stupidbodo

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

package main

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

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

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

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:

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);

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

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

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

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)

rgreenjr

-- show running queries (pre 9.2)
SELECT procpid, age(clock_timestamp(), query_start), usename, current_query 
FROM pg_stat_activity 
WHERE current_query != '<IDLE>' AND current_query NOT ILIKE '%pg_stat_activity%' 
ORDER BY query_start desc;

-- show running queries (9.2)
SELECT pid, age(clock_timestamp(), query_start), usename, query 
FROM pg_stat_activity 
WHERE query != '<IDLE>' AND query NOT ILIKE '%pg_stat_activity%'