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

JeffPaine

Moved

Now located at https://github.com/JeffPaine/beautiful_idiomatic_python.

Why it was moved

Github gists don't support Pull Requests or any notifications, which made it impossible for me to maintain this (surprisingly popular) gist with fixes, respond to comments and so on. In the interest of maintaining the quality of this resource for others, I've moved it to a proper repo. Cheers!

pmeenan

// Support routines for automatically reporting user timing for common analytics platforms
// Currently supports Google Analytics, Boomerang and SOASTA mPulse
// In the case of boomerang, you will need to map the event names you want reported
// to timer names (for mPulse these need to be custom0, custom1, etc) using a global variable:
// rumMapping = {'aft': 'custom0'};
(function() {
var wtt = function(n, t, b) {
  t = Math.round(t);
  if (t >= 0 && t < 3600000) {
    // Google Analytics

crowdmatt

{
"IAB1": "Arts & Entertainment",
"IAB1-1": "Books & Literature",
"IAB1-2": "Celebrity Fan/Gossip",
"IAB1-3": "Fine Art",
"IAB1-4": "Humor",
"IAB1-5": "Movies",
"IAB1-6": "Music",
"IAB1-7": "Television",
"IAB2": "Automotive",

koto

var page = require('webpage').create(),
    system = require('system'),
    address;

page.onInitialized = function () {
    page.evaluate(function () {
        // additional detection code here perhaps
        // f.e. detecting STORED/DOM XSS

    });

anacrolix

import io

class StringIteratorIO(io.TextIOBase):

    def __init__(self, iter):
        self._iter = iter
        self._left = ''

    def readable(self):
        return True

robertpitt

/**
 * SOCKS5 Proxy as per RFC1928
 * @see : http://www.ietf.org/rfc/rfc1928.txt
 */

/**
 * Load Dependancies
 */
var net = require('net'),

hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

TooTallNate

var net = require('net')

var sock = net.connect(1337)

process.stdin.pipe(sock)
sock.pipe(process.stdout)

sock.on('connect', function () {
  process.stdin.resume();
  process.stdin.setRawMode(true)

rafl

#!/usr/bin/env perl

# Parameters supported:       
#                             
# config                      
# autoconf                    
#                             
# Magic markers:              
#%# family=auto               
#%# capabilities=autoconf