milessabin

trait Companion[T] {
  type C
  def apply() : C
}

object Companion {
  implicit def companion[T](implicit comp : Companion[T]) = comp()
}

object TestCompanion {

sadache

This post aims to document a practical design implementation we came up with when designing some APIs in Scala. Of course the concept is not Scala specific and applies to other functional languages.

If you don't want to go into the full introduction, this post talks about how Applicatives are too restrictive and breaking them into two independent components can be interesting for Contravariant and Invariant Functors. Jump to implementation attached.

We are taking JSON serialization/deserialization as a motivating example.

JsValue is the name of the type that represents the JSON AST. What we need is to be able to read a JsValue and produce an A:

trait Reads[A]{

Kartones

PSQL

Magic words:

psql -U postgres

Some interesting flags (to see all, use -h or --help depending on your psql version):

• -E: will describe the underlaying queries of the \ commands (cool for learning!)
• -l: psql will list all databases and then exit (useful if the user you connect with doesn't has a default database, like at AWS RDS)

djspiewak

Introduction to scalaz-stream

Every application ever written can be viewed as some sort of transformation on data. Data can come from different sources, such as a network or a file or user input or the Large Hadron Collider. It can come from many sources all at once to be merged and aggregated in interesting ways, and it can be produced into many different output sinks, such as a network or files or graphical user interfaces. You might produce your output all at once, as a big data dump at the end of the world (right before your program shuts down), or you might produce it more incrementally. Every application fits into this model.

The scalaz-stream project is an attempt to make it easy to construct, test and scale programs that fit within this model (which is to say, everything). It does this by providing an abstraction around a "stream" of data, which is really just this notion of some number of data being sequentially pulled out of some unspecified data source. On top of this abstraction, sca

djspiewak

Git DMZ Flow

I've been asked a few times over the last few months to put together a full write-up of the Git workflow we use at RichRelevance (and at Precog before), since I have referenced it in passing quite a few times in tweets and in person. The workflow is appreciably different from GitFlow and its derivatives, and thus it brings with it a different set of tradeoffs and optimizations. To that end, it would probably be helpful to go over exactly what workflow benefits I find to be beneficial or even necessary.

• Two developers working on independent features must never be blocked by each other
  • No code freeze! Ever! For any reason!
• A developer must be able to base derivative work on another developer's work, without waiting for any third party
• Two developers working on inter-dependent features (or even the same feature) must be able to do so without interference from (or interfering with) any other parties
• Developers must be able to work on multiple features simultaneously, or at lea

odd

import akka.actor._
import scala.concurrent.duration.Duration
import scala.concurrent.{CanAwait, ExecutionContext, Future, TimeoutException}
import scala.util.{Failure, Success, Try}

object LocalExecutor {
  private case class Execute(runnable: Try[Runnable])

  private class LocalExecutionContext(target: ActorRef) extends ExecutionContext {
    override def execute(r: Runnable) = target ! Execute(Try(r))

OlivierBlanvillain

Revisiting Tagless Final Interpreters

Tageless Final interpreters are an alternative to the traditional Algebraic Data Type (and generalized ADT) based implementation of the interpreter pattern. This document presents the Tageless Final approach with Scala, and shows how Dotty with it's recently added implicits functions makes the approach even more appealing. All examples are direct translations of their Haskell version presented in the Typed Tagless Final Interpreters: Lecture Notes (section 2).

The interpreter pattern has recently received a lot of attention in the Scala community. A lot of efforts have been invested in trying to address the biggest shortcomings of ADT/GADT based solutions: extensibility. One can first look at cats' Inject typeclass for an implementation of [Data Type à la Carte](http://www.cs.ru.nl/~W.Swierstra/Publications/DataTypesA

gvolpe

Dependency Injection in Functional Programming

There exist several DI frameworks / libraries in the Scala ecosystem. But the more functional code you write the more you'll realize there's no need to use any of them.

A few of the most claimed benefits are the following:

• Dependency Injection.
• Life cycle management.
• Dependency graph rewriting.

abeln

Entry point

dotc/Compiler.scala

The entry point to the compiler contains the list of phases and their order.

Phases

Some phases executed independently, but others (miniphases) are grouped for efficiency. See the paper "Miniphases: Compilation using Modular and Efficient Tree Transformation" for details.

patrickjcurran

Vistors, F-Algebras, and Transducers

So Doug made an interesting PR, which uses the visitor pattern described in Haoyi's post.

As I was trying to understand the vistor pattern, I wanted to see how it compared to using F-algebras.

Haoyi describes the benefits of the vistor pattern to include:

• Composability
• Not creating intermediate datastructures