mdisec

# Nginx proxy for Elasticsearch + Kibana
#
# In this setup, we are password protecting the saving of dashboards. You may
# wish to extend the password protection to all paths.
#
# Even though these paths are being called as the result of an ajax request, the
# browser will prompt for a username/password on the first request
#
# If you use this, you'll want to point config.js at http://FQDN:443/ instead of
# http://FQDN:9200

drorata

# Initialize the scroll
page = es.search(
  index = 'yourIndex',
  doc_type = 'yourType',
  scroll = '2m',
  search_type = 'scan',
  size = 1000,
  body = {
    # Your query's body
    })

sebsto

sudo wget http://repos.fedorapeople.org/repos/dchen/apache-maven/epel-apache-maven.repo -O /etc/yum.repos.d/epel-apache-maven.repo
sudo sed -i s/\$releasever/6/g /etc/yum.repos.d/epel-apache-maven.repo
sudo yum install -y apache-maven
mvn --version

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

gitaarik

Git Submodules - Basic Explanation

Why submodules?

In Git you can add a submodule to a repository. This is basically a sub-repository embedded in your main repository. This can be very useful. A couple of usecases of submodules:

• Separate big codebases into multiple repositories.

debasishg

General Background and Overview

1. 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.
2. Models and Issues in Data Stream Systems
3. 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
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

jmduke

import requests
import operator

companies = ["facebook", "aws", "google", "yahoo", "dropbox", "twitter", "paypal", "linkedin", "mozilla", "adobe"]
api_url = "https://api.github.com/orgs/{}/repos"
data = {}

for company in companies:
	data[company] = {'forks': 0, 'stars': 0, 'repos': 0, 'languages': {}}
	r = requests.get(api_url.format(company))

karmi

tmp/

iandanforth

#############################################################################
# Full Imports

from __future__ import division
import math
import random

"""
This is a pure Python implementation of the K-means Clustering algorithmn. The
original can be found here:

captn3m0

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