jdegoes

package fpmax

import scala.util.Try
import scala.io.StdIn.readLine

object App0 {
  def main: Unit = {
    println("What is your name?")

    val name = readLine()

SystemFw

cats-effect

The cats-effect project defines a purely functional effect type (IO[A]), and associated typeclasses defining its behaviour. The ones we care about for this example are:

trait Sync[F[_]] extends MonadError[F, Throwable] {
   def delay[A](a: => A): F[A]
   ...
}

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.

Daenyth

The problem

I have some data which has adjacent entries that I want to group together and perform actions on. I know roughly that fs2.Pull can be used to "step" through a stream and do more complicated logic than the built in combinators allow. I don't know how to write one though!

In the end we should have something like

def combineAdjacent[F[_], A](
 shouldCombine: (A, A) => Boolean,

gvolpe

Shared State in pure Functional Programming

Newcomers to Functional Programming are often very confused about the proper way to share state without breaking purity and end up having a mix of pure and impure code that defeats the purpose of having pure FP code in the first place.

Reason why I decided to write up a beginner friendly guide :)

Use Case

We have a program that runs three computations at the same time and updates the internal state to keep track of the

patriknw

package flowcontrol

import scala.concurrent.duration._

import akka.actor.typed.ActorRef
import akka.actor.typed.ActorSystem
import akka.actor.typed.Behavior
import akka.actor.typed.scaladsl.Behaviors

/**

gvolpe

import java.io.FileOutputStream

import cats.effect.ExitCase.{Canceled, Completed, Error}
import cats.effect._
import cats.syntax.apply._
import cats.syntax.functor._

import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration._

lifeofguenter

#!/usr/bin/env bash

throw_exception() {
  echo "Ooops!"
  echo 'Stack trace:' 1>&2
  while caller $((n++)) 1>&2; do :; done;
  exit 1
}

set -E

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

mielientiev

// build.sbt
val slick = Seq(
  "com.typesafe.slick" %% "slick" % "3.2.1",
  "org.slf4j" % "slf4j-nop" % "1.6.4",
  "com.typesafe.slick" %% "slick-hikaricp" % "3.2.1",
  "com.h2database" % "h2" % "1.4.196"
)
val scalaz = Seq(
  "org.scalaz" %% "scalaz-core" % "7.2.17"
)