raulraja

import arrow.Kind
import arrow.core.Option
import arrow.core.Try
import arrow.core.functor
import arrow.effects.IO
import arrow.effects.fix
import arrow.effects.functor
import arrow.typeclasses.Functor

/* algebras */

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

lihaoyi

package test
package grepgit.core

import scala.collection.mutable
import scala.reflect.ClassTag

/**
  * A Generator of elements of type [[A]].
  *
  * [[Generator]] is basically the inverse of

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

jdegoes

case class IO[A](unsafePerformIO: () => A) {
  def map[B](ab: A => B): IO[B] = IO(() => ab(unsafePerformIO()))
  def flatMap[B](afb: A => IO[B]): IO[B] =IO(() => afb(unsafePerformIO()).unsafePerformIO())
  def tryIO(ta: Throwable => A): IO[A] = 
    IO(() => IO.tryIO(unsafePerformIO()).unsafePerformIO() match {
      case Left(t) => ta(t)
      case Right(a) => a
    })
}
object IO {

johnynek

/**
 * In a static language like scala, how could we repeatedly flatten a datastructure without reflection?
 * This is an interesting example of using implicit parameters to do the work for you.
 */
object DeepFlatten {

  // what should this really be called? ;)
  trait Flattenable[F[_]] {
    def flatten[A](f: F[F[A]]): F[A]
  }

kiritsuku

import java.awt.Dimension
import java.awt.event.MouseAdapter
import java.awt.event.MouseEvent
import javax.swing.JFrame

import scala.collection.immutable
import scala.concurrent.duration._

import akka.actor._
import akka.stream._

ryantanner

package org.ryantanner

import scala.annotation.implicitNotFound

import shapeless._
import Nat._
import ops.nat._
import LTEq._
import GTEq._
import RankGTEq._

kciesielski

package com.softwaremill.freemonads

import cats.free.Free
import cats.~>
import cats._, cats.std.all._
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.Future

sealed trait External[A]
case class Tickets(count: Int) extends AnyVal

cb372

Category theory jargon cheat sheet

A primer/refresher on the category theory concepts that most commonly crop up in conversations about Scala or FP. (Because it's embarassing when I forget this stuff!)

I'll be assuming Scalaz imports in code samples, and some of the code may be pseudo-Scala.

Functor

A functor is something that supports map.