pathikrit

/**
 * Solves the n-Queen puzzle in O(n!)
 * Let p[r] be the column of the queen on the rth row (must be exactly 1 queen per row)
 * There also must be exactly 1 queen per column and hence p must be a permuation of (0 until n)
 * There must be n distinct (col + diag) and n distinct (col - diag) for each queen (else bishop attacks)
 * @return returns a Iterator of solutions 
 *     Each solution is an array p of length n such that p[i] is the column of the queen on the ith row
 */
def nQueens(n: Int): Iterator[Seq[Int]] = 
  (0 until n)

Geal

#include <stdio.h>
#include <stdlib.h>
#include <pthread.h>
#include <unistd.h>
#include <stdbool.h>
#include <time.h>
#include <stdarg.h>
#include <string.h>

#include "future.h"

viktorklang

/*
Copyright 2012-2021 Viktor Klang

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software

davideicardi

Avro serialization

There are 4 possible serialization format when using avro:

• Avro Json encoding
• Avro Data Serialization (https://avro.apache.org/docs/current/spec.html#Data+Serialization) Binary format with an header that contains the full schema, this is the format usually used when writing Avro files
• Avro Single Object Encoding (https://avro.apache.org/docs/current/spec.html#single_object_encoding) Binary format with an header with only the fingerprint/id of the schema, this it the format used by Kafka (see this
• Avro Binary Encoding (https://avro.apache.org/docs/current/spec.html#binary_encoding)

eulerfx

Both command sourcing (CS) and event sourcing (ES) rely on determinism for correctness.

The correctness condition for ES is the determinism (purity) of the function State -> Event -> State. Given that this function is deterministic, aka it always maps the same inputs to the same ouputs, we can rely on it to reconstitute state at any point in time. Determinism is typically achieved by ensuring that the Event has all required information to make the state transition, ie no side effects. The Event then is a sort of "closure" of all pertinent information about the event.

The correctness condition for CS is the determinism of the function State -> Command -> Event. Herein lies one of the distinctions between command sourcing and event sourcing - a program can control its output, but it not its input. Since one can't control the input, aka command, one can't in general, enrich it with all required information to make the above function deterministic. A consequence of this is that you can't simply replay a

viktorklang

import scala.concurrent.duration._
import scala.concurrent.ExecutionContext
import scala.concurrent.Future
import akka.pattern.after
import akka.actor.Scheduler

/**
 * Given an operation that produces a T, returns a Future containing the result of T, unless an exception is thrown,
 * in which case the operation will be retried after _delay_ time, if there are more possible retries, which is configured through
 * the _retries_ parameter. If the operation does not succeed and there is no retries left, the resulting Future will contain the last failure.

Daenyth

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]

platy

import java.util.concurrent.ThreadPoolExecutor.AbortPolicy
import java.util.concurrent._

import scala.concurrent.{ Promise, Future }
import scala.concurrent.duration.FiniteDuration
import scala.language.implicitConversions
import scala.util.Try

object ScheduledExecutor {
  private val defaultHandler: RejectedExecutionHandler = new AbortPolicy

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,

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