# Preface

The aims of this book are two-fold: to introduce monads, functors, and other functional programming patterns as a way to structure program design, and to explain how these concepts are implemented in Cats.

Monads, and related concepts, are the functional programming equivalent of object-oriented design patterns—architectural building blocks that turn up over and over again in code. They differ from object-oriented patterns in two main ways:

- they are formally, and thus precisely, defined; and
- they are extremely (extremely) general.

This generality means they can be difficult to understand. *Everyone* finds abstraction difficult. However, it is generality that allows concepts like monads to be applied in such a wide variety of situations.

In this book we aim to show the concepts in a number of different ways, to help you build a mental model of how they work and where they are appropriate. We have extended case studies, a simple graphical notation, many smaller examples, and of course the mathematical definitions. Between them we hope you’ll find something that works for you.

Ok, let’s get started!

## Versions

This book is written for Scala 2.12.3 and Cats 1.0.0. Here is a minimal `build.sbt`

containing the relevant dependencies and settings^{1}:

```
scalaVersion := "2.12.3"
libraryDependencies +=
"org.typelevel" %% "cats-core" % "1.0.0"
scalacOptions ++= Seq(
"-Xfatal-warnings",
"-Ypartial-unification"
)
```

### Template Projects

For convenience, we have created a Giter8 template to get you started. To clone the template type the following:

`$ sbt new underscoreio/cats-seed.g8`

This will generate a sandbox project with Cats as a dependency. See the generated `README.md`

for instructions on how to run the sample code and/or start an interactive Scala console.

The `cats-seed`

template is as minimal as it gets. If you’d prefer a more batteries-included starting point, check out Typelevel’s `sbt-catalysts`

template:

`$ sbt new typelevel/sbt-catalysts.g8`

This will generate a project with a suite of library dependencies and compiler plugins, together with templates for unit tests and tut-enabled documentation. See the project pages for catalysts and sbt-catalysts for more information.

## Conventions Used in This Book

This book contains a lot of technical information and program code. We use the following typographical conventions to reduce ambiguity and highlight important concepts:

### Typographical Conventions

New terms and phrases are introduced in *italics*. After their initial introduction they are written in normal roman font.

Terms from program code, filenames, and file contents, are written in `monospace font`

. Note that we do not distinguish between singular and plural forms. For example, we might write `String`

or `Strings`

to refer to `java.lang.String`

.

References to external resources are written as hyperlinks. References to API documentation are written using a combination of hyperlinks and monospace font, for example: `scala.Option`

.

### Source Code

Source code blocks are written as follows. Syntax is highlighted appropriately where applicable:

```
object MyApp extends App {
println("Hello world!") // Print a fine message to the user!
}
```

Most code passes through tut to ensure it compiles. tut uses the Scala console behind the scenes, so we sometimes show console-style output as comments:

```
"Hello Cats!".toUpperCase
// res0: String = HELLO CATS!
```

### Callout Boxes

We use two types of *callout box* to highlight particular content:

Tip callouts indicate handy summaries, recipes, or best practices.

Advanced callouts provide additional information on corner cases or underlying mechanisms. Feel free to skip these on your first read-through—come back to them later for extra information.

## Acknowledgements

We’d like to thank our colleagues at Underscore, our friends at Typelevel, and everyone who helped contribute to this book. Special thanks to Jenny Clements for her fantastic artwork and Richard Dallaway for his proof reading expertise. Here is an alphabetical list of contributors:

Alessandro Marrella, Cody Koeninger, Connie Chen, Conor Fennell, Dani Rey, Daniela Sfregola, Danielle Ashley, David Castillo, David Piggott, Dennis Hunziker, Deokhwan Kim, Edd Steel, Evgeny Veretennikov, Francis Devereux, Ghislain Vaillant, Gregor Ihmor, Henk-Jan Meijer, Janne Pelkonen, Jason Scott, Javier Arrieta, Jenny Clements, Jérémie Jost, Joachim Hofer, Jonathon Ferguson, Lance Paine, Leif Wickland, ltbs, Marc Prud’hommeaux, Martin Carolan, Mr-SD, Narayan Iyer, Niccolo’ Paravanti, niqdev, Noor Nashid, Pablo Francisco Pérez Hidalgo, Pawel Jurczenko, Phil Derome, Philip Schwarz, Riccardo Sirigu, Richard Dallaway, Rodrigo B. de Oliveira, Seoh Char, Sergio Magnacco, Tim McIver, Toby Weston, Victor Osolovskiy, and Yinka Erinle.

If you spot an error or potential improvement, please raise an issue or submit a PR on the book’s Github page.

### Backers

We’d also like to extend very special thanks to our backers—fine people who helped fund the development of the book by buying a copy before we released it as open source. This book wouldn’t exist without you:

A battle-hardened technologist, Aaron Pritzlaff, Abhishek Srivastava, Aleksey “Daron” Terekhin, Algolia, Allen George (@allenageorge), Andrew Johnson, Andrew Kerr, Andy Dwelly, Anler, anthony@dribble.ai, Aravindh Sridaran, Araxis Ltd, ArtemK, Arthur Kushka (@arhelmus), Artur Zhurat, Arturas Smorgun, Attila Mravik, Axel Gschaider, Bamboo Le, bamine, Barry Kern, Ben Darfler (@bdarfler), Ben Letton, Benjamin Neil, Benoit Hericher, Bernt Andreas Langøien, Bill Leck, Blaze K, Boniface Kabaso, Brian Wongchaowart, Bryan Dragon, @cannedprimates, Ceschiatti (@6qat), Chris Gojlo, Chris Phelps, @CliffRedmond, Cody Koeninger, Constantin Gonciulea, Dadepo Aderemi, Damir Vandic, Damon Rolfs, Dan Todor, Daniel Arndt, Daniela Sfregola, David Greco, David Poltorak, Dennis Hunziker, Dennis Vriend, Derek Morr, Dimitrios Liapis, Don McNamara, Doug Clinton, Doug Lindholm (dlindhol), Edgar Mueller, Edward J Renauer Jr, Emiliano Martinez, esthom, Etienne Peiniau, Fede Silva, Filipe Azevedo, Franck Rasolo, Gary Coady, George Ball, Gerald Loeffler, Integrational, Giles Taylor, Guilherme Dantas (@gamsd), Harish Hurchurn, Hisham Ismail, Iurii Susuk, Ivan (SkyWriter) Kasatenko, Ivano Pagano, Jacob Baumbach, James Morris, Jan Vincent Liwanag, Javier Gonzalez, Jeff Gentry, Joel Chovanec, Jon Bates, Jorge Aliss (@jaliss), Juan Macias (@1macias1), Juan Ortega, Juan Pablo Romero Méndez, Jungsun Kim, Kaushik Chakraborty (@kaychaks), Keith Mannock, Ken Hoffman, Kevin Esler, Kevin Kyyro, kgillies, Klaus Rehm, Kostas Skourtis, Lance Linder, Liang, Guang Hua, Loïc Girault, Luke Tebbs, Makis A, Malcolm Robbins, Mansur Ashraf (@mansur_ashraf), Marcel Lüthi, Marek Prochera @hicolour, Marianudo (Mariano Navas), Mark Eibes, Mark van Rensburg, Martijn Blankestijn, Martin Studer, Matthew Edwards, Matthew Pflueger, mauropalsgraaf, mbarak, Mehitabel, Michael Pigg, Mikael Moghadam, Mike Gehard (@mikegehard), MonadicBind, arjun.mukherjee@gmail.com, Stephen Arbogast, Narayan Iyer, @natewave, Netanel Rabinowitz, Nick Peterson, Nicolas Sitbon, Oier Blasco Linares, Oliver Daff, Oliver Schrenk, Olly Shaw, P Villela, pandaforme, Patrick Garrity, Pawel Wlodarski from JUG Lodz, @peel, Peter Perhac, Phil Glover, Philipp Leser-Wolf, Rachel Bowyer, Radu Gancea (@radusw), Rajit Singh, Ramin Alidousti, Raymond Tay, Riccardo Sirigu, Richard (Yin-Wu) Chuo, Rob Vermazeren, Robert “Kemichal” Andersson, Robin Taylor (@badgermind), Rongcui Dong, Rui Morais, Rupert Bates, Rustem Suniev, Sanjiv Sahayam, Shane Delmore, Stefan Plantikow, Sundy Wiliam Yaputra, Tal Pressman, Tamas Neltz, theLXK, Tim Pigden, Tobias Lutz, Tom Duhourq, @tomzalt, Utz Westermann, Vadym Shalts, Val Akkapeddi, Vasanth Loka, Vladimir Bacvanski, Vladimir Bystrov aka udav_pit, William Benton, Wojciech Langiewicz, Yann Ollivier (@ya2o), Yoshiro Naito, zero323, and zeronone.

# 1 Introduction

Cats contains a wide variety of functional programming tools and allows developers to pick and choose the ones we want to use. The majority of these tools are delivered in the form of *type classes* that we can apply to existing Scala types.

Type classes are a programming pattern originating in Haskell^{2}. They allow us to extend existing libraries with new functionality, without using traditional inheritance, and without altering the original library source code.

In this chapter we will refresh our memory of type classes from Underscore’s Essential Scala book, and take a first look at the Cats codebase. We will look at two example type classes—`Show`

and `Eq`

—using them to identify patterns that lay the foundations for the rest of the book.

We’ll finish by tying type classes back into algebraic data types, pattern matching, value classes, and type aliases, presenting a structured approach to functional programming in Scala.

## 1.1 Anatomy of a Type Class

There are three important components to the type class pattern: the *type class* itself, *instances* for particular types, and the *interface* methods that we expose to users.

### 1.1.1 The Type Class

A *type class* is an interface or API that represents some functionality we want to implement. In Cats a type class is represented by a trait with at least one type parameter. For example, we can represent generic “serialize to JSON” behaviour as follows:

```
// Define a very simple JSON AST
sealed trait Json
final case class JsObject(get: Map[String, Json]) extends Json
final case class JsString(get: String) extends Json
final case class JsNumber(get: Double) extends Json
case object JsNull extends Json
// The "serialize to JSON" behaviour is encoded in this trait
trait JsonWriter[A] {
def write(value: A): Json
}
```

`JsonWriter`

is our type class in this example, with `Json`

and its subtypes providing supporting code.

### 1.1.2 Type Class Instances

The *instances* of a type class provide implementations for the types we care about, including types from the Scala standard library and types from our domain model.

In Scala we define instances by creating concrete implementations of the type class and tagging them with the `implicit`

keyword:

```
final case class Person(name: String, email: String)
object JsonWriterInstances {
implicit val stringWriter: JsonWriter[String] =
new JsonWriter[String] {
def write(value: String): Json =
JsString(value)
}
implicit val personWriter: JsonWriter[Person] =
new JsonWriter[Person] {
def write(value: Person): Json =
JsObject(Map(
"name" -> JsString(value.name),
"email" -> JsString(value.email)
))
}
// etc...
}
```

### 1.1.3 Type Class Interfaces

A type class *interface* is any functionality we expose to users. Interfaces are generic methods that accept instances of the type class as implicit parameters.

There are two common ways of specifying an interface: *Interface Objects* and *Interface Syntax*.

**Interface Objects**

The simplest way of creating an interface is to place methods in a singleton object:

```
object Json {
def toJson[A](value: A)(implicit w: JsonWriter[A]): Json =
w.write(value)
}
```

To use this object, we import any type class instances we care about and call the relevant method:

```
import JsonWriterInstances._
Json.toJson(Person("Dave", "dave@example.com"))
// res4: Json = JsObject(Map(name -> JsString(Dave), email -> JsString(dave@example.com)))
```

The compiler spots that we’ve called the `toJson`

method without providing the implicit parameters. It tries to fix this by searching for type class instances of the relevant types and inserting them at the call site:

`Json.toJson(Person("Dave", "dave@example.com"))(personWriter)`

**Interface Syntax**

We can alternatively use *extension methods* to extend existing types with interface methods^{3}. Cats refers to this as *“syntax”* for the type class:

```
object JsonSyntax {
implicit class JsonWriterOps[A](value: A) {
def toJson(implicit w: JsonWriter[A]): Json =
w.write(value)
}
}
```

We use interface syntax by importing it alongside the instances for the types we need:

```
import JsonWriterInstances._
import JsonSyntax._
Person("Dave", "dave@example.com").toJson
// res6: Json = JsObject(Map(name -> JsString(Dave), email -> JsString(dave@example.com)))
```

Again, the compiler searches for candidates for the implicit parameters and fills them in for us:

`Person("Dave", "dave@example.com").toJson(personWriter)`

**The implicitly Method**

The Scala standard library provides a generic type class interface called `implicitly`

. Its definition is very simple:

```
def implicitly[A](implicit value: A): A =
value
```

We can use `implicitly`

to summon any value from implicit scope. We provide the type we want and `implicitly`

does the rest:

```
import JsonWriterInstances._
// import JsonWriterInstances._
implicitly[JsonWriter[String]]
// res8: JsonWriter[String] = JsonWriterInstances$$anon$1@20da369
```

Most type classes in Cats provide other means to summon instances. However, `implicitly`

is a good fallback for debugging purposes. We can insert a call to `implicitly`

within the general flow of our code to ensure the compiler can find an instance of a type class and ensure that there are no ambiguous implicit errors.

## 1.2 Working with Implicits

Working with type classes in Scala means working with implicit values and implicit parameters. There are a few rules we need to know to do this effectively.

### 1.2.1 Packaging Implicits

In a curious quirk of the language, any definitions marked `implicit`

in Scala must be placed inside an object or trait rather than at the top level. In the example above we packaged our type class instances in an object called `JsonWriterInstances`

. We could equally have placed them in a companion object to `JsonWriter`

. Placing instances in a companion object to the type class has special significance in Scala because it plays into something called *implicit scope*.

### 1.2.2 Implicit Scope

As we saw above, the compiler searches for candidate type class instances by type. For example, in the following expression it will look for an instance of type `JsonWriter[String]`

:

`Json.toJson("A string!")`

The compiler searches for candidate instances in the *implicit scope* at the call site, which roughly consists of:

local or inherited definitions;

imported definitions;

definitions in the companion object of the type class or the parameter type (in this case

`JsonWriter`

or`String`

).

Definitions are only included in implicit scope if they are tagged with the `implicit`

keyword. Furthermore, if the compiler sees multiple candidate definitions, it fails with an *ambiguous implicit values* error:

```
implicit val writer1: JsonWriter[String] =
JsonWriterInstances.stringWriter
implicit val writer2: JsonWriter[String] =
JsonWriterInstances.stringWriter
Json.toJson("A string")
// <console>:23: error: ambiguous implicit values:
// both value stringWriter in object JsonWriterInstances of type => JsonWriter[String]
// and value writer1 of type => JsonWriter[String]
// match expected type JsonWriter[String]
// Json.toJson("A string")
// ^
```

The precise rules of implicit resolution are more complex than this, but the complexity is largely irrelevant for this book^{4}. For our purposes, we can package type class instances in roughly four ways:

- by placing them in an object such as
`JsonWriterInstances`

; - by placing them in a trait;
- by placing them in the companion object of the type class;
- by placing them in the companion object of the parameter type.

With option 1 we bring instances into scope by `importing`

them. With option 2 we bring them into scope with inheritance. With options 3 and 4, instances are *always* in implicit scope, regardless of where we try to use them.

### 1.2.3 Recursive Implicit Resolution

The power of type classes and implicits lies in the compiler’s ability to *combine* implicit definitions when searching for candidate instances.

Earlier we insinuated that all type class instances are `implicit vals`

. This was a simplification. We can actually define instances in two ways:

by defining concrete instances as

`implicit vals`

of the required type^{5};by defining

`implicit`

methods to construct instances from other type class instances.

Why would we construct instances from other instances? As a motivational example, consider defining a `JsonWriter`

for `Options`

. We would need a `JsonWriter[Option[A]]`

for every `A`

we care about in our application. We could try to brute force the problem by creating a library of `implicit vals`

:

```
implicit val optionIntWriter: JsonWriter[Option[Int]] =
???
implicit val optionPersonWriter: JsonWriter[Option[Person]] =
???
// and so on...
```

However, this approach clearly doesn’t scale. We end up requiring two `implicit vals`

for every type `A`

in our application: one for `A`

and one for `Option[A]`

.

Fortunately, we can abstract the code for handling `Option[A]`

into a common constructor based on the instance for `A`

:

if the option is

`Some(aValue)`

, write`aValue`

using the writer for`A`

;if the option is

`None`

, return`JsNull`

.

Here is the same code written out as an `implicit def`

:

```
implicit def optionWriter[A]
(implicit writer: JsonWriter[A]): JsonWriter[Option[A]] =
new JsonWriter[Option[A]] {
def write(option: Option[A]): Json =
option match {
case Some(aValue) => writer.write(aValue)
case None => JsNull
}
}
```

This method *constructs* a `JsonWriter`

for `Option[A]`

by relying on an implicit parameter to fill in the `A`

-specific functionality. When the compiler sees an expression like this:

`Json.toJson(Option("A string"))`

it searches for an implicit `JsonWriter[Option[String]]`

. It finds the implicit method for `JsonWriter[Option[A]]`

:

`Json.toJson(Option("A string"))(optionWriter[String])`

and recursively searches for a `JsonWriter[String]`

to use as the parameter to `optionWriter`

:

`Json.toJson(Option("A string"))(optionWriter(stringWriter))`

In this way, implicit resolution becomes a search through the space of possible combinations of implicit definitions, to find a combination that summons a type class instance of the correct overall type.

*Implicit Conversions*

When you create a type class instance constructor using an `implicit def`

, be sure to mark the parameters to the method as `implicit`

parameters. Without this keyword, the compiler won’t be able to fill in the parameters during implicit resolution.

`implicit`

methods with non-`implicit`

parameters form a different Scala pattern called an *implicit conversion*. This is also different from the previous section on `Interface Syntax`

, because in that case the `JsonWriter`

is an implicit class with `extension methods`

. Implicit conversion is an older programming pattern that is frowned upon in modern Scala code. Fortunately, the compiler will warn you when you do this. You have to manually enable implicit conversions by importing `scala.language.implicitConversions`

in your file:

```
implicit def optionWriter[A]
(writer: JsonWriter[A]): JsonWriter[Option[A]] =
???
// <console>:18: warning: implicit conversion method optionWriter should be enabled
// by making the implicit value scala.language.implicitConversions visible.
// This can be achieved by adding the import clause 'import scala.language.implicitConversions'
// or by setting the compiler option -language:implicitConversions.
// See the Scaladoc for value scala.language.implicitConversions for a discussion
// why the feature should be explicitly enabled.
// implicit def optionWriter[A]
// ^
// error: No warnings can be incurred under -Xfatal-warnings.
```

## 1.3 Exercise: Printable Library

Scala provides a `toString`

method to let us convert any value to a `String`

. However, this method comes with a few disadvantages: it is implemented for *every* type in the language, many implementations are of limited use, and we can’t opt-in to specific implementations for specific types.

Let’s define a `Printable`

type class to work around these problems:

Define a type class

`Printable[A]`

containing a single method`format`

.`format`

should accept a value of type`A`

and return a`String`

.Create an object

`PrintableInstances`

containing instances of`Printable`

for`String`

and`Int`

.Define an object

`Printable`

with two generic interface methods:`format`

accepts a value of type`A`

and a`Printable`

of the corresponding type. It uses the relevant`Printable`

to convert the`A`

to a`String`

.`print`

accepts the same parameters as`format`

and returns`Unit`

. It prints the`A`

value to the console using`println`

.

These steps define the three main components of our type class. First we define `Printable`

—the *type class* itself:

```
trait Printable[A] {
def format(value: A): String
}
```

Then we define some default *instances* of `Printable`

and package them in `PrintableInstances`

:

```
object PrintableInstances {
implicit val stringPrintable = new Printable[String] {
def format(input: String) = input
}
implicit val intPrintable = new Printable[Int] {
def format(input: Int) = input.toString
}
}
```

Finally we define an *interface* object, `Printable`

:

```
object Printable {
def format[A](input: A)(implicit p: Printable[A]): String =
p.format(input)
def print[A](input: A)(implicit p: Printable[A]): Unit =
println(format(input))
}
```

**Using the Library**

The code above forms a general purpose printing library that we can use in multiple applications. Let’s define an “application” now that uses the library.

First we’ll define a data type to represent a well-known type of furry animal:

`final case class Cat(name: String, age: Int, color: String)`

Next we’ll create an implementation of `Printable`

for `Cat`

that returns content in the following format:

`NAME is a AGE year-old COLOR cat.`

Finally, use the type class on the console or in a short demo app: create a `Cat`

and print it to the console:

```
// Define a cat:
val cat = Cat(/* ... */)
// Print the cat!
```

This is a standard use of the type class pattern. First we define a set of custom data types for our application:

`final case class Cat(name: String, age: Int, color: String)`

Then we define type class instances for the types we care about. These either go into the companion object of `Cat`

or a separate object to act as a namespace:

```
import PrintableInstances._
implicit val catPrintable = new Printable[Cat] {
def format(cat: Cat) = {
val name = Printable.format(cat.name)
val age = Printable.format(cat.age)
val color = Printable.format(cat.color)
s"$name is a $age year-old $color cat."
}
}
```

Finally, we use the type class by bringing the relevant instances into scope and using interface object/syntax. If we defined the instances in companion objects Scala brings them into scope for us automatically. Otherwise we use an `import`

to access them:

```
val cat = Cat("Garfield", 38, "ginger and black")
// cat: Cat = Cat(Garfield,38,ginger and black)
Printable.print(cat)
// Garfield is a 38 year-old ginger and black cat.
```

**Better Syntax**

Let’s make our printing library easier to use by defining some extension methods to provide better syntax:

Create an object called

`PrintableSyntax`

.Inside

`PrintableSyntax`

define an`implicit class PrintableOps[A]`

to wrap up a value of type`A`

.In

`PrintableOps`

define the following methods:`format`

accepts an implicit`Printable[A]`

and returns a`String`

representation of the wrapped`A`

;`print`

accepts an implicit`Printable[A]`

and returns`Unit`

. It prints the wrapped`A`

to the console.

Use the extension methods to print the example

`Cat`

you created in the previous exercise.

First we define an `implicit class`

containing our extension methods:

```
object PrintableSyntax {
implicit class PrintableOps[A](value: A) {
def format(implicit p: Printable[A]): String =
Printable.format(value)
def print(implicit p: Printable[A]): Unit =
Printable.print(value)
}
}
```

With `PrintableOps`

in scope, we can call the imaginary `print`

and `format`

methods on any value for which Scala can locate an implicit instance of `Printable`

:

```
import PrintableSyntax._
Cat("Garfield", 38, "ginger and black").print
// Garfield is a 38 year-old ginger and black cat.
```

We get a compile error if we haven’t defined an instance of `Printable`

for the relevant type:

```
import java.util.Date
new Date().print
// <console>:23: error: could not find implicit value for parameter p: Printable[java.util.Date]
// new Date().print
// ^
```

## 1.4 Meet Cats

In the previous section we saw how to implement type classes in Scala. In this section we will look at how type classes are implemented in Cats.

Cats is written using a modular structure that allows us to choose which type classes, instances, and interface methods we want to use. Let’s take a first look using `cats.Show`

as an example.

`Show`

is Cats’ equivalent of the `Printable`

type class we defined in the last section. It provides a mechanism for producing developer-friendly console output without using `toString`

. Here’s an abbreviated definition:

```
package cats
trait Show[A] {
def show(value: A): String
}
```

### 1.4.1 Importing Type Classes

The type classes in Cats are defined in the `cats`

package. We can import `Show`

directly from this package:

`import cats.Show`

The companion object of every Cats type class has an `apply`

method that locates an instance for any type we specify:

```
val showInt = Show.apply[Int]
// <console>:13: error: could not find implicit value for parameter instance: cats.Show[Int]
// val showInt = Show.apply[Int]
// ^
```

Oops—that didn’t work! The `apply`

method uses *implicits* to look up individual instances, so we’ll have to bring some instances into scope.

### 1.4.2 Importing Default Instances

The `cats.instances`

package provides default instances for a wide variety of types. We can import these as shown in the table below. Each import provides instances of all Cats’ type classes for a specific parameter type:

`cats.instances.int`

provides instances for`Int`

`cats.instances.string`

provides instances for`String`

`cats.instances.list`

provides instances for`List`

`cats.instances.option`

provides instances for`Option`

`cats.instances.all`

provides all instances that are shipped out of the box with Cats

See the `cats.instances`

package for a complete list of available imports.

Let’s import the instances of `Show`

for `Int`

and `String`

:

```
import cats.instances.int._ // for Show
import cats.instances.string._ // for Show
val showInt: Show[Int] = Show.apply[Int]
val showString: Show[String] = Show.apply[String]
```

That’s better! We now have access to two instances of `Show`

, and can use them to print `Ints`

and `Strings`

:

```
val intAsString: String =
showInt.show(123)
// intAsString: String = 123
val stringAsString: String =
showString.show("abc")
// stringAsString: String = abc
```

### 1.4.3 Importing Interface Syntax

We can make `Show`

easier to use by importing the *interface syntax* from `cats.syntax.show`

. This adds an extension method called `show`

to any type for which we have an instance of `Show`

in scope:

```
import cats.syntax.show._ // for show
val shownInt = 123.show
// shownInt: String = 123
val shownString = "abc".show
// shownString: String = abc
```

Cats provides separate syntax imports for each type class. We will introduce these as we encounter them in later sections and chapters.

### 1.4.4 Importing All The Things!

In this book we will use specific imports to show you exactly which instances and syntax you need in each example. However, this can be time consuming for many use cases. You should feel free to take one of the following shortcuts to simplify your imports:

`import cats._`

imports all of Cats’ type classes in one go;`import cats.instances.all._`

imports all of the type class instances for the standard library in one go;`import cats.syntax.all._`

imports all of the syntax in one go;`import cats.implicits._`

imports all of the standard type class instances*and*all of the syntax in one go.

Most people start their files with the following imports, reverting to more specific imports only if they encounter naming conflicts or problems ambiguous implicits:

```
import cats._
import cats.implicits._
```

### 1.4.5 Defining Custom Instances

We can define an instance of `Show`

simply by implementing the trait for a given type:

```
import java.util.Date
implicit val dateShow: Show[Date] =
new Show[Date] {
def show(date: Date): String =
s"${date.getTime}ms since the epoch."
}
```

However, Cats also provides a couple of convenient methods to simplify the process. There are two construction methods on the companion object of `Show`

that we can use to define instances for our own types:

```
object Show {
// Convert a function to a `Show` instance:
def show[A](f: A => String): Show[A] =
???
// Create a `Show` instance from a `toString` method:
def fromToString[A]: Show[A] =
???
}
```

These allows us to quickly construct instances with less ceremony than defining them from scratch:

```
implicit val dateShow: Show[Date] =
Show.show(date => s"${date.getTime}ms since the epoch.")
```

As you can see, the code using construction methods is much terser than the code without. Many type classes in Cats provide helper methods like these for constructing instances, either from scratch or by transforming existing instances for other types.

### 1.4.6 Exercise: Cat Show

Re-implement the `Cat`

application from the previous section using `Show`

instead of `Printable`

.

First let’s import everything we need from Cats: the `Show`

type class, the instances for `Int`

and `String`

, and the interface syntax:

```
import cats.Show
import cats.instances.int._ // for Show
import cats.instances.string._ // for Show
import cats.syntax.show._ // for show
```

Our definition of `Cat`

remains the same:

`final case class Cat(name: String, age: Int, color: String)`

In the companion object we replace our `Printable`

with an instance of `Show`

using one of the definition helpers discussed above:

```
implicit val catShow = Show.show[Cat] { cat =>
val name = cat.name.show
val age = cat.age.show
val color = cat.color.show
s"$name is a $age year-old $color cat."
}
```

Finally, we use the `Show`

interface syntax to print our instance of `Cat`

:

```
println(Cat("Garfield", 38, "ginger and black").show)
// Garfield is a 38 year-old ginger and black cat.
```

## 1.5 Example: Eq

We will finish off this chapter by looking at another useful type class: `cats.Eq`

. `Eq`

is designed to support *type-safe equality* and address annoyances using Scala’s built-in `==`

operator.

Almost every Scala developer has written code like this before:

```
List(1, 2, 3).map(Option(_)).filter(item => item == 1)
// res0: List[Option[Int]] = List()
```

Ok, many of you won’t have made such a simple mistake as this, but the principle is sound. The predicate in the `filter`

clause always returns `false`

because it is comparing an `Int`

to an `Option[Int]`

.

This is programmer error—we should have compared `item`

to `Some(1)`

instead of `1`

. However, it’s not technically a type error because `==`

works for any pair of objects, no matter what types we compare. `Eq`

is designed to add some type safety to equality checks and work around this problem.

### 1.5.1 Equality, Liberty, and Fraternity

We can use `Eq`

to define type-safe equality between instances of any given type:

```
package cats
trait Eq[A] {
def eqv(a: A, b: A): Boolean
// other concrete methods based on eqv...
}
```

The interface syntax, defined in `cats.syntax.eq`

, provides two methods for performing equality checks provided there is an instance `Eq[A]`

in scope:

`===`

compares two objects for equality;`=!=`

compares two objects for inequality.

### 1.5.2 Comparing Ints

Let’s look at a few examples. First we import the type class:

`import cats.Eq`

Now let’s grab an instance for `Int`

:

```
import cats.instances.int._ // for Eq
val eqInt = Eq[Int]
```

We can use `eqInt`

directly to test for equality:

```
eqInt.eqv(123, 123)
// res2: Boolean = true
eqInt.eqv(123, 234)
// res3: Boolean = false
```

Unlike Scala’s `==`

method, if we try to compare objects of different types using `eqv`

we get a compile error:

```
eqInt.eqv(123, "234")
// <console>:18: error: type mismatch;
// found : String("234")
// required: Int
// eqInt.eqv(123, "234")
// ^
```

We can also import the interface syntax in `cats.syntax.eq`

to use the `===`

and `=!=`

methods:

```
import cats.syntax.eq._ // for === and =!=
123 === 123
// res5: Boolean = true
123 =!= 234
// res6: Boolean = true
```

Again, comparing values of different types causes a compiler error:

```
123 === "123"
// <console>:20: error: type mismatch;
// found : String("123")
// required: Int
// 123 === "123"
// ^
```

### 1.5.3 Comparing Options

Now for a more interesting example—`Option[Int]`

. To compare values of type `Option[Int]`

we need to import instances of `Eq`

for `Option`

as well as `Int`

:

```
import cats.instances.int._ // for Eq
import cats.instances.option._ // for Eq
```

Now we can try some comparisons:

```
Some(1) === None
// <console>:26: error: value === is not a member of Some[Int]
// Some(1) === None
// ^
```

We have received an error here because the types don’t quite match up. We have `Eq`

instances in scope for `Int`

and `Option[Int]`

but the values we are comparing are of type `Some[Int]`

. To fix the issue we have to re-type the arguments as `Option[Int]`

:

```
(Some(1) : Option[Int]) === (None : Option[Int])
// res9: Boolean = false
```

We can do this in a friendlier fashion using the `Option.apply`

and `Option.empty`

methods from the standard library:

```
Option(1) === Option.empty[Int]
// res10: Boolean = false
```

or using special syntax from `cats.syntax.option`

:

```
import cats.syntax.option._ // for some and none
1.some === none[Int]
// res11: Boolean = false
1.some =!= none[Int]
// res12: Boolean = true
```

### 1.5.4 Comparing Custom Types

We can define our own instances of `Eq`

using the `Eq.instance`

method, which accepts a function of type `(A, A) => Boolean`

and returns an `Eq[A]`

:

```
import java.util.Date
import cats.instances.long._ // for Eq
implicit val dateEq: Eq[Date] =
Eq.instance[Date] { (date1, date2) =>
date1.getTime === date2.getTime
}
val x = new Date() // now
val y = new Date() // a bit later than now
x === x
// res13: Boolean = true
x === y
// res14: Boolean = false
```

### 1.5.5 Exercise: Equality, Liberty, and Felinity

Implement an instance of `Eq`

for our running `Cat`

example:

`final case class Cat(name: String, age: Int, color: String)`

Use this to compare the following pairs of objects for equality and inequality:

```
val cat1 = Cat("Garfield", 38, "orange and black")
val cat2 = Cat("Heathcliff", 33, "orange and black")
val optionCat1 = Option(cat1)
val optionCat2 = Option.empty[Cat]
```

First we need our Cats imports. In this exercise we’ll be using the `Eq`

type class and the `Eq`

interface syntax. We’ll bring instances of `Eq`

into scope as we need them below:

```
import cats.Eq
import cats.syntax.eq._ // for ===
```

Our `Cat`

class is the same as ever:

`final case class Cat(name: String, age: Int, color: String)`

We bring the `Eq`

instances for `Int`

and `String`

into scope for the implementation of `Eq[Cat]`

:

```
import cats.instances.int._ // for Eq
import cats.instances.string._ // for Eq
implicit val catEqual: Eq[Cat] =
Eq.instance[Cat] { (cat1, cat2) =>
(cat1.name === cat2.name ) &&
(cat1.age === cat2.age ) &&
(cat1.color === cat2.color)
}
```

Finally, we test things out in a sample application:

```
val cat1 = Cat("Garfield", 38, "orange and black")
// cat1: Cat = Cat(Garfield,38,orange and black)
val cat2 = Cat("Heathcliff", 32, "orange and black")
// cat2: Cat = Cat(Heathcliff,32,orange and black)
cat1 === cat2
// res17: Boolean = false
cat1 =!= cat2
// res18: Boolean = true
import cats.instances.option._ // for Eq
val optionCat1 = Option(cat1)
// optionCat1: Option[Cat] = Some(Cat(Garfield,38,orange and black))
val optionCat2 = Option.empty[Cat]
// optionCat2: Option[Cat] = None
optionCat1 === optionCat2
// res19: Boolean = false
optionCat1 =!= optionCat2
// res20: Boolean = true
```

## 1.6 Controlling Instance Selection

When working with type classes we must consider two issues that control instance selection:

What is the relationship between an instance defined on a type and its subtypes?

For example, if we define a

`JsonWriter[Option[Int]]`

, will the expression`Json.toJson(Some(1))`

select this instance? (Remember that`Some`

is a subtype of`Option`

).How do we choose between type class instances when there are many available?

What if we define two

`JsonWriters`

for`Person`

? When we write`Json.toJson(aPerson)`

, which instance is selected?

### 1.6.1 Variance

When we define type classes we can add variance annotations to the type parameter to affect the variance of the type class and the compiler’s ability to select instances during implicit resolution.

To recap Essential Scala, variance relates to subtypes. We say that `B`

is a subtype of `A`

if we can use a value of type `B`

anywhere we expect a value of type `A`

.

Co- and contravariance annotations arise when working with type constructors. For example, we denote covariance with a `+`

symbol:

`trait F[+A] // the "+" means "covariant"`

**Covariance**

Covariance means that the type `F[B]`

is a subtype of the type `F[A]`

if `B`

is a subtype of `A`

. This is useful for modelling many types, including collections like `List`

and `Option`

:

```
trait List[+A]
trait Option[+A]
```

The covariance of Scala collections allows us to substitute collections of one type for another in our code. For example, we can use a `List[Circle]`

anywhere we expect a `List[Shape]`

because `Circle`

is a subtype of `Shape`

:

```
sealed trait Shape
case class Circle(radius: Double) extends Shape
val circles: List[Circle] = ???
val shapes: List[Shape] = circles
```

What about contravariance? We write contravariant type constructors with a `-`

symbol like this:

`trait F[-A]`

**Contravariance**

Confusingly, contravariance means that the type `F[B]`

is a subtype of `F[A]`

if `A`

is a subtype of `B`

. This is useful for modelling types that represent processes, like our `JsonWriter`

type class above:

```
trait JsonWriter[-A] {
def write(value: A): Json
}
// defined trait JsonWriter
```

Let’s unpack this a bit further. Remember that variance is all about the ability to substitute one value for another. Consider a scenario where we have two values, one of type `Shape`

and one of type `Circle`

, and two `JsonWriters`

, one for `Shape`

and one for `Circle`

:

```
val shape: Shape = ???
val circle: Circle = ???
val shapeWriter: JsonWriter[Shape] = ???
val circleWriter: JsonWriter[Circle] = ???
def format[A](value: A, writer: JsonWriter[A]): Json =
writer.write(value)
```

Now ask yourself the question: “Which of combinations of value and writer can I pass to `format`

?” We can combine `circle`

with either writer because all `Circles`

are `Shapes`

. Conversely, we can’t combine `shape`

with `circleWriter`

because not all `Shapes`

are `Circles`

.

This relationship is what we formally model using contravariance. `JsonWriter[Shape]`

is a subtype of `JsonWriter[Circle]`

because `Circle`

is a subtype of `Shape`

. This means we can use `shapeWriter`

anywhere we expect to see a `JsonWriter[Circle]`

.

**Invariance**

Invariance is actually the easiest situation to describe. It’s what we get when we don’t write a `+`

or `-`

in a type constructor:

`trait F[A]`

This means the types `F[A]`

and `F[B]`

are never subtypes of one another, no matter what the relationship between `A`

and `B`

. This is the default semantics for Scala type constructors.

When the compiler searches for an implicit it looks for one matching the type *or subtype*. Thus we can use variance annotations to control type class instance selection to some extent.

There are two issues that tend to arise. Let’s imagine we have an algebraic data type like:

```
sealed trait A
final case object B extends A
final case object C extends A
```

The issues are:

Will an instance defined on a supertype be selected if one is available? For example, can we define an instance for

`A`

and have it work for values of type`B`

and`C`

?Will an instance for a subtype be selected in preference to that of a supertype. For instance, if we define an instance for

`A`

and`B`

, and we have a value of type`B`

, will the instance for`B`

be selected in preference to`A`

?

It turns out we can’t have both at once. The three choices give us behaviour as follows:

Type Class Variance | Invariant | Covariant | Contravariant |
---|---|---|---|

Supertype instance used? | No | No | Yes |

More specific type preferred? | No | Yes | No |

It’s clear there is no perfect system. Cats generally prefers to use invariant type classes. This allows us to specify more specific instances for subtypes if we want. It does mean that if we have, for example, a value of type `Some[Int]`

, our type class instance for `Option`

will not be used. We can solve this problem with a type annotation like `Some(1) : Option[Int]`

or by using “smart constructors” like the `Option.apply`

, `Option.empty`

, `some`

, and `none`

methods we saw in Section 1.5.3.

## 1.7 Summary

In this chapter we took a first look at type classes. We implemented our own `Printable`

type class using plain Scala before looking at two examples from Cats—`Show`

and `Eq`

.

We have now seen the general patterns in Cats type classes:

The type classes themselves are generic traits in the

`cats`

package.Each type class has a companion object with, an

`apply`

method for materializing instances, one or more*construction*methods for creating instances, and a collection of other relevant helper methods.Default instances are provided via objects in the

`cats.instances`

package, and are organized by parameter type rather than by type class.Many type classes have

*syntax*provided via the`cats.syntax`

package.

In the remaining chapters of Part I we will look at several broad and powerful type classes—`Semigroup`

, `Monoid`

, `Functor`

, `Monad`

, `Semigroupal`

, `Applicative`

, `Traverse`

, and more. In each case we will learn what functionality the type class provides, the formal rules it follows, and how it is implemented in Cats. Many of these type classes are more abstract than `Show`

or `Eq`

. While this makes them harder to learn, it makes them far more useful for solving general problems in our code.

# 2 Monoids and Semigroups

In this section we explore our first type classes, **monoid** and **semigroup**. These allow us to add or combine values. There are instances for `Ints`

, `Strings`

, `Lists`

, `Options`

, and many more. Let’s start by looking at a few simple types and operations to see what common principles we can extract.

**Integer addition**

Addition of `Ints`

is a binary operation that is *closed*, meaning that adding two `Ints`

always produces another `Int`

:

```
2 + 1
// res0: Int = 3
```

There is also the *identity* element `0`

with the property that `a + 0 == 0 + a == a`

for any `Int`

`a`

:

```
2 + 0
// res1: Int = 2
0 + 2
// res2: Int = 2
```

There are also other properties of addition. For instance, it doesn’t matter in what order we add elements because we always get the same result. This is a property known as *associativity*:

```
(1 + 2) + 3
// res3: Int = 6
1 + (2 + 3)
// res4: Int = 6
```

**Integer multiplication**

The same properties for addition also apply for multiplication, provided we use `1`

as the identity instead of `0`

:

```
1 * 3
// res5: Int = 3
3 * 1
// res6: Int = 3
```

Multiplication, like addition, is associative:

```
(1 * 2) * 3
// res7: Int = 6
1 * (2 * 3)
// res8: Int = 6
```

**String and sequence concatenation**

We can also add `Strings`

, using string concatenation as our binary operator:

```
"One" ++ "two"
// res9: String = Onetwo
```

and the empty string as the identity:

```
"" ++ "Hello"
// res10: String = Hello
"Hello" ++ ""
// res11: String = Hello
```

Once again, concatenation is associative:

```
("One" ++ "Two") ++ "Three"
// res12: String = OneTwoThree
"One" ++ ("Two" ++ "Three")
// res13: String = OneTwoThree
```

Note that we used `++`

above instead of the more usual `+`

to suggest a parallel with sequences. We can do the same with other types of sequence, using concatenation as the binary operator and the empty sequence as our identity.

## 2.1 Definition of a Monoid

We’ve seen a number of “addition” scenarios above each with an associative binary addition and an identity element. It will be no surprise to learn that this is a monoid. Formally, a monoid for a type `A`

is:

- an operation
`combine`

with type`(A, A) => A`

- an element
`empty`

of type`A`

This definition translates nicely into Scala code. Here is a simplified version of the definition from Cats:

```
trait Monoid[A] {
def combine(x: A, y: A): A
def empty: A
}
```

In addition to providing the `combine`

and `empty`

operations, monoids must formally obey several *laws*. For all values `x`

, `y`

, and `z`

, in `A`

, `combine`

must be associative and `empty`

must be an identity element:

```
def associativeLaw[A](x: A, y: A, z: A)
(implicit m: Monoid[A]): Boolean = {
m.combine(x, m.combine(y, z)) ==
m.combine(m.combine(x, y), z)
}
def identityLaw[A](x: A)
(implicit m: Monoid[A]): Boolean = {
(m.combine(x, m.empty) == x) &&
(m.combine(m.empty, x) == x)
}
```

Integer subtraction, for example, is not a monoid because subtraction is not associative:

```
(1 - 2) - 3
// res15: Int = -4
1 - (2 - 3)
// res16: Int = 2
```

In practice we only need to think about laws when we are writing our own `Monoid`

instances. Unlawful instances are dangerous because they can yield unpredictable results when used with the rest of Cats’ machinery. Most of the time we can rely on the instances provided by Cats and assume the library authors know what they’re doing.

## 2.2 Definition of a Semigroup

A semigroup is just the `combine`

part of a monoid. While many semigroups are also monoids, there are some data types for which we cannot define an `empty`

element. For example, we have just seen that sequence concatenation and integer addition are monoids. However, if we restrict ourselves to non-empty sequences and positive integers, we are no longer able to define a sensible `empty`

element. Cats has a `NonEmptyList`

data type that has an implementation of `Semigroup`

but no implementation of `Monoid`

.

A more accurate (though still simplified) definition of Cats’ `Monoid`

is:

```
trait Semigroup[A] {
def combine(x: A, y: A): A
}
trait Monoid[A] extends Semigroup[A] {
def empty: A
}
```

We’ll see this kind of inheritance often when discussing type classes. It provides modularity and allows us to re-use behaviour. If we define a `Monoid`

for a type `A`

, we get a `Semigroup`

for free. Similarly, if a method requires a parameter of type `Semigroup[B]`

, we can pass a `Monoid[B]`

instead.

## 2.3 Exercise: The Truth About Monoids

We’ve seen a few examples of monoids but there are plenty more to be found. Consider `Boolean`

. How many monoids can you define for this type? For each monoid, define the `combine`

and `empty`

operations and convince yourself that the monoid laws hold. Use the following definitions as a starting point:

```
trait Semigroup[A] {
def combine(x: A, y: A): A
}
trait Monoid[A] extends Semigroup[A] {
def empty: A
}
object Monoid {
def apply[A](implicit monoid: Monoid[A]) =
monoid
}
```

There are four monoids for `Boolean`

! First, we have *and* with operator `&&`

and identity `true`

:

```
implicit val booleanAndMonoid: Monoid[Boolean] =
new Monoid[Boolean] {
def combine(a: Boolean, b: Boolean) = a && b
def empty = true
}
```

Second, we have *or* with operator `||`

and identity `false`

:

```
implicit val booleanOrMonoid: Monoid[Boolean] =
new Monoid[Boolean] {
def combine(a: Boolean, b: Boolean) = a || b
def empty = false
}
```

Third, we have *exclusive or* with identity `false`

:

```
implicit val booleanEitherMonoid: Monoid[Boolean] =
new Monoid[Boolean] {
def combine(a: Boolean, b: Boolean) =
(a && !b) || (!a && b)
def empty = false
}
```

Finally, we have *exclusive nor* (the negation of exclusive or) with identity `true`

:

```
implicit val booleanXnorMonoid: Monoid[Boolean] =
new Monoid[Boolean] {
def combine(a: Boolean, b: Boolean) =
(!a || b) && (a || !b)
def empty = true
}
```

Showing that the identity law holds in each case is straightforward. Similarly associativity of the `combine`

operation can be shown by enumerating the cases.

## 2.4 Exercise: All Set for Monoids

What monoids and semigroups are there for sets?

*Set union* forms a monoid along with the empty set:

```
implicit def setUnionMonoid[A]: Monoid[Set[A]] =
new Monoid[Set[A]] {
def combine(a: Set[A], b: Set[A]) = a union b
def empty = Set.empty[A]
}
```

We need to define `setUnionMonoid`

as a method rather than a value so we can accept the type parameter `A`

. The type parameter allows us to use the same definition to summon `Monoids`

for `Sets`

of any type of data:

```
val intSetMonoid = Monoid[Set[Int]]
val strSetMonoid = Monoid[Set[String]]
intSetMonoid.combine(Set(1, 2), Set(2, 3))
// res2: Set[Int] = Set(1, 2, 3)
strSetMonoid.combine(Set("A", "B"), Set("B", "C"))
// res3: Set[String] = Set(A, B, C)
```

Set intersection forms a semigroup, but doesn’t form a monoid because it has no identity element:

```
implicit def setIntersectionSemigroup[A]: Semigroup[Set[A]] =
new Semigroup[Set[A]] {
def combine(a: Set[A], b: Set[A]) =
a intersect b
}
```

Set complement and set difference are not associative, so they cannot be considered for either monoids or semigroups. However, symmetric difference (the union less the intersection) does also form a monoid with the empty set:

```
implicit def symDiffMonoid[A]: Monoid[Set[A]] =
new Monoid[Set[A]] {
def combine(a: Set[A], b: Set[A]): Set[A] =
(a diff b) union (b diff a)
def empty: Set[A] = Set.empty
}
```

## 2.5 Monoids in Cats

Now we’ve seen what monoids are, let’s look at their implementation in Cats. Once again we’ll look at the three main aspects of the implementation: the *type class*, the *instances*, and the *interface*.

### 2.5.1 The Monoid Type Class

The monoid type class is `cats.kernel.Monoid`

, which is aliased as `cats.Monoid`

. `Monoid`

extends `cats.kernel.Semigroup`

, which is aliased as `cats.Semigroup`

. When using Cats we normally import type classes from the `cats`

package:

```
import cats.Monoid
import cats.Semigroup
```

*Cats Kernel?*

Cats Kernel is a subproject of Cats providing a small set of typeclasses for libraries that don’t require the full Cats toolbox. While these core type classes are technically defined in the `cats.kernel`

package, they are all aliased to the `cats`

package so we rarely need to be aware of the distinction.

The Cats Kernel type classes covered in this book are `Eq`

, `Semigroup`

, and `Monoid`

. All the other type classes we cover are part of the main Cats project and are defined directly in the `cats`

package.

### 2.5.2 Monoid Instances

`Monoid`

follows the standard Cats pattern for the user interface: the companion object has an `apply`

method that returns the type class instance for a particular type. For example, if we want the monoid instance for `String`

, and we have the correct implicits in scope, we can write the following:

```
import cats.Monoid
import cats.instances.string._ // for Monoid
Monoid[String].combine("Hi ", "there")
// res0: String = Hi there
Monoid[String].empty
// res1: String = ""
```

which is equivalent to:

```
Monoid.apply[String].combine("Hi ", "there")
// res2: String = Hi there
Monoid.apply[String].empty
// res3: String = ""
```

As we know, `Monoid`

extends `Semigroup`

. If we don’t need `empty`

we can equivalently write:

```
import cats.Semigroup
Semigroup[String].combine("Hi ", "there")
// res4: String = Hi there
```

The type class instances for `Monoid`

are organised under `cats.instances`

in the standard way described in Chapter 1. For example, if we want to pull in instances for `Int`

we import from `cats.instances.int`

:

```
import cats.Monoid
import cats.instances.int._ // for Monoid
Monoid[Int].combine(32, 10)
// res5: Int = 42
```

Similarly, we can assemble a `Monoid[Option[Int]]`

using instances from `cats.instances.int`

and `cats.instances.option`

:

```
import cats.Monoid
import cats.instances.int._ // for Monoid
import cats.instances.option._ // for Monoid
val a = Option(22)
// a: Option[Int] = Some(22)
val b = Option(20)
// b: Option[Int] = Some(20)
Monoid[Option[Int]].combine(a, b)
// res6: Option[Int] = Some(42)
```

Refer back to Chapter 1 for a more comprehensive list of imports.

### 2.5.3 Monoid Syntax

Cats provides syntax for the `combine`

method in the form of the `|+|`

operator. Because `combine`

technically comes from `Semigroup`

, we access the syntax by importing from `cats.syntax.semigroup`

:

```
import cats.instances.string._ // for Monoid
import cats.syntax.semigroup._ // for |+|
val stringResult = "Hi " |+| "there" |+| Monoid[String].empty
// stringResult: String = Hi there
import cats.instances.int._ // for Monoid
val intResult = 1 |+| 2 |+| Monoid[Int].empty
// intResult: Int = 3
```

### 2.5.4 Exercise: Adding All The Things

The cutting edge *SuperAdder v3.5a-32* is the world’s first choice for adding together numbers. The main function in the program has signature `def add(items: List[Int]): Int`

. In a tragic accident this code is deleted! Rewrite the method and save the day!

We can write the addition as a simple `foldLeft`

using `0`

and the `+`

operator:

```
def add(items: List[Int]): Int =
items.foldLeft(0)(_ + _)
```

We can alternatively write the fold using `Monoids`

, although there’s not a compelling use case for this yet:

```
import cats.Monoid
import cats.instances.int._ // for Monoid
import cats.syntax.semigroup._ // for |+|
def add(items: List[Int]): Int =
items.foldLeft(Monoid[Int].empty)(_ |+| _)
```

Well done! SuperAdder’s market share continues to grow, and now there is demand for additional functionality. People now want to add `List[Option[Int]]`

. Change `add`

so this is possible. The SuperAdder code base is of the highest quality, so make sure there is no code duplication!

Now there is a use case for `Monoids`

. We need a single method that adds `Ints`

and instances of `Option[Int]`

. We can write this as a generic method that accepts an implicit `Monoid`

as a parameter:

```
import cats.Monoid
import cats.instances.int._ // for Monoid
import cats.syntax.semigroup._ // for |+|
def add[A](items: List[A])(implicit monoid: Monoid[A]): A =
items.foldLeft(monoid.empty)(_ |+| _)
```

We can optionally use Scala’s *context bound* syntax to write the same code in a friendlier way:

```
def add[A: Monoid](items: List[A]): A =
items.foldLeft(Monoid[A].empty)(_ |+| _)
```

We can use this code to add values of type `Int`

and `Option[Int]`

as requested:

```
import cats.instances.int._ // for Monoid
add(List(1, 2, 3))
// res9: Int = 6
import cats.instances.option._ // for Monoid
add(List(Some(1), None, Some(2), None, Some(3)))
// res10: Option[Int] = Some(6)
```

Note that if we try to add a list consisting entirely of `Some`

values, we get a compile error:

```
add(List(Some(1), Some(2), Some(3)))
// <console>:61: error: could not find implicit value for evidence parameter of type cats.Monoid[Some[Int]]
// add(List(Some(1), Some(2), Some(3)))
// ^
```

This happens because the inferred type of the list is `List[Some[Int]]`

, while Cats will only generate a `Monoid`

for `Option[Int]`

. We’ll see how to get around this in a moment.

SuperAdder is entering the POS (point-of-sale, not the other POS) market. Now we want to add up `Orders`

:

`case class Order(totalCost: Double, quantity: Double)`

We need to release this code really soon so we can’t make any modifications to `add`

. Make it so!

Easy—we simply define a monoid instance for `Order`

!

```
implicit val monoid: Monoid[Order] = new Monoid[Order] {
def combine(o1: Order, o2: Order) =
Order(
o1.totalCost + o2.totalCost,
o1.quantity + o2.quantity
)
def empty = Order(0, 0)
}
```

## 2.6 Applications of Monoids

We now know what a monoid is—an abstraction of the concept of adding or combining—but where is it useful? Here are a few big ideas where monoids play a major role. These are explored in more detail in case studies later in the book.

### 2.6.1 Big Data

In big data applications like Spark and Hadoop we distribute data analysis over many machines, giving fault tolerance and scalability. This means each machine will return results over a portion of the data, and we must then combine these results to get our final result. In the vast majority of cases this can be viewed as a monoid.

If we want to calculate how many total visitors a web site has received, that means calculating an `Int`

on each portion of the data. We know the monoid instance of `Int`

is addition, which is the right way to combine partial results.

If we want to find out how many unique visitors a website has received, that’s equivalent to building a `Set[User]`

on each portion of the data. We know the monoid instance for `Set`

is the set union, which is the right way to combine partial results.

If we want to calculate 99% and 95% response times from our server logs, we can use a data structure called a `QTree`

for which there is a monoid.

Hopefully you get the idea. Almost every analysis that we might want to do over a large data set is a monoid, and therefore we can build an expressive and powerful analytics system around this idea. This is exactly what Twitter’s Algebird and Summingbird projects have done. We explore this idea further in the map-reduce case study.

### 2.6.2 Distributed Systems

In a distributed system, different machines may end up with different views of data. For example, one machine may receive an update that other machines did not receive. We would like to reconcile these different views, so every machine has the same data if no more updates arrive. This is called *eventual consistency*.

A particular class of data types support this reconciliation. These data types are called commutative replicated data types (CRDTs). The key operation is the ability to merge two data instances, with a result that captures all the information in both instances. This operation relies on having a monoid instance. We explore this idea further in the CRDT case study.

### 2.6.3 Monoids in the Small

The two examples above are cases where monoids inform the entire system architecture. There are also many cases where having a monoid around makes it easier to write a small code fragment. We’ll see lots of examples in the case studies in this book.

## 2.7 Summary

We hit a big milestone in this chapter—we covered our first type classes with fancy functional programming names:

- a
`Semigroup`

represents an addition or combination operation; - a
`Monoid`

extends a`Semigroup`

by adding an identity or “zero” element.

We can use `Semigroups`

and `Monoids`

by importing three things: the type classes themselves, the instances for the types we care about, and the semigroup syntax to give us the `|+|`

operator:

```
import cats.Monoid
import cats.instances.string._ // for Monoid
import cats.syntax.semigroup._ // for |+|
"Scala" |+| " with " |+| "Cats"
// res0: String = Scala with Cats
```

With the correct instances in scope, we can set about adding anything we want:

```
import cats.instances.int._ // for Monoid
import cats.instances.option._ // for Monoid
Option(1) |+| Option(2)
// res1: Option[Int] = Some(3)
import cats.instances.map._ // for Monoid
val map1 = Map("a" -> 1, "b" -> 2)
val map2 = Map("b" -> 3, "d" -> 4)
map1 |+| map2
// res3: Map[String,Int] = Map(b -> 5, d -> 4, a -> 1)
import cats.instances.tuple._ // for Monoid
val tuple1 = ("hello", 123)
val tuple2 = ("world", 321)
tuple1 |+| tuple2
// res6: (String, Int) = (helloworld,444)
```

We can also write generic code that works with any type for which we have an instance of `Monoid`

:

```
def addAll[A](values: List[A])
(implicit monoid: Monoid[A]): A =
values.foldRight(monoid.empty)(_ |+| _)
addAll(List(1, 2, 3))
// res7: Int = 6
addAll(List(None, Some(1), Some(2)))
// res8: Option[Int] = Some(3)
```

`Monoids`

are a great gateway to Cats. They’re easy to understand and simple to use. However, they’re just the tip of the iceberg in terms of the abstractions Cats enables us to make. In the next chapter we’ll look at *functors*, the type class personification of the beloved `map`

method. That’s where the fun really begins!

# 3 Functors

In this chapter we will investigate **functors**, an abstraction that allows us to represent sequences of operations within a context such as a `List`

, an `Option`

, or any one of a thousand other possibilities. Functors on their own aren’t so useful, but special cases of functors such as **monads** and **applicative functors** are some of the most commonly used abstractions in Cats.

## 3.1 Examples of Functors

Informally, a functor is anything with a `map`

method. You probably know lots of types that have this: `Option`

, `List`

, and `Either`

, to name a few.

We typically first encounter `map`

when iterating over `Lists`

. However, to understand functors we need to think of the method in another way. Rather than traversing the list, we should think of it as transforming all of the values inside in one go. We specify the function to apply, and `map`

ensures it is applied to every item. The values change but the structure of the list remains the same:

```
List(1, 2, 3).map(n => n + 1)
// res0: List[Int] = List(2, 3, 4)
```

Similarly, when we `map`

over an `Option`

, we transform the contents but leave the `Some`

or `None`

context unchanged. The same principle applies to `Either`

with its `Left`

and `Right`

contexts. This general notion of transformation, along with the common pattern of type signatures shown in Figure 1, is what connects the behaviour of `map`

across different data types.

Because `map`

leaves the structure of the context unchanged, we can call it repeatedly to sequence multiple computations on the contents of an initial data structure:

```
List(1, 2, 3).
map(n => n + 1).
map(n => n * 2).
map(n => n + "!")
// res1: List[String] = List(4!, 6!, 8!)
```

We should think of `map`

not as an iteration pattern, but as a way of sequencing computations on values ignoring some complication dictated by the relevant data type:

`Option`

—the value may or may not be present;`Either`

—there may be a value or an error;`List`

—there may be zero or more values.

## 3.2 More Examples of Functors

The `map`

methods of `List`

, `Option`

, and `Either`

apply functions eagerly. However, the idea of sequencing computations is more general than this. Let’s investigate the behaviour of some other functors that apply the pattern in different ways.

**Futures**

`Future`

is a functor that sequences asynchronous computations by queueing them and applying them as their predecessors complete. The type signature of its `map`

method, shown in Figure 2, has the same shape as the signatures above. However, the behaviour is very different.

When we work with a `Future`

we have no guarantees about its internal state. The wrapped computation may be ongoing, complete, or rejected. If the `Future`

is complete, our mapping function can be called immediately. If not, some underlying thread pool queues the function call and comes back to it later. We don’t know *when* our functions will be called, but we do know *what order* they will be called in. In this way, `Future`

provides the same sequencing behaviour seen in `List`

, `Option`

, and `Either`

:

```
import scala.concurrent.{Future, Await}
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration._
val future: Future[String] =
Future(123).
map(n => n + 1).
map(n => n * 2).
map(n => n + "!")
Await.result(future, 1.second)
// res3: String = 248!
```

*Futures and Referential Transparency*

Note that Scala’s `Futures`

aren’t a great example of pure functional programming because they aren’t *referentially transparent*. `Future`

always computes and caches a result and there’s no way for us to tweak this behaviour. This means we can get unpredictable results when we use `Future`

to wrap side-effecting computations. For example:

```
import scala.util.Random
val future1 = {
// Initialize Random with a fixed seed:
val r = new Random(0L)
// nextInt has the side-effect of moving to
// the next random number in the sequence:
val x = Future(r.nextInt)
for {
a <- x
b <- x
} yield (a, b)
}
val future2 = {
val r = new Random(0L)
for {
a <- Future(r.nextInt)
b <- Future(r.nextInt)
} yield (a, b)
}
val result1 = Await.result(future1, 1.second)
// result1: (Int, Int) = (-1155484576,-1155484576)
val result2 = Await.result(future2, 1.second)
// result2: (Int, Int) = (-1155484576,-723955400)
```

Ideally we would like `result1`

and `result2`

to contain the same value. However, the computation for `future1`

calls `nextInt`

once and the computation for `future2`

calls it twice. Because `nextInt`

returns a different result every time we get a different result in each case.

This kind of discrepancy makes it hard to reason about programs involving `Futures`

and side-effects. There also are other problematic aspects of `Future's`

behaviour, such as the way it always starts computations immediately rather than allowing the user to dictate when the program should run. For more information see this excellent Reddit answer by Rob Norris.

If `Future`

isn’t referentially transparent, perhaps we should look at another similar data-type that is. You should recognise this one…

**Functions (?!)**

It turns out that single argument functions are also functors. To see this we have to tweak the types a little. A function `A => B`

has two type parameters: the parameter type `A`

and the result type `B`

. To coerce them to the correct shape we can fix the parameter type and let the result type vary:

- start with
`X => A`

; - supply a function
`A => B`

; - get back
`X => B`

.

If we alias `X => A`

as `MyFunc[A]`

, we see the same pattern of types we saw with the other examples in this chapter. We also see this in Figure 3:

- start with
`MyFunc[A]`

; - supply a function
`A => B`

; - get back
`MyFunc[B]`

.

In other words, “mapping” over a `Function1`

is function composition:

```
import cats.instances.function._ // for Functor
import cats.syntax.functor._ // for map
val func1: Int => Double =
(x: Int) => x.toDouble
val func2: Double => Double =
(y: Double) => y * 2
(func1 map func2)(1) // composition using map
// res7: Double = 2.0
(func1 andThen func2)(1) // composition using andThen
// res8: Double = 2.0
func2(func1(1)) // composition written out by hand
// res9: Double = 2.0
```

How does this relate to our general pattern of sequencing operations? If we think about it, function composition *is* sequencing. We start with a function that performs a single operation and every time we use `map`

we append another operation to the chain. Calling `map`

doesn’t actually *run* any of the operations, but if we can pass an argument to the final function all of the operations are run in sequence. We can think of this as lazily queueing up operations similar to `Future`

:

```
val func =
((x: Int) => x.toDouble).
map(x => x + 1).
map(x => x * 2).
map(x => x + "!")
func(123)
// res10: String = 248.0!
```

*Partial Unification*

For the above examples to work we need to add the following compiler option to `build.sbt`

:

`scalacOptions += "-Ypartial-unification"`

otherwise we’ll get a compiler error:

```
func1.map(func2)
// <console>: error: value map is not a member of Int => Double
// func1.map(func2)
^
```

We’ll look at why this happens in detail in Section 3.8.

## 3.3 Definition of a Functor

Every example we’ve looked at so far is a functor: a class that encapsulates sequencing computations. Formally, a functor is a type `F[A]`

with an operation `map`

with type `(A => B) => F[B]`

. The general type chart is shown in Figure 4.

Cats encodes `Functor`

as a type class, `cats.Functor`

, so the method looks a little different. It accepts the initial `F[A]`

as a parameter alongside the transformation function. Here’s a simplified version of the definition:

```
package cats
import scala.language.higherKinds
trait Functor[F[_]] {
def map[A, B](fa: F[A])(f: A => B): F[B]
}
```

If you haven’t seen syntax like `F[_]`

before, it’s time to take a brief detour to discuss *type constructors* and *higher kinded types*. We’ll explain that `scala.language`

import as well.

*Functor Laws*

Functors guarantee the same semantics whether we sequence many small operations one by one, or combine them into a larger function before `mapping`

. To ensure this is the case the following laws must hold:

*Identity*: calling `map`

with the identity function is the same as doing nothing:

`fa.map(a => a) == fa`

*Composition*: `mapping`

with two functions `f`

and `g`

is the same as `mapping`

with `f`

and then `mapping`

with `g`

:

`fa.map(g(f(_))) == fa.map(f).map(g)`

## 3.4 Aside: Higher Kinds and Type Constructors

Kinds are like types for types. They describe the number of “holes” in a type. We distinguish between regular types that have no holes and “type constructors” that have holes we can fill to produce types.

For example, `List`

is a type constructor with one hole. We fill that hole by specifying a parameter to produce a regular type like `List[Int]`

or `List[A]`

. The trick is not to confuse type constructors with generic types. `List`

is a type constructor, `List[A]`

is a type:

```
List // type constructor, takes one parameter
List[A] // type, produced using a type parameter
```

There’s a close analogy here with functions and values. Functions are “value constructors”—they produce values when we supply parameters:

```
math.abs // function, takes one parameter
math.abs(x) // value, produced using a value parameter
```

In Scala we declare type constructors using underscores. Once we’ve declared them, however, we refer to them as simple identifiers:

```
// Declare F using underscores:
def myMethod[F[_]] = {
// Reference F without underscores:
val functor = Functor.apply[F]
// ...
}
```

This is analogous to specifying a function’s parameters in its definition and omitting them when referring to it:

```
// Declare f specifying parameters:
val f = (x: Int) => x * 2
// Reference f without parameters:
val f2 = f andThen f
```

Armed with this knowledge of type constructors, we can see that the Cats definition of `Functor`

allows us to create instances for any single-parameter type constructor, such as `List`

, `Option`

, `Future`

, or a type alias such as `MyFunc`

.

*Language Feature Imports*

Higher kinded types are considered an advanced language feature in Scala. Whenever we declare a type constructor with `A[_]`

syntax, we need to “enable” the higher kinded type language feature to suppress warnings from the compiler. We can either do this with a “language import” as above:

`import scala.language.higherKinds`

or by adding the following to `scalacOptions`

in `build.sbt`

:

`scalacOptions += "-language:higherKinds"`

We’ll use the language import in this book to ensure we are as explicit as possible. In practice, however, we find the `scalacOptions`

flag to be simpler and less verbose.

## 3.5 Functors in Cats

Let’s look at the implementation of functors in Cats. We’ll examine the aspects we did for monoids: the *type class*, the *instances*, and the *syntax*.

### 3.5.1 The Functor Type Class

The functor type class is `cats.Functor`

. We obtain instances using the standard `Functor.apply`

method on the companion object. As usual, default instances are arranged by type in the `cats.instances`

package:

```
import scala.language.higherKinds
import cats.Functor
import cats.instances.list._ // for Functor
import cats.instances.option._ // for Functor
val list1 = List(1, 2, 3)
// list1: List[Int] = List(1, 2, 3)
val list2 = Functor[List].map(list1)(_ * 2)
// list2: List[Int] = List(2, 4, 6)
val option1 = Option(123)
// option1: Option[Int] = Some(123)
val option2 = Functor[Option].map(option1)(_.toString)
// option2: Option[String] = Some(123)
```

`Functor`

also provides the `lift`

method, which converts a function of type `A => B`

to one that operates over a functor and has type `F[A] => F[B]`

:

```
val func = (x: Int) => x + 1
// func: Int => Int = <function1>
val liftedFunc = Functor[Option].lift(func)
// liftedFunc: Option[Int] => Option[Int] = cats.Functor$$Lambda$11699/1790382668@37dabefe
liftedFunc(Option(1))
// res0: Option[Int] = Some(2)
```

### 3.5.2 Functor Syntax

The main method provided by the syntax for `Functor`

is `map`

. It’s difficult to demonstrate this with `Options`

and `Lists`

as they have their own built-in `map`

methods and the Scala compiler will always prefer a built-in method over an extension method. We’ll work around this with two examples.

First let’s look at mapping over functions. Scala’s `Function1`

type doesn’t have a `map`

method (it’s called `andThen`

instead) so there are no naming conflicts:

```
import cats.instances.function._ // for Functor
import cats.syntax.functor._ // for map
val func1 = (a: Int) => a + 1
val func2 = (a: Int) => a * 2
val func3 = (a: Int) => a + "!"
val func4 = func1.map(func2).map(func3)
func4(123)
// res1: String = 248!
```

Let’s look at another example. This time we’ll abstract over functors so we’re not working with any particular concrete type. We can write a method that applies an equation to a number no matter what functor context it’s in:

```
def doMath[F[_]](start: F[Int])
(implicit functor: Functor[F]): F[Int] =
start.map(n => n + 1 * 2)
import cats.instances.option._ // for Functor
import cats.instances.list._ // for Functor
doMath(Option(20))
// res3: Option[Int] = Some(22)
doMath(List(1, 2, 3))
// res4: List[Int] = List(3, 4, 5)
```

To illustrate how this works, let’s take a look at the definition of the `map`

method in `cats.syntax.functor`

. Here’s a simplified version of the code:

```
implicit class FunctorOps[F[_], A](src: F[A]) {
def map[B](func: A => B)
(implicit functor: Functor[F]): F[B] =
functor.map(src)(func)
}
```

The compiler can use this extension method to insert a `map`

method wherever no built-in `map`

is available:

`foo.map(value => value + 1)`

Assuming `foo`

has no built-in `map`

method, the compiler detects the potential error and wraps the expression in a `FunctorOps`

to fix the code:

`new FunctorOps(foo).map(value => value + 1)`

The `map`

method of `FunctorOps`

requires an implicit `Functor`

as a parameter. This means this code will only compile if we have a `Functor`

for `expr1`

in scope. If we don’t, we get a compiler error:

```
final case class Box[A](value: A)
val box = Box[Int](123)
box.map(value => value + 1)
// <console>:34: error: value map is not a member of Box[Int]
// box.map(value => value + 1)
// ^
```

### 3.5.3 Instances for Custom Types

We can define a functor simply by defining its map method. Here’s an example of a `Functor`

for `Option`

, even though such a thing already exists in `cats.instances`

. The implementation is trivial—we simply call `Option's`

`map`

method:

```
implicit val optionFunctor: Functor[Option] =
new Functor[Option] {
def map[A, B](value: Option[A])(func: A => B): Option[B] =
value.map(func)
}
```

Sometimes we need to inject dependencies into our instances. For example, if we had to define a custom `Functor`

for `Future`

(another hypothetical example—Cats provides one in `cats.instances.future`

) we would need to account for the implicit `ExecutionContext`

parameter on `future.map`

. We can’t add extra parameters to `functor.map`

so we have to account for the dependency when we create the instance:

```
import scala.concurrent.{Future, ExecutionContext}
implicit def futureFunctor
(implicit ec: ExecutionContext): Functor[Future] =
new Functor[Future] {
def map[A, B](value: Future[A])(func: A => B): Future[B] =
value.map(func)
}
```

Whenever we summon a `Functor`

for `Future`

, either directly using `Functor.apply`

or indirectly via the `map`

extension method, the compiler will locate `futureFunctor`

by implicit resolution and recursively search for an `ExecutionContext`

at the call site. This is what the expansion might look like:

```
// We write this:
Functor[Future]
// The compiler expands to this first:
Functor[Future](futureFunctor)
// And then to this:
Functor[Future](futureFunctor(executionContext))
```

### 3.5.4 Exercise: Branching out with Functors

Write a `Functor`

for the following binary tree data type. Verify that the code works as expected on instances of `Branch`

and `Leaf`

:

```
sealed trait Tree[+A]
final case class Branch[A](left: Tree[A], right: Tree[A])
extends Tree[A]
final case class Leaf[A](value: A) extends Tree[A]
```

The semantics are similar to writing a `Functor`

for `List`

. We recurse over the data structure, applying the function to every `Leaf`

we find. The functor laws intuitively require us to retain the same structure with the same pattern of `Branch`

and `Leaf`

nodes:

```
import cats.Functor
implicit val treeFunctor: Functor[Tree] =
new Functor[Tree] {
def map[A, B](tree: Tree[A])(func: A => B): Tree[B] =
tree match {
case Branch(left, right) =>
Branch(map(left)(func), map(right)(func))
case Leaf(value) =>
Leaf(func(value))
}
}
```

Let’s use our `Functor`

to transform some `Trees`

:

```
Branch(Leaf(10), Leaf(20)).map(_ * 2)
// <console>:42: error: value map is not a member of wrapper.Branch[Int]
// Branch(Leaf(10), Leaf(20)).map(_ * 2)
// ^
```

Oops! This falls foul of the same invariance problem we discussed in Section 1.6.1. The compiler can find a `Functor`

instance for `Tree`

but not for `Branch`

or `Leaf`

. Let’s add some smart constructors to compensate:

```
object Tree {
def branch[A](left: Tree[A], right: Tree[A]): Tree[A] =
Branch(left, right)
def leaf[A](value: A): Tree[A] =
Leaf(value)
}
```

Now we can use our `Functor`

properly:

```
Tree.leaf(100).map(_ * 2)
// res10: wrapper.Tree[Int] = Leaf(200)
Tree.branch(Tree.leaf(10), Tree.leaf(20)).map(_ * 2)
// res11: wrapper.Tree[Int] = Branch(Leaf(20),Leaf(40))
```

## 3.6 *Contravariant* and Invariant Functors

As we have seen, we can think of `Functor's`

`map`

method as “appending” a transformation to a chain. We’re now going to look at two other type classes, one representing *prepending* operations to a chain, and one representing building a *bidirectional* chain of operations. These are called *contravariant* and *invariant functors* respectively.

*This Section is Optional!*

You don’t need to know about contravariant and invariant functors to understand monads, which are the most important pattern in this book and the focus of the next chapter. However, contravariant and invariant do come in handy in our discussion of `Semigroupal`

and `Applicative`

in Chapter 6.

If you want to move on to monads now, feel free to skip straight to Chapter 4. Come back here before you read Chapter 6.

### 3.6.1 Contravariant Functors and the *contramap* Method

The first of our type classes, the *contravariant functor*, provides an operation called `contramap`

that represents “prepending” an operation to a chain. The general type signature is shown in Figure 5.

The `contramap`

method only makes sense for data types that represent *transformations*. For example, we can’t define `contramap`

for an `Option`

because there is no way of feeding a value in an `Option[B]`

backwards through a function `A => B`

. However, we can define `contramap`

for the `Printable`

type class we discussed in Chapter 1:

```
trait Printable[A] {
def format(value: A): String
}
```

A `Printable[A]`

represents a transformation from `A`

to `String`

. Its `contramap`

method accepts a function `func`

of type `B => A`

and creates a new `Printable[B]`

:

```
trait Printable[A] {
def format(value: A): String
def contramap[B](func: B => A): Printable[B] =
???
}
def format[A](value: A)(implicit p: Printable[A]): String =
p.format(value)
```

#### 3.6.1.1 Exercise: Showing off with Contramap

Implement the `contramap`

method for `Printable`

above. Start with the following code template and replace the `???`

with a working method body:

```
trait Printable[A] {
def format(value: A): String
def contramap[B](func: B => A): Printable[B] =
new Printable[B] {
def format(value: B): String =
???
}
}
```

If you get stuck, think about the types. You need to turn `value`

, which is of type `B`

, into a `String`

. What functions and methods do you have available and in what order do they need to be combined?

Here’s a working implementation. We call `func`

to turn the `B`

into an `A`

and then use our original `Printable`

to turn the `A`

into a `String`

. In a small show of sleight of hand we use a `self`

alias to distinguish the outer and inner `Printables`

:

```
trait Printable[A] {
self =>
def format(value: A): String
def contramap[B](func: B => A): Printable[B] =
new Printable[B] {
def format(value: B): String =
self.format(func(value))
}
}
def format[A](value: A)(implicit p: Printable[A]): String =
p.format(value)
```

For testing purposes, let’s define some instances of `Printable`

for `String`

and `Boolean`

:

```
implicit val stringPrintable: Printable[String] =
new Printable[String] {
def format(value: String): String =
"\"" + value + "\""
}
implicit val booleanPrintable: Printable[Boolean] =
new Printable[Boolean] {
def format(value: Boolean): String =
if(value) "yes" else "no"
}
format("hello")
// res3: String = "hello"
format(true)
// res4: String = yes
```

Now define an instance of `Printable`

for the following `Box`

case class. You’ll need to write this as an `implicit def`

as described in Section 1.2.3:

`final case class Box[A](value: A)`

Rather than writing out the complete definition from scratch (`new Printable[Box]`

etc…), create your instance from an existing instance using `contramap`

.

To make the instance generic across all types of `Box`

, we base it on the `Printable`

for the type inside the `Box`

. We can either write out the complete definition by hand:

```
implicit def boxPrintable[A](implicit p: Printable[A]) =
new Printable[Box[A]] {
def format(box: Box[A]): String =
p.format(box.value)
}
```

or use `contramap`

to base the new instance on the implicit parameter:

```
implicit def boxPrintable[A](implicit p: Printable[A]) =
p.contramap[Box[A]](_.value)
```

Using `contramap`

is much simpler, and conveys the functional programming approach of building solutions by combining simple building blocks using pure functional combinators.

Your instance should work as follows:

```
format(Box("hello world"))
// res5: String = "hello world"
format(Box(true))
// res6: String = yes
```

If we don’t have a `Printable`

for the type inside the `Box`

, calls to `format`

should fail to compile:

```
format(Box(123))
// <console>:21: error: could not find implicit value for parameter p: Printable[Box[Int]]
// format(Box(123))
// ^
```

### 3.6.2 Invariant functors and the *imap* method

*Invariant functors* implement a method called `imap`

that is informally equivalent to a combination of `map`

and `contramap`

. If `map`

generates new type class instances by appending a function to a chain, and `contramap`

generates them by prepending an operation to a chain, `imap`

generates them via a pair of bidirectional transformations.

The most intuitive examples of this are a type class that represents encoding and decoding as some data type, such as Play JSON’s `Format`

and scodec’s `Codec`

. We can build our own `Codec`

by enhancing `Printable`

to support encoding and decoding to/from a `String`

:

```
trait Codec[A] {
def encode(value: A): String
def decode(value: String): A
def imap[B](dec: A => B, enc: B => A): Codec[B] = ???
}
def encode[A](value: A)(implicit c: Codec[A]): String =
c.encode(value)
def decode[A](value: String)(implicit c: Codec[A]): A =
c.decode(value)
```

The type chart for `imap`

is shown in Figure 6. If we have a `Codec[A]`

and a pair of functions `A => B`

and `B => A`

, the `imap`

method creates a `Codec[B]`

:

As an example use case, imagine we have a basic `Codec[String]`

, whose `encode`

and `decode`

methods are both a no-op:

```
implicit val stringCodec: Codec[String] =
new Codec[String] {
def encode(value: String): String = value
def decode(value: String): String = value
}
```

We can construct many useful `Codecs`

for other types by building off of `stringCodec`

using `imap`

:

```
implicit val intCodec: Codec[Int] =
stringCodec.imap(_.toInt, _.toString)
implicit val booleanCodec: Codec[Boolean] =
stringCodec.imap(_.toBoolean, _.toString)
```

*Coping with Failure*

Note that the `decode`

method of our `Codec`

type class doesn’t account for failures. If we want to model more sophisticated relationships we can move beyond functors to look at *lenses* and *optics*.

Optics are beyond the scope of this book. However, Julien Truffaut’s library Monocle provides a great starting point for further investigation.

#### 3.6.2.1 Transformative Thinking with *imap*

Implement the `imap`

method for `Codec`

above.

Here’s a working implementation:

```
trait Codec[A] {
def encode(value: A): String
def decode(value: String): A
def imap[B](dec: A => B, enc: B => A): Codec[B] = {
val self = this
new Codec[B] {
def encode(value: B): String =
self.encode(enc(value))
def decode(value: String): B =
dec(self.decode(value))
}
}
}
```

Demonstrate your `imap`

method works by creating a `Codec`

for `Double`

.

We can implement this using the `imap`

method of `stringCodec`

:

```
implicit val doubleCodec: Codec[Double] =
stringCodec.imap[Double](_.toDouble, _.toString)
```

Finally, implement a `Codec`

for the following `Box`

type:

`case class Box[A](value: A)`

We need a generic `Codec`

for `Box[A]`

for any given `A`

. We create this by calling `imap`

on a `Codec[A]`

, which we bring into scope using an implicit parameter:

```
implicit def boxCodec[A](implicit c: Codec[A]): Codec[Box[A]] =
c.imap[Box[A]](Box(_), _.value)
```

Your instances should work as follows:

```
encode(123.4)
// res0: String = 123.4
decode[Double]("123.4")
// res1: Double = 123.4
encode(Box(123.4))
// res2: String = 123.4
decode[Box[Double]]("123.4")
// res3: Box[Double] = Box(123.4)
```

*What’s With the Names?*

What’s the relationship between the terms “contravariance”, “invariance”, and “covariance” and these different kinds of functor?

If you recall from Section 1.6.1, variance affects subtyping, which is essentially our ability to use a value of one type in place of a value of another type without breaking the code.

Subtyping can be viewed as a conversion. If `B`

is a subtype of `A`

, we can always convert a `B`

to an `A`

.

Equivalently we could say that `B`

is a subtype of `A`

if there exists a function `A => B`

. A standard covariant functor captures exactly this. If `F`

is a covariant functor, wherever we have an `F[A]`

and a conversion `A => B`

we can always convert to an `F[B]`

.

A contravariant functor captures the opposite case. If `F`

is a contravariant functor, whenever we have a `F[A]`

and a conversion `B => A`

we can convert to an `F[B]`

.

Finally, invariant functors capture the case where we can convert from `F[A]`

to `F[B]`

via a function `A => B`

and vice versa via a function `B => A`

.

## 3.7 *Contravariant* and Invariant in Cats

Let’s look at the implementation of contravariant and invariant functors in Cats, provided by the `cats.Contravariant`

and `cats.Invariant`

type classes. Here’s a simplified version of the code:

```
trait Contravariant[F[_]] {
def contramap[A, B](fa: F[A])(f: B => A): F[B]
}
trait Invariant[F[_]] {
def imap[A, B](fa: F[A])(f: A => B)(g: B => A): F[B]
}
```

### 3.7.1 Contravariant in Cats

We can summon instances of `Contravariant`

using the `Contravariant.apply`

method. Cats provides instances for data types that consume parameters, including `Eq`

, `Show`

, and `Function1`

. Here’s an example:

```
import cats.Contravariant
import cats.Show
import cats.instances.string._
val showString = Show[String]
val showSymbol = Contravariant[Show].
contramap(showString)((sym: Symbol) => s"'${sym.name}")
showSymbol.show('dave)
// res2: String = 'dave
```

More conveniently, we can use `cats.syntax.contravariant`

, which provides a `contramap`

extension method:

```
import cats.syntax.contravariant._ // for contramap
showString.contramap[Symbol](_.name).show('dave)
// res3: String = dave
```

### 3.7.2 Invariant in Cats

Among other types, Cats provides an instance of `Invariant`

for `Monoid`

. This is a little different from the `Codec`

example we introduced in Section 3.6.2. If you recall, this is what `Monoid`

looks like:

```
package cats
trait Monoid[A] {
def empty: A
def combine(x: A, y: A): A
}
```

Imagine we want to produce a `Monoid`

for Scala’s `Symbol`

type. Cats doesn’t provide a `Monoid`

for `Symbol`

but it does provide a `Monoid`

for a similar type: `String`

. We can write our new semigroup with an `empty`

method that relies on the empty `String`

, and a `combine`

method that works as follows:

- accept two
`Symbols`

as parameters; - convert the
`Symbols`

to`Strings`

; - combine the
`Strings`

using`Monoid[String]`

; - convert the result back to a
`Symbol`

.

We can implement `combine`

using `imap`

, passing functions of type `String => Symbol`

and `Symbol => String`

as parameters. Here’ the code, written out using the `imap`

extension method provided by `cats.syntax.invariant`

:

```
import cats.Monoid
import cats.instances.string._ // for Monoid
import cats.syntax.invariant._ // for imap
import cats.syntax.semigroup._ // for |+|
implicit val symbolMonoid: Monoid[Symbol] =
Monoid[String].imap(Symbol.apply)(_.name)
Monoid[Symbol].empty
// res5: Symbol = '
'a |+| 'few |+| 'words
// res6: Symbol = 'afewwords
```

## 3.8 Aside: Partial Unification

In Section 3.2 we saw a curious compiler error. The following code compiled perfectly if we had the `-Ypartial-unification`

compiler flag enabled:

```
import cats.Functor
import cats.instances.function._ // for Functor
import cats.syntax.functor._ // for map
val func1 = (x: Int) => x.toDouble
val func2 = (y: Double) => y * 2
val func3 = func1.map(func2)
// func3: Int => Double = scala.runtime.AbstractFunction1$$Lambda$7405/1636482024@210b3e5c
```

but failed if the flag was missing:

```
val func3 = func1.map(func2)
// <console>: error: value map is not a member of Int => Double
// val func3 = func1.map(func2)
^
```

Obviously “partial unification” is some kind of optional compiler behaviour, without which our code will not compile. We should take a moment to describe this behaviour and discuss some gotchas and workarounds.

### 3.8.1 Unifying Type Constructors

In order to compile an expression like `func1.map(func2)`

above, the compiler has to search for a `Functor`

for `Function1`

. However, `Functor`

accepts a type constructor with one parameter:

```
trait Functor[F[_]] {
def map[A, B](fa: F[A])(func: A => B): F[B]
}
```

and `Function1`

has two type parameters (the function argument and the result type):

```
trait Function1[-A, +B] {
def apply(arg: A): B
}
```

The compiler has to fix one of the two parameters of `Function1`

to create a type constructor of the correct kind to pass to `Functor`

. It has two options to choose from:

```
type F[A] = Int => A
type F[A] = A => Double
```

*We* know that the former of these is the correct choice. However, earlier versions of the Scala compiler were not able to make this inference. This infamous limitation, known as SI-2712, prevented the compiler from “unifying” type constructors of different arities. This compiler limitation is now fixed, although we have to enable the fix via a compiler flag in `build.sbt`

:

`scalacOptions += "-Ypartial-unification"`

### 3.8.2 Left-to-Right Elimination

The partial unification in the Scala compiler works by fixing type parameters from left to right. In the above example, the compiler fixes the `Int`

in `Int => Double`

and looks for a `Functor`

for functions of type `Int => ?`

:

```
type F[A] = Int => A
val functor = Functor[F]
```

This left-to-right elimination works for a wide variety of common scenarios, including `Functors`

for types such as `Function1`

and `Either`

:

```
import cats.instances.either._ // for Functor
val either: Either[String, Int] = Right(123)
// either: Either[String,Int] = Right(123)
either.map(_ + 1)
// res2: scala.util.Either[String,Int] = Right(124)
```

However, there are situations where left-to-right elimination is not the correct choice. One example is the `Or`

type in Scalactic, which is a conventionally left-biased equivalent of `Either`

:

`type PossibleResult = ActualResult Or Error`

Another example is the `Contravariant`

functor for `Function1`

.

While the covariant `Functor`

for `Function1`

implements `andThen`

-style left-to-right function composition, the `Contravariant`

functor implements `compose`

-style right-to-left composition. In other words, the following expressions are all equivalent:

```
val func3a: Int => Double =
a => func2(func1(a))
val func3b: Int => Double =
func2.compose(func1)
// Hypothetical example. This won't actually compile:
val func3c: Int => Double =
func2.contramap(func1)
```

If we try this for real, however, our code won’t compile:

```
import cats.syntax.contravariant._ // for contramap
val func3c = func2.contramap(func1)
// <console>:27: error: value contramap is not a member of Double => Double
// val func3c = func2.contramap(func1)
// ^
```

The problem here is that the `Contravariant`

for `Function1`

fixes the return type and leaves the parameter type varying, requiring the compiler to eliminate type parameters from right to left, as shown below and in Figure 7:

`type F[A] = A => Double`

The compiler fails simply because of its left-to-right bias. We can prove this by creating a type alias that flips the parameters on Function1:

```
type <=[B, A] = A => B
type F[A] = Double <= A
```

If we re-type `func2`

as an instance of `<=`

, we reset the required order of elimination and we can call `contramap`

as desired:

```
val func2b: Double <= Double = func2
val func3c = func2b.contramap(func1)
// func3c: Double <= Int = scala.runtime.AbstractFunction1$$Lambda$7405/1636482024@4990d5e2
```

The difference between `func2`

and `func2b`

is purely syntactic—both refer to the same value and the type aliases are otherwise completely compatible. Incredibly, however, this simple rephrasing is enough to give the compiler the hint it needs to solve the problem.

It is rare that we have to do this kind of right-to-left elimination. Most multi-parameter type constructors are designed to be right-biased, requiring the left-to-right elimination that is supported by the compiler out of the box. However, it is useful to know about `-Ypartial-unification`

and this quirk of elimination order in case you ever come across an odd scenario like the one above.

## 3.9 Summary

Functors represent sequencing behaviours. We covered three types of functor in this chapter:

Regular covariant

`Functors`

, with their`map`

method, represent the ability to apply functions to a value in some context. Successive calls to`map`

apply these functions in*sequence*, each accepting the result of its predecessor as a parameter.`Contravariant`

functors, with their`contramap`

method, represent the ability to “prepend” functions to a function-like context. Successive calls to`contramap`

sequence these functions in the opposite order to`map`

.`Invariant`

functors, with their`imap`

method, represent bidirectional transformations.

Regular `Functors`

are by far the most common of these type classes, but even then it is rare to use them on their own. Functors form a foundational building block of several more interesting abstractions that we use all the time. In the following chapters we will look at two of these abstractions: *monads* and *applicative functors*.

Functors for collections are extremely important, as they transform each element independently of the rest. This allows us to parallelise or distribute transformations on large collections, a technique leveraged heavily in “map-reduce” frameworks like Hadoop. We will investigate this approach in more detail in the Map-reduce case study later in the book.

The `Contravariant`

and `Invariant`

type classes are less widely applicable but are still useful for building data types that represent transformations. We will revisit them to discuss the `Semigroupal`

type class later in Chapter 6.

# 4 Monads

*Monads* are one of the most common abstractions in Scala. Many Scala programmers quickly become intuitively familiar with monads, even if we don’t know them by name.

Informally, a monad is anything with a constructor and a `flatMap`

method. All of the functors we saw in the last chapter are also monads, including `Option`

, `List`

, and `Future`

. We even have special syntax to support monads: for comprehensions. However, despite the ubiquity of the concept, the Scala standard library lacks a concrete type to encompass “things that can be `flatMapped`

”. This type class is one of the benefits brought to us by Cats.

In this chapter we will take a deep dive into monads. We will start by motivating them with a few examples. We’ll proceed to their formal definition and their implementation in Cats. Finally, we’ll tour some interesting monads that you may not have seen, providing introductions and examples of their use.

## 4.1 What is a Monad?

This is the question that has been posed in a thousand blog posts, with explanations and analogies involving concepts as diverse as cats, Mexican food, space suits full of toxic waste, and monoids in the category of endofunctors (whatever that means). We’re going to solve the problem of explaining monads once and for all by stating very simply:

A monad is a mechanism for

sequencing computations.

That was easy! Problem solved, right? But then again, last chapter we said functors were a control mechanism for exactly the same thing. Ok, maybe we need some more discussion…

In Section 3.1 we said that functors allow us to sequence computations ignoring some complication. However, functors are limited in that they only allow this complication to occur once at the beginning of the sequence. They don’t account further complications at each step in the sequence.

This is where monads come in. A monad’s `flatMap`

method allows us to specify what happens next, taking into account an intermediate complication. The `flatMap`

method of `Option`

takes intermediate `Options`

into account. The `flatMap`

method of `List`

handles intermediate `Lists`

. And so on. In each case, the function passed to `flatMap`

specifies the application-specific part of the computation, and `flatMap`

itself takes care of the complication allowing us to `flatMap`

again. Let’s ground things by looking at some examples.

**Options**

`Option`

allows us to sequence computations that may or may not return values. Here are some examples:

```
def parseInt(str: String): Option[Int] =
scala.util.Try(str.toInt).toOption
def divide(a: Int, b: Int): Option[Int] =
if(b == 0) None else Some(a / b)
```

Each of these methods may “fail” by returning `None`

. The `flatMap`

method allows us to ignore this when we sequence operations:

```
def stringDivideBy(aStr: String, bStr: String): Option[Int] =
parseInt(aStr).flatMap { aNum =>
parseInt(bStr).flatMap { bNum =>
divide(aNum, bNum)
}
}
```

We know the semantics well:

- the first call to
`parseInt`

returns a`None`

or a`Some`

; - if it returns a
`Some`

, the`flatMap`

method calls our function and passes us the integer`aNum`

; - the second call to
`parseInt`

returns a`None`

or a`Some`

; - if it returns a
`Some`

, the`flatMap`

method calls our function and passes us`bNum`

; - the call to
`divide`

returns a`None`

or a`Some`

, which is our result.

At each step, `flatMap`

chooses whether to call our function, and our function generates the next computation in the sequence. This is shown in Figure 8.

The result of the computation is an `Option`

, allowing us to call `flatMap`

again and so the sequence continues. This results in the fail-fast error handling behaviour that we know and love, where a `None`

at any step results in a `None`

overall:

```
stringDivideBy("6", "2")
// res1: Option[Int] = Some(3)
stringDivideBy("6", "0")
// res2: Option[Int] = None
stringDivideBy("6", "foo")
// res3: Option[Int] = None
stringDivideBy("bar", "2")
// res4: Option[Int] = None
```

Every monad is also a functor (see below for proof), so we can rely on both `flatMap`

and `map`

to sequence computations that do and don’t introduce a new monad. Plus, if we have both `flatMap`

and `map`

we can use for comprehensions to clarify the sequencing behaviour:

```
def stringDivideBy(aStr: String, bStr: String): Option[Int] =
for {
aNum <- parseInt(aStr)
bNum <- parseInt(bStr)
ans <- divide(aNum, bNum)
} yield ans
```

**Lists**

When we first encounter `flatMap`

as budding Scala developers, we tend to think of it as a pattern for iterating over `Lists`

. This is reinforced by the syntax of for comprehensions, which look very much like imperative for loops:

```
for {
x <- (1 to 3).toList
y <- (4 to 5).toList
} yield (x, y)
// res5: List[(Int, Int)] = List((1,4), (1,5), (2,4), (2,5), (3,4), (3,5))
```

However, there is another mental model we can apply that highlights the monadic behaviour of `List`

. If we think of `Lists`

as sets of intermediate results, `flatMap`

becomes a construct that calculates permutations and combinations.

For example, in the for comprehension above there are three possible values of `x`

and two possible values of `y`

. This means there are six possible values of `(x, y)`

. `flatMap`

is generating these combinations from our code, which states the sequence of operations:

- get
`x`

- get
`y`

- create a tuple
`(x, y)`

**Futures**

`Future`

is a monad that sequences computations without worrying that they are asynchronous:

```
import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration._
def doSomethingLongRunning: Future[Int] = ???
def doSomethingElseLongRunning: Future[Int] = ???
def doSomethingVeryLongRunning: Future[Int] =
for {
result1 <- doSomethingLongRunning
result2 <- doSomethingElseLongRunning
} yield result1 + result2
```

Again, we specify the code to run at each step, and `flatMap`

takes care of all the horrifying underlying complexities of thread pools and schedulers.

If you’ve made extensive use of `Future`

, you’ll know that the code above is running each operation *in sequence*. This becomes clearer if we expand out the for comprehension to show the nested calls to `flatMap`

:

```
def doSomethingVeryLongRunning: Future[Int] =
doSomethingLongRunning.flatMap { result1 =>
doSomethingElseLongRunning.map { result2 =>
result1 + result2
}
}
```

Each `Future`

in our sequence is created by a function that receives the result from a previous `Future`

. In other words, each step in our computation can only start once the previous step is finished. This is born out by the type chart for `flatMap`

in Figure 9, which shows the function parameter of type `A => Future[B]`

.

We *can* run futures in parallel, of course, but that is another story and shall be told another time. Monads are all about sequencing.

### 4.1.1 Definition of a Monad

While we have only talked about `flatMap`

above, monadic behaviour is formally captured in two operations:

`pure`

, of type`A => F[A]`

;`flatMap`

^{6}, of type`(F[A], A => F[B]) => F[B]`

.

`pure`

abstracts over constructors, providing a way to create a new monadic context from a plain value. `flatMap`

provides the sequencing step we have already discussed, extracting the value from a context and generating the next context in the sequence. Here is a simplified version of the `Monad`

type class in Cats:

```
import scala.language.higherKinds
trait Monad[F[_]] {
def pure[A](value: A): F[A]
def flatMap[A, B](value: F[A])(func: A => F[B]): F[B]
}
```

*Monad Laws*

`pure`

and `flatMap`

must obey a set of laws that allow us to sequence operations freely without unintended glitches and side-effects:

*Left identity*: calling `pure`

and transforming the result with `func`

is the same as calling `func`

:

`pure(a).flatMap(func) == func(a)`

*Right identity*: passing `pure`

to `flatMap`

is the same as doing nothing:

`m.flatMap(pure) == m`

*Associativity*: `flatMapping`

over two functions `f`

and `g`

is the same as `flatMapping`

over `f`

and then `flatMapping`

over `g`

:

`m.flatMap(f).flatMap(g) == m.flatMap(x => f(x).flatMap(g))`

### 4.1.2 Exercise: Getting Func-y

Every monad is also a functor. We can define `map`

in the same way for every monad using the existing methods, `flatMap`

and `pure`

:

```
import scala.language.higherKinds
trait Monad[F[_]] {
def pure[A](a: A): F[A]
def flatMap[A, B](value: F[A])(func: A => F[B]): F[B]
def map[A, B](value: F[A])(func: A => B): F[B] =
???
}
```

Try defining `map`

yourself now.

At first glance this seems tricky, but if we follow the types we’ll see there’s only one solution. We are passed a `value`

of type `F[A]`

. Given the tools available there’s only one thing we can do: call `flatMap`

:

```
trait Monad[F[_]] {
def pure[A](value: A): F[A]
def flatMap[A, B](value: F[A])(func: A => F[B]): F[B]
def map[A, B](value: F[A])(func: A => B): F[B] =
flatMap(value)(a => ???)
}
```

We need a function of type `A => F[B]`

as the second parameter. We have two function building blocks available: the `func`

parameter of type `A => B`

and the `pure`

function of type `A => F[A]`

. Combining these gives us our result:

```
trait Monad[F[_]] {
def pure[A](value: A): F[A]
def flatMap[A, B](value: F[A])(func: A => F[B]): F[B]
def map[A, B](value: F[A])(func: A => B): F[B] =
flatMap(value)(a => pure(func(a)))
}
```

## 4.2 Monads in Cats

It’s time to give monads our standard Cats treatment. As usual we’ll look at the type class, instances, and syntax.

### 4.2.1 The Monad Type Class

The monad type class is `cats.Monad`

. `Monad`

extends two other type classes: `FlatMap`

, which provides the `flatMap`

method, and `Applicative`

, which provides `pure`

. `Applicative`

also extends `Functor`

, which gives every `Monad`

a `map`

method as we saw in the exercise above. We’ll discuss `Applicatives`

in Chapter 6.

Here are some examples using `pure`

and `flatMap`

, and `map`

directly:

```
import cats.Monad
import cats.instances.option._ // for Monad
import cats.instances.list._ // for Monad
val opt1 = Monad[Option].pure(3)
// opt1: Option[Int] = Some(3)
val opt2 = Monad[Option].flatMap(opt1)(a => Some(a + 2))
// opt2: Option[Int] = Some(5)
val opt3 = Monad[Option].map(opt2)(a => 100 * a)
// opt3: Option[Int] = Some(500)
val list1 = Monad[List].pure(3)
// list1: List[Int] = List(3)
val list2 = Monad[List].
flatMap(List(1, 2, 3))(a => List(a, a*10))
// list2: List[Int] = List(1, 10, 2, 20, 3, 30)
val list3 = Monad[List].map(list2)(a => a + 123)
// list3: List[Int] = List(124, 133, 125, 143, 126, 153)
```

`Monad`

provides many other methods, including all of the methods from `Functor`

. See the scaladoc for more information.

### 4.2.2 Default Instances

Cats provides instances for all the monads in the standard library (`Option`

, `List`

, `Vector`

and so on) via `cats.instances`

:

```
import cats.instances.option._ // for Monad
Monad[Option].flatMap(Option(1))(a => Option(a*2))
// res0: Option[Int] = Some(2)
import cats.instances.list._ // for Monad
Monad[List].flatMap(List(1, 2, 3))(a => List(a, a*10))
// res1: List[Int] = List(1, 10, 2, 20, 3, 30)
import cats.instances.vector._ // for Monad
Monad[Vector].flatMap(Vector(1, 2, 3))(a => Vector(a, a*10))
// res2: Vector[Int] = Vector(1, 10, 2, 20, 3, 30)
```

Cats also provides a `Monad`

for `Future`

. Unlike the methods on the `Future`

class itself, the `pure`

and `flatMap`

methods on the monad can’t accept implicit `ExecutionContext`

parameters (because the parameters aren’t part of the definitions in the `Monad`

trait). To work around this, Cats requires us to have an `ExecutionContext`

in scope when we summon a `Monad`

for `Future`

:

```
import cats.instances.future._ // for Monad
import scala.concurrent._
import scala.concurrent.duration._
val fm = Monad[Future]
// <console>:37: error: could not find implicit value for parameter instance: cats.Monad[scala.concurrent.Future]
// val fm = Monad[Future]
// ^
```

Bringing the `ExecutionContext`

into scope fixes the implicit resolution required to summon the instance:

```
import scala.concurrent.ExecutionContext.Implicits.global
val fm = Monad[Future]
// fm: cats.Monad[scala.concurrent.Future] = cats.instances.FutureInstances$$anon$1@52f2cf14
```

The `Monad`

instance uses the captured `ExecutionContext`

for subsequent calls to `pure`

and `flatMap`

:

```
val future = fm.flatMap(fm.pure(1))(x => fm.pure(x + 2))
Await.result(future, 1.second)
// res3: Int = 3
```

In addition to the above, Cats provides a host of new monads that we don’t have in the standard library. We’ll familiarise ourselves with some of these in a moment.

### 4.2.3 Monad Syntax

The syntax for monads comes from three places:

`cats.syntax.flatMap`

provides syntax for`flatMap`

;`cats.syntax.functor`

provides syntax for`map`

;`cats.syntax.applicative`

provides syntax for`pure`

.

In practice it’s often easier to import everything in one go from `cats.implicits`

. However, we’ll use the individual imports here for clarity.

We can use `pure`

to construct instances of a monad. We’ll often need to specify the type parameter to disambiguate the particular instance we want.

```
import cats.instances.option._ // for Monad
import cats.instances.list._ // for Monad
import cats.syntax.applicative._ // for pure
1.pure[Option]
// res4: Option[Int] = Some(1)
1.pure[List]
// res5: List[Int] = List(1)
```

It’s difficult to demonstrate the `flatMap`

and `map`

methods directly on Scala monads like `Option`

and `List`

, because they define their own explicit versions of those methods. Instead we’ll write a generic function that performs a calculation on parameters that come wrapped in a monad of the user’s choice:

```
import cats.Monad
import cats.syntax.functor._ // for map
import cats.syntax.flatMap._ // for flatMap
import scala.language.higherKinds
def sumSquare[F[_]: Monad](a: F[Int], b: F[Int]): F[Int] =
a.flatMap(x => b.map(y => x*x + y*y))
import cats.instances.option._ // for Monad
import cats.instances.list._ // for Monad
sumSquare(Option(3), Option(4))
// res8: Option[Int] = Some(25)
sumSquare(List(1, 2, 3), List(4, 5))
// res9: List[Int] = List(17, 26, 20, 29, 25, 34)
```

We can rewrite this code using for comprehensions. The compiler will “do the right thing” by rewriting our comprehension in terms of `flatMap`

and `map`

and inserting the correct implicit conversions to use our `Monad`

:

```
def sumSquare[F[_]: Monad](a: F[Int], b: F[Int]): F[Int] =
for {
x <- a
y <- b
} yield x*x + y*y
sumSquare(Option(3), Option(4))
// res10: Option[Int] = Some(25)
sumSquare(List(1, 2, 3), List(4, 5))
// res11: List[Int] = List(17, 26, 20, 29, 25, 34)
```

That’s more or less everything we need to know about the generalities of monads in Cats. Now let’s take a look at some useful monad instances that we haven’t seen in the Scala standard library.

## 4.3 The Identity Monad

In the previous section we demonstrated Cats’ `flatMap`

and `map`

syntax by writing a method that abstracted over different monads:

```
import scala.language.higherKinds
import cats.Monad
import cats.syntax.functor._ // for map
import cats.syntax.flatMap._ // for flatMap
def sumSquare[F[_]: Monad](a: F[Int], b: F[Int]): F[Int] =
for {
x <- a
y <- b
} yield x*x + y*y
```

This method works well on `Options`

and `Lists`

but we can’t call it passing in plain values:

```
sumSquare(3, 4)
// <console>:22: error: no type parameters for method sumSquare: (a: F[Int], b: F[Int])(implicit evidence$1: cats.Monad[F])F[Int] exist so that it can be applied to arguments (Int, Int)
// --- because ---
// argument expression's type is not compatible with formal parameter type;
// found : Int
// required: ?F[Int]
// sumSquare(3, 4)
// ^
// <console>:22: error: type mismatch;
// found : Int(3)
// required: F[Int]
// sumSquare(3, 4)
// ^
// <console>:22: error: type mismatch;
// found : Int(4)
// required: F[Int]
// sumSquare(3, 4)
// ^
```

It would be incredibly useful if we could use `sumSquare`

with parameters that were either in a monad or not in a monad at all. This would allow us to abstract over monadic and non-monadic code. Fortunately, Cats provides the `Id`

type to bridge the gap:

```
import cats.Id
sumSquare(3 : Id[Int], 4 : Id[Int])
// res2: cats.Id[Int] = 25
```

`Id`

allows us to call our monadic method using plain values. However, the exact semantics are difficult to understand. We cast the parameters to `sumSquare`

as `Id[Int]`

and received an `Id[Int]`

back as a result!

What’s going on? Here is the definition of `Id`

to explain:

```
package cats
type Id[A] = A
```

`Id`

is actually a type alias that turns an atomic type into a single-parameter type constructor. We can cast any value of any type to a corresponding `Id`

:

```
"Dave" : Id[String]
// res3: cats.Id[String] = Dave
123 : Id[Int]
// res4: cats.Id[Int] = 123
List(1, 2, 3) : Id[List[Int]]
// res5: cats.Id[List[Int]] = List(1, 2, 3)
```

Cats provides instances of various type classes for `Id`

, including `Functor`

and `Monad`

. These let us call `map`

, `flatMap`

, and `pure`

passing in plain values:

```
val a = Monad[Id].pure(3)
// a: cats.Id[Int] = 3
val b = Monad[Id].flatMap(a)(_ + 1)
// b: cats.Id[Int] = 4
import cats.syntax.functor._ // for map
import cats.syntax.flatMap._ // for flatMap
for {
x <- a
y <- b
} yield x + y
// res6: cats.Id[Int] = 7
```

The ability to abstract over monadic and non-monadic code is extremely powerful. For example, we can run code asynchronously in production using `Future`

and synchronously in test using `Id`

. We’ll see this in our first case study in Chapter 8.

### 4.3.1 Exercise: Monadic Secret Identities

Implement `pure`

, `map`

, and `flatMap`

for `Id`

! What interesting discoveries do you uncover about the implementation?

Let’s start by defining the method signatures:

```
import cats.Id
def pure[A](value: A): Id[A] =
???
def map[A, B](initial: Id[A])(func: A => B): Id[B] =
???
def flatMap[A, B](initial: Id[A])(func: A => Id[B]): Id[B] =
???
```

Now let’s look at each method in turn. The `pure`

operation creates an `Id[A]`

from an `A`

. But `A`

and `Id[A]`

are the same type! All we have to do is return the initial value:

```
def pure[A](value: A): Id[A] =
value
pure(123)
// res10: cats.Id[Int] = 123
```

The `map`

method takes a parameter of type `Id[A]`

, applies a function of type `A => B`

, and returns an `Id[B]`

. But `Id[A]`

is simply `A`

and `Id[B]`

is simply `B`

! All we have to do is call the function—no packing or unpacking required:

```
def map[A, B](initial: Id[A])(func: A => B): Id[B] =
func(initial)
map(123)(_ * 2)
// res11: cats.Id[Int] = 246
```

The final punch line is that, once we strip away the `Id`

type constructors, `flatMap`

and `map`

are actually identical:

```
def flatMap[A, B](initial: Id[A])(func: A => Id[B]): Id[B] =
func(initial)
// flatMap: [A, B](initial: cats.Id[A])(func: A => cats.Id[B])cats.Id[B]
flatMap(123)(_ * 2)
// res12: cats.Id[Int] = 246
```

This ties in with our understanding of functors and monads as sequencing type classes. Each type class allows us to sequence operations ignoring some kind of complication. In the case of `Id`

there is no complication, making `map`

and `flatMap`

the same thing.

Notice that we haven’t had to write type annotations in the method bodies above. The compiler is able to interpret values of type `A`

as `Id[A]`

and vice versa by the context in which they are used.

The only restriction we’ve seen to this is that Scala cannot unify types and type constructors when searching for implicits. Hence our need to re-type `Int`

as `Id[Int]`

in the call to `sumSquare`

at the opening of this section:

`sumSquare(3 : Id[Int], 4 : Id[Int])`

## 4.4 Either

Let’s look at another useful monad: the `Either`

type from the Scala standard library. In Scala 2.11 and earlier, many people didn’t consider `Either`

a monad because it didn’t have `map`

and `flatMap`

methods. In Scala 2.12, however, `Either`

became *right biased*.

### 4.4.1 Left and Right Bias

In Scala 2.11, `Either`

had no default `map`

or `flatMap`

method. This made the Scala 2.11 version of `Either`

inconvenient to use in for comprehensions. We had to insert calls to `.right`

in every generator clause:

```
val either1: Either[String, Int] = Right(10)
val either2: Either[String, Int] = Right(32)
for {
a <- either1.right
b <- either2.right
} yield a + b
// res0: scala.util.Either[String,Int] = Right(42)
```

In Scala 2.12, `Either`

was redesigned. The modern `Either`

makes the decision that the right side represents the success case and thus supports `map`

and `flatMap`

directly. This makes for comprehensions much more pleasant:

```
for {
a <- either1
b <- either2
} yield a + b
// res1: scala.util.Either[String,Int] = Right(42)
```

Cats back-ports this behaviour to Scala 2.11 via the `cats.syntax.either`

import, allowing us to use right-biased `Either`

in all supported versions of Scala. In Scala 2.12+ we can either omit this import or leave it in place without breaking anything:

```
import cats.syntax.either._ // for map and flatMap
for {
a <- either1
b <- either2
} yield a + b
```

### 4.4.2 Creating Instances

In addition to creating instances of `Left`

and `Right`

directly, we can also import the `asLeft`

and `asRight`

extension methods from `cats.syntax.either`

:

```
import cats.syntax.either._ // for asRight
val a = 3.asRight[String]
// a: Either[String,Int] = Right(3)
val b = 4.asRight[String]
// b: Either[String,Int] = Right(4)
for {
x <- a
y <- b
} yield x*x + y*y
// res4: scala.util.Either[String,Int] = Right(25)
```

These “smart constructors” have advantages over `Left.apply`

and `Right.apply`

because they return results of type `Either`

instead of `Left`

and `Right`

. This helps avoid type inference bugs caused by over-narrowing, like the bug in the example below:

```
def countPositive(nums: List[Int]) =
nums.foldLeft(Right(0)) { (accumulator, num) =>
if(num > 0) {
accumulator.map(_ + 1)
} else {
Left("Negative. Stopping!")
}
}
// <console>:21: error: type mismatch;
// found : scala.util.Either[Nothing,Int]
// required: scala.util.Right[Nothing,Int]
// accumulator.map(_ + 1)
// ^
// <console>:23: error: type mismatch;
// found : scala.util.Left[String,Nothing]
// required: scala.util.Right[Nothing,Int]
// Left("Negative. Stopping!")
// ^
```

This code fails to compile for two reasons:

- the compiler infers the type of the accumulator as
`Right`

instead of`Either`

; - we didn’t specify type parameters for
`Right.apply`

so the compiler infers the left parameter as`Nothing`

.

Switching to `asRight`

avoids both of these problems. `asRight`

has a return type of `Either`

, and allows us to completely specify the type with only one type parameter:

```
def countPositive(nums: List[Int]) =
nums.foldLeft(0.asRight[String]) { (accumulator, num) =>
if(num > 0) {
accumulator.map(_ + 1)
} else {
Left("Negative. Stopping!")
}
}
countPositive(List(1, 2, 3))
// res5: Either[String,Int] = Right(3)
countPositive(List(1, -2, 3))
// res6: Either[String,Int] = Left(Negative. Stopping!)
```

`cats.syntax.either`

adds some useful extension methods to the `Either`

companion object. The `catchOnly`

and `catchNonFatal`

methods are great for capturing `Exceptions`

as instances of `Either`

:

```
Either.catchOnly[NumberFormatException]("foo".toInt)
// res7: Either[NumberFormatException,Int] = Left(java.lang.NumberFormatException: For input string: "foo")
Either.catchNonFatal(sys.error("Badness"))
// res8: Either[Throwable,Nothing] = Left(java.lang.RuntimeException: Badness)
```

There are also methods for creating an `Either`

from other data types:

```
Either.fromTry(scala.util.Try("foo".toInt))
// res9: Either[Throwable,Int] = Left(java.lang.NumberFormatException: For input string: "foo")
Either.fromOption[String, Int](None, "Badness")
// res10: Either[String,Int] = Left(Badness)
```

### 4.4.3 Transforming Eithers

`cats.syntax.either`

also adds some useful methods for instances of `Either`

. We can use `orElse`

and `getOrElse`

to extract values from the right side or return a default:

```
import cats.syntax.either._
"Error".asLeft[Int].getOrElse(0)
// res11: Int = 0
"Error".asLeft[Int].orElse(2.asRight[String])
// res12: Either[String,Int] = Right(2)
```

The `ensure`

method allows us to check whether the right-hand value satisfies a predicate:

```
-1.asRight[String].ensure("Must be non-negative!")(_ > 0)
// res13: Either[String,Int] = Left(Must be non-negative!)
```

The `recover`

and `recoverWith`

methods provide similar error handling to their namesakes on `Future`

:

```
"error".asLeft[Int].recover {
case str: String => -1
}
// res14: Either[String,Int] = Right(-1)
"error".asLeft[Int].recoverWith {
case str: String => Right(-1)
}
// res15: Either[String,Int] = Right(-1)
```

There are `leftMap`

and `bimap`

methods to complement `map`

:

```
"foo".asLeft[Int].leftMap(_.reverse)
// res16: Either[String,Int] = Left(oof)
6.asRight[String].bimap(_.reverse, _ * 7)
// res17: Either[String,Int] = Right(42)
"bar".asLeft[Int].bimap(_.reverse, _ * 7)
// res18: Either[String,Int] = Left(rab)
```

The `swap`

method lets us exchange left for right:

```
123.asRight[String]
// res19: Either[String,Int] = Right(123)
123.asRight[String].swap
// res20: scala.util.Either[Int,String] = Left(123)
```

Finally, Cats adds a host of conversion methods: `toOption`

, `toList`

, `toTry`

, `toValidated`

, and so on.

### 4.4.4 Error Handling

`Either`

is typically used to implement fail-fast error handling. We sequence computations using `flatMap`

as usual. If one computation fails, the remaining computations are not run:

```
for {
a <- 1.asRight[String]
b <- 0.asRight[String]
c <- if(b == 0) "DIV0".asLeft[Int]
else (a / b).asRight[String]
} yield c * 100
// res21: scala.util.Either[String,Int] = Left(DIV0)
```

When using `Either`

for error handling, we need to determine what type we want to use to represent errors. We could use `Throwable`

for this:

`type Result[A] = Either[Throwable, A]`

This gives us similar semantics to `scala.util.Try`

. The problem, however, is that `Throwable`

is an extremely broad type. We have (almost) no idea about what type of error occurred.

Another approach is to define an algebraic data type to represent errors that may occur in our program:

```
sealed trait LoginError extends Product with Serializable
final case class UserNotFound(username: String)
extends LoginError
final case class PasswordIncorrect(username: String)
extends LoginError
case object UnexpectedError extends LoginError
case class User(username: String, password: String)
type LoginResult = Either[LoginError, User]
```

This approach solves the problems we saw with `Throwable`

. It gives us a fixed set of expected error types and a catch-all for anything else that we didn’t expect. We also get the safety of exhaustivity checking on any pattern matching we do:

```
// Choose error-handling behaviour based on type:
def handleError(error: LoginError): Unit =
error match {
case UserNotFound(u) =>
println(s"User not found: $u")
case PasswordIncorrect(u) =>
println(s"Password incorrect: $u")
case UnexpectedError =>
println(s"Unexpected error")
}
val result1: LoginResult = User("dave", "passw0rd").asRight
// result1: LoginResult = Right(User(dave,passw0rd))
val result2: LoginResult = UserNotFound("dave").asLeft
// result2: LoginResult = Left(UserNotFound(dave))
result1.fold(handleError, println)
// User(dave,passw0rd)
result2.fold(handleError, println)
// User not found: dave
```

### 4.4.5 Exercise: What is Best?

Is the error handling strategy in the previous examples well suited for all purposes? What other features might we want from error handling?

This is an open question. It’s also kind of a trick question—the answer depends on the semantics we’re looking for. Some points to ponder:

Error recovery is important when processing large jobs. We don’t want to run a job for a day and then find it failed on the last element.

Error reporting is equally important. We need to know what went wrong, not just that something went wrong.

- In a number of cases, we want to collect all the errors, not just the first one we encountered. A typical example is validating a web form. It’s a far better experience to report all errors to the user when they submit a form than to report them one at a time.

## 4.5 Aside: Error Handling and MonadError

Cats provides an additional type class called `MonadError`

that abstracts over `Either`

-like data types that are used for error handling. `MonadError`

provides extra operations for raising and handling errors.

*This Section is Optional!*

You won’t need to use `MonadError`

unless you need to abstract over error handling monads. For example, you can use `MonadError`

to abstract over `Future`

and `Try`

, or over `Either`

and `EitherT`

(which we will meet in Chapter 5).

If you don’t need this kind of abstraction right now, feel free to skip onwards to Section 4.6.

### 4.5.1 The MonadError Type Class

Here is a simplified version of the definition of `MonadError`

:

```
package cats
trait MonadError[F[_], E] extends Monad[F] {
// Lift an error into the `F` context:
def raiseError[A](e: E): F[A]
// Handle an error, potentially recovering from it:
def handleError[A](fa: F[A])(f: E => A): F[A]
// Test an instance of `F`,
// failing if the predicate is not satisfied:
def ensure[A](fa: F[A])(e: E)(f: A => Boolean): F[A]
}
```

`MonadError`

is defined in terms of two type parameters:

`F`

is the type of the monad;`E`

is the type of error contained within`F`

.

To demonstrate how these parameters fit together, here’s an example where we instantiate the type class for `Either`

:

```
import cats.MonadError
import cats.instances.either._ // for MonadError
type ErrorOr[A] = Either[String, A]
val monadError = MonadError[ErrorOr, String]
```

*ApplicativeError*

In reality, `MonadError`

extends another type class called `ApplicativeError`

. However, we won’t encounter `Applicatives`

until Chapter 6. The semantics are the same for each type class so we can ignore this detail for now.

### 4.5.2 Raising and Handling Errors

The two most important methods of `MonadError`

are `raiseError`

and `handleError`

. `raiseError`

is like the `pure`

method for `Monad`

except that it creates an instance representing a failure:

```
val success = monadError.pure(42)
// success: ErrorOr[Int] = Right(42)
val failure = monadError.raiseError("Badness")
// failure: ErrorOr[Nothing] = Left(Badness)
```

`handleError`

is the complement of `raiseError`

. It allows us to consume an error and (possibly) turn it into a success, similar to the `recover`

method of `Future`

:

```
monadError.handleError(failure) {
case "Badness" =>
monadError.pure("It's ok")
case other =>
monadError.raiseError("It's not ok")
}
// res2: ErrorOr[ErrorOr[String]] = Right(Right(It's ok))
```

There is also a third useful method called `ensure`

that implements `filter`

-like behaviour. We test the value of a successful monad with a predicate and specify an error to raise if the predicate returns `false`

:

```
import cats.syntax.either._ // for asRight
monadError.ensure(success)("Number too low!")(_ > 1000)
// res3: ErrorOr[Int] = Left(Number too low!)
```

Cats provides syntax for `raiseError`

and `handleError`

via `cats.syntax.applicativeError`

and `ensure`

via `cats.syntax.monadError`

:

```
import cats.syntax.applicative._ // for pure
import cats.syntax.applicativeError._ // for raiseError etc
import cats.syntax.monadError._ // for ensure
val success = 42.pure[ErrorOr]
// success: ErrorOr[Int] = Right(42)
val failure = "Badness".raiseError[ErrorOr, Int]
// failure: ErrorOr[Int] = Left(Badness)
success.ensure("Number to low!")(_ > 1000)
// res4: Either[String,Int] = Left(Number to low!)
```

There are other useful variants of these methods. See the source of `cats.MonadError`

and `cats.ApplicativeError`

for more information.

### 4.5.3 Instances of MonadError

Cats provides instances of `MonadError`

for numerous data types including `Either`

, `Future`

, and `Try`

. The instance for `Either`

is customisable to any error type, whereas the instances for `Future`

and `Try`

always represent errors as `Throwables`

:

```
import scala.util.Try
import cats.instances.try_._ // for MonadError
val exn: Throwable =
new RuntimeException("It's all gone wrong")
exn.raiseError[Try, Int]
// res6: scala.util.Try[Int] = Failure(java.lang.RuntimeException: It's all gone wrong)
```

### 4.5.4 Exercise: Abstracting

## 4.6 The Eval Monad

`cats.Eval`

is a monad that allows us to abstract over different *models of evaluation*. We typically hear of two such models: *eager* and *lazy*. `Eval`

throws in a further distinction of whether or not a result is *memoized*.

### 4.6.1 Eager, Lazy, Memoized, Oh My!

What do these terms mean?

*Eager* computations happen immediately whereas *lazy* computations happen on access. *Memoized* computations are run once on first access, after which the results are cached.

For example, Scala `vals`

are eager and memoized. We can see this using a computation with a visible side-effect. In the following example, the code to compute the value of `x`

happens at the definition site rather than on access (eager). Accessing `x`

recalls the stored value without re-running the code (memoized).

```
val x = {
println("Computing X")
math.random
}
// Computing X
// x: Double = 0.874225390459179
x // first access
// res0: Double = 0.874225390459179
x // second access
// res1: Double = 0.874225390459179
```

By contrast, `defs`

are lazy and not memoized. The code to compute `y`

below is not run until we access it (lazy), and is re-run on every access (not memoized):

```
def y = {
println("Computing Y")
math.random
}
// y: Double
y // first access
// Computing Y
// res2: Double = 0.8762878254751856
y // second access
// Computing Y
// res3: Double = 0.7216435660069331
```

Last but not least, `lazy vals`

are lazy and memoized. The code to compute `z`

below is not run until we access it for the first time (lazy). The result is then cached and re-used on subsequent accesses (memoized):

```
lazy val z = {
println("Computing Z")
math.random
}
// z: Double = <lazy>
z // first access
// Computing Z
// res4: Double = 0.24849492235046577
z // second access
// res5: Double = 0.24849492235046577
```

### 4.6.2 Eval’s Models of Evaluation

`Eval`

has three subtypes: `Now`

, `Later`

, and `Always`

. We construct these with three constructor methods, which create instances of the three classes and return them typed as `Eval`

:

```
import cats.Eval
val now = Eval.now(math.random + 1000)
// now: cats.Eval[Double] = Now(1000.5473732231594)
val later = Eval.later(math.random + 2000)
// later: cats.Eval[Double] = cats.Later@68492ea2
val always = Eval.always(math.random + 3000)
// always: cats.Eval[Double] = cats.Always@4bbcc69c
```

We can extract the result of an `Eval`

using its `value`

method:

```
now.value
// res6: Double = 1000.5473732231594
later.value
// res7: Double = 2000.9010215354635
always.value
// res8: Double = 3000.283950879214
```

Each type of `Eval`

calculates its result using one of the evaluation models defined above. `Eval.now`

captures a value *right now*. Its semantics are similar to a `val`

—eager and memoized:

```
val x = Eval.now {
println("Computing X")
math.random
}
// Computing X
// x: cats.Eval[Double] = Now(0.0695011339240671)
x.value // first access
// res9: Double = 0.0695011339240671
x.value // second access
// res10: Double = 0.0695011339240671
```

`Eval.always`

captures a lazy computation, similar to a `def`

:

```
val y = Eval.always {
println("Computing Y")
math.random
}
// y: cats.Eval[Double] = cats.Always@70e1ac5a
y.value // first access
// Computing Y
// res11: Double = 0.5975419052288963
y.value // second access
// Computing Y
// res12: Double = 0.485795150642218
```

Finally, `Eval.later`

captures a lazy, memoized computation, similar to a `lazy val`

:

```
val z = Eval.later {
println("Computing Z")
math.random
}
// z: cats.Eval[Double] = cats.Later@49f1cb64
z.value // first access
// Computing Z
// res13: Double = 0.9527317294303527
z.value // second access
// res14: Double = 0.9527317294303527
```

The three behaviours are summarized below:

Scala | Cats | Properties |
---|---|---|

`val` |
`Now` |
eager, memoized |

`lazy val` |
`Later` |
lazy, memoized |

`def` |
`Always` |
lazy, not memoized |

### 4.6.3 Eval as a Monad

Like all monads, `Eval's`

`map`

and `flatMap`

methods add computations to a chain. In this case, however, the chain is stored explicitly as a list of functions. The functions aren’t run until we call `Eval's`

`value`

method to request a result:

```
val greeting = Eval.
always { println("Step 1"); "Hello" }.
map { str => println("Step 2"); s"$str world" }
// greeting: cats.Eval[String] = cats.Eval$$anon$8@6ea0d023
greeting.value
// Step 1
// Step 2
// res15: String = Hello world
```

Note that, while the semantics of the originating `Eval`

instances are maintained, mapping functions are always called lazily on demand (`def`

semantics):

```
val ans = for {
a <- Eval.now { println("Calculating A"); 40 }
b <- Eval.always { println("Calculating B"); 2 }
} yield {
println("Adding A and B")
a + b
}
// Calculating A
// ans: cats.Eval[Int] = cats.Eval$$anon$8@6c0993a2
ans.value // first access
// Calculating B
// Adding A and B
// res16: Int = 42
ans.value // second access
// Calculating B
// Adding A and B
// res17: Int = 42
```

`Eval`

has a `memoize`

method that allows us to memoize a chain of computations. The result of the chain up to the call to `memoize`

is cached, whereas calculations after the call retain their original semantics:

```
val saying = Eval.
always { println("Step 1"); "The cat" }.
map { str => println("Step 2"); s"$str sat on" }.
memoize.
map { str => println("Step 3"); s"$str the mat" }
// saying: cats.Eval[String] = cats.Eval$$anon$8@789e2b29
saying.value // first access
// Step 1
// Step 2
// Step 3
// res18: String = The cat sat on the mat
saying.value // second access
// Step 3
// res19: String = The cat sat on the mat
```

### 4.6.4 Trampolining and *Eval.defer*

One useful property of `Eval`

is that its `map`

and `flatMap`

methods are *trampolined*. This means we can nest calls to `map`

and `flatMap`

arbitrarily without consuming stack frames. We call this property *“stack safety”*.

For example, consider this function for calculating factorials:

```
def factorial(n: BigInt): BigInt =
if(n == 1) n else n * factorial(n - 1)
```

It is relatively easy to make this method stack overflow:

```
factorial(50000)
// java.lang.StackOverflowError
// ...
```

We can rewrite the method using `Eval`

to make it stack safe:

```
def factorial(n: BigInt): Eval[BigInt] =
if(n == 1) {
Eval.now(n)
} else {
factorial(n - 1).map(_ * n)
}
factorial(50000).value
// java.lang.StackOverflowError
// ...
```

Oops! That didn’t work—our stack still blew up! This is because we’re still making all the recursive calls to `factorial`

before we start working with `Eval's`

`map`

method. We can work around this using `Eval.defer`

, which takes an existing instance of `Eval`

and defers its evaluation. The `defer`

method is trampolined like `map`

and `flatMap`

, so we can use it as a quick way to make an existing operation stack safe:

```
def factorial(n: BigInt): Eval[BigInt] =
if(n == 1) {
Eval.now(n)
} else {
Eval.defer(factorial(n - 1).map(_ * n))
}
factorial(50000).value
// res20: BigInt = 3347320509597144836915476094071486477912773223810454807730100321990168022144365641697381231071916930879848043819020829989361638474306669374263057284536378403832575628212335998726824407823597235604085385444137338375356856553637116832740516607615516592140615607546129420179056747966549862924222002254155351071815980161547645181061667497021799653747497254113933819163882350063030764425687485727139465108190987490964348626858922980787003103100896286115455397991161294065232739697149721103126114286073379350968783735581183060955172890660383359253285163596173088527981195739949529945030635444247849264102899006955963488352990055767655092917547592078804480762256241516513045904631806851740676636001232955645406572422517547342818312102919571559378742364111719451383859303800641313297631250...
```

`Eval`

is a useful tool to enforce stack safety when working on very large computations and data structures. However, we must bear in mind that trampolining is not free. It avoids consuming stack by creating a chain of function objects on the heap. There are still limits on how deeply we can nest computations, but they are bounded by the size of the heap rather than the stack.

### 4.6.5 Exercise: Safer Folding using Eval

The naive implementation of `foldRight`

below is not stack safe. Make it so using `Eval`

:

```
def foldRight[A, B](as: List[A], acc: B)(fn: (A, B) => B): B =
as match {
case head :: tail =>
fn(head, foldRight(tail, acc)(fn))
case Nil =>
acc
}
```

The easiest way to fix this is to introduce a helper method called `foldRightEval`

. This is essentially our original method with every occurrence of `B`

replaced with `Eval[B]`

, and a call to `Eval.defer`

to protect the recursive call:

```
import cats.Eval
def foldRightEval[A, B](as: List[A], acc: Eval[B])
(fn: (A, Eval[B]) => Eval[B]): Eval[B] =
as match {
case head :: tail =>
Eval.defer(fn(head, foldRightEval(tail, acc)(fn)))
case Nil =>
acc
}
```

We can redefine `foldRight`

simply in terms of `foldRightEval`

and the resulting method is stack safe:

```
def foldRight[A, B](as: List[A], acc: B)(fn: (A, B) => B): B =
foldRightEval(as, Eval.now(acc)) { (a, b) =>
b.map(fn(a, _))
}.value
foldRight((1 to 100000).toList, 0L)(_ + _)
// res22: Long = 5000050000
```

## 4.7 The Writer Monad

`cats.data.Writer`

is a monad that lets us carry a log along with a computation. We can use it to record messages, errors, or additional data about a computation, and extract the log alongside the final result.

One common use for `Writers`

is recording sequences of steps in multi-threaded computations where standard imperative logging techniques can result in interleaved messages from different contexts. With `Writer`

the log for the computation is tied to the result, so we can run concurrent computations without mixing logs.

*Cats Data Types*

`Writer`

is the first data type we’ve seen from the `cats.data`

package. This package provides instances of various type classes that produce useful semantics. Other examples from `cats.data`

include the monad transformers that we will see in the next chapter, and the `Validated`

type we will encounter in Chapter 6.

### 4.7.1 Creating and Unpacking Writers

A `Writer[W, A]`

carries two values: a *log* of type `W`

and a *result* of type `A`

. We can create a `Writer`

from values of each type as follows:

```
import cats.data.Writer
import cats.instances.vector._ // for Monoid
Writer(Vector(
"It was the best of times",
"it was the worst of times"
), 1859)
// res0: cats.data.WriterT[cats.Id,scala.collection.immutable.Vector[String],Int] = WriterT((Vector(It was the best of times, it was the worst of times),1859))
```

Notice that the type reported on the console is actually `WriterT[Id, Vector[String], Int]`

instead of `Writer[Vector[String], Int]`

as we might expect. In the spirit of code reuse, Cats implements `Writer`

in terms of another type, `WriterT`

. `WriterT`

is an example of a new concept called a *monad transformer*, which we will cover in the next chapter.

Let’s try to ignore this detail for now. `Writer`

is a type alias for `WriterT`

, so we can read types like `WriterT[Id, W, A]`

as `Writer[W, A]`

:

`type Writer[W, A] = WriterT[Id, W, A]`

For convenience, Cats provides a way of creating `Writers`

specifying only the log or the result. If we only have a result we can use the standard `pure`

syntax. To do this we must have a `Monoid[W]`

in scope so Cats knows how to produce an empty log:

```
import cats.instances.vector._ // for Monoid
import cats.syntax.applicative._ // for pure
type Logged[A] = Writer[Vector[String], A]
123.pure[Logged]
// res2: Logged[Int] = WriterT((Vector(),123))
```

If we have a log and no result we can create a `Writer[Unit]`

using the `tell`

syntax from `cats.syntax.writer`

:

```
import cats.syntax.writer._ // for tell
Vector("msg1", "msg2", "msg3").tell
// res3: cats.data.Writer[scala.collection.immutable.Vector[String],Unit] = WriterT((Vector(msg1, msg2, msg3),()))
```

If we have both a result and a log, we can either use `Writer.apply`

or we can use the `writer`

syntax from `cats.syntax.writer`

:

```
import cats.syntax.writer._ // for writer
val a = Writer(Vector("msg1", "msg2", "msg3"), 123)
// a: cats.data.WriterT[cats.Id,scala.collection.immutable.Vector[String],Int] = WriterT((Vector(msg1, msg2, msg3),123))
val b = 123.writer(Vector("msg1", "msg2", "msg3"))
// b: cats.data.Writer[scala.collection.immutable.Vector[String],Int] = WriterT((Vector(msg1, msg2, msg3),123))
```

We can extract the result and log from a `Writer`

using the `value`

and `written`

methods respectively:

```
val aResult: Int =
a.value
// aResult: Int = 123
val aLog: Vector[String] =
a.written
// aLog: Vector[String] = Vector(msg1, msg2, msg3)
```

We can extract both values at the same time using the `run`

method:

```
val (log, result) = b.run
// log: scala.collection.immutable.Vector[String] = Vector(msg1, msg2, msg3)
// result: Int = 123
```

### 4.7.2 Composing and Transforming Writers

The log in a `Writer`

is preserved when we `map`

or `flatMap`

over it. `flatMap`

appends the logs from the source `Writer`

and the result of the user’s sequencing function. For this reason it’s good practice to use a log type that has an efficient append and concatenate operations, such as a `Vector`

:

```
val writer1 = for {
a <- 10.pure[Logged]
_ <- Vector("a", "b", "c").tell
b <- 32.writer(Vector("x", "y", "z"))
} yield a + b
// writer1: cats.data.WriterT[cats.Id,Vector[String],Int] = WriterT((Vector(a, b, c, x, y, z),42))
writer1.run
// res4: cats.Id[(Vector[String], Int)] = (Vector(a, b, c, x, y, z),42)
```

In addition to transforming the result with `map`

and `flatMap`

, we can transform the log in a `Writer`

with the `mapWritten`

method:

```
val writer2 = writer1.mapWritten(_.map(_.toUpperCase))
// writer2: cats.data.WriterT[cats.Id,scala.collection.immutable.Vector[String],Int] = WriterT((Vector(A, B, C, X, Y, Z),42))
writer2.run
// res5: cats.Id[(scala.collection.immutable.Vector[String], Int)] = (Vector(A, B, C, X, Y, Z),42)
```

We can transform both log and result simultaneously using `bimap`

or `mapBoth`

. `bimap`

takes two function parameters, one for the log and one for the result. `mapBoth`

takes a single function that accepts two parameters:

```
val writer3 = writer1.bimap(
log => log.map(_.toUpperCase),
res => res * 100
)
// writer3: cats.data.WriterT[cats.Id,scala.collection.immutable.Vector[String],Int] = WriterT((Vector(A, B, C, X, Y, Z),4200))
writer3.run
// res6: cats.Id[(scala.collection.immutable.Vector[String], Int)] = (Vector(A, B, C, X, Y, Z),4200)
val writer4 = writer1.mapBoth { (log, res) =>
val log2 = log.map(_ + "!")
val res2 = res * 1000
(log2, res2)
}
// writer4: cats.data.WriterT[cats.Id,scala.collection.immutable.Vector[String],Int] = WriterT((Vector(a!, b!, c!, x!, y!, z!),42000))
writer4.run
// res7: cats.Id[(scala.collection.immutable.Vector[String], Int)] = (Vector(a!, b!, c!, x!, y!, z!),42000)
```

Finally, we can clear the log with the `reset`

method and swap log and result with the `swap`

method:

```
val writer5 = writer1.reset
// writer5: cats.data.WriterT[cats.Id,Vector[String],Int] = WriterT((Vector(),42))
writer5.run
// res8: cats.Id[(Vector[String], Int)] = (Vector(),42)
val writer6 = writer1.swap
// writer6: cats.data.WriterT[cats.Id,Int,Vector[String]] = WriterT((42,Vector(a, b, c, x, y, z)))
writer6.run
// res9: cats.Id[(Int, Vector[String])] = (42,Vector(a, b, c, x, y, z))
```

### 4.7.3 Exercise: Show Your Working

`Writers`

are useful for logging operations in multi-threaded environments. Let’s confirm this by computing (and logging) some factorials.

The `factorial`

function below computes a factorial and prints out the intermediate steps as it runs. The `slowly`

helper function ensures this takes a while to run, even on the very small examples below:

```
def slowly[A](body: => A) =
try body finally Thread.sleep(100)
def factorial(n: Int): Int = {
val ans = slowly(if(n == 0) 1 else n * factorial(n - 1))
println(s"fact $n $ans")
ans
}
```

Here’s the output—a sequence of monotonically increasing values:

```
factorial(5)
// fact 0 1
// fact 1 1
// fact 2 2
// fact 3 6
// fact 4 24
// fact 5 120
// res11: Int = 120
```

If we start several factorials in parallel, the log messages can become interleaved on standard out. This makes it difficult to see which messages come from which computation:

```
import scala.concurrent._
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration._
Await.result(Future.sequence(Vector(
Future(factorial(3)),
Future(factorial(3))
)), 5.seconds)
// fact 0 1
// fact 0 1
// fact 1 1
// fact 1 1
// fact 2 2
// fact 2 2
// fact 3 6
// fact 3 6
// res14: scala.collection.immutable.Vector[Int] =
// Vector(120, 120)
```

Rewrite `factorial`

so it captures the log messages in a `Writer`

. Demonstrate that this allows us to reliably separate the logs for concurrent computations.

We’ll start by defining a type alias for `Writer`

so we can use it with `pure`

syntax:

```
import cats.data.Writer
import cats.syntax.applicative._ // for pure
type Logged[A] = Writer[Vector[String], A]
42.pure[Logged]
// res13: Logged[Int] = WriterT((Vector(),42))
```

We’ll import the `tell`

syntax as well:

```
import cats.syntax.writer._ // for tell
Vector("Message").tell
// res14: cats.data.Writer[scala.collection.immutable.Vector[String],Unit] = WriterT((Vector(Message),()))
```

Finally, we’ll import the `Semigroup`

instance for `Vector`

. We need this to `map`

and `flatMap`

over `Logged`

:

```
import cats.instances.vector._ // for Monoid
41.pure[Logged].map(_ + 1)
// res15: cats.data.WriterT[cats.Id,Vector[String],Int] = WriterT((Vector(),42))
```

With these in scope, the definition of `factorial`

becomes:

```
def factorial(n: Int): Logged[Int] =
for {
ans <- if(n == 0) {
1.pure[Logged]
} else {
slowly(factorial(n - 1).map(_ * n))
}
_ <- Vector(s"fact $n $ans").tell
} yield ans
```

When we call `factorial`

, we now have to `run`

the return value to extract the log and our factorial:

```
val (log, res) = factorial(5).run
// log: Vector[String] = Vector(fact 0 1, fact 1 1, fact 2 2, fact 3 6, fact 4 24, fact 5 120)
// res: Int = 120
```

We can run several `factorials`

in parallel as follows, capturing their logs independently without fear of interleaving:

```
val Vector((logA, ansA), (logB, ansB)) =
Await.result(Future.sequence(Vector(
Future(factorial(3).run),
Future(factorial(5).run)
)), 5.seconds)
// logA: Vector[String] = Vector(fact 0 1, fact 1 1, fact 2 2, fact 3 6)
// ansA: Int = 6
// logB: Vector[String] = Vector(fact 0 1, fact 1 1, fact 2 2, fact 3 6, fact 4 24, fact 5 120)
// ansB: Int = 120
```

## 4.8 The Reader Monad

`cats.data.Reader`

is a monad that allows us to sequence operations that depend on some input. Instances of `Reader`

wrap up functions of one argument, providing us with useful methods for composing them.

One common use for `Readers`

is dependency injection. If we have a number of operations that all depend on some external configuration, we can chain them together using a `Reader`

to produce one large operation that accepts the configuration as a parameter and runs our program in the order specified.

### 4.8.1 Creating and Unpacking Readers

We can create a `Reader[A, B]`

from a function `A => B`

using the `Reader.apply`

constructor:

```
import cats.data.Reader
case class Cat(name: String, favoriteFood: String)
// defined class Cat
val catName: Reader[Cat, String] =
Reader(cat => cat.name)
// catName: cats.data.Reader[Cat,String] = Kleisli(<function1>)
```

We can extract the function again using the `Reader's`

`run`

method and call it using `apply`

as usual:

```
catName.run(Cat("Garfield", "lasagne"))
// res0: cats.Id[String] = Garfield
```

So far so simple, but what advantage do `Readers`

give us over the raw functions?

### 4.8.2 Composing Readers

The power of `Readers`

comes from their `map`

and `flatMap`

methods, which represent different kinds of function composition. We typically create a set of `Readers`

that accept the same type of configuration, combine them with `map`

and `flatMap`

, and then call `run`

to inject the config at the end.

The `map`

method simply extends the computation in the `Reader`

by passing its result through a function:

```
val greetKitty: Reader[Cat, String] =
catName.map(name => s"Hello ${name}")
greetKitty.run(Cat("Heathcliff", "junk food"))
// res1: cats.Id[String] = Hello Heathcliff
```

The `flatMap`

method is more interesting. It allows us to combine readers that depend on the same input type. To illustrate this, let’s extend our greeting example to also feed the cat:

```
val feedKitty: Reader[Cat, String] =
Reader(cat => s"Have a nice bowl of ${cat.favoriteFood}")
val greetAndFeed: Reader[Cat, String] =
for {
greet <- greetKitty
feed <- feedKitty
} yield s"$greet. $feed."
greetAndFeed(Cat("Garfield", "lasagne"))
// res3: cats.Id[String] = Hello Garfield. Have a nice bowl of lasagne.
greetAndFeed(Cat("Heathcliff", "junk food"))
// res4: cats.Id[String] = Hello Heathcliff. Have a nice bowl of junk food.
```

### 4.8.3 Exercise: Hacking on Readers

The classic use of `Readers`

is to build programs that accept a configuration as a parameter. Let’s ground this with a complete example of a simple login system. Our configuration will consist of two databases: a list of valid users and a list of their passwords:

```
case class Db(
usernames: Map[Int, String],
passwords: Map[String, String]
)
```

Start by creating a type alias `DbReader`

for a `Reader`

that consumes a `Db`

as input. This will make the rest of our code shorter.

Our type alias fixes the `Db`

type but leaves the result type flexible:

`type DbReader[A] = Reader[Db, A]`

Now create methods that generate `DbReaders`

to look up the username for an `Int`

user ID, and look up the password for a `String`

username. The type signatures should be as follows:

```
def findUsername(userId: Int): DbReader[Option[String]] =
???
def checkPassword(
username: String,
password: String): DbReader[Boolean] =
???
```

Remember: the idea is to leave injecting the configuration until last. This means setting up functions that accept the config as a parameter and check it against the concrete user info we have been given:

```
def findUsername(userId: Int): DbReader[Option[String]] =
Reader(db => db.usernames.get(userId))
def checkPassword(
username: String,
password: String): DbReader[Boolean] =
Reader(db => db.passwords.get(username).contains(password))
```

Finally create a `checkLogin`

method to check the password for a given user ID. The type signature should be as follows:

```
def checkLogin(
userId: Int,
password: String): DbReader[Boolean] =
???
```

As you might expect, here we use `flatMap`

to chain `findUsername`

and `checkPassword`

. We use `pure`

to lift a `Boolean`

to a `DbReader[Boolean]`

when the username is not found:

```
import cats.syntax.applicative._ // for pure
def checkLogin(
userId: Int,
password: String): DbReader[Boolean] =
for {
username <- findUsername(userId)
passwordOk <- username.map { username =>
checkPassword(username, password)
}.getOrElse {
false.pure[DbReader]
}
} yield passwordOk
```

You should be able to use `checkLogin`

as follows:

```
val users = Map(
1 -> "dade",
2 -> "kate",
3 -> "margo"
)
val passwords = Map(
"dade" -> "zerocool",
"kate" -> "acidburn",
"margo" -> "secret"
)
val db = Db(users, passwords)
checkLogin(1, "zerocool").run(db)
// res10: cats.Id[Boolean] = true
checkLogin(4, "davinci").run(db)
// res11: cats.Id[Boolean] = false
```

### 4.8.4 When to Use Readers?

`Readers`

provide a tool for doing dependency injection. We write steps of our program as instances of `Reader`

, chain them together with `map`

and `flatMap`

, and build a function that accepts the dependency as input.

There are many ways of implementing dependency injection in Scala, from simple techniques like methods with multiple parameter lists, through implicit parameters and type classes, to complex techniques like the cake pattern and DI frameworks.

`Readers`

are most useful in situations where:

we are constructing a batch program that can easily be represented by a function;

we need to defer injection of a known parameter or set of parameters;

we want to be able to test parts of the program in isolation.

By representing the steps of our program as `Readers`

we can test them as easily as pure functions, plus we gain access to the `map`

and `flatMap`

combinators.

For more advanced problems where we have lots of dependencies, or where a program isn’t easily represented as a pure function, other dependency injection techniques tend to be more appropriate.

*Kleisli Arrows*

You may have noticed from console output that `Reader`

is implemented in terms of another type called `Kleisli`

. *Kleisli arrows* provide a more general form of `Reader`

that generalise over the type constructor of the result type. We will encounter Kleislis again in Chapter 5.

## 4.9 The State Monad

`cats.data.State`

allows us to pass additional state around as part of a computation. We define `State`

instances representing atomic state operations and thread them together using `map`

and `flatMap`

. In this way we can model mutable state in a purely functional way, without using mutation.

### 4.9.1 Creating and Unpacking State

Boiled down to their simplest form, instances of `State[S, A]`

represent functions of type `S => (S, A)`

. `S`

is the type of the state and `A`

is the type of the result.

```
import cats.data.State
val a = State[Int, String] { state =>
(state, s"The state is $state")
}
// a: cats.data.State[Int,String] = cats.data.IndexedStateT@1bb91466
```

In other words, an instance of `State`

is a function that does two things:

- transforms an input state to an output state;
- computes a result.

We can “run” our monad by supplying an initial state. `State`

provides three methods—`run`

, `runS`

, and `runA`

—that return different combinations of state and result. Each method returns an instance of `Eval`

, which `State`

uses to maintain stack safety. We call the `value`

method as usual to extract the actual result:

```
// Get the state and the result:
val (state, result) = a.run(10).value
// state: Int = 10
// result: String = The state is 10
// Get the state, ignore the result:
val state = a.runS(10).value
// state: Int = 10
// Get the result, ignore the state:
val result = a.runA(10).value
// result: String = The state is 10
```

### 4.9.2 Composing and Transforming State

As we’ve seen with `Reader`

and `Writer`

, the power of the `State`

monad comes from combining instances. The `map`

and `flatMap`

methods thread the state from one instance to another. Each individual instance represents an atomic state transformation, and their combination represents a complete sequence of changes:

```
val step1 = State[Int, String] { num =>
val ans = num + 1
(ans, s"Result of step1: $ans")
}
// step1: cats.data.State[Int,String] = cats.data.IndexedStateT@76fee935
val step2 = State[Int, String] { num =>
val ans = num * 2
(ans, s"Result of step2: $ans")
}
// step2: cats.data.State[Int,String] = cats.data.IndexedStateT@3567092a
val both = for {
a <- step1
b <- step2
} yield (a, b)
// both: cats.data.IndexedStateT[cats.Eval,Int,Int,(String, String)] = cats.data.IndexedStateT@1921ccfc
val (state, result) = both.run(20).value
// state: Int = 42
// result: (String, String) = (Result of step1: 21,Result of step2: 42)
```

As you can see, in this example the final state is the result of applying both transformations in sequence. State is threaded from step to step even though we don’t interact with it in the for comprehension.

The general model for using the `State`

monad is to represent each step of a computation as an instance and compose the steps using the standard monad operators. Cats provides several convenience constructors for creating primitive steps:

`get`

extracts the state as the result;`set`

updates the state and returns unit as the result;`pure`

ignores the state and returns a supplied result;`inspect`

extracts the state via a transformation function;`modify`

updates the state using an update function.

```
val getDemo = State.get[Int]
// getDemo: cats.data.State[Int,Int] = cats.data.IndexedStateT@1d17d7a
getDemo.run(10).value
// res3: (Int, Int) = (10,10)
val setDemo = State.set[Int](30)
// setDemo: cats.data.State[Int,Unit] = cats.data.IndexedStateT@bd058d5
setDemo.run(10).value
// res4: (Int, Unit) = (30,())
val pureDemo = State.pure[Int, String]("Result")
// pureDemo: cats.data.State[Int,String] = cats.data.IndexedStateT@1bdc8ea
pureDemo.run(10).value
// res5: (Int, String) = (10,Result)
val inspectDemo = State.inspect[Int, String](_ + "!")
// inspectDemo: cats.data.State[Int,String] = cats.data.IndexedStateT@3bb7f50e
inspectDemo.run(10).value
// res6: (Int, String) = (10,10!)
val modifyDemo = State.modify[Int](_ + 1)
// modifyDemo: cats.data.State[Int,Unit] = cats.data.IndexedStateT@308648d0
modifyDemo.run(10).value
// res7: (Int, Unit) = (11,())
```

We can assemble these building blocks using a for comprehension. We typically ignore the result of intermediate stages that only represent transformations on the state:

```
import State._
val program: State[Int, (Int, Int, Int)] = for {
a <- get[Int]
_ <- set[Int](a + 1)
b <- get[Int]
_ <- modify[Int](_ + 1)
c <- inspect[Int, Int](_ * 1000)
} yield (a, b, c)
// program: cats.data.State[Int,(Int, Int, Int)] = cats.data.IndexedStateT@6f5b85ba
val (state, result) = program.run(1).value
// state: Int = 3
// result: (Int, Int, Int) = (1,2,3000)
```

### 4.9.3 Exercise: Post-Order Calculator

The `State`

monad allows us to implement simple interpreters for complex expressions, passing the values of mutable registers along with the result. We can see a simple example of this by implementing a calculator for post-order integer arithmetic expressions.

In case you haven’t heard of post-order expressions before (don’t worry if you haven’t), they are a mathematical notation where we write the operator *after* its operands. So, for example, instead of writing `1 + 2`

we would write:

`1 2 +`

Although post-order expressions are difficult for humans to read, they are easy to evaluate in code. All we need to do is traverse the symbols from left to right, carrying a *stack* of operands with us as we go:

when we see a number, we push it onto the stack;

when we see an operator, we pop two operands off the stack, operate on them, and push the result in their place.

This allows us to evaluate complex expressions without using parentheses. For example, we can evaluate `(1 + 2) * 3)`

as follows:

```
1 2 + 3 * // see 1, push onto stack
2 + 3 * // see 2, push onto stack
+ 3 * // see +, pop 1 and 2 off of stack,
// push (1 + 2) = 3 in their place
3 3 * // see 3, push onto stack
3 * // see 3, push onto stack
* // see *, pop 3 and 3 off of stack,
// push (3 * 3) = 9 in their place
```

Let’s write an interpreter for these expressions. We can parse each symbol into a `State`

instance representing a transformation on the stack and an intermediate result. The `State`

instances can be threaded together using `flatMap`

to produce an interpreter for any sequence of symbols.

Start by writing a function `evalOne`

that parses a single symbol into an instance of `State`

. Use the code below as a template. Don’t worry about error handling for now—if the stack is in the wrong configuration, it’s OK to throw an exception.

```
import cats.data.State
type CalcState[A] = State[List[Int], A]
def evalOne(sym: String): CalcState[Int] = ???
```

If this seems difficult, think about the basic form of the `State`

instances you’re returning. Each instance represents a functional transformation from a stack to a pair of a stack and a result. You can ignore any wider context and focus on just that one step:

```
State[List[Int], Int] { oldStack =>
val newStack = someTransformation(oldStack)
val result = someCalculation
(newStack, result)
}
```

Feel free to write your `Stack`

instances in this form or as sequences of the convenience constructors we saw above.

The stack operation required is different for operators and operands. For clarity we’ll implement `evalOne`

in terms of two helper functions, one for each case:

```
def evalOne(sym: String): CalcState[Int] =
sym match {
case "+" => operator(_ + _)
case "-" => operator(_ - _)
case "*" => operator(_ * _)
case "/" => operator(_ / _)
case num => operand(num.toInt)
}
```

Let’s look at `operand`

first. All we have to do is push a number onto the stack. We also return the operand as an intermediate result:

```
def operand(num: Int): CalcState[Int] =
State[List[Int], Int] { stack =>
(num :: stack, num)
}
```

The `operator`

function is a little more complex. We have to pop two operands off the stack (having the second operand at the top of the stack) and push the result in their place. The code can fail if the stack doesn’t have enough operands on it, but the exercise description allows us to throw an exception in this case:

```
def operator(func: (Int, Int) => Int): CalcState[Int] =
State[List[Int], Int] {
case b :: a :: tail =>
val ans = func(a, b)
(ans :: tail, ans)
case _ =>
sys.error("Fail!")
}
```

`evalOne`

allows us to evaluate single-symbol expressions as follows. We call `runA`

supplying `Nil`

as an initial stack, and call `value`

to unpack the resulting `Eval`

instance:

```
evalOne("42").runA(Nil).value
// res3: Int = 42
```

We can represent more complex programs using `evalOne`

, `map`

, and `flatMap`

. Note that most of the work is happening on the stack, so we ignore the results of the intermediate steps for `evalOne("1")`

and `evalOne("2")`

:

```
val program = for {
_ <- evalOne("1")
_ <- evalOne("2")
ans <- evalOne("+")
} yield ans
// program: cats.data.IndexedStateT[cats.Eval,List[Int],List[Int],Int] = cats.data.IndexedStateT@6ef0b66b
program.runA(Nil).value
// res4: Int = 3
```

Generalise this example by writing an `evalAll`

method that computes the result of a `List[String]`

. Use `evalOne`

to process each symbol, and thread the resulting `State`

monads together using `flatMap`

. Your function should have the following signature:

```
def evalAll(input: List[String]): CalcState[Int] =
???
```

We implement `evalAll`

by folding over the input. We start with a pure `CalcState`

that returns `0`

if the list is empty. We `flatMap`

at each stage, ignoring the intermediate results as we saw in the example:

```
import cats.syntax.applicative._ // for pure
def evalAll(input: List[String]): CalcState[Int] =
input.foldLeft(0.pure[CalcState]) { (a, b) =>
a.flatMap(_ => evalOne(b))
}
```

We can use `evalAll`

to conveniently evaluate multi-stage expressions:

```
val program = evalAll(List("1", "2", "+", "3", "*"))
// program: CalcState[Int] = cats.data.IndexedStateT@49999058
program.runA(Nil).value
// res6: Int = 9
```

Because `evalOne`

and `evalAll`

both return instances of `State`

, we can thread these results together using `flatMap`

. `evalOne`

produces a simple stack transformation and `evalAll`

produces a complex one, but they’re both pure functions and we can use them in any order as many times as we like:

```
val program = for {
_ <- evalAll(List("1", "2", "+"))
_ <- evalAll(List("3", "4", "+"))
ans <- evalOne("*")
} yield ans
// program: cats.data.IndexedStateT[cats.Eval,List[Int],List[Int],Int] = cats.data.IndexedStateT@69d23509
program.runA(Nil).value
// res7: Int = 21
```

Complete the exercise by implementing an `evalInput`

function that splits an input `String`

into symbols, calls `evalAll`

, and runs the result with an initial stack.

We’ve done all the hard work now. All we need to do is split the input into terms and call `runA`

and `value`

to unpack the result:

```
def evalInput(input: String): Int =
evalAll(input.split(" ").toList).runA(Nil).value
evalInput("1 2 + 3 4 + *")
// res8: Int = 21
```

## 4.10 Defining Custom Monads

We can define a `Monad`

for a custom type by providing implementations of three methods: `flatMap`

, `pure`

, and a method we haven’t seen yet called `tailRecM`

. Here is an implementation of `Monad`

for `Option`

as an example:

```
import cats.Monad
import scala.annotation.tailrec
val optionMonad = new Monad[Option] {
def flatMap[A, B](opt: Option[A])
(fn: A => Option[B]): Option[B] =
opt flatMap fn
def pure[A](opt: A): Option[A] =
Some(opt)
@tailrec
def tailRecM[A, B](a: A)
(fn: A => Option[Either[A, B]]): Option[B] =
fn(a) match {
case None => None
case Some(Left(a1)) => tailRecM(a1)(fn)
case Some(Right(b)) => Some(b)
}
}
```

The `tailRecM`

method is an optimisation used in Cats to limit the amount of stack space consumed by nested calls to `flatMap`

. The technique comes from a 2015 paper by PureScript creator Phil Freeman. The method should recursively call itself until the result of `fn`

returns a `Right`

.

If we can make `tailRecM`

tail-recursive, Cats is able to guarantee stack safety in recursive situations such as folding over large lists (see Section 7.1). If we can’t make `tailRecM`

tail-recursive, Cats cannot make these guarantees and extreme use cases may result in `StackOverflowErrors`

. All of the built-in monads in Cats have tail-recursive implementations of `tailRecM`

, although writing one for custom monads can be a challenge… as we shall see.

### 4.10.1 Exercise: Branching out Further with Monads

Let’s write a `Monad`

for our `Tree`

data type from last chapter. Here’s the type again:

```
sealed trait Tree[+A]
final case class Branch[A](left: Tree[A], right: Tree[A])
extends Tree[A]
final case class Leaf[A](value: A) extends Tree[A]
def branch[A](left: Tree[A], right: Tree[A]): Tree[A] =
Branch(left, right)
def leaf[A](value: A): Tree[A] =
Leaf(value)
```

Verify that the code works on instances of `Branch`

and `Leaf`

, and that the `Monad`

provides `Functor`

-like behaviour for free.

Also verify that having a `Monad`

in scope allows us to use for comprehensions, despite the fact that we haven’t directly implemented `flatMap`

or `map`

on `Tree`

.

Don’t feel you have to make `tailRecM`

tail-recursive. Doing so is quite difficult. We’ve included both tail-recursive and non-tail-recursive implementations in the solutions so you can check your work.

The code for `flatMap`

is similar to the code for `map`

. Again, we recurse down the structure and use the results from `func`

to build a new `Tree`

.

The code for `tailRecM`

is fairly complex regardless of whether we make it tail-recursive or not.

If we follow the types, the non-tail-recursive solution falls out:

```
import cats.Monad
implicit val treeMonad = new Monad[Tree] {
def pure[A](value: A): Tree[A] =
Leaf(value)
def flatMap[A, B](tree: Tree[A])
(func: A => Tree[B]): Tree[B] =
tree match {
case Branch(l, r) =>
Branch(flatMap(l)(func), flatMap(r)(func))
case Leaf(value) =>
func(value)
}
def tailRecM[A, B](a: A)
(func: A => Tree[Either[A, B]]): Tree[B] =
flatMap(func(a)) {
case Left(value) =>
tailRecM(value)(func)
case Right(value) =>
Leaf(value)
}
}
```

The solution above is perfectly fine for this exercise. Its only downside is that Cats cannot make guarantees about stack safety.

The tail-recursive solution is much harder to write. We adapted this solution from this Stack Overflow post by Nazarii Bardiuk. It involves an explicit depth first traversal of the tree, maintaining an `open`

list of nodes to visit and a `closed`

list of nodes to use to reconstruct the tree:

```
import cats.Monad
implicit val treeMonad = new Monad[Tree] {
def pure[A](value: A): Tree[A] =
Leaf(value)
def flatMap[A, B](tree: Tree[A])
(func: A => Tree[B]): Tree[B] =
tree match {
case Branch(l, r) =>
Branch(flatMap(l)(func), flatMap(r)(func))
case Leaf(value) =>
func(value)
}
def tailRecM[A, B](arg: A)
(func: A => Tree[Either[A, B]]): Tree[B] = {
@tailrec
def loop(
open: List[Tree[Either[A, B]]],
closed: List[Option[Tree[B]]]): List[Tree[B]] =
open match {
case Branch(l, r) :: next =>
loop(l :: r :: next, None :: closed)
case Leaf(Left(value)) :: next =>
loop(func(value) :: next, closed)
case Leaf(Right(value)) :: next =>
loop(next, Some(pure(value)) :: closed)
case Nil =>
closed.foldLeft(Nil: List[Tree[B]]) { (acc, maybeTree) =>
maybeTree.map(_ :: acc).getOrElse {
val left :: right :: tail = acc
branch(left, right) :: tail
}
}
}
loop(List(func(arg)), Nil).head
}
}
```

Regardless of which version of `tailRecM`

we define, we can use our `Monad`

to `flatMap`

and `map`

on `Trees`

:

```
import cats.syntax.functor._ // for map
import cats.syntax.flatMap._ // for flatMap
branch(leaf(100), leaf(200)).
flatMap(x => branch(leaf(x - 1), leaf(x + 1)))
// res3: wrapper.Tree[Int] = Branch(Branch(Leaf(99),Leaf(101)),Branch(Leaf(199),Leaf(201)))
```

We can also transform `Trees`

using for comprehensions:

```
for {
a <- branch(leaf(100), leaf(200))
b <- branch(leaf(a - 10), leaf(a + 10))
c <- branch(leaf(b - 1), leaf(b + 1))
} yield c
// res4: wrapper.Tree[Int] = Branch(Branch(Branch(Leaf(89),Leaf(91)),Branch(Leaf(109),Leaf(111))),Branch(Branch(Leaf(189),Leaf(191)),Branch(Leaf(209),Leaf(211))))
```

The monad for `Option`

provides fail-fast semantics. The monad for `List`

provides concatenation semantics. What are the semantics of `flatMap`

for a binary tree? Every node in the tree has the potential to be replaced with a whole subtree, producing a kind of “growing” or “feathering” behaviour, reminiscent of list concatenation along two axes.

## 4.11 Summary

In this chapter we’ve seen monads up-close. We saw that `flatMap`

can be viewed as an operator for sequencing computations, dictating the order in which operations must happen. From this viewpoint, `Option`

represents a computation that can fail without an error message, `Either`

represents computations that can fail with a message, `List`

represents multiple possible results, and `Future`

represents a computation that may produce a value at some point in the future.

We’ve also seen some of the custom types and data structures that Cats provides, including `Id`

, `Reader`

, `Writer`

, and `State`

. These cover a wide range of use cases.

Finally, in the unlikely event that we have to implement a custom monad, we’ve learned about defining our own instance using `tailRecM`

. `tailRecM`

is an odd wrinkle that is a concession to building a functional programming library that is stack-safe by default. We don’t need to understand `tailRecM`

to understand monads, but having it around gives us benefits of which we can be grateful when writing monadic code.

# 5 Monad Transformers

Monads are like burritos, which means that once you acquire a taste, you’ll find yourself returning to them again and again. This is not without issues. As burritos can bloat the waist, monads can bloat the code base through nested for-comprehensions.

Imagine we are interacting with a database. We want to look up a user record. The user may or may not be present, so we return an `Option[User]`

. Our communication with the database could fail for many reasons (network issues, authentication problems, and so on), so this result is wrapped up in an `Either`

, giving us a final result of `Either[Error, Option[User]]`

.

To use this value we must nest `flatMap`

calls (or equivalently, for-comprehensions):

```
def lookupUserName(id: Long): Either[Error, Option[String]] =
for {
optUser <- lookupUser(id)
} yield {
for { user <- optUser } yield user.name
}
```

This quickly becomes very tedious.

## 5.1 Exercise: Composing Monads

A question arises. Given two arbitrary monads, can we combine them in some way to make a single monad? That is, do monads *compose*? We can try to write the code but we soon hit problems:

```
import cats.Monad
import cats.syntax.applicative._ // for pure
import cats.syntax.flatMap._ // for flatMap
import scala.language.higherKinds
// Hypothetical example. This won't actually compile:
def compose[M1[_]: Monad, M2[_]: Monad] = {
type Composed[A] = M1[M2[A]]
new Monad[Composed] {
def pure[A](a: A): Composed[A] =
a.pure[M2].pure[M1]
def flatMap[A, B](fa: Composed[A])
(f: A => Composed[B]): Composed[B] =
// Problem! How do we write flatMap?
???
}
}
```

It is impossible to write a general definition of `flatMap`

without knowing something about `M1`

or `M2`

. However, if we *do* know something about one or other monad, we can typically complete this code. For example, if we fix `M2`

above to be `Option`

, a definition of `flatMap`

comes to light:

```
def flatMap[A, B](fa: Composed[A])
(f: A => Composed[B]): Composed[B] =
fa.flatMap(_.fold(None.pure[M1])(f))
```

Notice that the definition above makes use of `None`

—an `Option`

-specific concept that doesn’t appear in the general `Monad`

interface. We need this extra detail to combine `Option`

with other monads. Similarly, there are things about other monads that help us write composed `flatMap`

methods for them. This is the idea behind monad transformers: Cats defines transformers for a variety of monads, each providing the extra knowledge we need to compose that monad with others. Let’s look at some examples.

## 5.2 A Transformative Example

Cats provides transformers for many monads, each named with a `T`

suffix: `EitherT`

composes `Either`

with other monads, `OptionT`

composes `Option`

, and so on.

Here’s an example that uses `OptionT`

to compose `List`

and `Option`

. We can use `OptionT[List, A]`

, aliased to `ListOption[A]`

for convenience, to transform a `List[Option[A]]`

into a single monad:

```
import cats.data.OptionT
type ListOption[A] = OptionT[List, A]
```

Note how we build `ListOption`

from the inside out: we pass `List`

, the type of the outer monad, as a parameter to `OptionT`

, the transformer for the inner monad.

We can create instances of `ListOption`

using the `OptionT`

constructor, or more conveniently using `pure`

:

```
import cats.Monad
import cats.instances.list._ // for Monad
import cats.syntax.applicative._ // for pure
val result1: ListOption[Int] = OptionT(List(Option(10)))
// result1: ListOption[Int] = OptionT(List(Some(10)))
val result2: ListOption[Int] = 32.pure[ListOption]
// result2: ListOption[Int] = OptionT(List(Some(32)))
```

The `map`

and `flatMap`

methods combine the corresponding methods of `List`

and `Option`

into single operations:

```
result1.flatMap { (x: Int) =>
result2.map { (y: Int) =>
x + y
}
}
// res1: cats.data.OptionT[List,Int] = OptionT(List(Some(42)))
```

This is the basis of all monad transformers. The combined `map`

and `flatMap`

methods allow us to use both component monads without having to recursively unpack and repack values at each stage in the computation. Now let’s look at the API in more depth.

*Complexity of Imports*

The imports in the code samples above hint at how everything bolts together.

We import `cats.syntax.applicative`

to get the `pure`

syntax. `pure`

requires an implicit parameter of type `Applicative[ListOption]`

. We haven’t met `Applicatives`

yet, but all `Monads`

are also `Applicatives`

so we can ignore that difference for now.

In order to generate our `Applicative[ListOption]`

we need instances of `Applicative`

for `List`

and `OptionT`

. `OptionT`

is a Cats data type so its instance is provided by its companion object. The instance for `List`

comes from `cats.instances.list`

.

Notice we’re not importing `cats.syntax.functor`

or `cats.syntax.flatMap`

. This is because `OptionT`

is a concrete data type with its own explicit `map`

and `flatMap`

methods. It wouldn’t cause problems if we imported the syntax—the compiler would ignore it in favour of the explicit methods.

Remember that we’re subjecting ourselves to these shenanigans because we’re stubbornly refusing to use the universal Cats import, `cats.implicits`

. If we did use that import, all of the instances and syntax we needed would be in scope and everything would just work.

## 5.3 Monad Transformers in Cats

Each monad transformer is a data type, defined in `cats.data`

, that allows us to *wrap* stacks of monads to produce new monads. We use the monads we’ve built via the `Monad`

type class. The main concepts we have to cover to understand monad transformers are:

- the available transformer classes;
- how to build stacks of monads using transformers;
- how to construct instances of a monad stack; and
- how to pull apart a stack to access the wrapped monads.

### 5.3.1 The Monad Transformer Classes

By convention, in Cats a monad `Foo`

will have a transformer class called `FooT`

. In fact, many monads in Cats are defined by combining a monad transformer with the `Id`

monad. Concretely, some of the available instances are:

`cats.data.OptionT`

for`Option`

;`cats.data.EitherT`

for`Either`

;`cats.data.ReaderT`

for`Reader`

;`cats.data.WriterT`

for`Writer`

;`cats.data.StateT`

for`State`

;`cats.data.IdT`

for the`Id`

monad.

*Kleisli Arrows*

In Section 4.8 we mentioned that the `Reader`

monad was a specialisation of a more general concept called a “kleisli arrow”, represented in Cats as `cats.data.Kleisli`

.

We can now reveal that `Kleisli`

and `ReaderT`

are, in fact, the same thing! `ReaderT`

is actually a type alias for `Kleisli`

. Hence, we were creating `Readers`

last chapter and seeing `Kleislis`

on the console.

### 5.3.2 Building Monad Stacks

All of these monad transformers follow the same convention. The transformer itself represents the *inner* monad in a stack, while the first type parameter specifies the outer monad. The remaining type parameters are the types we’ve used to form the corresponding monads.

For example, our `ListOption`

type above is an alias for `OptionT[List, A]`

but the result is effectively a `List[Option[A]]`

. In other words, we build monad stacks from the inside out:

`type ListOption[A] = OptionT[List, A]`

Many monads and all transformers have at least two type parameters, so we often have to define type aliases for intermediate stages.

For example, suppose we want to wrap `Either`

around `Option`

. `Option`

is the innermost type so we want to use the `OptionT`

monad transformer. We need to use `Either`

as the first type parameter. However, `Either`

itself has two type parameters and monads only have one. We need a type alias to convert the type constructor to the correct shape:

```
// Alias Either to a type constructor with one parameter:
type ErrorOr[A] = Either[String, A]
// Build our final monad stack using OptionT:
type ErrorOrOption[A] = OptionT[ErrorOr, A]
```

`ErrorOrOption`

is a monad, just like `ListOption`

. We can use `pure`

, `map`

, and `flatMap`

as usual to create and transform instances:

```
import cats.instances.either._ // for Monad
val a = 10.pure[ErrorOrOption]
// a: ErrorOrOption[Int] = OptionT(Right(Some(10)))
val b = 32.pure[ErrorOrOption]
// b: ErrorOrOption[Int] = OptionT(Right(Some(32)))
val c = a.flatMap(x => b.map(y => x + y))
// c: cats.data.OptionT[ErrorOr,Int] = OptionT(Right(Some(42)))
```

Things become even more confusing when we want to stack three or more monads.

For example, let’s create a `Future`

of an `Either`

of `Option`

. Once again we build this from the inside out with an `OptionT`

of an `EitherT`

of `Future`

. However, we can’t define this in one line because `EitherT`

has three type parameters:

```
case class EitherT[F[_], E, A](stack: F[Either[E, A]]) {
// etc...
}
```

The three type parameters are as follows:

`F[_]`

is the outer monad in the stack (`Either`

is the inner);`E`

is the error type for the`Either`

;`A`

is the result type for the`Either`

.

This time we create an alias for `EitherT`

that fixes `Future`

and `Error`

and allows `A`

to vary:

```
import scala.concurrent.Future
import cats.data.{EitherT, OptionT}
type FutureEither[A] = EitherT[Future, String, A]
type FutureEitherOption[A] = OptionT[FutureEither, A]
```

Our mammoth stack now composes three monads and our `map`

and `flatMap`

methods cut through three layers of abstraction:

```
import cats.instances.future._ // for Monad
import scala.concurrent.Await
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration._
val futureEitherOr: FutureEitherOption[Int] =
for {
a <- 10.pure[FutureEitherOption]
b <- 32.pure[FutureEitherOption]
} yield a + b
```

*Kind Projector*

If you frequently find yourself defining multiple type aliases when building monad stacks, you may want to try the Kind Projector compiler plugin. Kind Projector enhances Scala’s type syntax to make it easier to define partially applied type constructors. For example:

```
import cats.instances.option._ // for Monad
// import cats.instances.option._
123.pure[EitherT[Option, String, ?]]
// res7: cats.data.EitherT[Option,String,Int] = EitherT(Some(Right(123)))
```

Kind Projector can’t simplify all type declarations down to a single line, but it can reduce the number of intermediate type definitions needed to keep our code readable.

### 5.3.3 Constructing and Unpacking Instances

As we saw above, we can create transformed monad stacks using the relevant monad transformer’s `apply`

method or the usual `pure`

syntax^{7}:

```
// Create using apply:
val errorStack1 = OptionT[ErrorOr, Int](Right(Some(10)))
// errorStack1: cats.data.OptionT[ErrorOr,Int] = OptionT(Right(Some(10)))
// Create using pure:
val errorStack2 = 32.pure[ErrorOrOption]
// errorStack2: ErrorOrOption[Int] = OptionT(Right(Some(32)))
```

Once we’ve finished with a monad transformer stack, we can unpack it using its `value`

method. This returns the untransformed stack. We can then manipulate the individual monads in the usual way:

```
// Extracting the untransformed monad stack:
errorStack1.value
// res11: ErrorOr[Option[Int]] = Right(Some(10))
// Mapping over the Either in the stack:
errorStack2.value.map(_.getOrElse(-1))
// res13: scala.util.Either[String,Int] = Right(32)
```

Each call to `value`

unpacks a single monad transformer. We may need more than one call to completely unpack a large stack. For example, to `Await`

the `FutureEitherOption`

stack above, we need to call `value`

twice:

```
futureEitherOr
// res14: FutureEitherOption[Int] = OptionT(EitherT(Future(Success(Right(Some(42))))))
val intermediate = futureEitherOr.value
// intermediate: FutureEither[Option[Int]] = EitherT(Future(Success(Right(Some(42)))))
val stack = intermediate.value
// stack: scala.concurrent.Future[Either[String,Option[Int]]] = Future(Success(Right(Some(42))))
Await.result(stack, 1.second)
// res15: Either[String,Option[Int]] = Right(Some(42))
```

### 5.3.4 Default Instances

Many monads in Cats are defined using the corresponding transformer and the `Id`

monad. This is reassuring as it confirms that the APIs for monads and transformers are identical. `Reader`

, `Writer`

, and `State`

are all defined in this way:

```
type Reader[E, A] = ReaderT[Id, E, A] // = Kleisli[Id, E, A]
type Writer[W, A] = WriterT[Id, W, A]
type State[S, A] = StateT[Id, S, A]
```

In other cases monad transformers are defined separately to their corresponding monads. In these cases, the methods of the transformer tend to mirror the methods on the monad. For example, `OptionT`

defines `getOrElse`

, and `EitherT`

defines `fold`

, `bimap`

, `swap`

, and other useful methods.

### 5.3.5 Usage Patterns

Widespread use of monad transformers is sometimes difficult because they fuse monads together in predefined ways. Without careful thought, we can end up having to unpack and repack monads in different configurations to operate on them in different contexts.

We can cope with this in multiple ways. One approach involves creating a single “super stack” and sticking to it throughout our code base. This works if the code is simple and largely uniform in nature. For example, in a web application, we could decide that all request handlers are asynchronous and all can fail with the same set of HTTP error codes. We could design a custom ADT representing the errors and use a fusion `Future`

and `Either`

everywhere in our code:

```
sealed abstract class HttpError
final case class NotFound(item: String) extends HttpError
final case class BadRequest(msg: String) extends HttpError
// etc...
type FutureEither[A] = EitherT[Future, HttpError, A]
```

The “super stack” approach starts to fail in larger, more heterogeneous code bases where different stacks make sense in different contexts. Another design pattern that makes more sense in these contexts uses monad transformers as local “glue code”. We expose untransformed stacks at module boundaries, transform them to operate on them locally, and untransform them before passing them on. This allows each module of code to make its own decisions about which transformers to use:

```
import cats.data.Writer
type Logged[A] = Writer[List[String], A]
// Methods generally return untransformed stacks:
def parseNumber(str: String): Logged[Option[Int]] =
util.Try(str.toInt).toOption match {
case Some(num) => Writer(List(s"Read $str"), Some(num))
case None => Writer(List(s"Failed on $str"), None)
}
// Consumers use monad transformers locally to simplify composition:
def addAll(a: String, b: String, c: String): Logged[Option[Int]] = {
import cats.data.OptionT
val result = for {
a <- OptionT(parseNumber(a))
b <- OptionT(parseNumber(b))
c <- OptionT(parseNumber(c))
} yield a + b + c
result.value
}
// This approach doesn't force OptionT on other users' code:
val result1 = addAll("1", "2", "3")
// result1: Logged[Option[Int]] = WriterT((List(Read 1, Read 2, Read 3),Some(6)))
val result2 = addAll("1", "a", "3")
// result2: Logged[Option[Int]] = WriterT((List(Read 1, Failed on a),None))
```

Unfortunately, there aren’t one-size-fits-all approaches to working with monad transformers. The best approach for you may depend on a lot of factors: the size and experience of your team, the complexity of your code base, and so on. You may need to experiment and gather feedback from colleagues to determine whether monad transformers are a good fit.

## 5.4 Exercise: Monads: Transform and Roll Out

The Autobots, well-known robots in disguise, frequently send messages during battle requesting the power levels of their team mates. This helps them coordinate strategies and launch devastating attacks. The message sending method looks like this:

```
def getPowerLevel(autobot: String): Response[Int] =
???
```

Transmissions take time in Earth’s viscous atmosphere, and messages are occasionally lost due to satellite malfunction or sabotage by pesky Decepticons^{8}. `Responses`

are therefore represented as a stack of monads:

```
type Response[A] = Future[Either[String, A]]
// defined type alias Response
```

Optimus Prime is getting tired of the nested for comprehensions in his neural matrix. Help him by rewriting `Response`

using a monad transformer.

This is a relatively simple combination. We want `Future`

on the outside and `Either`

on the inside, so we build from the inside out using an `EitherT`

of `Future`

:

```
import cats.data.EitherT
import scala.concurrent.Future
type Response[A] = EitherT[Future, String, A]
```

Now test the code by implementing `getPowerLevel`

to retrieve data from a set of imaginary allies. Here’s the data we’ll use:

```
val powerLevels = Map(
"Jazz" -> 6,
"Bumblebee" -> 8,
"Hot Rod" -> 10
)
```

If an Autobot isn’t in the `powerLevels`

map, return an error message reporting that they were unreachable. Include the `name`

in the message for good effect.

```
import cats.instances.future._ // for Monad
import cats.syntax.flatMap._ // for flatMap
import scala.concurrent.ExecutionContext.Implicits.global
type Response[A] = EitherT[Future, String, A]
def getPowerLevel(ally: String): Response[Int] = {
powerLevels.get(ally) match {
case Some(avg) => EitherT.right(Future(avg))
case None => EitherT.left(Future(s"$ally unreachable"))
}
}
```

Two autobots can perform a special move if their combined power level is greater than 15. Write a second method, `canSpecialMove`

, that accepts the names of two allies and checks whether a special move is possible. If either ally is unavailable, fail with an appropriate error message:

```
def canSpecialMove(ally1: String, ally2: String): Response[Boolean] =
???
```

We request the power level from each ally and use `map`

and `flatMap`

to combine the results:

```
def canSpecialMove(ally1: String, ally2: String): Response[Boolean] =
for {
power1 <- getPowerLevel(ally1)
power2 <- getPowerLevel(ally2)
} yield (power1 + power2) > 15
```

Finally, write a method `tacticalReport`

that takes two ally names and prints a message saying whether they can perform a special move:

```
def tacticalReport(ally1: String, ally2: String): String =
???
```

We use the `value`

method to unpack the monad stack and `Await`

and `fold`

to unpack the `Future`

and `Either`

:

```
import scala.concurrent.Await
import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration._
def tacticalReport(ally1: String, ally2: String): String = {
val stack = canSpecialMove(ally1, ally2).value
Await.result(stack, 1.second) match {
case Left(msg) =>
s"Comms error: $msg"
case Right(true) =>
s"$ally1 and $ally2 are ready to roll out!"
case Right(false) =>
s"$ally1 and $ally2 need a recharge."
}
}
```

You should be able to use `report`

as follows:

```
tacticalReport("Jazz", "Bumblebee")
// res28: String = Jazz and Bumblebee need a recharge.
tacticalReport("Bumblebee", "Hot Rod")
// res29: String = Bumblebee and Hot Rod are ready to roll out!
tacticalReport("Jazz", "Ironhide")
// res30: String = Comms error: Ironhide unreachable
```

## 5.5 Summary

In this chapter we introduced monad transformers, which eliminate the need for nested for comprehensions and pattern matching when working with “stacks” of nested monads.

Each monad transformer, such as `FutureT`

, `OptionT`

or `EitherT`

, provides the code needed to merge its related monad with other monads. The transformer is a data structure that wraps a monad stack, equipping it with `map`

and `flatMap`

methods that unpack and repack the whole stack.

The type signatures of monad transformers are written from the inside out, so an `EitherT[Option, String, A]`

is a wrapper for an `Option[Either[String, A]]`

. It is often useful to use type aliases when writing transformer types for deeply nested monads.

With this look at monad transformers, we have now covered everything we need to know about monads and the sequencing of computations using `flatMap`

. In the next chapter we will switch tack and discuss two new type classes, `Semigroupal`

and `Applicative`

, that support new kinds of operation such as `zipping`

independent values within a context.

# 6 Semigroupal and Applicative

In previous chapters we saw how functors and monads let us sequence operations using `map`

and `flatMap`

. While functors and monads are both immensely useful abstractions, there are certain types of program flow that they cannot represent.

One such example is form validation. When we validate a form we want to return *all* the errors to the user, not stop on the first error we encounter. If we model this with a monad like `Either`

, we fail fast and lose errors. For example, the code below fails on the first call to `parseInt`

and doesn’t go any further:

```
import cats.syntax.either._ // for catchOnly
def parseInt(str: String): Either[String, Int] =
Either.catchOnly[NumberFormatException](str.toInt).
leftMap(_ => s"Couldn't read $str")
for {
a <- parseInt("a")
b <- parseInt("b")
c <- parseInt("c")
} yield (a + b + c)
// res1: scala.util.Either[String,Int] = Left(Couldn't read a)
```

Another example is the concurrent evaluation of `Futures`

. If we have several long-running independent tasks, it makes sense to execute them concurrently. However, monadic comprehension only allows us to run them in sequence. `map`

and `flatMap`

aren’t quite capable of capturing what we want because they make the assumption that each computation is *dependent* on the previous one:

```
// context2 is dependent on value1:
context1.flatMap(value1 => context2)
```

The calls to `parseInt`

and `Future.apply`

above are *independent* of one another, but `map`

and `flatMap`

can’t exploit this. We need a weaker construct—one that doesn’t guarantee sequencing—to achieve the result we want. In this chapter we will look at two type classes that support this pattern:

`Semigroupal`

encompasses the notion of composing pairs of contexts. Cats provides a`cats.syntax.apply`

module that makes use of`Semigroupal`

and`Functor`

to allow users to sequence functions with multiple arguments.`Applicative`

extends`Semigroupal`

and`Functor`

. It provides a way of applying functions to parameters within a context.`Applicative`

is the source of the`pure`

method we introduced in Chapter 4.

Applicatives are often formulated in terms of function application, instead of the semigroupal formulation that is emphasised in Cats. This alternative formulation provides a link to other libraries and languages such as Scalaz and Haskell. We’ll take a look at different formulations of Applicative, as well as the relationships between `Semigroupal`

, `Functor`

, `Applicative`

, and `Monad`

, towards the end of the chapter.

## 6.1 Semigroupal

`cats.Semigroupal`

is a type class that allows us to combine contexts^{9}. If we have two objects of type `F[A]`

and `F[B]`

, a `Semigroupal[F]`

allows us to combine them to form an `F[(A, B)]`

. Its definition in Cats is:

```
trait Semigroupal[F[_]] {
def product[A, B](fa: F[A], fb: F[B]): F[(A, B)]
}
```

As we discussed at the beginning of this chapter, the parameters `fa`

and `fb`

are independent of one another: we can compute them in either order before passing them to `product`

. This is in contrast to `flatMap`

, which imposes a strict order on its parameters. This gives us more freedom when defining instances of `Semigroupal`

than we get when defining `Monads`

.

### 6.1.1 Joining Two Contexts

While `Semigroup`

allows us to join values, `Semigroupal`

allows us to join contexts. Let’s join some `Options`

as an example:

```
import cats.Semigroupal
import cats.instances.option._ // for Semigroupal
Semigroupal[Option].product(Some(123), Some("abc"))
// res0: Option[(Int, String)] = Some((123,abc))
```

If both parameters are instances of `Some`

, we end up with a tuple of the values within. If either parameter evaluates to `None`

, the entire result is `None`

:

```
Semigroupal[Option].product(None, Some("abc"))
// res1: Option[(Nothing, String)] = None
Semigroupal[Option].product(Some(123), None)
// res2: Option[(Int, Nothing)] = None
```

### 6.1.2 Joining Three or More Contexts

The companion object for `Semigroupal`

defines a set of methods on top of `product`

. For example, the methods `tuple2`

through `tuple22`

generalise `product`

to different arities:

```
import cats.instances.option._ // for Semigroupal
Semigroupal.tuple3(Option(1), Option(2), Option(3))
// res3: Option[(Int, Int, Int)] = Some((1,2,3))
Semigroupal.tuple3(Option(1), Option(2), Option.empty[Int])
// res4: Option[(Int, Int, Int)] = None
```

The methods `map2`

through `map22`

apply a user-specified function to the values inside 2 to 22 contexts:

```
Semigroupal.map3(Option(1), Option(2), Option(3))(_ + _ + _)
// res5: Option[Int] = Some(6)
Semigroupal.map2(Option(1), Option.empty[Int])(_ + _)
// res6: Option[Int] = None
```

There are also methods `contramap2`

through `contramap22`

and `imap2`

through `imap22`

, that require instances of `Contravariant`

and `Invariant`

respectively.

## 6.2 Apply Syntax

Cats provides a convenient *apply syntax* that provides a shorthand for the methods described above. We import the syntax from `cats.syntax.apply`

. Here’s an example:

```
import cats.instances.option._ // for Semigroupal
import cats.syntax.apply._ // for tupled and mapN
```

The `tupled`

method is implicitly added to the tuple of `Options`

. It uses the `Semigroupal`

for `Option`

to zip the values inside the `Options`

, creating a single `Option`

of a tuple:

```
(Option(123), Option("abc")).tupled
// res7: Option[(Int, String)] = Some((123,abc))
```

We can use the same trick on tuples of up to 22 values. Cats defines a separate `tupled`

method for each arity:

```
(Option(123), Option("abc"), Option(true)).tupled
// res8: Option[(Int, String, Boolean)] = Some((123,abc,true))
```

In addition to `tupled`

, Cats’ apply syntax provides a method called `mapN`

that accepts an implicit `Functor`

and a function of the correct arity to combine the values:

```
case class Cat(name: String, born: Int, color: String)
(
Option("Garfield"),
Option(1978),
Option("Orange & black")
).mapN(Cat.apply)
// res9: Option[Cat] = Some(Cat(Garfield,1978,Orange & black))
```

Internally `mapN`

uses the `Semigroupal`

to extract the values from the `Option`

and the `Functor`

to apply the values to the function.

It’s nice to see that this syntax is type checked. If we supply a function that accepts the wrong number or types of parameters, we get a compile error:

```
val add: (Int, Int) => Int = (a, b) => a + b
// add: (Int, Int) => Int = <function2>
(Option(1), Option(2), Option(3)).mapN(add)
// <console>:27: error: type mismatch;
// found : (Int, Int) => Int
// required: (Int, Int, Int) => ?
// (Option(1), Option(2), Option(3)).mapN(add)
// ^
(Option("cats"), Option(true)).mapN(add)
// <console>:27: error: type mismatch;
// found : (Int, Int) => Int
// required: (String, Boolean) => ?
// (Option("cats"), Option(true)).mapN(add)
// ^
```

### 6.2.1 Fancy Functors and Apply Syntax

Apply syntax also has `contramapN`

and `imapN`

methods that accept Contravariant and Invariant functors. For example, we can combine `Monoids`

using `Invariant`

. Here’s an example:

```
import cats.Monoid
import cats.instances.int._ // for Monoid
import cats.instances.invariant._ // for Semigroupal
import cats.instances.list._ // for Monoid
import cats.instances.string._ // for Monoid
import cats.syntax.apply._ // for imapN
case class Cat(
name: String,
yearOfBirth: Int,
favoriteFoods: List[String]
)
val tupleToCat: (String, Int, List[String]) => Cat =
Cat.apply _
val catToTuple: Cat => (String, Int, List[String]) =
cat => (cat.name, cat.yearOfBirth, cat.favoriteFoods)
implicit val catMonoid: Monoid[Cat] = (
Monoid[String],
Monoid[Int],
Monoid[List[String]]
).imapN(tupleToCat)(catToTuple)
```

Our `Monoid`

allows us to create “empty” `Cats`

, and add `Cats`

together using the syntax from Chapter 2:

```
import cats.syntax.semigroup._ // for |+|
val garfield = Cat("Garfield", 1978, List("Lasagne"))
val heathcliff = Cat("Heathcliff", 1988, List("Junk Food"))
garfield |+| heathcliff
// res17: Cat = Cat(GarfieldHeathcliff,3966,List(Lasagne, Junk Food))
```

## 6.3 Semigroupal Applied to Different Types

`Semigroupal`

doesn’t always provide the behaviour we expect, particularly for types that also have instances of `Monad`

. We have seen the behaviour of the `Semigroupal`

for `Option`

. Let’s look at some examples for other types.

**Future**

The semantics for `Future`

provide parallel as opposed to sequential execution:

```
import cats.Semigroupal
import cats.instances.future._ // for Semigroupal
import scala.concurrent._
import scala.concurrent.duration._
import scala.concurrent.ExecutionContext.Implicits.global
import scala.language.higherKinds
val futurePair = Semigroupal[Future].
product(Future("Hello"), Future(123))
Await.result(futurePair, 1.second)
// res1: (String, Int) = (Hello,123)
```

The two `Futures`

start executing the moment we create them, so they are already calculating results by the time we call `product`

. We can use apply syntax to zip fixed numbers of `Futures`

:

```
import cats.syntax.apply._ // for mapN
case class Cat(
name: String,
yearOfBirth: Int,
favoriteFoods: List[String]
)
val futureCat = (
Future("Garfield"),
Future(1978),
Future(List("Lasagne"))
).mapN(Cat.apply)
Await.result(futureCat, 1.second)
// res4: Cat = Cat(Garfield,1978,List(Lasagne))
```

**List**

Combining `Lists`

with `Semigroupal`

produces some potentially unexpected results. We might expect code like the following to *zip* the lists, but we actually get the cartesian product of their elements:

```
import cats.Semigroupal
import cats.instances.list._ // for Semigroupal
Semigroupal[List].product(List(1, 2), List(3, 4))
// res5: List[(Int, Int)] = List((1,3), (1,4), (2,3), (2,4))
```

This is perhaps surprising. Zipping lists tends to be a more common operation. We’ll see why we get this behaviour in a moment.

**Either**

We opened this chapter with a discussion of fail-fast versus accumulating error-handling. We might expect `product`

applied to `Either`

to accumulate errors instead of fail fast. Again, perhaps surprisingly, we find that `product`

implements the same fail-fast behaviour as `flatMap`

:

```
import cats.instances.either._ // for Semigroupal
type ErrorOr[A] = Either[Vector[String], A]
Semigroupal[ErrorOr].product(
Left(Vector("Error 1")),
Left(Vector("Error 2"))
)
// res7: ErrorOr[(Nothing, Nothing)] = Left(Vector(Error 1))
```

In this example `product`

sees the first failure and stops, even though it is possible to examine the second parameter and see that it is also a failure.

### 6.3.1 Semigroupal Applied to Monads

The reason for the surprising results for `List`

and `Either`

is that they are both monads. To ensure consistent semantics, Cats’ `Monad`

(which extends `Semigroupal`

) provides a standard definition of `product`

in terms of `map`

and `flatMap`

. This gives what we might think of as unexpected and less useful behaviour for a number of data types. The consistency of semantics is important for higher level abstractions, but we don’t know about those yet.

Even our results for `Future`

are a trick of the light. `flatMap`

provides sequential ordering, so `product`

provides the same. The parallel execution we observe occurs because our constituent `Futures`

start running before we call `product`

. This is equivalent to the classic create-then-flatMap pattern:

```
val a = Future("Future 1")
val b = Future("Future 2")
for {
x <- a
y <- b
} yield (x, y)
```

So why bother with `Semigroupal`

at all? The answer is that we can create useful data types that have instances of `Semigroupal`

(and `Applicative`

) but not `Monad`

. This frees us to implement `product`

in different ways. We’ll examine this further in a moment when we look at an alternative data type for error handling.

#### 6.3.1.1 Exercise: The Product of Monads

Implement `product`

in terms of `flatMap`

:

```
import cats.Monad
def product[M[_]: Monad, A, B](x: M[A], y: M[B]): M[(A, B)] =
???
```

We can implement `product`

in terms of `map`

and `flatMap`

like so:

```
import cats.syntax.flatMap._ // for flatMap
import cats.syntax.functor._ // for map
def product[M[_]: Monad, A, B](x: M[A], y: M[B]): M[(A, B)] =
x.flatMap(a => y.map(b => (a, b)))
```

Unsurprisingly, this code is equivalent to a for comprehension:

```
def product[M[_]: Monad, A, B](x: M[A], y: M[B]): M[(A, B)] =
for {
a <- x
b <- y
} yield (a, b)
```

The semantics of `flatMap`

are what give rise to the behaviour for `List`

and `Either`

:

```
import cats.instances.list._ // for Semigroupal
product(List(1, 2), List(3, 4))
// res12: List[(Int, Int)] = List((1,3), (1,4), (2,3), (2,4))
type ErrorOr[A] = Either[Vector[String], A]
product[ErrorOr, Int, Int](
Left(Vector("Error 1")),
Left(Vector("Error 2"))
)
// res13: ErrorOr[(Int, Int)] = Left(Vector(Error 1))
```

## 6.4 Validated

By now we are familiar with the fail-fast error handling behaviour of `Either`

. Furthermore, because `Either`

is a monad, we know that the semantics of `product`

are the same as those for `flatMap`

. In fact, it is impossible for us to design a monadic data type that implements error accumulating semantics without breaking the consistency of these two methods.

Fortunately, Cats provides a data type called `Validated`

that has an instance of `Semigroupal`

but *no* instance of `Monad`

. The implementation of `product`

is therefore free to accumulate errors:

```
import cats.Semigroupal
import cats.data.Validated
import cats.instances.list._ // for Monoid
type AllErrorsOr[A] = Validated[List[String], A]
Semigroupal[AllErrorsOr].product(
Validated.invalid(List("Error 1")),
Validated.invalid(List("Error 2"))
)
// res1: AllErrorsOr[(Nothing, Nothing)] = Invalid(List(Error 1, Error 2))
```

`Validated`

complements `Either`

nicely. Between the two we have support for both of the common types of error handling: fail-fast and accumulating.

### 6.4.1 Creating Instances of Validated

`Validated`

has two subtypes, `Validated.Valid`

and `Validated.Invalid`

, that correspond loosely to `Right`

and `Left`

. There are a lot of ways to create instances of these types. We can create them directly using their `apply`

methods:

```
val v = Validated.Valid(123)
// v: cats.data.Validated.Valid[Int] = Valid(123)
val i = Validated.Invalid(List("Badness"))
// i: cats.data.Validated.Invalid[List[String]] = Invalid(List(Badness))
```

However, it is often easier to use the `valid`

and `invalid`

smart constructors, which widen the return type to `Validated`

:

```
val v = Validated.valid[List[String], Int](123)
// v: cats.data.Validated[List[String],Int] = Valid(123)
val i = Validated.invalid[List[String], Int](List("Badness"))
// i: cats.data.Validated[List[String],Int] = Invalid(List(Badness))
```

As a third option we can import the `valid`

and `invalid`

extension methods from `cats.syntax.validated`

:

```
import cats.syntax.validated._ // for valid and invalid
123.valid[List[String]]
// res2: cats.data.Validated[List[String],Int] = Valid(123)
List("Badness").invalid[Int]
// res3: cats.data.Validated[List[String],Int] = Invalid(List(Badness))
```

As a fourth option we can use `pure`

and `raiseError`

from `cats.syntax.applicative`

and `cats.syntax.applicativeError`

respectively:

```
import cats.syntax.applicative._ // for pure
import cats.syntax.applicativeError._ // for raiseError
type ErrorsOr[A] = Validated[List[String], A]
123.pure[ErrorsOr]
// res5: ErrorsOr[Int] = Valid(123)
List("Badness").raiseError[ErrorsOr, Int]
// res6: ErrorsOr[Int] = Invalid(List(Badness))
```

Finally, there are helper methods to create instances of `Validated`

from different sources. We can create them from `Exceptions`

, as well as instances of `Try`

, `Either`

, and `Option`

:

```
Validated.catchOnly[NumberFormatException]("foo".toInt)
// res7: cats.data.Validated[NumberFormatException,Int] = Invalid(java.lang.NumberFormatException: For input string: "foo")
Validated.catchNonFatal(sys.error("Badness"))
// res8: cats.data.Validated[Throwable,Nothing] = Invalid(java.lang.RuntimeException: Badness)
Validated.fromTry(scala.util.Try("foo".toInt))
// res9: cats.data.Validated[Throwable,Int] = Invalid(java.lang.NumberFormatException: For input string: "foo")
Validated.fromEither[String, Int](Left("Badness"))
// res10: cats.data.Validated[String,Int] = Invalid(Badness)
Validated.fromOption[String, Int](None, "Badness")
// res11: cats.data.Validated[String,Int] = Invalid(Badness)
```

### 6.4.2 Combining Instances of Validated

We can combine instances of `Validated`

using any of the methods or syntax described for `Semigroupal`

above.

All of these techniques require an instance of `Semigroupal`

to be in scope. As with `Either`

, we need to fix the error type to create a type constructor with the correct number of parameters for `Semigroupal`

:

`type AllErrorsOr[A] = Validated[String, A]`

`Validated`

accumulates errors using a `Semigroup`

, so we need one of those in scope to summon the `Semigroupal`

. If no `Semigroup`

is visible at the call site, we get an annoyingly unhelpful compilation error:

```
Semigroupal[AllErrorsOr]
// <console>:28: error: could not find implicit value for parameter instance: cats.Semigroupal[AllErrorsOr]
// Semigroupal[AllErrorsOr]
// ^
```

Once we import a `Semigroup`

for the error type, everything works as expected:

```
import cats.instances.string._ // for Semigroup
Semigroupal[AllErrorsOr]
// res13: cats.Semigroupal[AllErrorsOr] = cats.data.ValidatedInstances$$anon$1@3e73d5ae
```

As long as the compiler has all the implicits in scope to summon a `Semigroupal`

of the correct type, we can use apply syntax or any of the other `Semigroupal`

methods to accumulate errors as we like:

```
import cats.syntax.apply._ // for tupled
(
"Error 1".invalid[Int],
"Error 2".invalid[Int]
).tupled
// res14: cats.data.Validated[String,(Int, Int)] = Invalid(Error 1Error 2)
```

As you can see, `String`

isn’t an ideal type for accumulating errors. We commonly use `Lists`

or `Vectors`

instead:

```
import cats.instances.vector._ // for Semigroupal
(
Vector(404).invalid[Int],
Vector(500).invalid[Int]
).tupled
// res15: cats.data.Validated[scala.collection.immutable.Vector[Int],(Int, Int)] = Invalid(Vector(404, 500))
```

The `cats.data`

package also provides the `NonEmptyList`

and `NonEmptyVector`

types that prevent us failing without at least one error:

```
import cats.data.NonEmptyVector
(
NonEmptyVector.of("Error 1").invalid[Int],
NonEmptyVector.of("Error 2").invalid[Int]
).tupled
// res16: cats.data.Validated[cats.data.NonEmptyVector[String],(Int, Int)] = Invalid(NonEmptyVector(Error 1, Error 2))
```

### 6.4.3 Methods of Validated

`Validated`

comes with a suite of methods that closely resemble those available for `Either`

, including the methods from `cats.syntax.either`

. We can use `map`

, `leftMap`

, and `bimap`

to transform the values inside the valid and invalid sides:

```
123.valid.map(_ * 100)
// res17: cats.data.Validated[Nothing,Int] = Valid(12300)
"?".invalid.leftMap(_.toString)
// res18: cats.data.Validated[String,Nothing] = Invalid(?)
123.valid[String].bimap(_ + "!", _ * 100)
// res19: cats.data.Validated[String,Int] = Valid(12300)
"?".invalid[Int].bimap(_ + "!", _ * 100)
// res20: cats.data.Validated[String,Int] = Invalid(?!)
```

We can’t `flatMap`

because `Validated`

isn’t a monad. However, Cats does provide a stand-in for `flatMap`

called `andThen`

. The type signature of `andThen`

is identical to that of `flatMap`

, but it has a different name because it is not a lawful implementation with respect to the monad laws:

```
32.valid.andThen { a =>
10.valid.map { b =>
a + b
}
}
// res21: cats.data.Validated[Nothing,Int] = Valid(42)
```

If we want to do more than just `flatMap`

, we can convert back and forth between `Validated`

and `Either`

using the `toEither`

and `toValidated`

methods. Note that `toValidated`

comes from [`cats.syntax.either`

]:

```
import cats.syntax.either._ // for toValidated
// import cats.syntax.either._
"Badness".invalid[Int]
// res22: cats.data.Validated[String,Int] = Invalid(Badness)
"Badness".invalid[Int].toEither
// res23: Either[String,Int] = Left(Badness)
"Badness".invalid[Int].toEither.toValidated
// res24: cats.data.Validated[String,Int] = Invalid(Badness)
```

As with `Either`

, we can use the `ensure`

method to fail with a specified error if a predicate does not hold:

`// 123.valid[String].ensure("Negative!")(_ > 0)`

Finally, we can call `getOrElse`

or `fold`

to extract values from the `Valid`

and `Invalid`

cases:

```
"fail".invalid[Int].getOrElse(0)
// res26: Int = 0
"fail".invalid[Int].fold(_ + "!!!", _.toString)
// res27: String = fail!!!
```

### 6.4.4 Exercise: Form Validation

Let’s get used to `Validated`

by implementing a simple HTML registration form. We receive request data from the client in a `Map[String, String]`

and we want to parse it to create a `User`

object:

`case class User(name: String, age: Int)`

Our goal is to implement code that parses the incoming data enforcing the following rules:

- the name and age must be specified;
- the name must not be blank;
- the age must be a valid non-negative integer.

If all the rules pass our parser we should return a `User`

. If any rules fail we should return a `List`

of the error messages.

To implement this example we’ll need to combine rules in sequence and in parallel. We’ll use `Either`

to combine computations in sequence using fail-fast semantics, and `Validated`

to combine them in parallel using accumulating semantics.

Let’s start with some sequential combination. We’ll define two methods to read the `"name"`

and `"age"`

fields:

`readName`

will take a`Map[String, String]`

parameter, extract the`"name"`

field, check the relevant validation rules, and return an`Either[List[String], String]`

.`readAge`

will take a`Map[String, String]`

parameter, extract the`"age"`

field, check the relevant validation rules, and return an`Either[List[String], Int]`

.

We’ll build these methods up from smaller building blocks. Start by defining a method `getValue`

that reads a `String`

from the `Map`

given a field name.

We’ll be using `Either`

and `Validated`

so we’ll start with some imports:

```
import cats.data.Validated
type FormData = Map[String, String]
type FailFast[A] = Either[List[String], A]
type FailSlow[A] = Validated[List[String], A]
```

The `getValue`

rule extracts a `String`

from the form data. We’ll be using it in sequence with rules for parsing `Ints`

and checking values, so we’ll define it to return an `Either`

:

```
def getValue(name: String)(data: FormData): FailFast[String] =
data.get(name).
toRight(List(s"$name field not specified"))
```

We can create and use an instance of `getValue`

as follows:

```
val getName = getValue("name") _
// getName: FormData => FailFast[String] = <function1>
getName(Map("name" -> "Dade Murphy"))
// res29: FailFast[String] = Right(Dade Murphy)
```

In the event of a missing field, our instance returns an error message containing an appropriate field name:

```
getName(Map())
// res30: FailFast[String] = Left(List(name field not specified))
```

Next define a method `parseInt`

that consumes a `String`

and parses it as an `Int`

.

We’ll use `Either`

again here. We use `Either.catchOnly`

to consume the `NumberFormatException`

from `toInt`

, and we use `leftMap`

to turn it into an error message:

```
import cats.syntax.either._ // for catchOnly
type NumFmtExn = NumberFormatException
def parseInt(name: String)(data: String): FailFast[Int] =
Either.catchOnly[NumFmtExn](data.toInt).
leftMap(_ => List(s"$name must be an integer"))
```

Note that our solution accepts an extra parameter to name the field we’re parsing. This is useful for creating better error messages, but it’s fine if you leave it out in your code.

If we provide valid input, `parseInt`

converts it to an `Int`

:

```
parseInt("age")("11")
// res33: FailFast[Int] = Right(11)
```

If we provide erroneous input, we get a useful error message:

```
parseInt("age")("foo")
// res34: FailFast[Int] = Left(List(age must be an integer))
```

Next implement the validation checks: `nonBlank`

to check `Strings`

, and `nonNegative`

to check `Ints`

.

These definitions use the same patterns as above:

```
def nonBlank(name: String)(data: String): FailFast[String] =
Right(data).
ensure(List(s"$name cannot be blank"))(_.nonEmpty)
def nonNegative(name: String)(data: Int): FailFast[Int] =
Right(data).
ensure(List(s"$name must be non-negative"))(_ >= 0)
```

Here are some examples of use:

```
nonBlank("name")("Dade Murphy")
// res36: FailFast[String] = Right(Dade Murphy)
nonBlank("name")("")
// res37: FailFast[String] = Left(List(name cannot be blank))
nonNegative("age")(11)
// res38: FailFast[Int] = Right(11)
nonNegative("age")(-1)
// res39: FailFast[Int] = Left(List(age must be non-negative))
```

Now combine `getValue`

, `parseInt`

, `nonBlank`

and `nonNegative`

to create `readName`

and `readAge`

:

We use `flatMap`

to combine the rules sequentially:

```
def readName(data: FormData): FailFast[String] =
getValue("name")(data).
flatMap(nonBlank("name"))
def readAge(data: FormData): FailFast[Int] =
getValue("age")(data).
flatMap(nonBlank("age")).
flatMap(parseInt("age")).
flatMap(nonNegative("age"))
```

The rules pick up all the error cases we’ve seen so far:

```
readName(Map("name" -> "Dade Murphy"))
// res41: FailFast[String] = Right(Dade Murphy)
readName(Map("name" -> ""))
// res42: FailFast[String] = Left(List(name cannot be blank))
readName(Map())
// res43: FailFast[String] = Left(List(name field not specified))
readAge(Map("age" -> "11"))
// res44: FailFast[Int] = Right(11)
readAge(Map("age" -> "-1"))
// res45: FailFast[Int] = Left(List(age must be non-negative))
readAge(Map())
// res46: FailFast[Int] = Left(List(age field not specified))
```

Finally, use a `Semigroupal`

to combine the results of `readName`

and `readAge`

to produce a `User`

. Make sure you switch from `Either`

to `Validated`

to accumulate errors.

We can do this by switching from `Either`

to `Validated`

and using apply syntax:

```
import cats.instances.list._ // for Semigroupal
import cats.syntax.apply._ // for mapN
def readUser(data: FormData): FailSlow[User] =
(
readName(data).toValidated,
readAge(data).toValidated
).mapN(User.apply)
readUser(Map("name" -> "Dave", "age" -> "37"))
// res48: FailSlow[User] = Valid(User(Dave,37))
readUser(Map("age" -> "-1"))
// res49: FailSlow[User] = Invalid(List(name field not specified, age must be non-negative))
```

The need to switch back and forth between `Either`

and `Validated`

is annoying. The choice of whether to use `Either`

or `Validated`

as a default is determined by context. In application code, we typically find areas that favour accumulating semantics and areas that favour fail-fast semantics. We pick the data type that best suits our need and switch to the other as necessary in specific situations.

## 6.5 Apply and Applicative

Semigroupals aren’t mentioned frequently in the wider functional programming literature. They provide a subset of the functionality of a related type class called an *applicative functor* (“applicative” for short).

`Semigroupal`

and `Applicative`

effectively provide alternative encodings of the same notion of joining contexts. Both encodings are introduced in the same 2008 paper by Conor McBride and Ross Paterson^{10}.

Cats models applicatives using two type classes. The first, `cats.Apply`

, extends `Semigroupal`

and `Functor`

and adds an `ap`

method that applies a parameter to a function within a context. The second, `cats.Applicative`

, extends `Apply`

, adds the `pure`

method introduced in Chapter 4. Here’s a simplified definition in code:

`trait Apply[F[_]] extends Semigroupal[F] with Fu`