djspiewak

Understanding Comparative Benchmarks

I'm going to do something that I don't normally do, which is to say I'm going to talk about comparative benchmarks. In general, I try to confine performance discussion to absolute metrics as much as possible, or comparisons to other well-defined neutral reference points. This is precisely why Cats Effect's readme mentions a comparison to a fixed thread pool, rather doing comparisons with other asynchronous runtimes like Akka or ZIO. Comparisons in general devolve very quickly into emotional marketing.

But, just once, today we're going to talk about the emotional marketing. In particular, we're going to look at Cats Effect 3 and ZIO 2. Now, for context, as of this writing ZIO 2 has released their first milestone; they have not released a final 2.0 version. This implies straight off the bat that we're comparing apples to oranges a bit, since Cats Effect 3 has been out and in production for months. However, there has been a post going around which cites various compar

bpholt

#!/usr/bin/env bash
set -o errexit -o nounset -o pipefail
IFS=$'\n\t'
RED='\033[1;31m'
NC='\033[0m' # No Color
readonly RED NC

function log_error () {
  echo -e "$1" > /dev/stderr
}

OlegYch

val compileTimes = inputKey[Unit]("Get compilation time for each folder")
compileTimes / aggregate := false,
compileTimes := {
  import complete.DefaultParsers._
  //  val ((depth, _), filter) = (NatBasic ~ " " ~ StringBasic).parsed
  val (depth, filter) = spaceDelimited("depth filter").parsed match {
    case Seq(depth, filter) => (depth.toInt, filter)
    case _ => sys.error("expected depth and filter arguments, eg '2 /'")
  }
  val s = state.value

gatorcse

import cats._
import cats.implicits._
import cats.effect._
import cats.effect.implicits._
import fs2._

class StreamMerger[F[_]] {
  def priorityOrderBy[A, B: Order](s1: Stream[F, A], s2: Stream[F, A])(f: A => B): Stream[F, A] = {

    def go(p1: Stream.StepLeg[F, A], p2: Stream.StepLeg[F, A]): Pull[F, A, Unit] = {

mpilquist

// Save as ~/.ammonite/predef.sc
// To use fs2 from ammonite repl, type `load.fs2` from repl prompt.
// You'll get all fs2 & cats imports, ContextShift and Timer instances
// for IO, and a globalBlocker

import $plugin.$ivy.`org.typelevel:::kind-projector:0.11.0`

if (!repl.compiler.settings.isScala213)
  repl.load.apply("interp.configureCompiler(_.settings.YpartialUnification.value = true)")

gvolpe

import cats.Traverse
import cats.effect._
import cats.effect.concurrent.Semaphore
import cats.temp.par._
import cats.syntax.all._

import scala.concurrent.duration._

object Main extends IOApp {
  import ParTask._

kiambogo

import fs2.concurrent.Queue
import cats.implicits._
import cats.effect.Concurrent
import cats.effect.concurrent.Ref
 
def groupBy[F[_], A, K](selector: A => F[K])(implicit F: Concurrent[F]): Pipe[F, A, (K, Stream[F, A])] = {
  in =>
  Stream.eval(Ref.of[F, Map[K, Queue[F, Option[A]]]](Map.empty)).flatMap { st =>
    val cleanup = {
      import alleycats.std.all._

SystemFw

 // Grows with the number of distinct `K`
  def partitions[F[_], A, K](selector: A => F[K])(implicit F: Effect[F], ec: ExecutionContext)  : Pipe[F, A, (K, Stream[F, A])] = in => 
  Stream.eval(async.refOf[F, Map[K, Queue[F, Option[A]]]](Map.empty)).flatMap { st =>
    val cleanup = {
      import alleycats.std.all._
      st.get.flatMap(_.traverse_(_.enqueue1(None)))
    }

    (in ++ Stream.eval_(cleanup)).evalMap { el =>
      (selector(el), st.get).mapN { (key, queues) =>

filosganga

import cats.effect.Async
import com.google.common.util.concurrent.{FutureCallback, Futures, ListenableFuture}

import scala.concurrent.ExecutionContext

object GuavaFutures {

  implicit class RichListenableFuture[T](lf: ListenableFuture[T]) {

    def toAsync[F[_]: Async](implicit ec: ExecutionContext): F[T] = {

djspiewak

Thread Pools

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

1. CPU-bound
2. Blocking IO
3. Non-blocking IO polling

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