DavidWittman

A Brief Introduction to Fabric

Fabric is a deployment management framework written in Python which makes remotely managing multiple servers incredibly easy. If you've ever had to issue a change to a group servers, this should look pretty familiar:

for s in $(cat servers.txt); do ssh $s service httpd graceful; done

Fabric improves on this process by providing a suite of functions to run commands on the servers, as well as a number of other features which just aren't possible in a simple for loop. While a working knowledge of Python is helpful when using Fabric, it certainly isn't necessary. This tutorial will cover the steps necessary to get started with the framework and introduce how it can be used to improve on administering groups of servers.

RedBeard0531

// derived from http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm

function map() {
    emit(1, // Or put a GROUP BY key here
         {sum: this.value, // the field you want stats for
          min: this.value,
          max: this.value,
          count:1,
          diff: 0, // M2,n:  sum((val-mean)^2)
    });

mattweber

This is an example how to perform multi-select faceting in ElasticSearch.

Selecting multiple values from the same facet will result in an OR filter between each of the values:
(facet1.value1 OR facet1.value2)

Faceting on more than one facet will result in an AND filter between each facet:
(facet1.value1 OR facet1.value2) AND (facet2.value1)

I have chosen to update the counts for each facet the selected value DOES NOT belong to since we are performing an AND between each facet.  I have included an example that shows how to keep the counts if you don't want to do this (filter0.sh).

fairchild

input {
  redis {
    host => "127.0.0.1"
    type => "redis-input"
    # these settings should match the output of the agent
    data_type => "list"
    key => "logstash"
    # We use json_event here since the sender is a logstash agent
    message_format => "json_event"
  }

mikhailov

# Nginx+Unicorn best-practices congifuration guide. Heartbleed fixed.
# We use latest stable nginx with fresh **openssl**, **zlib** and **pcre** dependencies.
# Some extra handy modules to use: --with-http_stub_status_module --with-http_gzip_static_module
#
# Deployment structure
#
# SERVER:
#  /etc/init.d/nginx                             (1. nginx)
#  /home/app/public_html/app_production/current  (Capistrano directory)
#

turtlesoupy

 http {
    proxy_cache_path  /var/cache/nginx levels=1:2 keys_zone=one:8m max_size=3000m inactive=600m;
    proxy_temp_path /var/tmp;
    include       mime.types;
    default_type  application/octet-stream;
    sendfile        on;
    keepalive_timeout  65;

    gzip on;
    gzip_comp_level 6;

mrjoes

1. pip install -r reqs.pip
2. server.py
3. open client.html in browser
4. redis-cli publish push '123456'
5. check browser console

dbr

"""Playing with tornado.websocket, to add markers to a Google Map using WebSockets

$ pip install tornado
$ python livemap.py --port=8888

Open http://localhost:8888 in one window

Each time http://localhost:8888/ping is opened in a second window, a
new marker is added to the map (at a random location)

npinto

import cv2
import cv2.cv as cv


def detect(img, cascade_fn='haarcascades/haarcascade_frontalface_alt.xml',
           scaleFactor=1.3, minNeighbors=4, minSize=(20, 20),
           flags=cv.CV_HAAR_SCALE_IMAGE):

    cascade = cv2.CascadeClassifier(cascade_fn)
    rects = cascade.detectMultiScale(img, scaleFactor=scaleFactor,

jasdeepkhalsa

// Long Polling (Recommened Technique - Creates An Open Connection To Server ∴ Fast)
(function poll(){
    $.ajax({ url: "server", success: function(data){
        //Update your dashboard gauge
        salesGauge.setValue(data.value);
    }, dataType: "json", complete: poll, timeout: 30000 });
})();