jackcviers

So the hacker news post said my comment was too long. Warning, long, opinionated post:

Scala dev for 10+ years here. Spark is weird. Databricks has a style guide that deliberately chooses not to use scala features that the rest of the community uses, and doesn't follow the same best practices around library and scala major version usage that the rest of the community uses [1]. It's no surprise that the project has trouble interoperating with libraries outside of the spark ecosystem, and is therefore a maintenance problem.

Spark's style and compatibility problems

Scala isn't a maintenance nightmare, but it does attract a lot of newcomers who dive in, don't stick within one of its many ecosystems, get confused, and generally leave a mess, and that is a direct result of the fact that scala is a multi-paradigm, relatively expressive language to the one(s) it is competing with and pulling developers from, and that those developers, for the large part, don't really want to change and think that Scala is just a

sirupsen

class Book < Airrecord::Table
  class Endorser < Airrecord::Table
    self.base_key = ""
    self.table_name = "Endorser"
  end

  self.base_key = ""
  self.table_name = "Books"

  has_many :endorsements, class: 'Book::Endorser', column: 'Endorsements'

nolanlawson

Promises puzzle answer sheet

Solution to https://twitter.com/nolanlawson/status/578948854411878400.

Puzzle #1

doSomething().then(function () {

maxwell

#got this error in your Rubymine Test runner, and have to "spring stop" all the time?
#/Users/maxwell/.rvm/gems/ruby-2.1.2/gems/activesupport-4.1.6/lib/active_support/dependencies.rb:247:in `require': cannot load such file -- teamcity/spec/runner/formatter/teamcity/formatter (LoadError)

#!/usr/bin/env ruby
begin
  load File.expand_path("../spring", __FILE__)
rescue LoadError
end
require 'bundler/setup'

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

relaxdiego

Follow these steps to install graphite on OS X Mavericks.

Prerequisites

• Homebrew
• Python 2.7
• Git

Install dependencies

Install Cairo and friends

kafecho

package org.kafecho.learning.midi
import javax.sound.midi.MidiSystem
import javax.sound.midi.ShortMessage
import scala.util.Random

object MidiTest extends App {
  val rcvr = MidiSystem.getReceiver
  while (true) {
    val myMsg = new ShortMessage
    myMsg.setMessage(ShortMessage.NOTE_ON, 8, Random.nextInt.abs % 127, 93)

coffeemug

After spending the better part of the month implementing date support
in RethinkDB, Mike Lucy sent the team the following e-mail. It would
have been funny, if it didn't cause thousands of programmers so much
pain. Read it, laugh, and weep!

-----

So, it turns out that we're only going to support dates between the
year 1400 and the year 10000 (inclusive), because that's what boost
supports.

dehora

object ಠ_ಠ {

  class ಠ_ಠ(m: String) extends Exception(m) 

  def main(a: Array[String]) = {
    throw new ಠ_ಠ("ಠ_ಠ")
  }
}

clintongormley

Yesterday I upgraded our running elasticsearch cluster on a site which serves a few million search requests a day, with zero downtime. I've been asked to describe the process, hence this blogpost.

To make it more complicated, the cluster was running elasticsearch version 0.17.8 (released 6 Oct 2011) and I upgraded it to the latest 0.19.10. There have been 21 releases between those two versions, with a lot of functional changes, so I needed to be ready to roll back if necessary.

Our setup:

• elasticsearch

We run elasticsearch on two biggish boxes: 16 cores plus 32GB of RAM. All indices have 1 replica, so all data is stored on both boxes (about 45GB of data). The primary data for our main indices is also stored in our database. We have a few other indices whose data is stored only in elasticsearch, but are updated once daily only. Finally, we store our sessions in elasticsearch, but active sessions are cached in memcached.