pchiusano

package scalaworld.interpreters

/*
This file shows a simple language, an interpreter, and two
partial evaluators for that language, along with a profiling suite.
*/

trait Expr // denotes a Vector[Double] => Vector[Double]

object Expr {

mpilquist

  def switchMap[F[_]: Async, A, B](f: A => Stream[F, B]): Pipe[F, A, B] = {
    def go(
      outer: ScopedFuture[F, Pull[F, Nothing, (NonEmptyChunk[A], Handle[F, A])]],
      inner: ScopedFuture[F, Pull[F, Nothing, (NonEmptyChunk[B], Handle[F, B])]]
    ): Pull[F, B, Nothing] = {
      (outer race inner).pull.flatMap {
        case Left(outer) =>
          outer.optional.flatMap {
            case None =>
              inner.pull.flatMap(identity).flatMap { case (hd, tl) => Pull.output(hd) >> tl.echo }

viktorklang

/*
Copyright 2015 Viktor Klang
 
   Licensed 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

johnynek

/**
 * To get started:
 * git clone https://github.com/twitter/algebird
 * cd algebird
 * ./sbt algebird-core/console
 */

/**
 * Let's get some data. Here is Alice in Wonderland, line by line
 */

jdegoes

Enterprise Scala

A guide to writing high-performance, robust, Enterprise-grade Scala applications.

Scala is a new language for the JVM that is rapidly spreading beyond tech companies like Twitter, Netflix, and LinkedIn, and into large Enterprises. Engineering teams in companies of all types and sizes are turning to Scala for its strong type system, its extensive ecosystem (Scalaz, Shapeless, Spire), and its seamless interoperability with legacy Java code bases.

While there are many books geared at introducing developers to the Scala programming language, none specifically address the question of what it means to write idiomatic Scala in today’s modern Enterprise. As a large, complex language with many advanced features, there are many ways to write Scala apps, and not all of them were created equal.

Enterprise Scala is the first book to systematize decades of real world, production experience writing large-scale, Enterprise-grade systems in Scala. The result is a comprehensive overview of what idioma

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.

---

kevinwright

As compiled by Kevin Wright a.k.a @thecoda

(executive producer of the movie, and I didn't even know it... clever huh?)

please, please, please - If you know of any slides/code/whatever not on here, then ping me on twitter or comment this Gist!

This gist will be updated as and when I find new information. So it's probably best not to fork it, or you'll miss the updates!

Monday June 16th

runarorama

sealed trait Interact[A]

case class Ask(prompt: String)
  extends Interact[String]

case class Tell(msg: String)
  extends Interact[Unit]

trait Monad[M[_]] {
  def pure[A](a: A): M[A]

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

pozorvlak

CvRDTs are (almost?) as general as they can be

What are you talking about, and why should I care?

Now that we live in the Big Data, Web 3.14159 era, lots of people want to build databases that are too big to fit on a single machine. But there's a problem in the form of the CAP theorem, which states that if your network ever partitions (a machine goes down, or part of the network loses its connection to the rest) then you can keep consistency (all machines return the same answer to