alexandru

import concurrent.{Future, Await, Promise, ExecutionContext}
import java.util.concurrent.{Future => JavaFuture, TimeUnit, Callable, CancellationException, ExecutorService}
import concurrent.duration._
import scala.util.Success
import scala.util.Failure
import scala.util.control.NonFatal
import scala.collection.JavaConverters._
import concurrent.duration.TimeUnit
import java.{util => jutil}

pathikrit

val n = 9
val s = Math.sqrt(n).toInt
type Board = IndexedSeq[IndexedSeq[Int]]
 
def solve(board: Board, cell: Int = 0): Option[Board] = (cell%n, cell/n) match {
  case (r, `n`) => Some(board)
  case (r, c) if board(r)(c) > 0 => solve(board, cell + 1)
  case (r, c) =>
    def guess(x: Int) = solve(board.updated(r, board(r).updated(c, x)), cell + 1)  
    val used = board.indices.flatMap(i => Seq(board(r)(i), board(i)(c), board(s*(r/s) + i/s)(s*(c/s) + i%s)))

smarter

Generalized Algebraic Data Types in Scala

Basic GADTs

Here's an ADT which is not a GADT, in Haskell:

data Expr = IntExpr Int | BoolExpr Bool

OlivierBlanvillain

Revisiting Tagless Final Interpreters

Tageless Final interpreters are an alternative to the traditional Algebraic Data Type (and generalized ADT) based implementation of the interpreter pattern. This document presents the Tageless Final approach with Scala, and shows how Dotty with it's recently added implicits functions makes the approach even more appealing. All examples are direct translations of their Haskell version presented in the Typed Tagless Final Interpreters: Lecture Notes (section 2).

The interpreter pattern has recently received a lot of attention in the Scala community. A lot of efforts have been invested in trying to address the biggest shortcomings of ADT/GADT based solutions: extensibility. One can first look at cats' Inject typeclass for an implementation of [Data Type à la Carte](http://www.cs.ru.nl/~W.Swierstra/Publications/DataTypesA

non

Laws

A law is a group of two or more expressions which are required to be the same. The expressions will usually involve one or more typed holes ("inputs") which vary.

Some examples:

x.map(id) === x

d1egoaz

If you want to learn Scala, I’d suggest:

• Install IntelliJ + Scala Plugin
• Don’t do the Coursera courses yet.
• Don’t do the “red book” Functional Programming in Scala yet.
• Do: http://underscore.io/books/
  • Essential Scala
  • Essential Play
  • Essential Slick
• Do Scala for the Impatient: https://www.amazon.com/Scala-Impatient-Cay-S-Horstmann/dp/0321774094
• The Neophyte’s Guide to Scala http://danielwestheide.com/scala/neophytes.html

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.

hobwekiva

Preliminaries

type Void <: Nothing
type ¬[-A] = A => Void
type ¬¬[+A] = ¬[¬[A]]
type ⋁[+A, +B] = Either[A, B]
type ⋀[+A, +B] = Tuple2[A, B]

wogan

package retry

import scala.concurrent.duration.{Duration, FiniteDuration}
import scala.util.Random

import scala.concurrent.duration._

object Backoff {

  val none: Backoff = _ => Duration.Zero

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,