justindossey

  def all_replicas_in_sync?(verbose=false)
    in_sync = true
    # connect to the primary
    mongo = Mongo::Connection.new(*MongoMapper.connection.primary)
    db = mongo.db('local')
    last_op=db.collection('oplog.rs').find.sort([['$natural',-1]]).limit(1).to_a[0]
    db.collection('slaves').find().to_a.each do |slave|
      opdiff = last_op['ts'].increment - slave['syncedTo'].increment
      diff = (last_op['ts'].seconds - slave['syncedTo'].seconds)/1000.0
      in_sync = false if diff > 60

ryandotsmith

The Worker Pattern

2011-05-22

Introduction

This article is the accumulation of a tutorial that was given as a training session at Red Dirt Ruby Conf (2011) and a formal talk given at Rails Conf (2011). To understand the theory behind The Worker Pattern, read over the slides and the slide notes. From there, you can follow the instructions in the Tutorial

eric

module Resque
  module Plugins
    # If you want only one instance of your job queued at a time,
    # extend it with this module.
    #
    # For example:
    #
    # require 'resque/plugins/zookeeper_lock'
    #
    # class UpdateNetworkGraph

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;
});

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:'}

ryandotsmith

Process Partitioning

The Problem

When working with large, high volume, low latency systems, it is often the case that processing data sequentially becomes detrimental to the system's health. If we only allow 1 process to work on our data we run into several challenges:

• Our process may fall behind resulting in a situation which it is impossible for our process to catch up.
• Our singleton process could crash and leave our system in a degraded state.
• The average latency of data processing could be dramatically affected by outlying cases.

jpignata

require "raindrops"

class UnicornConnectionMonitor
  def initialize(app, options = {})
    @app = app
    @statsd = options.fetch(:statsd)
  end

  def call(env)
    if linux? && unicorn?

practicingruby

require 'socket'

module EventEmitter
  def _callbacks
    @_callbacks ||= Hash.new { |h, k| h[k] = [] }
  end

  def on(type, &blk)
    _callbacks[type] << blk
    self

bravoecho

require 'pry'

data = {
  a: {
    alfa: "alfa",
    beta: "beta"
  },
  b: 2,
  c: {
    alfa: {

jqr

require 'fileutils'

class Reader
  def self.read(filename, processing_filename)
    return [] unless File.exists?(filename)
    FileUtils.move(filename, processing_filename)
    lines = []
    File.open(processing_filename, 'r') do |f|
      f.flock(File::LOCK_EX)
      while !f.eof?