Vitor Vieira vitorsvieira

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

Operation	Input	Result	Notes
map	`F[A]` , `A => B`	`F[B]`	Functor
apply	`F[A]` , `F[A => B]`	`F[B]`	Applicative
(fa, fb, ...).mapN	`(F[A], F[B], ...)` , `(A, B, ...) => C`	`F[C]`	Applicative
(fa, fb, ...).tupled	`(F[A], F[B], ...)`	`F[(A, B, ...)]`	Applicative
flatMap	`F[A]` , `A => F[B]`	`F[B]`	Monad
traverse	`F[A]` , `A => G[B]`	`G[F[A]]`	Traversable; `fa.traverse(f)` == `fa.map(f).sequence`; "foreach with effects"
sequence	`F[G[A]]`	`G[F[A]]`	Same as `fga.traverse(identity)`
attempt	`F[A]`	`F[Either[E, A]]`	Given `ApplicativeError[F, E]`

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

Thread Pools

Thread pools on the JVM should usually be divided into the following three categories:

CPU-bound
Blocking IO
Non-blocking IO polling

Each of these categories has a different optimal configuration and usage pattern.

Swift Concurrency Manifesto

Author: Chris Lattner

Chinese Translation by Jason Yu

Applied Functional Programming with Scala - Notes

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

A curated list of AWS resources to prepare for the AWS Certifications

A curated list of awesome AWS resources you need to prepare for the all 5 AWS Certifications. This gist will include: open source repos, blogs & blogposts, ebooks, PDF, whitepapers, video courses, free lecture, slides, sample test and many other resources.

Recursion and Trampolines in Scala

Recursion is beautiful. As an example, let's consider this perfectly acceptable example of defining the functions even and odd in Scala, whose semantics you can guess:

def even(i: Int): Boolean = i match {
  case 0 => true
  case _ => odd(i - 1)
}

def odd(i: Int): Boolean = i match {

	import cats.{ ApplicativeError, MonadError }
	import cats.data.{ Kleisli, OptionT }
	import cats.effect.Sync
	import cats.effect.concurrent.Ref
	import cats.syntax.all._
	import io.circe.generic.auto._
	import io.circe.syntax._
	import org.http4s._
	import org.http4s.circe.CirceEntityDecoder._
	import org.http4s.circe._


	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._