agaoglu

/**
 * This file is licensed to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance with the
 * License. You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

afair

==========================================          ==========================================
             TMUX COMMAND                                        WINDOW (TAB)
==========================================          ==========================================

List    tmux ls                                     List         ^b w
New          new -s <session>                       Create       ^b c
Attach       att -t <session>                       Rename       ^b , <name>
Rename       rename-session -t <old> <new>          Last         ^b l               (lower-L)
Kill         kill-session -t <session>              Close        ^b &

pbailis

A friend asked me for a few pointers to interesting, mostly recent papers on data warehousing and "big data" database systems, with an eye towards real-world deployments. I figured I'd share the list. It's biased and rather incomplete but maybe of interest to someone. While many are obvious choices (I've omitted several, like MapReduce), I think there are a few underappreciated gems.

###Dataflow Engines:

Dryad--general-purpose distributed parallel dataflow engine
http://research.microsoft.com/en-us/projects/dryad/eurosys07.pdf

Spark--in memory dataflow
http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf

macintux

• https://ferd.ca/a-distributed-systems-reading-list.html
• http://the-paper-trail.org/blog/distributed-systems-theory-for-the-distributed-systems-engineer/
• https://github.com/palvaro/CMPS290S-Winter16/blob/master/readings.md
• http://muratbuffalo.blogspot.com/2015/12/my-distributed-systems-seminars-reading.html
• http://christophermeiklejohn.com/distributed/systems/2013/07/12/readings-in-distributed-systems.html
• http://michaelrbernste.in/2013/11/06/distributed-systems-archaeology-works-cited.html
• http://rxin.github.io/db-readings/
• http://research.microsoft.com/en-us/um/people/lamport/pubs/pubs.html
• http://pdos.csail.mit.edu/dsrg/papers/
• http://scalingsystems.com/2011/09/07/reading-list-for-distributed-systems/

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

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

negator

    
    
    /**
      The Play (2.3) json combinator library is arguably the best in the scala world. However it doesnt
      work with case classes with greater than 22 fields.
      The following gist leverages the shapeless 'Automatic Typeclass Derivation' facility to work around this
      limitation. Simply stick it in a common location in your code base, and use like so:
      Note: ** Requires Play 2.3 and shapeless 2.0.0  
      
  

pierzchalski

import shapeless._
import shapeless.ops.coproduct.Length
import shapeless.ops.nat.ToInt

import scala.util.Random
import scalaz.Reader

sealed trait T
case class A(s: String) extends T
case class B(n: Int, t: T) extends T

tpolecat

EDIT: This gist has been promoted and is now a blog post.

Kinda-Curried Type Parameters

Scala methods can have multiple lists of value parameters but only one list of type parameters, which is occasionally irritating when some are inferable and others are not. Consider this method which has two type parameters, one inferable and one not.

import scalaz._, Scalaz._