone (as the control group)
• Nginx proxy to Go HTTP
• Nginx fastcgi to Go TCP FastCGI
• Nginx fastcgi to Go Unix Socket FastCGI