Yuriy Bogomolov YBogomolov

Haskeller Competency Matrix

See also List of materials about Software Design in Haskell

	Junior	Middle	Senior	Architect
Haskell level	Basic Haskell	Intermediate Haskell	Advanced Haskell	Language-agnostic
Haskell knowledge scope	Learn you a Haskell	Get programming with Haskell	Haskell in Depth	Knows several languages from different categories
	Get programming with Haskell	Haskell in Depth	Functional Design and Architecture
	Soar with Haskell	[Soar

Magic

It’s folklore that if you’re summing a list of numbers, then you should always use strict foldl. Is that really true though? foldr is useful for lists when the function we use is lazy in its second argument. For (+) :: Int -> Int -> Int this is tyically not the case, but in some sense that’s because Int is “too strict”. An alternative representation of numbers is to represent them inductively. If we do this, sumation can be lazy, and foldr can do things that foldl simply can’t!

First, let’s define natural numbers inductively, and say how to add them:

data Nat = Zero | OnePlus Nat deriving Show

one :: Nat

Monads and delimited control are very closely related, so it isn’t too hard to understand them in terms of one another. From a monadic point of view, the big idea is that if you have the computation m >>= f, then f is m’s continuation. It’s the function that is called with m’s result to continue execution after m returns.

If you have a long chain of binds, the continuation is just the composition of all of them. So, for example, if you have

m >>= f >>= g >>= h

then the continuation of m is f >=> g >=> h. Likewise, the continuation of m >>= f is g >=> h.

Generic validation of nested data

TLDR

This technique helps validate arbitrary conditions in deeply nested structures without writing additional code — with the help of Haskell Generics.

It boils down to this pattern:

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

type StringBool = "true"|"false";


interface AnyNumber { prev?: any, isZero: StringBool };
interface PositiveNumber { prev: any, isZero: "false" };

type IsZero<TNumber extends AnyNumber> = TNumber["isZero"];
type Next<TNumber extends AnyNumber> = { prev: TNumber, isZero: "false" };
type Prev<TNumber extends PositiveNumber> = TNumber["prev"];

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

	// Adapted from http://code.slipthrough.net/2016/08/10/approximating-gadts-in-purescript/

	import { Kind, URIS } from 'fp-ts/lib/HKT'
	import { URI } from 'fp-ts/lib/Identity'
	import { identity } from 'fp-ts/lib/function'

	// ------------------------------------------
	// Leibniz
	// ------------------------------------------

	import { getParameterFromSsm } from 'aws-ssm.sdk'

	function getParameterValue(name: string, defaultValue: string) {
	return process.env[name] \|\| getParameterFromSsm(name, defaultValue);
	}

	export class LazyParameter extends LazyValue<string> {
	constructor(name: string, defaultValue?: string) {
	super(async () => {
	return getParameterValue(name, defaultValue);