vbezhenar

#!/bin/sh

url='http://pgl.yoyo.org/as/serverlist.php?hostformat=nohtml'

ftp -o - "$url" | awk '{printf "local-zone: \"%s.\" static\n", $1}' > /var/unbound/etc/blacklist.conf.new || exit $?
mv /var/unbound/etc/blacklist.conf.new /var/unbound/etc/blacklist.conf
rcctl reload unbound

rafrombrc

require "string"
require "math"
require "table"
require "cjson"

local status_codes = {}
local request_times = {}

local ticker_interval = read_config("ticker_interval") or error("must provide ticker_interval")
local percent_thresh = read_config("percent_threshold") or 90

jjjake

# Assign identifier and collection to variables for use in final output.
.metadata.identifier as $i |
.metadata.collection as $c |

# Filter out any items that do not have files metadata.
select(.files != null) |

# Get all non-derivative files that have a file size, and slim down the metadata.
.files |
map(

bbinet

#! /bin/bash
 
# certificates are valids during 365 days
VALID=365
TMPPASS="test"

while [[ -z "$password" ]]
do
  read -s -p "Enter password for the Certificate Authority (CA) key: " password
  echo

skurfuerst

require "cjson"

local dt = require 'date_time'
-- Generic decoder for JSON logs. This will extract all JSON
-- keys and add them to the `Fields` variable of the created
-- Heka message.
--
-- Example use:
--
-- [NginxJsonLogDecoder]

slouma2000

Step 1: Upgrade Packages 
# yum update
# yum groupinstall "Development Tools"

Step 2: Installing Recommended Packages
# yum install gcc-c++ patch readline readline-devel zlib zlib-devel 
# yum install libyaml-devel libffi-devel openssl-devel make 
# yum install bzip2 autoconf automake libtool bison iconv-devel

Step 3: Install RVM ( Ruby Version Manager ) 

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

rep

#!/usr/bin/python
# -*- coding: utf8 -*-

import sys
import os
import time
import select
import socket

import pycares

rafaelrosafu

More info on http://hekad.readthedocs.org/en/latest/installing.html

Build and test hekad

yum install git gcc patch python-devel
rpm -ihv http://pkgs.repoforge.org/cmake/cmake-2.8.8-1.el6.rfx.x86_64.rpm

git clone git@github.com:mozilla-services/heka-build.git
cd heka-build

miekg

for tld in $(awk ' { print $1 }' root-zone | egrep '^[a-z0-9-]+\.$' | sort -u); do                                                   
    echo -n $tld:                                                                                                                    
    if dig +dnssec DNSKEY $tld | grep -q RRSIG; then                                                                                 
        echo -n DNSSEC:                                                                                                              
    else                                                                                                                             
        echo -n dnssec:                                                                                                              
    fi                                                                                                                               
    echo $(dig +noall +answer ${tld}cc.jpmens.net txt | awk ' { print $5" "$6" "$7" "$