arthurnn

require 'active_support/all'
require 'active_record'
require 'logger'

ActiveRecord::Base.logger = Logger.new(STDOUT)

spec = { 'test' => {adapter: 'sqlite3', database: ':memory:'},
         'test_readonly' => {adapter: 'sqlite3', database: ':memory:'},
         'shared_test' => {adapter: 'sqlite3', database: ':memory:'},
         'shared_test_readonly' => {adapter: 'sqlite3', database: ':memory:'}

halilim

1. Prepare your RDS instance and [authorize access to it][1] (basically make it public).

2. Put your app in maintenance mode:

   heroku maintenance:on

3. Save the latest SQL dump of the DB:

brianhempel

require "net/http"

def start_server
  # Remove the X to enable the parameters for tuning.
  # These are the default values as of Ruby 2.2.0.
  @child = spawn(<<-EOC.split.join(" "))
    XRUBY_GC_HEAP_FREE_SLOTS=4096
    XRUBY_GC_HEAP_INIT_SLOTS=10000
    XRUBY_GC_HEAP_GROWTH_FACTOR=1.8
    XRUBY_GC_HEAP_GROWTH_MAX_SLOTS=0

acolyer

Internet Scale Services Checklist

A checklist for designing and developing internet scale services, inspired by James Hamilton's 2007 paper "On Desgining and Deploying Internet-Scale Services."

• http://mvdirona.com/jrh/talksandpapers/jamesrh_lisa.pdf

Basic tenets

• Does the design expect failures to happen regularly and handle them gracefully?
• Have we kept things as simple as possible?

elithrar

package main

import (
    "fmt"
    "log"
    "net/http"

    "html/template"

    "github.com/gorilla/sessions"

lelandbatey

Description

This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.

The script is here:

#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"

Results

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

rick

$("button.mark-all-as-read").parent().prepend('<button class="box-action mute-all-unread css-truncate tooltipped upwards" original-title="mute all unread notifications"> <span class="octicon octicon-mute"></span> </button>');

$('button.mute-all-unread').click(function(event) {
  // mute all unread notifications
  $(this).parent().parent().parent().find('li.unread').find('.js-mute-notification button').click();
  return false;
});

rcrowley

package main

import (
	"log"
	"net"
	"os"
	"os/signal"
	"sync"
	"syscall"
	"time"

jstorimer

This is a proof-of-concept of a couple of concurrent data structures written in Ruby.

The implementations are heavily commented for those interested. There are benchmarks (with results) included below. The results are interesting, but, as always, take with a grain of salt.

Data structures

AtomicLinkedQueue is a lock-free queue, built on atomic CAS operations.