m2ym

import Data.Maybe (isJust)

data Tree a = Leaf | Node (Tree a) a (Tree a)

replace :: a -> Tree a -> Tree a
replace a Leaf = Node Leaf a Leaf
replace a (Node l _ r) = Node l a r

rotateR :: Tree a -> Tree a
rotateR (Node (Node x a y) b z) = Node x a (Node y b z)

jhickner

import Control.Proxy
import Control.Proxy.TCP

main :: IO ()
main = serve (Host "0.0.0.0") "8000" $ \(socket,_) ->
         runProxy $ socketReadS 4096 socket >-> socketWriteD socket

NicolasT

import Control.Monad (forever, forM_)
import Control.Concurrent
import Control.Concurrent.STM

data Event = Timeout
           | Message Int
  deriving (Show)

pusher :: TChan Event -> IO ()
pusher c = forM_ [0 ..] $ \i -> do

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

t-mat

#include <stdio.h>
#include <memory>

int main() {
    std::unique_ptr<FILE, int(*)(FILE*)> fp(fopen("test.txt", "r"), fclose);

    if(fp) {
        char buf[4096];
        while(fgets(buf, sizeof(buf), fp.get())) {
            printf("%s", buf);

rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

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

vratiu

#  Customize BASH PS1 prompt to show current GIT repository and branch.
#  by Mike Stewart - http://MediaDoneRight.com

#  SETUP CONSTANTS
#  Bunch-o-predefined colors.  Makes reading code easier than escape sequences.
#  I don't remember where I found this.  o_O

# Reset
Color_Off="\[\033[0m\]"       # Text Reset

stupidbodo

// Playbook - http://play.golang.org/p/3wFl4lacjX

package main

import (
    "bytes"
    "crypto/aes"
    "crypto/cipher"
    "crypto/rand"
    "encoding/base64"

isaacs

# Hello, and welcome to makefile basics.
#
# You will learn why `make` is so great, and why, despite its "weird" syntax,
# it is actually a highly expressive, efficient, and powerful way to build
# programs.
#
# Once you're done here, go to
# http://www.gnu.org/software/make/manual/make.html
# to learn SOOOO much more.