# ssledz blog

## Everything should be made as simple as possible, but no simpler.

In functional programming monad is a design pattern which is used to express how states of computations are changing. It can take a form of some abstract data type constructor with two abstract functions.

In scala we can define this contract using Monad type class

Functions pure and flatMap for a given monad M[_] have to follow some laws - I will talk about them later.

Function map can be defined in terms of flatMap and pure and this is a bonus which we get for a free when we provide an instance of a Monad for a type M[_]. Moreover many useful functions can be defined in terms of flatMap and pure like map2, ap, filter and so on. This interface is so powerful then often is treated as a primitive one when goes to implement other functions.

We can think about M[A] like about some smart container for a value (values) of type A. This container abstracts away from how this value is kept. We can have many flavors of them like container:

• aware of whether or not the value exists
• with more then one value
• for which getting the value would trigger some kind of IO operation
• with value which eventually could appear in future
• with value or error
• with value dependent on some kind of state
• with value and some logging information
• etc

Monad let us focus on what we want to do with the contained value. Monad is like a context in which the value exists. When we want to do some computation we are abstracting over the context so we aren’t disrupted whether or not the value exists, we have many of them or the value may appear in a future. We want just to get the value out of the container for a moment to make some computation and then put it there again. The context is important only when we want to pull out a value permanently.

Another advantage of the monad is an ability of sequencing the computations. Having let’s say two computations we can very easily make dependence between them saying that the computations of the second depends on a result of the first. Of course this can be scaled to more than two.

At first glance, it may seem to be not so impressive because it is very common to make such things during coding. But be aware that monad frees us from thinking about the context in which the value exists. The context can be for example an asynchronous computation. Dealing with concurrency is challenging - we have to be very careful to not make a hard to spot mistakes. Monad takes care about this complexity, providing a result of the first computation as soon as possible giving us possibility to spawn another computation in asynchronous manner.

### Laws

• Left identity: return a >>= f ≡ f a
• Right identity: m >>= return ≡ m
• Associativity: (m >>= f) >>= g ≡ m >>= (\x -> f x >>= g)

These laws was taken from haskell because expressions there are very compact and easy to follow. Function >>= in scala maps to flatMap, return is just a pure, f x is an application of function f with x and the last one \x -> ... is a lambda expression.

Laws in scala can be written in a following way (using ScalaCheck)

If you are curious about implementation details take a look on this class

This section is a placeholder for a list of posts about monads mentioned in this article. I will try my best to deliver a missing content. Watch my blog for an update.

• Option
• Either
• Id
• Writer