8bitreid

package example

import zio.{IO, Layer, Task, ULayer, ZIO, ZIOAppDefault, ZLayer}
// Main
object ZioApp extends ZIOAppDefault:
  val app: Task[Unit] =
    ZIO.serviceWithZIO[GreetingService](_.sayHello)
      .provideLayer(GreetingModule.helloLayer)
  def run: Task[Unit] = app

quelgar

Every Argument for Static Typing Applies to Typed Errors

Think of all the arguments you've heard as to why static typing is desirable — every single one of those arguments applies equally well to using types to represent error conditions.

An odd thing I’ve observed about the Scala community is how many of its members believe that a) a language with a sophisticated static type system is very valuable; and b) that using types for error handling is basically a waste of time. If static types are useful—and if you like Scala, presumably you think they are—then using them to represent error conditions is also useful.

Here's a little secret of functional programming: errors aren't some special thing that operate under a different set of rules to everything else. Yes, there are a set of common patterns we group under the loose heading "error handling", but fundamentally we're just dealing with more values. Values that can have types associated with them. There's absolutely no reason why the benefits of static ty

djspiewak

Quick Tips for Fast Code on the JVM

I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.

This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea

jdegoes

Applied Functional Programming with Scala - Notes

Copyright © 2016-2018 Fantasyland Institute of Learning. All rights reserved.

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

folone

def ackermann(m: Int, n: Int): Int = {
   (m, n) match {
     case (0, _)                   ⇒ n + 1
     case (m, 0) if m > 0          ⇒ ackermann(m - 1, 1)
     case (m, n) if m > 0 && n > 0 ⇒ ackermann(m - 1, ackermann(m, n - 1))
   }
 }
 
import scalaz._, Scalaz._, Free.{suspend ⇒ _, _}, Trampoline._
def ackermannTramp(m: Int, n: Int): Trampoline[Int] = {