maxivak

#!/bin/bash
# xvfb - this script starts and stops Xvfb
#
# chkconfig: 345 95 50
# description: Starts xvfb on display 99
# processname: Xvfb
# pidfile:    /var/log/xvfb/xvfb.pid


# Source function library.

kunev

import asyncio

@asyncio.coroutine
def open_file(name):
    print("opening {}".format(name))
    return open(name)

@asyncio.coroutine
def close_file(file):
    print("closing {}".format(file.name))

hellwen

import xlwt
import StringIO
import mimetypes
from flask import Response
from werkzeug.datastructures import Headers

#... code for setting up Flask

@app.route('/export/')
def export_view():

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

wowo

# install gender http://www.php.net/manual/en/book.gender.php
sudo apt-get install libpcre3-dev
sudo pecl install gender

# generate data
mkdir ~/gender
sudo pear run-scripts pecl/gender

# eneble module
echo 'extension=gender.so' >> /etc/php5/cli/php.ini

mardambey

import java.util.Properties
import kafka.server.KafkaServer
import kafka.server.KafkaConfig
import kafka.producer.ProducerConfig
import kafka.producer.Producer
import kafka.message.Message
import kafka.producer.ProducerData
import kafka.consumer.ConsumerConfig
import kafka.consumer.Consumer
import kafka.utils.Utils