Scala with Cats

Noel Welsh and Dave Gurnell

April 2020

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:

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 2.13.1 and Cats 2.1.0. Here is a minimal build.sbt containing the relevant dependencies and settings1:

scalaVersion := "2.13.1"

libraryDependencies +=
  "org.typelevel" %% "cats-core" % "2.1.0"

scalacOptions ++= Seq(
  "-Xfatal-warnings"
)

Template Projects

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

$ sbt new scalawithcats/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 very minimal. 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 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 mdoc to ensure it compiles. mdoc 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 Inner Product and 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, Denis Zjukow, 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, mizuno, Mr-SD, Narayan Iyer, Niccolo’ Paravanti, niqdev, Noor Nashid, Pablo Francisco Pérez Hidalgo, Pawel Jurczenko, Phil Derome, Philip Schwarz, Riccardo Sirigu, Richard Dallaway, Robert Stoll, Rodney Jacobsen, Rodrigo B. de Oliveira, Rud Wangrungarun, 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 Haskell2. 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 methods that use type classes.

Type classes in Scala are implemented using implicit values and parameters, and optionally using implicit classes. Scala language constructs correspond to the components of type classes as follows:

Let’s see how this works in detail.

1.1.1 The Type Class

A type class is an interface or API that represents some functionality we want to implement. In Scala 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
final 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. When we come to implement instances of JsonWriter, the type parameter A will be the concrete type of data we are writing.

1.1.2 Type Class Instances

The instances of a type class provide implementations of the type class for specific types we care about, which can include 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...
}

These are known as implicit values.

1.1.3 Type Class Use

A type class use is any functionality that requires a type class instance to work. In Scala this means any method that accepts instances of the type class as implicit parameters.

Cats provides utilities that make type classes easier to use, and you will sometimes seem these patterns in other libraries. There are two ways it does this: Interface Objects and Interface Syntax.

Interface Objects

The simplest way of creating an interface that uses a type class 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"))
// res1: 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 methods3. 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
// res3: 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._

implicitly[JsonWriter[String]]
// res5: JsonWriter[String] = repl.Session$App0$JsonWriterInstances$$anon$1@76f60d45

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 places where the compiler searches for candidate instances is known as the implicit scope. The implicit scope applies at the call site; that is the point where we call a method with an implicit parameter. The implicit scope which roughly consists of:

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")
// error: ambiguous implicit values:
//  both value writer1 in object App0 of type => repl.Session.App0.JsonWriter[String]
//  and value writer2 in object App0 of type => repl.Session.App0.JsonWriter[String]
//  match expected type repl.Session.App0.JsonWriter[String]
// Json.toJson("A string")
// ^^^^^^^^^^^^^^^^^^^^^^^

The precise rules of implicit resolution are more complex than this, but the complexity is largely irrelevant for day-to-day use4. For our purposes, we can package type class instances in roughly four ways:

  1. by placing them in an object such as JsonWriterInstances;
  2. by placing them in a trait;
  3. by placing them in the companion object of the type class;
  4. 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.

It is conventional to put type class instances in a companion object (option 3 and 4 above) if there is only one sensible implementation, or at least one implementation that is widely accepted as the default. This makes type class instances easier to use as no import is required to bring them into the implicit scope.

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. This is sometimes known as type class composition.

Earlier we insinuated that all type class instances are implicit vals. This was a simplification. We can actually define instances in two ways:

  1. by defining concrete instances as implicit vals of the required type5;

  2. 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 Option. 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:

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 creates 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]] =
  ???
// warning: implicit conversion method foo 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.

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:

  1. Define a type class Printable[A] containing a single method format. format should accept a value of type A and return a String.

  2. Create an object PrintableInstances containing instances of Printable for String and Int.

  3. 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 formatted 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", 41, "ginger and black")
// cat: Cat = Cat("Garfield", 41, "ginger and black")

Printable.print(cat)
// Garfield is a 41 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:

  1. Create an object called PrintableSyntax.

  2. Inside PrintableSyntax define an implicit class PrintableOps[A] to wrap up a value of type A.

  3. 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.

  4. 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 =
      p.format(value)

    def print(implicit p: Printable[A]): Unit =
      println(format(p))
  }
}

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", 41, "ginger and black").print
// Garfield is a 41 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
// error: could not find implicit value for parameter p: repl.Session.App0.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]
// error: could not find implicit value for parameter instance: cats.Show[Int]
// val showInt:    Show[Int]    = 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:

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 is doesn’t add value in production code. It is simpler and faster to use the following imports:

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."
  }

new Date().show
// res1: String = "1594215596646ms 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 allow 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[Cat] = 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)
// warning: Option[Int] and Int are unrelated: they will most likely never compare equal
// res: 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:

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)
// res1: Boolean = true
eqInt.eqv(123, 234)
// res2: 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")
// error: type mismatch;
//  found   : String("234")
//  required: Int

We can also import the interface syntax in cats.syntax.eq to use the === and =!= methods:

import cats.syntax.eq._ // for === and =!=

123 === 123
// res4: Boolean = true
123 =!= 234
// res5: Boolean = true

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

123 === "123"
// 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
// 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])
// res8: 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]
// res9: Boolean = false

or using special syntax from cats.syntax.option:

import cats.syntax.option._ // for some and none

1.some === none[Int]
// res10: Boolean = false
1.some =!= none[Int]
// res11: 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
// res12: Boolean = true
x === y
// res13: 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
// res15: Boolean = false
cat1 =!= cat2
// res16: 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
// res17: Boolean = false
optionCat1 =!= optionCat2
// res18: Boolean = true

1.6 Controlling Instance Selection

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

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 with a collection of a subtype 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

Generally speaking, covariance is used for outputs: data that we can later get out of a container type such as List, or otherwise returned by some method.

Contravariance

What about contravariance? We write contravariant type constructors with a - symbol like this:

trait F[-A]

Perhaps 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 inputs, like our JsonWriter type class above:

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

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 combinations of value and writer can I pass to format?” We can write a Circle with either writer because all Circles are Shapes. Conversely, we can’t write a 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 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:

  1. 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?

  2. 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 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 saw the components that make up a type class:

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

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:

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
// res14: Int = -4

1 - (2 - 3)
// res15: 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, without the empty part. 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))
// res18: Set[Int] = Set(1, 2, 3)
strSetMonoid.combine(Set("A", "B"), Set("B", "C"))
// res19: 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.

As always, unless we have a good reason to import individual instances, we can just import everything.

import cats._
import cats.implicits._

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 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.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 shorter 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))
// res10: Int = 6

import cats.instances.option._ // for Monoid

add(List(Some(1), None, Some(2), None, Some(3)))
// res11: 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)))
// 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:

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
// res2: 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
// res3: (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))
// res4: Int = 6
addAll(List(None, Some(1), Some(2)))
// res5: 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 (the number of elements and their order) 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.

Figure 1: Type chart: mapping over List, Option, and Either

Figure 1: Type chart: mapping over List, Option, and Either

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 => s"${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:

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.

Figure 2: Type chart: mapping over a Future

Figure 2: Type chart: mapping over a Future

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 => s"${n}!")

Await.result(future, 1.second)
// res2: 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.

When we look at Cats Effect we’ll see that the IO type solves these problems.

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:

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:

Figure 3: Type chart: mapping over a Function1

Figure 3: Type chart: mapping over a Function1

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
// res3: Double = 2.0     // composition using map
(func1 andThen func2)(1) // composition using andThen
// res4: Double = 2.0 // composition using andThen
func2(func1(1))          // composition written out by hand
// res5: 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 => s"${x}!")

func(123)
// res6: String = "248.0!"

Partial Unification

For the above examples to work, in versions of Scala before 2.13, 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 3.7.

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.

Figure 4: Type chart: generalised functor map

Figure 4: Type chart: generalised functor map

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


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.

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 by applying 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 by applying a value parameter

In Scala we declare type constructors using underscores. This specifies how many “holes” the type constructor has. However, to use them we refer to just the name.

// Declare F using underscores:
def myMethod[F[_]] = {

  // Reference F without underscores:
  val functor = Functor.apply[F]

  // ...
}

This is analogous to specifying function parameter types. When we declare a parameter we also give its type. However, to use them we refer to just the name.

// Declare f specifying parameter types
def f(x: Int): Int = 
  // Reference x without type
  x * 2

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

In versions of Scala before 2.13 we need to “enable” the higher kinded type language feature, to suppress warnings from the compiler, whenever we declare a type constructor with A[_] syntax. 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"

In practice we find the scalacOptions flag to be the simpler of the two options.

3.5 Functors in Cats

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

3.5.1 The Functor Type Class and Instances

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 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 provides a method called lift, 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$11546/665425203@13439aca

liftedFunc(Option(1))
// res1: Option[Int] = Some(2)

The as method is the other method you are likely to use. It replaces with value inside the Functor with the given value.

Functor[List].as(list1, "As")
// res2: List[String] = List("As", "As", "As")

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) => s"${a}!"
val func4 = func1.map(func2).map(func3)

func4(123)
// res3: 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))
// res4: Option[Int] = Some(22)
doMath(List(1, 2, 3))
// res5: 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 F 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)
// error: value map is not a member of repl.Session.App0.Box[Int]
// box.map(value => value + 1)
// ^^^^^^^

The as method is also available as syntax.

List(1, 2, 3).as("As")
// res7: List[String] = List("As", "As", "As")

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)
// error: value map is not a member of repl.Session.App0.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)
// res9: Tree[Int] = Leaf(200)

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

## Contravariant and Invariant Functors {#contravariant-invariant}

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.

3.5.5 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.

Figure 5: Type chart: the contramap method

Figure 5: Type chart: the contramap method

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)

3.5.5.1 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 =
      s"'${value}'"
  }

implicit val booleanPrintable: Printable[Boolean] =
  new Printable[Boolean] {
    def format(value: Boolean): String =
      if(value) "yes" else "no"
  }

format("hello")
// res2: String = "'hello'"
format(true)
// res3: 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.

Your instance should work as follows:

format(Box("hello world"))
// res4: String = "'hello world'"
format(Box(true))
// res5: 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))
// error: could not find implicit value for parameter p: repl.Session.App1.Printable[repl.Session.App1.Box[Int]]
//       def encode(value: B): String =
//                 ^

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]
): Printable[Box[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]): Printable[Box[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.

3.5.6 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]:

Figure 6: Type chart: the imap method

Figure 6: Type chart: the imap method

As an example use case, imagine we have a basic Codec[String], whose encode and decode methods both simply return the value they are passed:

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.

3.5.6.1 Transformative Thinking with imap

Implement the imap method for Codec above.

Here’s a working implementation:

trait Codec[A] { self =>
  def encode(value: A): String
  def decode(value: String): A

  def imap[B](dec: A => B, enc: B => A): Codec[B] = {
    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:

final 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)
// res11: String = "123.4"
decode[Double]("123.4")
// res12: Double = 123.4

encode(Box(123.4))
// res13: String = "123.4"
decode[Box[Double]]("123.4")
// res14: 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 B => A. A standard covariant functor captures exactly this. If F is a covariant functor, wherever we have an F[B] and a conversion B => A we can always convert to an F[A].

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.

3.6 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]
}

3.6.1 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(Symbol("dave"))
// res1: 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](sym => s"'${sym.name}")
  .show(Symbol("dave"))
// res2: String = "'dave"

3.6.2 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 3.5.6. 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:

  1. accept two Symbols as parameters;
  2. convert the Symbols to Strings;
  3. combine the Strings using Monoid[String];
  4. 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
// res3: Symbol = '

Symbol("a") |+| Symbol("few") |+| Symbol("words")
// res4: Symbol = 'afewwords

3.7 Aside: Partial Unification

In Section 3.2 we saw a functor instance for Function1.

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.Function1$$Lambda$6493/63932183@157213ca

Function1 has two type parameters (the function argument and the result type):

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

However, Functor accepts a type constructor with one parameter:

trait Functor[F[_]] {
  def map[A, B](fa: F[A])(func: A => B): F[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 the compiler doesn’t understand what the code means. Instead it relies on a simple rule, implementing what is called “partial unification”.

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:

val either: Either[String, Int] = Right(123)
// either: Either[String, Int] = Right(123)

either.map(_ + 1)
// res0: Either[String, Int] = Right(124)

Partial unification is the default behaviour in Scala 2.13. In earlier versions of Scala we need to add the -Ypartial-unification compiler flag. In sbt we would add the compiler flag in build.sbt:

scalacOptions += "-Ypartial-unification"

The rationale behind this change is discussed in SI-2712.

3.7.1 Limitations of Partial Unification

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)
// 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
Figure 7: Type chart: contramapping over a Function1

Figure 7: Type chart: contramapping over a Function1

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: Int => Double = scala.Function1$$Lambda$6493/63932183@6d3804ec

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

3.8 Summary

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

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 for 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)
    }
  }

The semantics are:

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.

Figure 8: Type chart: flatMap for Option

Figure 8: Type chart: flatMap for Option

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")
// res0: Option[Int] = Some(3)
stringDivideBy("6", "0")
// res1: Option[Int] = None
stringDivideBy("6", "foo")
// res2: Option[Int] = None
stringDivideBy("bar", "2")
// res3: 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:

Futures

Future is a monad that sequences computations without worrying that they may be asynchronous:

import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global

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].

Figure 9: Type chart: flatMap for Future

Figure 9: Type chart: flatMap for Future

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 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:


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:


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]
// error: Could not find an instance of Monad for 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: Monad[Future] = cats.instances.FutureInstances$$anon$1@5493a1be

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)
// res4: 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:

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]
// res5: Option[Int] = Some(1)
1.pure[List]
// res6: 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

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))
// res7: Option[Int] = Some(25)
sumSquare(List(1, 2, 3), List(4, 5))
// res8: 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 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)
// 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   : 3
//  required: ?F[Int]
// error: type mismatch;
//  found   : Int(3)
//  required: F[Int]
// error: type mismatch;
//  found   : Int(4)
//  required: F[Int]

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])
// res1: 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]
// res2: Id[String] = "Dave"
123 : Id[Int]
// res3: Id[Int] = 123
List(1, 2, 3) : Id[List[Int]]
// res4: 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: Id[Int] = 3
val b = Monad[Id].flatMap(a)(_ + 1)
// b: Id[Int] = 4

import cats.syntax.functor._ // for map
import cats.syntax.flatMap._ // for flatMap

for {
  x <- a
  y <- b
} yield x + y
// res5: 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)
// res7: 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 boxing or unboxing required:

def map[A, B](initial: Id[A])(func: A => B): Id[B] =
  func(initial)

map(123)(_ * 2)
// res8: 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(123)(_ * 2)
// res9: 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

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: 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
// res3: 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 problems caused by over-narrowing, like the issue 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!")
    }
  }
// error: type mismatch;
//  found   : scala.util.Either[Nothing,Int]
//  required: scala.util.Right[Nothing,Int]
//       accumulator.map(_ + 1)
//       ^^^^^^^^^^^^^^^^^^^^^^
// 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:

  1. the compiler infers the type of the accumulator as Right instead of Either;
  2. 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.

Users of Scala 2.11 or 2.12 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 _: String => -1
}
// res14: Either[String, Int] = Right(-1)

"error".asLeft[Int].recoverWith {
  case _: 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: 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: 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 handleErrorWith[A](fa: F[A])(f: E => F[A]): F[A]
  
  // Handle all errors, recovering from them:
  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:

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 handleErrorWith. 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")

handleErrorWith 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.handleErrorWith(failure) {
  case "Badness" =>
    monadError.pure("It's ok")

  case _ =>
    monadError.raiseError("It's not ok")
}
// res0: ErrorOr[String] = Right("It's ok")

If we know we can handle all possible errors we can use handleWith.

monadError.handleError(failure) {
  case "Badness" => 42

  case _ => -1
}
// res1: ErrorOr[Int] = Right(42)

There is another 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:

monadError.ensure(success)("Number too low!")(_ > 1000)
// res2: ErrorOr[Int] = Left("Number too low!")

Cats provides syntax for raiseError and handleErrorWith 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")
failure.handleErrorWith{
  case "Badness" =>
    256.pure

  case _ =>
    ("It's not ok").raiseError
}
// res4: ErrorOr[Int] = Right(256)
success.ensure("Number to low!")(_ > 1000)
// res5: ErrorOr[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: Try[Int] = Failure(java.lang.RuntimeException: It's all gone wrong)

4.5.4 Exercise: Abstracting

Implement a method validateAdult with the following signature

def validateAdult[F[_]](age: Int)(implicit me: MonadError[F, Throwable]): F[Int] =
  ???

When passed an age greater than or equal to 18 it should return that value as a success. Otherwise it should return a error represented as an IllegalArgumentException.

Here are some examples of use.

validateAdult[Try](18)
// res7: Try[Int] = Success(18)
validateAdult[Try](8)
// res8: Try[Int] = Failure(
//   java.lang.IllegalArgumentException: Age must be greater than or equal to 18
// )
type ExceptionOr[A] = Either[Throwable, A]
validateAdult[ExceptionOr](-1)
// res9: ExceptionOr[Int] = Left(
//   java.lang.IllegalArgumentException: Age must be greater than or equal to 18
// )

We can solve this using pure and raiseError. Note the use of type parameters to these methods, to aid type inference.

def validateAdult[F[_]](age: Int)(implicit me: MonadError[F, Throwable]): F[Int] =
  if(age >= 18) age.pure[F]
  else new IllegalArgumentException("Age must be greater than or equal to 18").raiseError[F, Int]

4.6 The Eval Monad

cats.Eval is a monad that allows us to abstract over different models of evaluation. We typically talk of two such models: eager and lazy, also called call-by-value and call-by-name respectively. Eval also allows for a result to be memoized, which gives us call-by-need evaluation.

Eval is also stack-safe, which means we can use it in very deep recursions without blowing up the stack.

4.6.1 Eager, Lazy, Memoized, Oh My!

What do these terms for models of evaluation mean? Let’s see some examples.

Let’s first look at Scala vals. We can see the evaluation model using a computation with a visible side-effect. In the following example, the code to compute the value of x happens at place where it is defined rather than on access. Accessing x recalls the stored value without re-running the code.

val x = {
  println("Computing X")
  math.random
}
// Computing X
// x: Double = 0.15241729989551633

x // first access
// res0: Double = 0.15241729989551633 // first access
x // second access
// res1: Double = 0.15241729989551633

This is an example of call-by-value evaluation:

Let’s look at an example using a def. The code to compute y below is not run until we use it, and is re-run on every access:

def y = {
  println("Computing Y")
  math.random
}

y // first access
// Computing Y
// res2: Double = 0.6963618800921411 // first access
y // second access
// Computing Y
// res3: Double = 0.7321640587866993

These are the properties of call-by-name evaluation:

Last but not least, lazy vals are an example of call-by-need evaluation. The code to compute z below is not run until we use 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 // first access
// Computing Z
// res4: Double = 0.18457255119783122 // first access
z // second access
// res5: Double = 0.18457255119783122

Let’s summarize. There are two properties of interest:

There are three possible combinations of these properties:

The final combination, eager and not memoized, is not possible.

4.6.2 Eval’s Models of Evaluation

Eval has three subtypes: Now, Always, and Later. They correspond to call-by-value, call-by-name, and call-by-need respectively. 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: Eval[Double] = Now(1000.020590704322)
val always = Eval.always(math.random + 3000)
// always: Eval[Double] = cats.Always@4d8ca6eb
val later = Eval.later(math.random + 2000)
// later: Eval[Double] = cats.Later@601dc0b2

We can extract the result of an Eval using its value method:

now.value
// res6: Double = 1000.020590704322
always.value
// res7: Double = 3000.97102818157
later.value
// res8: Double = 2000.0126977436273

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: Eval[Double] = Now(0.681816469770503)

x.value // first access
// res10: Double = 0.681816469770503 // first access
x.value // second access
// res11: Double = 0.681816469770503

Eval.always captures a lazy computation, similar to a def:

val y = Eval.always{
  println("Computing Y")
  math.random
}
// y: Eval[Double] = cats.Always@414a351

y.value // first access
// Computing Y
// res12: Double = 0.09982997820703643 // first access
y.value // second access
// Computing Y
// res13: Double = 0.34240334819463436

Finally, Eval.later captures a lazy, memoized computation, similar to a lazy val:

val z = Eval.later{
  println("Computing Z")
  math.random
}
// z: Eval[Double] = cats.Later@b0a344a

z.value // first access
// Computing Z
// res14: Double = 0.3604236919233441 // first access
z.value // second access
// res15: Double = 0.3604236919233441

The three behaviours are summarized below:

Scala Cats Properties
val Now eager, memoized
def Always lazy, not memoized
lazy val Later lazy, 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: Eval[String] = cats.Eval$$anon$4@2319703e

greeting.value
// Step 1
// Step 2
// res16: 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: Eval[Int] = cats.Eval$$anon$4@2d0f2cbf

ans.value // first access
// Calculating B
// Adding A and B
// res17: Int = 42 // first access
ans.value // second access
// Calculating B
// Adding A and B
// res18: 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: Eval[String] = cats.Eval$$anon$4@ca01c64

saying.value // first access
// Step 1
// Step 2
// Step 3
// res19: String = "The cat sat on the mat" // first access
saying.value // second access
// Step 3
// res20: 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
// res: A very big value

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)(_ + _)
// res24: Long = 5000050000L

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.package.Id, 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]
// res1: 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
// res2: Writer[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.package.Id, Vector[String], Int] = WriterT(
//   (Vector("msg1", "msg2", "msg3"), 123)
// )
val b = 123.writer(Vector("msg1", "msg2", "msg3"))
// b: Writer[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: 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.package.Id, Vector[String], Int] = WriterT(
//   (Vector("a", "b", "c", "x", "y", "z"), 42)
// )

writer1.run
// res3: (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.package.Id, Vector[String], Int] = WriterT(
//   (Vector("A", "B", "C", "X", "Y", "Z"), 42)
// )

writer2.run
// res4: (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.package.Id, Vector[String], Int] = WriterT(
//   (Vector("A", "B", "C", "X", "Y", "Z"), 4200)
// )

writer3.run
// res5: (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.package.Id, Vector[String], Int] = WriterT(
//   (Vector("a!", "b!", "c!", "x!", "y!", "z!"), 42000)
// )

writer4.run
// res6: (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.package.Id, Vector[String], Int] = WriterT(
//   (Vector(), 42)
// )

writer5.run
// res7: (Vector[String], Int) = (Vector(), 42)

val writer6 = writer1.swap
// writer6: cats.data.WriterT[cats.package.Id, Int, Vector[String]] = WriterT(
//   (42, Vector("a", "b", "c", "x", "y", "z"))
// )

writer6.run
// res8: (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
// res9: 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._
import scala.concurrent.duration._

Await.result(Future.sequence(Vector(
  Future(factorial(5)),
  Future(factorial(5))
)), 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
// fact 4 24
// fact 4 24
// fact 5 120
// fact 5 120
// res: 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.instances.vector._
import cats.syntax.applicative._ // for pure

type Logged[A] = Writer[Vector[String], A]

42.pure[Logged]
// res11: Logged[Int] = WriterT((Vector(), 42))

We’ll import the tell syntax as well:

import cats.syntax.writer._ // for tell

Vector("Message").tell
// res12: Writer[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)
// res13: cats.data.WriterT[cats.package.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:

Await.result(Future.sequence(Vector(
  Future(factorial(5)),
  Future(factorial(5))
)).map(_.map(_.written)), 5.seconds)
// res: scala.collection.immutable.Vector[cats.Id[Vector[String]]] = 
//   Vector(
//     Vector(fact 0 1, fact 1 1, fact 2 2, fact 3 6, fact 4 24, fact 5 120), 
//     Vector(fact 0 1, fact 1 1, fact 2 2, fact 3 6, fact 4 24, fact 5 120)
//   )

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

final case class Cat(name: String, favoriteFood: String)

val catName: Reader[Cat, String] =
  Reader(cat => cat.name)
// catName: 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"))
// res1: cats.package.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"))
// res2: cats.package.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.package.Id[String] = "Hello Garfield. Have a nice bowl of lasagne."
greetAndFeed(Cat("Heathcliff", "junk food"))
// res4: cats.package.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:

final 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)
// res7: cats.package.Id[Boolean] = true
checkLogin(4, "davinci").run(db)
// res8: cats.package.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:

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 complicated 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 actual 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")
}

In other words, an instance of State is a function that does two things:

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 justTheState = a.runS(10).value
// justTheState: Int = 10

// Get the result, ignore the state:
val justTheResult = a.runA(10).value
// justTheResult: 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")
}

val step2 = State[Int, String]{ num =>
  val ans = num * 2
  (ans, s"Result of step2: $ans")
}

val both = for {
  a <- step1
  b <- step2
} yield (a, b)

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:

val getDemo = State.get[Int]
// getDemo: State[Int, Int] = cats.data.IndexedStateT@741518c8
getDemo.run(10).value
// res1: (Int, Int) = (10, 10)

val setDemo = State.set[Int](30)
// setDemo: State[Int, Unit] = cats.data.IndexedStateT@509fb0a
setDemo.run(10).value
// res2: (Int, Unit) = (30, ())

val pureDemo = State.pure[Int, String]("Result")
// pureDemo: State[Int, String] = cats.data.IndexedStateT@562ae0a8
pureDemo.run(10).value
// res3: (Int, String) = (10, "Result")

val inspectDemo = State.inspect[Int, String](x => s"${x}!")
// inspectDemo: State[Int, String] = cats.data.IndexedStateT@2dc6b50f
inspectDemo.run(10).value
// res4: (Int, String) = (10, "10!")

val modifyDemo = State.modify[Int](_ + 1)
// modifyDemo: State[Int, Unit] = cats.data.IndexedStateT@71c93b27
modifyDemo.run(10).value
// res5: (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 cats.data.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: State[Int, (Int, Int, Int)] = cats.data.IndexedStateT@3b525fbf

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:

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)i 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
// res10: 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@3afcc7dd

program.runA(Nil).value
// res11: 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 multistageProgram = evalAll(List("1", "2", "+", "3", "*"))
// multistageProgram: CalcState[Int] = cats.data.IndexedStateT@228a6340

multistageProgram.runA(Nil).value
// res13: 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 biggerProgram = for {
  _   <- evalAll(List("1", "2", "+"))
  _   <- evalAll(List("3", "4", "+"))
  ans <- evalOne("*")
} yield ans
// biggerProgram: cats.data.IndexedStateT[cats.Eval, List[Int], List[Int], Int] = cats.data.IndexedStateT@1a227435

biggerProgram.runA(Nil).value
// res14: 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 + *")
// res15: 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.

To motivate its use let’s use the following example: Suppose we want to write a method that calls a function until the function indicates it should stop. The function will return a monad instance because, as we know, monads represent sequencing and many monads have some notion of stopping.

We can write this method in terms of flatMap.

import cats.syntax.flatMap._ // For flatMap

def retry[F[_]: Monad, A](start: A)(f: A => F[A]): F[A] =
  f(start).flatMap{ a =>
    retry(a)(f)
  }

Unfortunately it is not stack-safe. It works for small input.

import cats.instances.option._

retry(100)(a => if(a == 0) None else Some(a - 1))
// res1: Option[Int] = None

but if we try large input we get a StackOverflowError.

retry(100000)(a => if(a == 0) None else Some(a - 1))
// KABLOOIE!!!!

We can instead rewrite this method using tailRecM.

import cats.syntax.functor._ // for map

def retryTailRecM[F[_]: Monad, A](start: A)(f: A => F[A]): F[A] =
  Monad[F].tailRecM(start){ a =>
    f(a).map(a2 => Left(a2))
  }

Now it runs successfully no matter how many time we recurse.

retryTailRecM(100000)(a => if(a == 0) None else Some(a - 1))
// res2: Option[Int] = None

It’s important to note that we have to explicitly call tailRecM. There isn’t a code transformation that will convert non-tail recursive code into tail recursive code that uses tailRecM. However there are several utilities provided by the Monad type class that makes these kinds of methods easier to write. For example, we can rewrite retry in terms of iterateWhileM and we don’t have to explicitly call tailRecM.

import cats.syntax.monad._ // for iterateWhileM

def retryM[F[_]: Monad, A](start: A)(f: A => F[A]): F[A] =
  start.iterateWhileM(f)(a => true)

retryM(100000)(a => if(a == 0) None else Some(a - 1))
// res3: Option[Int] = None

We’ll see more methods that use tailRecM in Section 7.1.

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
import scala.annotation.tailrec

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)))
// res5: 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
// res6: 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.syntax.applicative._ // for pure

// 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[Composed[B]](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.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: 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:

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:

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: 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:

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 // for Monad

123.pure[EitherT[Option, String, *]]
// res3: 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 syntax7:

// Create using apply:
val errorStack1 = OptionT[ErrorOr, Int](Right(Some(10)))
// errorStack1: 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
// res4: ErrorOr[Option[Int]] = Right(Some(10))

// Mapping over the Either in the stack:
errorStack2.value.map(_.getOrElse(-1))
// res5: 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
// res6: 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: Future[Either[String, Option[Int]]] = Future(Success(Right(Some(42))))

Await.result(stack, 1.second)
// res7: 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 Decepticons8. Responses are therefore represented as a stack of monads:

type Response[A] = Future[Either[String, A]]

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.data.EitherT
import scala.concurrent.Future
val powerLevels = Map(
  "Jazz"      -> 6,
  "Bumblebee" -> 8,
  "Hot Rod"   -> 10
)

import cats.instances.future._ // for Monad
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 canSpecialMove(ally1: String, ally2: String): Response[Boolean] =
  for {
    power1 <- getPowerLevel(ally1)
    power2 <- getPowerLevel(ally2)
  } yield (power1 + power2) > 15

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")
// res13: String = "Jazz and Bumblebee need a recharge."
tacticalReport("Bumblebee", "Hot Rod")
// res14: String = "Bumblebee and Hot Rod are ready to roll out!"
tacticalReport("Jazz", "Ironhide")
// res15: 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)
// res0: 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 three type classes that support this pattern:

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 contexts9. 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"))
// res1: 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"))
// res2: Option[Tuple2[Nothing, String]] = None
Semigroupal[Option].product(Some(123), None)
// res3: Option[Tuple2[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))
// res4: Option[(Int, Int, Int)] = Some((1, 2, 3))
Semigroupal.tuple3(Option(1), Option(2), Option.empty[Int])
// res5: 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))(_ + _ + _)
// res6: Option[Int] = Some(6)

Semigroupal.map2(Option(1), Option.empty[Int])(_ + _)
// res7: Option[Int] = None

There are also methods contramap2 through contramap22 and imap2 through imap22, that require instances of Contravariant and Invariant respectively.

6.1.3 Semigroupal Laws

There is only one law for Semigroupal: the product method must be associative.

product(a, product(b, c)) == product(product(a, b), c)

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
// res8: 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
// res9: 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.

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

(
  Option("Garfield"),
  Option(1978),
  Option("Orange & black")
).mapN(Cat.apply)
// res10: Option[Cat] = Some(Cat("Garfield", 1978, "Orange & black"))

Of all the methods mentioned here, it is most common to use mapN.

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)
// error: ':' expected but '(' found.
//   Option("Garfield"),
//         ^
// error: identifier expected but '}' found.

(Option("cats"), Option(true)).mapN(add)
// error: ':' expected but '(' found.
//   Option("Garfield"),
//         ^
// error: identifier expected but '}' found.

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

final 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
// res14: 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

val futurePair = Semigroupal[Future].
  product(Future("Hello"), Future(123))

Await.result(futurePair, 1.second)
// res0: (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)
// res1: 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))
// res2: 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"))
)
// res3: ErrorOr[Tuple2[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. If we have a monad we can implement product as follows.

import cats.Monad
import cats.syntax.functor._ // for map
import cats.syntax.flatMap._ // for flatmap

def product[F[_]: Monad, A, B](fa: F[A], fb: F[B]): F[(A,B)] =
  fa.flatMap(a => 
    fb.map(b =>
      (a, b)
    )
  )

It would be very strange if we had different semantics for product depending on how we implemented it. To ensure consistent semantics, Cats’ Monad (which extends Semigroupal) provides a standard definition of product in terms of map and flatMap as we showed above.

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 Lists

Why does product for List produce the Cartesian product? We saw an example above. Here it is again.

Semigroupal[List].product(List(1, 2), List(3, 4))
// res5: List[(Int, Int)] = List((1, 3), (1, 4), (2, 3), (2, 4))

We can also write this in terms of tupled.

(List(1, 2), List(3, 4)).tupled
// res6: List[(Int, Int)] = List((1, 3), (1, 4), (2, 3), (2, 4))

This exercise is checking that you understood the definition of product in terms of flatMap and map.

import cats.syntax.functor._ // for map
import cats.syntax.flatMap._ // for flatMap

def product[F[_]: Monad, A, B](x: F[A], y: F[B]): F[(A, B)] =
  x.flatMap(a => y.map(b => (a, b)))

This code is equivalent to a for comprehension:

def product[F[_]: Monad, A, B](x: F[A], y: F[B]): F[(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))
// res9: List[(Int, Int)] = List((1, 3), (1, 4), (2, 3), (2, 4))

6.4 Parallel

In the previous section we saw that when call product on a type that has a Monad instance we get sequential semantics. This makes sense from the point-of-view of keeping consistency with implementations of product in terms of flatMap and map. However it’s not always what we want. The Parallel type class, and its associated syntax, allows us to access alternate semantics for certain monads.

We’ve seen how the product method on Either stops at the first error.

import cats.Semigroupal
import cats.instances.either._ // for Semigroupal

type ErrorOr[A] = Either[Vector[String], A]
val error1: ErrorOr[Int] = Left(Vector("Error 1"))
val error2: ErrorOr[Int] = Left(Vector("Error 2"))

Semigroupal[ErrorOr].product(error1, error2)
// res0: ErrorOr[(Int, Int)] = Left(Vector("Error 1"))

We can also write this using tupled as a short-cut.

import cats.syntax.apply._ // for tupled
import cats.instances.vector._ // for Semigroup on Vector

(error1, error2).tupled
// res1: ErrorOr[(Int, Int)] = Left(Vector("Error 1"))

To collect all the errors we simply replace tupled with its “parallel” version called parTupled.

import cats.syntax.parallel._ // for parTupled

(error1, error2).parTupled
// res2: ErrorOr[(Int, Int)] = Left(Vector("Error 1", "Error 2"))

Notice that both errors are returned! This behaviour is not special to using Vector as the error type. Any type that has a Semigroup instance will work. For example, here we use List instead.

import cats.instances.list._ // for Semigroup on List

type ErrorOrList[A] = Either[List[String], A]
val errStr1: ErrorOrList[Int] = Left(List("error 1"))
val errStr2: ErrorOrList[Int] = Left(List("error 2"))

(errStr1, errStr2).parTupled
// res3: ErrorOrList[(Int, Int)] = Left(List("error 1", "error 2"))

There are many syntax methods provided by Parallel for methods on Semigroupal and related types, but the most commonly used is parMapN. Here’s an example of parMapN in an error handling situation.

val success1: ErrorOr[Int] = Right(1)
val success2: ErrorOr[Int] = Right(2)
val addTwo = (x: Int, y: Int) => x + y

(error1, error2).parMapN(addTwo)
// res4: ErrorOr[Int] = Left(Vector("Error 1", "Error 2"))
(success1, success2).parMapN(addTwo)
// res5: ErrorOr[Int] = Right(3)

Let’s dig into how Parallel works. The definition below is the core of Parallel.

trait Parallel[M[_]] {
  type F[_]
  
  def applicative: Applicative[F]
  def monad: Monad[M]
  def parallel: ~>[M, F]
}

This tells us if there is a Parallel instance for some type constructor M then:

We haven’t seen ~> before. It’s a type alias for FunctionK and is what performs the conversion from M to F. A normal function A => B converts values of type A to values of type B. Remember that M and F are not types; they are type constructors. A FunctionK M ~> F is a function from a value with type M[A] to a value with type F[A]. Let’s see a quick example by defining a FunctionK that converts an Option to a List.

import cats.arrow.FunctionK

object optionToList extends FunctionK[Option, List] {
  def apply[A](fa: Option[A]): List[A] =
    fa match {
      case None    => List.empty[A]
      case Some(a) => List(a)
    }
}

optionToList(Some(1))
// res6: List[Int] = List(1)
optionToList(None)
// res7: List[Nothing] = List()

As the type parameter A is generic a FunctionK cannot inspect any values contained with the type constructor M. The conversion must be performed purely in terms of the structure of the type constructors M and F. We can in optionToList above this is indeed the case.

So in summary, Parallel allows us to take a type that has a monad instance and convert it to some related type that instead has an applicative (or semigroupal) instance. This related type will have some useful alternate semantics. We’ve seen the case above where the related applicative for Either allows for accumulation of errors instead of fail-fast semantics.

Now we’ve seen Parallel it’s time to finally learn about Applicative.

6.4.0.1 Exercise: Parallel List

Does List have a Parallel instance? If so, what does the Parallel instance do?

List does have a Parallel instance, and it zips the List insted of creating the cartesian product.

We can see by writing a little bit of code.

import cats.instances.list._

(List(1, 2), List(3, 4)).tupled
// res8: List[(Int, Int)] = List((1, 3), (1, 4), (2, 3), (2, 4))
(List(1, 2), List(3, 4)).parTupled
// res9: List[(Int, Int)] = List((1, 3), (2, 4))

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 Paterson10.

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 and adds the pure method introduced in Chapter 4. Here’s a simplified definition in code:

trait Apply[F[_]] extends Semigroupal[F] with Functor[F] {
  def ap[A, B](ff: F[A => B])(fa: F[A]): F[B]

  def product[A, B](fa: F[A], fb: F[B]): F[(A, B)] =
    ap(map(fa)(a => (b: B) => (a, b)))(fb)
}

trait Applicative[F[_]] extends Apply[F] {
  def pure[A](a: A): F[A]
}

Breaking this down, the ap method applies a parameter fa to a function ff within a context F[_]. The product method from Semigroupal is defined in terms of ap and map.

Don’t worry too much about the implementation of product—it’s difficult to read and the details aren’t particuarly important. The main point is that there is a tight relationship between product, ap, and map that allows any one of them to be defined in terms of the other two.

Applicative also introduces the pure method. This is the same pure we saw in Monad. It constructs a new applicative instance from an unwrapped value. In this sense, Applicative is related to Apply as Monoid is related to Semigroup.

6.5.1 The Hierarchy of Sequencing Type Classes

With the introduction of Apply and Applicative, we can zoom out and see a whole family of type classes that concern themselves with sequencing computations in different ways. Figure 10 shows the relationship between the type classes covered in this book11.

Figure 10: Monad type class hierarchy

Figure 10: Monad type class hierarchy

Each type class in the hierarchy represents a particular set of sequencing semantics, introduces a set of characteristic methods, and defines the functionality of its supertypes in terms of them:

Because of the lawful nature of the relationships between the type classes, the inheritance relationships are constant across all instances of a type class. Apply defines product in terms of ap and map; Monad defines product, ap, and map, in terms of pure and flatMap.

To illustrate this let’s consider two hypothetical data types:

What can we say about these two data types without knowing more about their implementation?

We know strictly more about Foo than Bar: Monad is a subtype of Applicative, so we can guarantee properties of Foo (namely flatMap) that we cannot guarantee with Bar. Conversely, we know that Bar may have a wider range of behaviours than Foo. It has fewer laws to obey (no flatMap), so it can implement behaviours that Foo cannot.

This demonstrates the classic trade-off of power (in the mathematical sense) versus constraint. The more constraints we place on a data type, the more guarantees we have about its behaviour, but the fewer behaviours we can model.

Monads happen to be a sweet spot in this trade-off. They are flexible enough to model a wide range of behaviours and restrictive enough to give strong guarantees about those behaviours. However, there are situations where monads aren’t the right tool for the job. Sometimes we want Thai food, and burritos just won’t satisfy.

Whereas monads impose a strict sequencing on the computations they model, applicatives and semigroupals impose no such restriction. This puts them in a different sweet spot in the hierarchy. We can use them to represent classes of parallel / independent computations that monads cannot.

We choose our semantics by choosing our data structures. If we choose a monad, we get strict sequencing. If we choose an applicative, we lose the ability to flatMap. This is the trade-off enforced by the consistency laws. So choose your types carefully!

6.6 Summary

While monads and functors are the most widely used sequencing data types we’ve covered in this book, semigroupals and applicatives are the most general. These type classes provide a generic mechanism to combine values and apply functions within a context, from which we can fashion monads and a variety of other combinators.

Semigroupal and Applicative are most commonly used as a means of combining independent values such as the results of validation rules. Cats provides the Validated type for this specific purpose, along with apply syntax as a convenient way to express the combination of rules.

We have almost covered all of the functional programming concepts on our agenda for this book. The next chapter covers Traverse and Foldable, two powerful type classes for converting between data types. After that we’ll look at several case studies that bring together all of the concepts from Part I.

7 Foldable and Traverse

In this chapter we’ll look at two type classes that capture iteration over collections:

We’ll start by looking at Foldable, and then examine cases where folding becomes complex and Traverse becomes convenient.

7.1 Foldable

The Foldable type class captures the foldLeft and foldRight methods we’re used to in sequences like Lists, Vectors, and Streams. Using Foldable, we can write generic folds that work with a variety of sequence types. We can also invent new sequences and plug them into our code. Foldable gives us great use cases for Monoids and the Eval monad.

7.1.1 Folds and Folding

Let’s start with a quick recap of the general concept of folding. We supply an accumulator value and a binary function to combine it with each item in the sequence:

def show[A](list: List[A]): String =
  list.foldLeft("nil")((accum, item) => s"$item then $accum")

show(Nil)
// res0: String = "nil"

show(List(1, 2, 3))
// res1: String = "3 then 2 then 1 then nil"

The foldLeft method works recursively down the sequence. Our binary function is called repeatedly for each item, the result of each call becoming the accumulator for the next. When we reach the end of the sequence, the final accumulator becomes our final result.

Depending on the operation we’re performing, the order in which we fold may be important. Because of this there are two standard variants of fold:

Figure 11 illustrates each direction.

Figure 11: Illustration of foldLeft and foldRight

Figure 11: Illustration of foldLeft and foldRight

foldLeft and foldRight are equivalent if our binary operation is associative. For example, we can sum a List[Int] by folding in either direction, using 0 as our accumulator and addition as our operation:

List(1, 2, 3).foldLeft(0)(_ + _)
// res2: Int = 6
List(1, 2, 3).foldRight(0)(_ + _)
// res3: Int = 6

If we provide a non-associative operator the order of evaluation makes a difference. For example, if we fold using subtraction, we get different results in each direction:

List(1, 2, 3).foldLeft(0)(_ - _)
// res4: Int = -6
List(1, 2, 3).foldRight(0)(_ - _)
// res5: Int = 2

7.1.2 Exercise: Reflecting on Folds

Try using foldLeft and foldRight with an empty list as the accumulator and :: as the binary operator. What results do you get in each case?

Folding from left to right reverses the list:

List(1, 2, 3).foldLeft(List.empty[Int])((a, i) => i :: a)
// res6: List[Int] = List(3, 2, 1)

Folding right to left copies the list, leaving the order intact:

List(1, 2, 3).foldRight(List.empty[Int])((i, a) => i :: a)
// res7: List[Int] = List(1, 2, 3)

Note that we have to carefully specify the type of the accumulator to avoid a type error. We use List.empty[Int] to avoid inferring the accumulator type as Nil.type or List[Nothing]:

List(1, 2, 3).foldRight(Nil)(_ :: _)
// error: type mismatch;
//  found   : List[Int]
//  required: scala.collection.immutable.Nil.type
// List(1, 2, 3).foldRight(Nil)(_ :: _)
//                                ^^^^

7.1.3 Exercise: Scaf-fold-ing Other Methods

foldLeft and foldRight are very general methods. We can use them to implement many of the other high-level sequence operations we know. Prove this to yourself by implementing substitutes for List's map, flatMap, filter, and sum methods in terms of foldRight.

Here are the solutions:

def map[A, B](list: List[A])(func: A => B): List[B] =
  list.foldRight(List.empty[B]) { (item, accum) =>
    func(item) :: accum
  }

map(List(1, 2, 3))(_ * 2)
// res9: List[Int] = List(2, 4, 6)

def flatMap[A, B](list: List[A])(func: A => List[B]): List[B] =
  list.foldRight(List.empty[B]) { (item, accum) =>
    func(item) ::: accum
  }

flatMap(List(1, 2, 3))(a => List(a, a * 10, a * 100))
// res10: List[Int] = List(1, 10, 100, 2, 20, 200, 3, 30, 300)

def filter[A](list: List[A])(func: A => Boolean): List[A] =
  list.foldRight(List.empty[A]) { (item, accum) =>
    if(func(item)) item :: accum else accum
  }

filter(List(1, 2, 3))(_ % 2 == 1)
// res11: List[Int] = List(1, 3)

We’ve provided two definitions of sum, one using scala.math.Numeric (which recreates the built-in functionality accurately)…

import scala.math.Numeric

def sumWithNumeric[A](list: List[A])
      (implicit numeric: Numeric[A]): A =
  list.foldRight(numeric.zero)(numeric.plus)

sumWithNumeric(List(1, 2, 3))
// res12: Int = 6

and one using cats.Monoid (which is more appropriate to the content of this book):

import cats.Monoid

def sumWithMonoid[A](list: List[A])
      (implicit monoid: Monoid[A]): A =
  list.foldRight(monoid.empty)(monoid.combine)

import cats.instances.int._ // for Monoid

sumWithMonoid(List(1, 2, 3))
// res13: Int = 6

7.1.4 Foldable in Cats

Cats’ Foldable abstracts foldLeft and foldRight into a type class. Instances of Foldable define these two methods and inherit a host of derived methods. Cats provides out-of-the-box instances of Foldable for a handful of Scala data types: List, Vector, LazyList, and Option.

We can summon instances as usual using Foldable.apply and call their implementations of foldLeft directly. Here is an example using List:

import cats.Foldable
import cats.instances.list._ // for Foldable

val ints = List(1, 2, 3)

Foldable[List].foldLeft(ints, 0)(_ + _)
// res0: Int = 6

Other sequences like Vector and LazyList work in the same way. Here is an example using Option, which is treated like a sequence of zero or one elements:

import cats.instances.option._ // for Foldable

val maybeInt = Option(123)

Foldable[Option].foldLeft(maybeInt, 10)(_ * _)
// res1: Int = 1230

7.1.4.1 Folding Right

Foldable defines foldRight differently to foldLeft, in terms of the Eval monad:

def foldRight[A, B](fa: F[A], lb: Eval[B])
                     (f: (A, Eval[B]) => Eval[B]): Eval[B]

Using Eval means folding is always stack safe, even when the collection’s default definition of foldRight is not. For example, the default implementation of foldRight for LazyList is not stack safe. The longer the lazy list, the larger the stack requirements for the fold. A sufficiently large lazy list will trigger a StackOverflowError:

import cats.Eval
import cats.Foldable

def bigData = (1 to 100000).to(LazyList)

bigData.foldRight(0L)(_ + _)
// java.lang.StackOverflowError ...

Using Foldable forces us to use stack safe operations, which fixes the overflow exception:

import cats.instances.lazyList._ // for Foldable

val eval: Eval[Long] =
  Foldable[LazyList].
    foldRight(bigData, Eval.now(0L)) { (num, eval) =>
      eval.map(_ + num)
    }

eval.value
// res3: Long = 5000050000L

Stack Safety in the Standard Library

Stack safety isn’t typically an issue when using the standard library. The most commonly used collection types, such as List and Vector, provide stack safe implementations of foldRight:

(1 to 100000).toList.foldRight(0L)(_ + _)
// res4: Long = 5000050000L
(1 to 100000).toVector.foldRight(0L)(_ + _)
// res5: Long = 5000050000L

We’ve called out Stream because it is an exception to this rule. Whatever data type we’re using, though, it’s useful to know that Eval has our back.

7.1.4.2 Folding with Monoids

Foldable provides us with a host of useful methods defined on top of foldLeft. Many of these are facsimiles of familiar methods from the standard library: find, exists, forall, toList, isEmpty, nonEmpty, and so on:

Foldable[Option].nonEmpty(Option(42))
// res6: Boolean = true

Foldable[List].find(List(1, 2, 3))(_ % 2 == 0)
// res7: Option[Int] = Some(2)

In addition to these familiar methods, Cats provides two methods that make use of Monoids:

For example, we can use combineAll to sum over a List[Int]:

import cats.instances.int._ // for Monoid

Foldable[List].combineAll(List(1, 2, 3))
// res8: Int = 6

Alternatively, we can use foldMap to convert each Int to a String and concatenate them:

import cats.instances.string._ // for Monoid

Foldable[List].foldMap(List(1, 2, 3))(_.toString)
// res9: String = "123"

Finally, we can compose Foldables to support deep traversal of nested sequences:

import cats.instances.vector._ // for Monoid

val ints = List(Vector(1, 2, 3), Vector(4, 5, 6))

(Foldable[List] compose Foldable[Vector]).combineAll(ints)
// res11: Int = 21

7.1.4.3 Syntax for Foldable

Every method in Foldable is available in syntax form via cats.syntax.foldable. In each case, the first argument to the method on Foldable becomes the receiver of the method call:

import cats.syntax.foldable._ // for combineAll and foldMap

List(1, 2, 3).combineAll
// res12: Int = 6

List(1, 2, 3).foldMap(_.toString)
// res13: String = "123"

Explicits over Implicits

Remember that Scala will only use an instance of Foldable if the method isn’t explicitly available on the receiver. For example, the following code will use the version of foldLeft defined on List:

List(1, 2, 3).foldLeft(0)(_ + _)
// res14: Int = 6

whereas the following generic code will use Foldable:



def sum[F[_]: Foldable](values: F[Int]): Int =
  values.foldLeft(0)(_ + _)

We typically don’t need to worry about this distinction. It’s a feature! We call the method we want and the compiler uses a Foldable when needed to ensure our code works as expected. If we need a stack-safe implementation of foldRight, using Eval as the accumulator is enough to force the compiler to select the method from Cats.

7.2 Traverse

foldLeft and foldRight are flexible iteration methods but they require us to do a lot of work to define accumulators and combinator functions. The Traverse type class is a higher level tool that leverages Applicatives to provide a more convenient, more lawful, pattern for iteration.

7.2.1 Traversing with Futures

We can demonstrate Traverse using the Future.traverse and Future.sequence methods in the Scala standard library. These methods provide Future-specific implementations of the traverse pattern. As an example, suppose we have a list of server hostnames and a method to poll a host for its uptime:

import scala.concurrent._
import scala.concurrent.duration._
import scala.concurrent.ExecutionContext.Implicits.global

val hostnames = List(
  "alpha.example.com",
  "beta.example.com",
  "gamma.demo.com"
)

def getUptime(hostname: String): Future[Int] =
  Future(hostname.length * 60) // just for demonstration

Now, suppose we want to poll all of the hosts and collect all of their uptimes. We can’t simply map over hostnames because the result—a List[Future[Int]]—would contain more than one Future. We need to reduce the results to a single Future to get something we can block on. Let’s start by doing this manually using a fold:

val allUptimes: Future[List[Int]] =
  hostnames.foldLeft(Future(List.empty[Int])) {
    (accum, host) =>
      val uptime = getUptime(host)
      for {
        accum  <- accum
        uptime <- uptime
      } yield accum :+ uptime
  }

Await.result(allUptimes, 1.second)
// res0: List[Int] = List(1020, 960, 840)

Intuitively, we iterate over hostnames, call func for each item, and combine the results into a list. This sounds simple, but the code is fairly unwieldy because of the need to create and combine Futures at every iteration. We can improve on things greatly using Future.traverse, which is tailor-made for this pattern:

val allUptimes: Future[List[Int]] =
  Future.traverse(hostnames)(getUptime)

Await.result(allUptimes, 1.second)
// res2: List[Int] = List(1020, 960, 840)

This is much clearer and more concise—let’s see how it works. If we ignore distractions like CanBuildFrom and ExecutionContext, the implementation of Future.traverse in the standard library looks like this:

def traverse[A, B](values: List[A])
    (func: A => Future[B]): Future[List[B]] =
  values.foldLeft(Future(List.empty[B])) { (accum, host) =>
    val item = func(host)
    for {
      accum <- accum
      item  <- item
    } yield accum :+ item
  }

This is essentially the same as our example code above. Future.traverse is abstracting away the pain of folding and defining accumulators and combination functions. It gives us a clean high-level interface to do what we want:

The standard library also provides another method, Future.sequence, that assumes we’re starting with a List[Future[B]] and don’t need to provide an identity function:

object Future {
  def sequence[B](futures: List[Future[B]]): Future[List[B]] =
    traverse(futures)(identity)

  // etc...
}

In this case the intuitive understanding is even simpler:

Future.traverse and Future.sequence solve a very specific problem: they allow us to iterate over a sequence of Futures and accumulate a result. The simplified examples above only work with Lists, but the real Future.traverse and Future.sequence work with any standard Scala collection.

Cats’ Traverse type class generalises these patterns to work with any type of Applicative: Future, Option, Validated, and so on. We’ll approach Traverse in the next sections in two steps: first we’ll generalise over the Applicative, then we’ll generalise over the sequence type. We’ll end up with an extremely valuable tool that trivialises many operations involving sequences and other data types.

7.2.2 Traversing with Applicatives

If we squint, we’ll see that we can rewrite traverse in terms of an Applicative. Our accumulator from the example above:

Future(List.empty[Int])

is equivalent to Applicative.pure:

import cats.Applicative
import cats.instances.future._   // for Applicative
import cats.syntax.applicative._ // for pure

List.empty[Int].pure[Future]

Our combinator, which used to be this:

def oldCombine(
  accum : Future[List[Int]],
  host  : String
): Future[List[Int]] = {
  val uptime = getUptime(host)
  for {
    accum  <- accum
    uptime <- uptime
  } yield accum :+ uptime
}

is now equivalent to Semigroupal.combine:

import cats.syntax.apply._ // for mapN

// Combining accumulator and hostname using an Applicative:
def newCombine(accum: Future[List[Int]],
      host: String): Future[List[Int]] =
  (accum, getUptime(host)).mapN(_ :+ _)

By substituting these snippets back into the definition of traverse we can generalise it to to work with any Applicative:


def listTraverse[F[_]: Applicative, A, B]
      (list: List[A])(func: A => F[B]): F[List[B]] =
  list.foldLeft(List.empty[B].pure[F]) { (accum, item) =>
    (accum, func(item)).mapN(_ :+ _)
  }

def listSequence[F[_]: Applicative, B]
      (list: List[F[B]]): F[List[B]] =
  listTraverse(list)(identity)

We can use listTraverse to re-implement our uptime example:

val totalUptime = listTraverse(hostnames)(getUptime)

Await.result(totalUptime, 1.second)
// res5: List[Int] = List(1020, 960, 840)

or we can use it with other Applicative data types as shown in the following exercises.

7.2.2.1 Exercise: Traversing with Vectors

What is the result of the following?

import cats.instances.vector._ // for Applicative

listSequence(List(Vector(1, 2), Vector(3, 4)))

The argument is of type List[Vector[Int]], so we’re using the Applicative for Vector and the return type is going to be Vector[List[Int]].

Vector is a monad, so its semigroupal combine function is based on flatMap. We’ll end up with a Vector of Lists of all the possible combinations of List(1, 2) and List(3, 4):

listSequence(List(Vector(1, 2), Vector(3, 4)))
// res7: Vector[List[Int]] = Vector(
//   List(1, 3),
//   List(1, 4),
//   List(2, 3),
//   List(2, 4)
// )

What about a list of three parameters?

listSequence(List(Vector(1, 2), Vector(3, 4), Vector(5, 6)))

With three items in the input list, we end up with combinations of three Ints: one from the first item, one from the second, and one from the third:

listSequence(List(Vector(1, 2), Vector(3, 4), Vector(5, 6)))
// res9: Vector[List[Int]] = Vector(
//   List(1, 3, 5),
//   List(1, 3, 6),
//   List(1, 4, 5),
//   List(1, 4, 6),
//   List(2, 3, 5),
//   List(2, 3, 6),
//   List(2, 4, 5),
//   List(2, 4, 6)
// )

7.2.2.2 Exercise: Traversing with Options

Here’s an example that uses Options:

import cats.instances.option._ // for Applicative

def process(inputs: List[Int]) =
  listTraverse(inputs)(n => if(n % 2 == 0) Some(n) else None)

What is the return type of this method? What does it produce for the following inputs?

process(List(2, 4, 6))
process(List(1, 2, 3))

The arguments to listTraverse are of types List[Int] and Int => Option[Int], so the return type is Option[List[Int]]. Again, Option is a monad, so the semigroupal combine function follows from flatMap. The semantics are therefore fail-fast error handling: if all inputs are even, we get a list of outputs. Otherwise we get None:

process(List(2, 4, 6))
// res12: Option[List[Int]] = Some(List(2, 4, 6))
process(List(1, 2, 3))
// res13: Option[List[Int]] = None

7.2.2.3 Exercise: Traversing with Validated

Finally, here is an example that uses Validated:

import cats.data.Validated
import cats.instances.list._ // for Monoid

type ErrorsOr[A] = Validated[List[String], A]

def process(inputs: List[Int]): ErrorsOr[List[Int]] =
  listTraverse(inputs) { n =>
    if(n % 2 == 0) {
      Validated.valid(n)
    } else {
      Validated.invalid(List(s"$n is not even"))
    }
  }

What does this method produce for the following inputs?

process(List(2, 4, 6))
process(List(1, 2, 3))

The return type here is ErrorsOr[List[Int]], which expands to Validated[List[String], List[Int]]. The semantics for semigroupal combine on validated are accumulating error handling, so the result is either a list of even Ints, or a list of errors detailing which Ints failed the test:

process(List(2, 4, 6))
// res17: ErrorsOr[List[Int]] = Valid(List(2, 4, 6))
process(List(1, 2, 3))
// res18: ErrorsOr[List[Int]] = Invalid(List("1 is not even", "3 is not even"))

7.2.3 Traverse in Cats

Our listTraverse and listSequence methods work with any type of Applicative, but they only work with one type of sequence: List. We can generalise over different sequence types using a type class, which brings us to Cats’ Traverse. Here’s the abbreviated definition:

package cats

trait Traverse[F[_]] {
  def traverse[G[_]: Applicative, A, B]
      (inputs: F[A])(func: A => G[B]): G[F[B]]

  def sequence[G[_]: Applicative, B]
      (inputs: F[G[B]]): G[F[B]] =
    traverse(inputs)(identity)
}

Cats provides instances of Traverse for List, Vector, Stream, Option, Either, and a variety of other types. We can summon instances as usual using Traverse.apply and use the traverse and sequence methods as described in the previous section:

import cats.Traverse
import cats.instances.future._ // for Applicative
import cats.instances.list._   // for Traverse

val totalUptime: Future[List[Int]] =
  Traverse[List].traverse(hostnames)(getUptime)

Await.result(totalUptime, 1.second)
// res0: List[Int] = List(1020, 960, 840)

val numbers = List(Future(1), Future(2), Future(3))

val numbers2: Future[List[Int]] =
  Traverse[List].sequence(numbers)

Await.result(numbers2, 1.second)
// res1: List[Int] = List(1, 2, 3)

There are also syntax versions of the methods, imported via cats.syntax.traverse:

import cats.syntax.traverse._ // for sequence and traverse

Await.result(hostnames.traverse(getUptime), 1.second)
// res2: List[Int] = List(1020, 960, 840)
Await.result(numbers.sequence, 1.second)
// res3: List[Int] = List(1, 2, 3)

As you can see, this is much more compact and readable than the foldLeft code we started with earlier this chapter!

7.3 Summary

In this chapter we were introduced to Foldable and Traverse, two type classes for iterating over sequences.

Foldable abstracts the foldLeft and foldRight methods we know from collections in the standard library. It adds stack-safe implementations of these methods to a handful of extra data types, and defines a host of situationally useful additions. That said, Foldable doesn’t introduce much that we didn’t already know.

The real power comes from Traverse, which abstracts and generalises the traverse and sequence methods we know from Future. Using these methods we can turn an F[G[A]] into a G[F[A]] for any F with an instance of Traverse and any G with an instance of Applicative. In terms of the reduction we get in lines of code, Traverse is one of the most powerful patterns in this book. We can reduce folds of many lines down to a single foo.traverse.


…and with that, we’ve finished all of the theory in this book. There’s plenty more to come, though, as we put everything we’ve learned into practice in a series of in-depth case studies in Part II!

8 Case Study: Testing Asynchronous Code

We’ll start with a straightforward case study: how to simplify unit tests for asynchronous code by making them synchronous.

Let’s return to the example from Chapter 7 where we’re measuring the uptime on a set of servers. We’ll flesh out the code into a more complete structure. There will be two components. The first is an UptimeClient that polls remote servers for their uptime:

import scala.concurrent.Future

trait UptimeClient {
  def getUptime(hostname: String): Future[Int]
}

We’ll also have an UptimeService that maintains a list of servers and allows the user to poll them for their total uptime:

import cats.instances.future._ // for Applicative
import cats.instances.list._   // for Traverse
import cats.syntax.traverse._  // for traverse
import scala.concurrent.ExecutionContext.Implicits.global

class UptimeService(client: UptimeClient) {
  def getTotalUptime(hostnames: List[String]): Future[Int] =
    hostnames.traverse(client.getUptime).map(_.sum)
}

We’ve modelled UptimeClient as a trait because we’re going to want to stub it out in unit tests. For example, we can write a test client that allows us to provide dummy data rather than calling out to actual servers:

class TestUptimeClient(hosts: Map[String, Int]) extends UptimeClient {
  def getUptime(hostname: String): Future[Int] =
    Future.successful(hosts.getOrElse(hostname, 0))
}

Now, suppose we’re writing unit tests for UptimeService. We want to test its ability to sum values, regardless of where it is getting them from. Here’s an example:

def testTotalUptime() = {
  val hosts    = Map("host1" -> 10, "host2" -> 6)
  val client   = new TestUptimeClient(hosts)
  val service  = new UptimeService(client)
  val actual   = service.getTotalUptime(hosts.keys.toList)
  val expected = hosts.values.sum
  assert(actual == expected)
}
//

The code doesn’t compile because we’ve made a classic error12. We forgot that our application code is asynchronous. Our actual result is of type Future[Int] and our expected result is of type Int. We can’t compare them directly!

There are a couple of ways to solve this problem. We could alter our test code to accommodate the asynchronousness. However, there is another alternative. Let’s make our service code synchronous so our test works without modification!

8.1 Abstracting over Type Constructors

We need to implement two versions of UptimeClient: an asynchronous one for use in production and a synchronous one for use in our unit tests:

trait RealUptimeClient extends UptimeClient {
  def getUptime(hostname: String): Future[Int]
}

trait TestUptimeClient extends UptimeClient {
  def getUptime(hostname: String): Int
}

The question is: what result type should we give to the abstract method in UptimeClient? We need to abstract over Future[Int] and Int:

trait UptimeClient {
  def getUptime(hostname: String): ???
}

At first this may seem difficult. We want to retain the Int part from each type but “throw away” the Future part in the test code. Fortunately, Cats provides a solution in terms of the identity type, Id, that we discussed way back in Section 4.3. Id allows us to “wrap” types in a type constructor without changing their meaning:

package cats

type Id[A] = A

Id allows us to abstract over the return types in UptimeClient. Implement this now:

Here’s the implementation:

import cats.Id

trait UptimeClient[F[_]] {
  def getUptime(hostname: String): F[Int]
}

trait RealUptimeClient extends UptimeClient[Future] {
  def getUptime(hostname: String): Future[Int]
}

trait TestUptimeClient extends UptimeClient[Id] {
  def getUptime(hostname: String): Id[Int]
}

Note that, because Id[A] is just a simple alias for A, we don’t need to refer to the type in TestUptimeClient as Id[Int]—we can simply write Int instead:

trait TestUptimeClient extends UptimeClient[Id] {
  def getUptime(hostname: String): Int
}

Of course, technically speaking we don’t need to redeclare getUptime in RealUptimeClient or TestUptimeClient. However, writing everything out helps illustrate the technique.

You should now be able to flesh your definition of TestUptimeClient out into a full class based on a Map[String, Int] as before.

The final code is similar to our original implementation of TestUptimeClient, except we no longer need the call to Future.successful:

class TestUptimeClient(hosts: Map[String, Int])
  extends UptimeClient[Id] {
  def getUptime(hostname: String): Int =
    hosts.getOrElse(hostname, 0)
}

8.2 Abstracting over Monads

Let’s turn our attention to UptimeService. We need to rewrite it to abstract over the two types of UptimeClient. We’ll do this in two stages: first we’ll rewrite the class and method signatures, then the method bodies. Starting with the method signatures:

The code should look like this:

class UptimeService[F[_]](client: UptimeClient[F]) {
  def getTotalUptime(hostnames: List[String]): F[Int] =
    ???
    // hostnames.traverse(client.getUptime).map(_.sum)
}

Now uncomment the body of getTotalUptime. You should get a compilation error similar to the following:

// <console>:28: error: could not find implicit value for
//               evidence parameter of type cats.Applicative[F]
//            hostnames.traverse(client.getUptime).map(_.sum)
//                              ^

The problem here is that traverse only works on sequences of values that have an Applicative. In our original code we were traversing a List[Future[Int]]. There is an applicative for Future so that was fine. In this version we are traversing a List[F[Int]]. We need to prove to the compiler that F has an Applicative. Do this by adding an implicit constructor parameter to UptimeService.

We can write this as an implicit parameter:

import cats.Applicative
import cats.syntax.functor._ // for map

class UptimeService[F[_]](client: UptimeClient[F])
    (implicit a: Applicative[F]) {

  def getTotalUptime(hostnames: List[String]): F[Int] =
    hostnames.traverse(client.getUptime).map(_.sum)
}

or more tersely as a context bound:

class UptimeService[F[_]: Applicative]
    (client: UptimeClient[F]) {

  def getTotalUptime(hostnames: List[String]): F[Int] =
    hostnames.traverse(client.getUptime).map(_.sum)
}

Note that we need to import cats.syntax.functor as well as cats.Applicative. This is because we’re switching from using future.map to the Cats’ generic extension method that requires an implicit Functor parameter.

Finally, let’s turn our attention to our unit tests. Our test code now works as intended without any modification. We create an instance of TestUptimeClient and wrap it in an UptimeService. This effectively binds F to Id, allowing the rest of the code to operate synchronously without worrying about monads or applicatives:

def testTotalUptime() = {
  val hosts    = Map("host1" -> 10, "host2" -> 6)
  val client   = new TestUptimeClient(hosts)
  val service  = new UptimeService(client)
  val actual   = service.getTotalUptime(hosts.keys.toList)
  val expected = hosts.values.sum
  assert(actual == expected)
}

testTotalUptime()

8.3 Summary

This case study provides an example of how Cats can help us abstract over different computational scenarios. We used the Applicative type class to abstract over asynchronous and synchronous code. Leaning on a functional abstraction allows us to specify the sequence of computations we want to perform without worrying about the details of the implementation.

Back in Figure 10, we showed a “stack” of computational type classes that are meant for exactly this kind of abstraction. Type classes like Functor, Applicative, Monad, and Traverse provide abstract implementations of patterns such as mapping, zipping, sequencing, and iteration. The mathematical laws on those types ensure that they work together with a consistent set of semantics.

We used Applicative in this case study because it was the least powerful type class that did what we needed. If we had required flatMap, we could have swapped out Applicative for Monad. If we had needed to abstract over different sequence types, we could have used Traverse. There are also type classes like ApplicativeError and MonadError that help model failures as well as successful computations.

Let’s move on now to a more complex case study where type classes will help us produce something more interesting: a map-reduce-style framework for parallel processing.

9 Case Study: Map-Reduce

In this case study we’re going to implement a simple-but-powerful parallel processing framework using Monoids, Functors, and a host of other goodies.

If you have used Hadoop or otherwise worked in “big data” you will have heard of MapReduce, which is a programming model for doing parallel data processing across clusters of machines (aka “nodes”). As the name suggests, the model is built around a map phase, which is the same map function we know from Scala and the Functor type class, and a reduce phase, which we usually call fold13 in Scala.

9.1 Parallelizing map and fold

Recall the general signature for map is to apply a function A => B to a F[A], returning a F[B]:

Figure 12: Type chart: functor map

Figure 12: Type chart: functor map

map transforms each individual element in a sequence independently. We can easily parallelize map because there are no dependencies between the transformations applied to different elements (the type signature of the function A => B shows us this, assuming we don’t use side-effects not reflected in the types).

What about fold? We can implement this step with an instance of Foldable. Not every functor also has an instance of foldable but we can implement a map-reduce system on top of any data type that has both of these type classes. Our reduction step becomes a foldLeft over the results of the distributed map.

Figure 13: Type chart: fold

Figure 13: Type chart: fold

By distributing the reduce step we lose control over the order of traversal. Our overall reduction may not be entirely left-to-right—we may reduce left-to-right across several subsequences and then combine the results. To ensure correctness we need a reduction operation that is associative:

reduce(a1, reduce(a2, a3)) == reduce(reduce(a1, a2), a3)

If we have associativity, we can arbitrarily distribute work between our nodes provided the subsequences at every node stay in the same order as the initial dataset.

Our fold operation requires us to seed the computation with an element of type B. Since fold may be split into an arbitrary number of parallel steps, the seed should not affect the result of the computation. This naturally requires the seed to be an identity element:

reduce(seed, a1) == reduce(a1, seed) == a1

In summary, our parallel fold will yield the correct results if:

What does this pattern sound like? That’s right, we’ve come full circle back to Monoid, the first type class we discussed in this book. We are not the first to recognise the importance of monoids. The monoid design pattern for map-reduce jobs is at the core of recent big data systems such as Twitter’s Summingbird.

In this project we’re going to implement a very simple single-machine map-reduce. We’ll start by implementing a method called foldMap to model the data-flow we need.

9.2 Implementing foldMap

We saw foldMap briefly back when we covered Foldable. It is one of the derived operations that sits on top of foldLeft and foldRight. However, rather than use Foldable, we will re-implement foldMap here ourselves as it will provide useful insight into the structure of map-reduce.

Start by writing out the signature of foldMap. It should accept the following parameters:

You will have to add implicit parameters or context bounds to complete the type signature.

import cats.Monoid

/** Single-threaded map-reduce function.
  * Maps `func` over `values` and reduces using a `Monoid[B]`.
  */
def foldMap[A, B: Monoid](values: Vector[A])(func: A => B): B =
  ???

Now implement the body of foldMap. Use the flow chart in Figure 14 as a guide to the steps required:

  1. start with a sequence of items of type A;
  2. map over the list to produce a sequence of items of type B;
  3. use the Monoid to reduce the items to a single B.
Figure 14: foldMap algorithm

Figure 14: foldMap algorithm

Here’s some sample output for reference:

import cats.instances.int._ // for Monoid

foldMap(Vector(1, 2, 3))(identity)
// res1: Int = 6

import cats.instances.string._ // for Monoid

// Mapping to a String uses the concatenation monoid:
foldMap(Vector(1, 2, 3))(_.toString + "! ")
// res2: String = "1! 2! 3! "

// Mapping over a String to produce a String:
foldMap("Hello world!".toVector)(_.toString.toUpperCase)
// res3: String = "HELLO WORLD!"

We have to modify the type signature to accept a Monoid for B. With that change we can use the Monoid empty and |+| syntax as described in Section 2.5.3:

import cats.Monoid
import cats.syntax.semigroup._ // for |+|

def foldMap[A, B : Monoid](as: Vector[A])(func: A => B): B =
  as.map(func).foldLeft(Monoid[B].empty)(_ |+| _)

We can make a slight alteration to this code to do everything in one step:

def foldMap[A, B : Monoid](as: Vector[A])(func: A => B): B =
  as.foldLeft(Monoid[B].empty)(_ |+| func(_))

9.3 Parallelising foldMap

Now we have a working single-threaded implementation of foldMap, let’s look at distributing work to run in parallel. We’ll use our single-threaded version of foldMap as a building block.

We’ll write a multi-CPU implementation that simulates the way we would distribute work in a map-reduce cluster as shown in Figure 15:

  1. we start with an initial list of all the data we need to process;
  2. we divide the data into batches, sending one batch to each CPU;
  3. the CPUs run a batch-level map phase in parallel;
  4. the CPUs run a batch-level reduce phase in parallel, producing a local result for each batch;
  5. we reduce the results for each batch to a single final result.
Figure 15: parallelFoldMap algorithm

Figure 15: parallelFoldMap algorithm

Scala provides some simple tools to distribute work amongst threads. We could use the parallel collections library to implement a solution, but let’s challenge ourselves by diving a bit deeper and implementing the algorithm ourselves using Futures.

9.3.1 Futures, Thread Pools, and ExecutionContexts

We already know a fair amount about the monadic nature of Futures. Let’s take a moment for a quick recap, and to describe how Scala futures are scheduled behind the scenes.

Futures run on a thread pool, determined by an implicit ExecutionContext parameter. Whenever we create a Future, whether through a call to Future.apply or some other combinator, we must have an implicit ExecutionContext in scope:

import scala.concurrent.Future
import scala.concurrent.ExecutionContext.Implicits.global

val future1 = Future {
  (1 to 100).toList.foldLeft(0)(_ + _)
}
// future1: Future[Int] = Future(Success(5050))

val future2 = Future {
  (100 to 200).toList.foldLeft(0)(_ + _)
}
// future2: Future[Int] = Future(Success(15150))

In this example we’ve imported a ExecutionContext.Implicits.global. This default context allocates a thread pool with one thread per CPU in our machine. When we create a Future the ExecutionContext schedules it for execution. If there is a free thread in the pool, the Future starts executing immediately. Most modern machines have at least two CPUs, so in our example it is likely that future1 and future2 will execute in parellel.

Some combinators create new Futures that schedule work based on the results of other Futures. The map and flatMap methods, for example, schedule computations that run as soon as their input values are computed and a CPU is available:

val future3 = future1.map(_.toString)
// future3: Future[String] = Future(Success(5050))

val future4 = for {
  a <- future1
  b <- future2
} yield a + b
// future4: Future[Int] = Future(Success(20200))

As we saw in Section 7.2, we can convert a List[Future[A]] to a Future[List[A]] using Future.sequence:

Future.sequence(List(Future(1), Future(2), Future(3)))
// res6: Future[List[Int]] = Future(Success(List(1, 2, 3)))

or an instance of Traverse:

import cats.instances.future._ // for Applicative
import cats.instances.list._   // for Traverse
import cats.syntax.traverse._  // for sequence

List(Future(1), Future(2), Future(3)).sequence
// res7: Future[List[Int]] = Future(Success(List(1, 2, 3)))

An ExecutionContext is required in either case. Finally, we can use Await.result to block on a Future until a result is available:

import scala.concurrent._
import scala.concurrent.duration._

Await.result(Future(1), 1.second) // wait for the result
// res8: Int = 1

There are also Monad and Monoid implementations for Future available from cats.instances.future:

import cats.{Monad, Monoid}
import cats.instances.int._    // for Monoid
import cats.instances.future._ // for Monad and Monoid

Monad[Future].pure(42)

Monoid[Future[Int]].combine(Future(1), Future(2))

9.3.2 Dividing Work

Now we’ve refreshed our memory of Futures, let’s look at how we can divide work into batches. We can query the number of available CPUs on our machine using an API call from the Java standard library:

Runtime.getRuntime.availableProcessors
// res11: Int = 2

We can partition a sequence (actually anything that implements Vector) using the grouped method. We’ll use this to split off batches of work for each CPU:

(1 to 10).toList.grouped(3).toList
// res12: List[List[Int]] = List(
//   List(1, 2, 3),
//   List(4, 5, 6),
//   List(7, 8, 9),
//   List(10)
// )

9.3.3 Implementing parallelFoldMap

Implement a parallel version of foldMap called parallelFoldMap. Here is the type signature:

def parallelFoldMap[A, B : Monoid]
      (values: Vector[A])
      (func: A => B): Future[B] = ???

Use the techniques described above to split the work into batches, one batch per CPU. Process each batch in a parallel thread. Refer back to Figure 15 if you need to review the overall algorithm.

For bonus points, process the batches for each CPU using your implementation of foldMap from above.

Here is an annotated solution that splits out each map and fold into a separate line of code:

def parallelFoldMap[A, B: Monoid]
      (values: Vector[A])
      (func: A => B): Future[B] = {
  // Calculate the number of items to pass to each CPU:
  val numCores  = Runtime.getRuntime.availableProcessors
  val groupSize = (1.0 * values.size / numCores).ceil.toInt

  // Create one group for each CPU:
  val groups: Iterator[Vector[A]] =
    values.grouped(groupSize)

  // Create a future to foldMap each group:
  val futures: Iterator[Future[B]] =
    groups map { group =>
      Future {
        group.foldLeft(Monoid[B].empty)(_ |+| func(_))
      }
    }

  // foldMap over the groups to calculate a final result:
  Future.sequence(futures) map { iterable =>
    iterable.foldLeft(Monoid[B].empty)(_ |+| _)
  }
}

val result: Future[Int] =
  parallelFoldMap((1 to 1000000).toVector)(identity)

Await.result(result, 1.second)
// res14: Int = 1784293664

We can re-use our definition of foldMap for a more concise solution. Note that the local maps and reduces in steps 3 and 4 of Figure 15 are actually equivalent to a single call to foldMap, shortening the entire algorithm as follows:

def parallelFoldMap[A, B: Monoid]
      (values: Vector[A])
      (func: A => B): Future[B] = {
  val numCores  = Runtime.getRuntime.availableProcessors
  val groupSize = (1.0 * values.size / numCores).ceil.toInt

  val groups: Iterator[Vector[A]] =
    values.grouped(groupSize)

  val futures: Iterator[Future[B]] =
    groups.map(group => Future(foldMap(group)(func)))

  Future.sequence(futures) map { iterable =>
    iterable.foldLeft(Monoid[B].empty)(_ |+| _)
  }
}

val result: Future[Int] =
  parallelFoldMap((1 to 1000000).toVector)(identity)

Await.result(result, 1.second)
// res16: Int = 1784293664

9.3.4 parallelFoldMap with more Cats

Although we implemented foldMap ourselves above, the method is also available as part of the Foldable type class we discussed in Section 7.1.

Reimplement parallelFoldMap using Cats’ Foldable and Traverseable type classes.

We’ll restate all of the necessary imports for completeness:

import cats.Monoid

import cats.instances.int._    // for Monoid
import cats.instances.future._ // for Applicative and Monad
import cats.instances.vector._ // for Foldable and Traverse

import cats.syntax.foldable._  // for combineAll and foldMap
import cats.syntax.traverse._  // for traverse

import scala.concurrent._
import scala.concurrent.duration._
import scala.concurrent.ExecutionContext.Implicits.global

Here’s the implementation of parallelFoldMap delegating as much of the method body to Cats as possible:

def parallelFoldMap[A, B: Monoid]
      (values: Vector[A])
      (func: A => B): Future[B] = {
  val numCores  = Runtime.getRuntime.availableProcessors
  val groupSize = (1.0 * values.size / numCores).ceil.toInt

  values
    .grouped(groupSize)
    .toVector
    .traverse(group => Future(group.toVector.foldMap(func)))
    .map(_.combineAll)
}

val future: Future[Int] =
  parallelFoldMap((1 to 1000).toVector)(_ * 1000)

Await.result(future, 1.second)
// res18: Int = 500500000

The call to vector.grouped returns an Iterable[Iterator[Int]]. We sprinkle calls to toVector through the code to convert the data back to a form that Cats can understand. The call to traverse creates a Future[Vector[Int]] containing one Int per batch. The call to map then combines the match using the combineAll method from Foldable.

9.4 Summary

In this case study we implemented a system that imitates map-reduce as performed on a cluster. Our algorithm followed three steps:

  1. batch the data and send one batch to each “node”;
  2. perform a local map-reduce on each batch;
  3. combine the results using monoid addition.

Our toy system emulates the batching behaviour of real-world map-reduce systems such as Hadoop. However, in reality we are running all of our work on a single machine where communcation between nodes is negligible. We don’t actually need to batch data to gain efficient parallel processing of a list. We can simply map using a Functor and reduce using a Monoid.

Regardless of the batching strategy, mapping and reducing with Monoids is a powerful and general framework that isn’t limited to simple tasks like addition and string concatenation. Most of the tasks data scientists perform in their day-to-day analyses can be cast as monoids. There are monoids for all the following:

to name but a few.

10 Case Study: Data Validation

In this case study we will build a library for validation. What do we mean by validation? Almost all programs must check their input meets certain criteria. Usernames must not be blank, email addresses must be valid, and so on. This type of validation often occurs in web forms, but it could be performed on configuration files, on web service responses, and any other case where we have to deal with data that we can’t guarantee is correct. Authentication, for example, is just a specialised form of validation.

We want to build a library that performs these checks. What design goals should we have? For inspiration, let’s look at some examples of the types of checks we want to perform:

With these examples in mind we can state some goals:

These goals assume we’re checking a single piece of data. We will also need to combine checks across multiple pieces of data. For a login form, for example, we’ll need to combine the check results for the username and the password. This will turn out to be quite a small component of the library, so the majority of our time will focus on checking a single data item.

10.1 Sketching the Library Structure

Let’s start at the bottom, checking individual pieces of data. Before we start coding let’s try to develop a feel for what we’ll be building. We can use a graphical notation to help us. We’ll go through our goals one by one.

Providing error messages

Our first goal requires us to associate useful error messages with a check failure. The output of a check could be either the value being checked, if it passed the check, or some kind of error message. We can abstractly represent this as a value in a context, where the context is the possibility of an error message as shown in Figure 16.

Figure 16: A validation result

Figure 16: A validation result

A check itself is therefore a function that transforms a value into a value in a context as shown in Figure 17.

Figure 17: A validation check

Figure 17: A validation check

Combine checks

How do we combine smaller checks into larger ones? Is this an applicative or semigroupal as shown in Figure 18?

Figure 18: Applicative combination of checks

Figure 18: Applicative combination of checks

Not really. With applicative combination, both checks are applied to the same value and result in a tuple with the value repeated. What we want feels more like a monoid as shown in Figure 19. We can define a sensible identity—a check that always passes—and two binary combination operators—and and or:

Figure 19: Monoid combination of checks

Figure 19: Monoid combination of checks

We’ll probably be using and and or about equally often with our validation library and it will be annoying to continuously switch between two monoids for combining rules. We consequently won’t actually use the monoid API: we’ll use two separate methods, and and or, instead.

Accumulating errors as we check

Monoids also feel like a good mechanism for accumulating error messages. If we store messages as a List or NonEmptyList, we can even use a pre-existing monoid from inside Cats.

Transforming data as we check it

In addition to checking data, we also have the goal of transforming it. This seems like it should be a map or a flatMap depending on whether the transform can fail or not, so it seems we also want checks to be a monad as shown in Figure 20.

Figure 20: Monadic combination of checks

Figure 20: Monadic combination of checks

We’ve now broken down our library into familiar abstractions and are in a good position to begin development.

10.2 The Check Datatype

Our design revolves around a Check, which we said was a function from a value to a value in a context. As soon as you see this description you should think of something like

type Check[A] = A => Either[String, A]

Here we’ve represented the error message as a String. This is probably not the best representation. We may want to accumulate messages in a List, for example, or even use a different representation that allows for internationalization or standard error codes.

We could attempt to build some kind of ErrorMessage type that holds all the information we can think of. However, we can’t predict the user’s requirements. Instead let’s let the user specify what they want. We can do this by adding a second type parameter to Check:

type Check[E, A] = A => Either[E, A]

We will probably want to add custom methods to Check so let’s declare it as a trait instead of a type alias:

trait Check[E, A] {
  def apply(value: A): Either[E, A]

  // other methods...
}

As we said in Essential Scala, there are two functional programming patterns that we should consider when defining a trait:

Type classes allow us to unify disparate data types with a common interface. This doesn’t seem like what we’re trying to do here. That leaves us with an algebraic data type. Let’s keep that thought in mind as we explore the design a bit further.

10.3 Basic Combinators

Let’s add some combinator methods to Check, starting with and. This method combines two checks into one, succeeding only if both checks succeed. Think about implementing this method now. You should hit some problems. Read on when you do!

trait Check[E, A] {
  def and(that: Check[E, A]): Check[E, A] =
    ???

  // other methods...
}

The problem is: what do you do when both checks fail? The correct thing to do is to return both errors, but we don’t currently have any way to combine Es. We need a type class that abstracts over the concept of “accumulating” errors as shown in Figure 21 What type class do we know that looks like this? What method or operator should we use to implement the operation?

Figure 21: Combining error messages

Figure 21: Combining error messages

We need a Semigroup for E. Then we can combine values of E using the combine method or its associated |+| syntax:

import cats.Semigroup
import cats.instances.list._   // for Semigroup
import cats.syntax.semigroup._ // for |+|

val semigroup = Semigroup[List[String]]

// Combination using methods on Semigroup
semigroup.combine(List("Badness"), List("More badness"))
// res3: List[String] = List("Badness", "More badness")

// Combination using Semigroup syntax
List("Oh noes") |+| List("Fail happened")
// res4: List[String] = List("Oh noes", "Fail happened")

Note we don’t need a full Monoid because we don’t need the identity element. We should always try to keep our constraints as small as possible!

There is another semantic issue that will come up quite quickly: should and short-circuit if the first check fails. What do you think the most useful behaviour is?

We want to report all the errors we can, so we should prefer not short-circuiting whenever possible.

In the case of the and method, the two checks we’re combining are independent of one another. We can always run both rules and combine any errors we see.

Use this knowledge to implement and. Make sure you end up with the behaviour you expect!

There are at least two implementation strategies.

In the first we represent checks as functions. The Check data type becomes a simple wrapper for a function that provides our library of combinator methods. For the sake of disambiguation, we’ll call this implementation CheckF:

import cats.Semigroup
import cats.syntax.either._    // for asLeft and asRight
import cats.syntax.semigroup._ // for |+|

final case class CheckF[E, A](func: A => Either[E, A]) {
  def apply(a: A): Either[E, A] =
    func(a)

  def and(that: CheckF[E, A])
        (implicit s: Semigroup[E]): CheckF[E, A] =
    CheckF { a =>
      (this(a), that(a)) match {
        case (Left(e1),  Left(e2))  => (e1 |+| e2).asLeft
        case (Left(e),   Right(_))  => e.asLeft
        case (Right(_),  Left(e))   => e.asLeft
        case (Right(_), Right(_)) => a.asRight
      }
    }
}

Let’s test the behaviour we get. First we’ll setup some checks:

import cats.instances.list._ // for Semigroup

val a: CheckF[List[String], Int] =
  CheckF { v =>
    if(v > 2) v.asRight
    else List("Must be > 2").asLeft
  }

val b: CheckF[List[String], Int] =
  CheckF { v =>
    if(v < -2) v.asRight
    else List("Must be < -2").asLeft
  }

val check: CheckF[List[String], Int] =
  a and b

Now run the check with some data:

check(5)
// res5: Either[List[String], Int] = Left(List("Must be < -2"))
check(0)
// res6: Either[List[String], Int] = Left(List("Must be > 2", "Must be < -2"))

Excellent! Everything works as expected! We’re running both checks and accumulating errors as required.

What happens if we try to create checks that fail with a type that we can’t accumulate? For example, there is no Semigroup instance for Nothing. What happens if we create instances of CheckF[Nothing, A]?

val a: CheckF[Nothing, Int] =
  CheckF(v => v.asRight)

val b: CheckF[Nothing, Int] =
  CheckF(v => v.asRight)

We can create checks just fine but when we come to combine them we get an error we might expect:

val check = a and b
// error: could not find implicit value for parameter s: cats.Semigroup[Nothing]
//   a and b
//   ^^^^^^^

Now let’s see another implementation strategy. In this approach we model checks as an algebraic data type, with an explicit data type for each combinator. We’ll call this implementation Check:

sealed trait Check[E, A] {
  import Check._

  def and(that: Check[E, A]): Check[E, A] =
    And(this, that)

  def apply(a: A)(implicit s: Semigroup[E]): Either[E, A] =
    this match {
      case Pure(func) =>
        func(a)

      case And(left, right) =>
        (left(a), right(a)) match {
          case (Left(e1),  Left(e2))  => (e1 |+| e2).asLeft
          case (Left(e),   Right(_))  => e.asLeft
          case (Right(_),  Left(e))   => e.asLeft
          case (Right(_), Right(_)) => a.asRight
        }
    }
}
object Check {
  final case class And[E, A](
    left: Check[E, A],
    right: Check[E, A]) extends Check[E, A]
  
  final case class Pure[E, A](
    func: A => Either[E, A]) extends Check[E, A]
    
  def pure[E, A](f: A => Either[E, A]): Check[E, A] =
    Pure(f)
}

Let’s see an example:

val a: Check[List[String], Int] =
  Check.pure { v =>
    if(v > 2) v.asRight
    else List("Must be > 2").asLeft
  }

val b: Check[List[String], Int] =
  Check.pure { v =>
    if(v < -2) v.asRight
    else List("Must be < -2").asLeft
  }

val check: Check[List[String], Int] =
  a and b

While the ADT implementation is more verbose than the function wrapper implementation, it has the advantage of cleanly separating the structure of the computation (the ADT instance we create) from the process that gives it meaning (the apply method). From here we have a number of options:

  • inspect and refactor checks after they are created;
  • move the apply “interpreter” out into its own module;
  • implement alternative interpreters providing other functionality (for example visualizing checks).

Because of its flexibility, we will use the ADT implementation for the rest of this case study.

Strictly speaking, Either[E, A] is the wrong abstraction for the output of our check. Why is this the case? What other data type could we use instead? Switch your implementation over to this new data type.

The implementation of apply for And is using the pattern for applicative functors. Either has an Applicative instance, but it doesn’t have the semantics we want. It fails fast instead of accumulating errors.

If we want to accumulate errors Validated is a more appropriate abstraction. As a bonus, we get more code reuse because we can lean on the applicative instance of Validated in the implementation of apply.

Here’s the complete implementation:

import cats.Semigroup
import cats.data.Validated
import cats.syntax.apply._     // for mapN

sealed trait Check[E, A] {
  import Check._

  def and(that: Check[E, A]): Check[E, A] =
    And(this, that)

  def apply(a: A)(implicit s: Semigroup[E]): Validated[E, A] =
    this match {
      case Pure(func) =>
        func(a)

      case And(left, right) =>
        (left(a), right(a)).mapN((_, _) => a)
    }
}
object Check {
  final case class And[E, A](
    left: Check[E, A],
    right: Check[E, A]) extends Check[E, A]
  
  final case class Pure[E, A](
    func: A => Validated[E, A]) extends Check[E, A]
}

Our implementation is looking pretty good now. Implement an or combinator to complement and.

This reuses the same technique for and. We have to do a bit more work in the apply method. Note that it’s OK to short-circuit in this case because the choice of rules is implicit in the semantics of “or”.

import cats.Semigroup
import cats.data.Validated
import cats.syntax.semigroup._ // for |+|
import cats.syntax.apply._     // for mapN
import cats.data.Validated._   // for Valid and Invalid

sealed trait Check[E, A] {
  import Check._

  def and(that: Check[E, A]): Check[E, A] =
    And(this, that)

  def or(that: Check[E, A]): Check[E, A] =
    Or(this, that)

  def apply(a: A)(implicit s: Semigroup[E]): Validated[E, A] =
    this match {
      case Pure(func) =>
        func(a)

      case And(left, right) =>
        (left(a), right(a)).mapN((_, _) => a)

      case Or(left, right) =>
        left(a) match {
          case Valid(a)    => Valid(a)
          case Invalid(e1) =>
            right(a) match {
              case Valid(a)    => Valid(a)
              case Invalid(e2) => Invalid(e1 |+| e2)
            }
        }
    }
}
object Check {
  final case class And[E, A](
    left: Check[E, A],
    right: Check[E, A]) extends Check[E, A]
  
  final case class Or[E, A](
    left: Check[E, A],
    right: Check[E, A]) extends Check[E, A]
  
  final case class Pure[E, A](
    func: A => Validated[E, A]) extends Check[E, A]
}

With and and or we can implement many of checks we’ll want in practice. However, we still have a few more methods to add. We’ll turn to map and related methods next.

10.4 Transforming Data

One of our requirements is the ability to transform data. This allows us to support additional scenarios like parsing input. In this section we’ll extend our check library with this additional functionality.

The obvious starting point is map. When we try to implement this, we immediately run into a wall. Our current definition of Check requires the input and output types to be the same:

type Check[E, A] = A => Either[E, A]

When we map over a check, what type do we assign to the result? It can’t be A and it can’t be B. We are at an impasse:

def map(check: Check[E, A])(func: A => B): Check[E, ???]

To implement map we need to change the definition of Check. Specifically, we need to a new type variable to separate the input type from the output:

type Check[E, A, B] = A => Either[E, B]

Checks can now represent operations like parsing a String as an Int:

val parseInt: Check[List[String], String, Int] =
  // etc...

However, splitting our input and output types raises another issue. Up until now we have operated under the assumption that a Check always returns its input when successful. We used this in and and or to ignore the output of the left and right rules and simply return the original input on success:

(this(a), that(a)) match {
  case And(left, right) =>
    (left(a), right(a))
      .mapN((result1, result2) => Right(a))

  // etc...
}

In our new formulation we can’t return Right(a) because its type is Either[E, A] not Either[E, B]. We’re forced to make an arbitrary choice between returning Right(result1) and Right(result2). The same is true of the or method. From this we can derive two things:

10.4.1 Predicates

We can make progress by pulling apart the concept of a predicate, which can be combined using logical operations such as and and or, and the concept of a check, which can transform data.

What we have called Check so far we will call Predicate. For Predicate we can state the following identity law encoding the notion that a predicate always returns its input if it succeeds:

For a predicate p of type Predicate[E, A] and elements a1 and a2 of type A, if p(a1) == Success(a2) then a1 == a2.

Making this change gives us the following code:

import cats.Semigroup
import cats.data.Validated
import cats.syntax.semigroup._ // for |+|
import cats.syntax.apply._     // for mapN
import cats.data.Validated._   // for Valid and Invalid

sealed trait Predicate[E, A] {
  def and(that: Predicate[E, A]): Predicate[E, A] =
    And(this, that)

  def or(that: Predicate[E, A]): Predicate[E, A] =
    Or(this, that)

  def apply(a: A)(implicit s: Semigroup[E]): Validated[E, A] =
    this match {
      case Pure(func) =>
        func(a)

      case And(left, right) =>
        (left(a), right(a)).mapN((_, _) => a)

      case Or(left, right) =>
        left(a) match {
          case Valid(_)   => Valid(a)
          case Invalid(e1) =>
            right(a) match {
              case Valid(_)   => Valid(a)
              case Invalid(e2) => Invalid(e1 |+| e2)
            }
        }
    }
}

final case class And[E, A](
  left: Predicate[E, A],
  right: Predicate[E, A]) extends Predicate[E, A]

final case class Or[E, A](
  left: Predicate[E, A],
  right: Predicate[E, A]) extends Predicate[E, A]

final case class Pure[E, A](
  func: A => Validated[E, A]) extends Predicate[E, A]

10.4.2 Checks

We’ll use Check to represent a structure we build from a Predicate that also allows transformation of its input. Implement Check with the following interface:

sealed trait Check[E, A, B] {
  def apply(a: A): Validated[E, B] =
    ???

  def map[C](func: B => C): Check[E, A, C] =
    ???
}

If you follow the same strategy as Predicate you should be able to create code similar to the below:

import cats.Semigroup
import cats.data.Validated

sealed trait Check[E, A, B] {
  import Check._

  def apply(in: A)(implicit s: Semigroup[E]): Validated[E, B]

  def map[C](f: B => C): Check[E, A, C] =
    Map[E, A, B, C](this, f)
}

object Check {
  final case class Map[E, A, B, C](
    check: Check[E, A, B],
    func: B => C) extends Check[E, A, C] {
  
    def apply(in: A)(implicit s: Semigroup[E]): Validated[E, C] =
      check(in).map(func)
  }
  
  final case class Pure[E, A](
    pred: Predicate[E, A]) extends Check[E, A, A] {
  
    def apply(in: A)(implicit s: Semigroup[E]): Validated[E, A] =
      pred(in)
  }

  def apply[E, A](pred: Predicate[E, A]): Check[E, A, A] =
    Pure(pred)
}

What about flatMap? The semantics are a bit unclear here. The method is simple enough to declare but it’s not so obvious what it means or how we should implement apply. The general shape of flatMap is shown in Figure 22.

Figure 22: Type chart for flatMap

Figure 22: Type chart for flatMap

How do we relate F in the figure to Check in our code? Check has three type variables while F only has one.

To unify the types we need to fix two of the type parameters. The idiomatic choices are the error type E and the input type A. This gives us the relationships shown in Figure 23. In other words, the semantics of applying a FlatMap are:

Figure 23: Type chart for flatMap applied to Check

Figure 23: Type chart for flatMap applied to Check

This is quite an odd method. We can implement it, but it is hard to find a use for it. Go ahead and implement flatMap for Check, and then we’ll see a more generally useful method.

It’s the same implementation strategy as before with one wrinkle: Validated doesn’t have a flatMap method. To implement flatMap we must momentarily switch to Either and then switch back to Validated. The withEither method on Validated does exactly this. From here we can just follow the types to implement apply.

import cats.Semigroup
import cats.data.Validated

sealed trait Check[E, A, B] {
  def apply(in: A)(implicit s: Semigroup[E]): Validated[E, B]

  def flatMap[C](f: B => Check[E, A, C]) =
    FlatMap[E, A, B, C](this, f)

  // other methods...
}

final case class FlatMap[E, A, B, C](
  check: Check[E, A, B],
  func: B => Check[E, A, C]) extends Check[E, A, C] {

  def apply(a: A)(implicit s: Semigroup[E]): Validated[E, C] =
    check(a).withEither(_.flatMap(b => func(b)(a).toEither))
}

// other data types...

We can write a more useful combinator that chains together two Checks. The output of the first check is connected to the input of the second. This is analogous to function composition using andThen:

val f: A => B = ???
val g: B => C = ???
val h: A => C = f andThen g

A Check is basically a function A => Validated[E, B] so we can define an analagous andThen method:

trait Check[E, A, B] {
  def andThen[C](that: Check[E, B, C]): Check[E, A, C]
}

Implement andThen now!

Here’s a minimal definition of andThen and its corresponding AndThen class:

sealed trait Check[E, A, B] {
  def apply(in: A)(implicit s: Semigroup[E]): Validated[E, B]

  def andThen[C](that: Check[E, B, C]): Check[E, A, C] =
    AndThen[E, A, B, C](this, that)
}

final case class AndThen[E, A, B, C](
  check1: Check[E, A, B],
  check2: Check[E, B, C]) extends Check[E, A, C] {

  def apply(a: A)(implicit s: Semigroup[E]): Validated[E, C] =
    check1(a).withEither(_.flatMap(b => check2(b).toEither))
}

10.4.3 Recap

We now have two algebraic data types, Predicate and Check, and a host of combinators with their associated case class implementations. Look at the following solution for a complete definition of each ADT.

Here’s our final implementaton, including some tidying and repackaging of the code:

import cats.Semigroup
import cats.data.Validated
import cats.data.Validated._   // for Valid and Invalid
import cats.syntax.semigroup._ // for |+|
import cats.syntax.apply._     // for mapN
import cats.syntax.validated._ // for valid and invalid

Here is our complete implementation of Predicate, including the and and or combinators and a Predicate.apply method to create a Predicate from a function:

sealed trait Predicate[E, A] {
  import Predicate._
  import Validated._

  def and(that: Predicate[E, A]): Predicate[E, A] =
    And(this, that)

  def or(that: Predicate[E, A]): Predicate[E, A] =
    Or(this, that)

  def apply(a: A)(implicit s: Semigroup[E]): Validated[E, A] =
    this match {
      case Pure(func) =>
        func(a)

      case And(left, right) =>
        (left(a), right(a)).mapN((_, _) => a)

      case Or(left, right) =>
        left(a) match {
          case Valid(_)   => Valid(a)
          case Invalid(e1) =>
            right(a) match {
              case Valid(_)   => Valid(a)
              case Invalid(e2) => Invalid(e1 |+| e2)
            }
        }
    }
}

object Predicate {
  final case class And[E, A](
    left: Predicate[E, A],
    right: Predicate[E, A]) extends Predicate[E, A]

  final case class Or[E, A](
    left: Predicate[E, A],
    right: Predicate[E, A]) extends Predicate[E, A]

  final case class Pure[E, A](
    func: A => Validated[E, A]) extends Predicate[E, A]

  def apply[E, A](f: A => Validated[E, A]): Predicate[E, A] =
    Pure(f)

  def lift[E, A](err: E, fn: A => Boolean): Predicate[E, A] =
    Pure(a => if(fn(a)) a.valid else err.invalid)
}

Here is a complete implementation of Check. Due to a type inference bug in Scala’s pattern matching, we’ve switched to implementing apply using inheritance:

import cats.Semigroup
import cats.data.Validated
import cats.syntax.apply._     // for mapN
import cats.syntax.validated._ // for valid and invalid

sealed trait Check[E, A, B] {
  import Check._

  def apply(in: A)(implicit s: Semigroup[E]): Validated[E, B]

  def map[C](f: B => C): Check[E, A, C] =
    Map[E, A, B, C](this, f)

  def flatMap[C](f: B => Check[E, A, C]) =
    FlatMap[E, A, B, C](this, f)

  def andThen[C](next: Check[E, B, C]): Check[E, A, C] =
    AndThen[E, A, B, C](this, next)
}

object Check {
  final case class Map[E, A, B, C](
    check: Check[E, A, B],
    func: B => C) extends Check[E, A, C] {

    def apply(a: A)
        (implicit s: Semigroup[E]): Validated[E, C] =
      check(a) map func
  }

  final case class FlatMap[E, A, B, C](
    check: Check[E, A, B],
    func: B => Check[E, A, C]) extends Check[E, A, C] {

    def apply(a: A)
        (implicit s: Semigroup[E]): Validated[E, C] =
      check(a).withEither(_.flatMap(b => func(b)(a).toEither))
  }

  final case class AndThen[E, A, B, C](
    check: Check[E, A, B],
    next: Check[E, B, C]) extends Check[E, A, C] {

    def apply(a: A)
        (implicit s: Semigroup[E]): Validated[E, C] =
      check(a).withEither(_.flatMap(b => next(b).toEither))
  }

  final case class Pure[E, A, B](
    func: A => Validated[E, B]) extends Check[E, A, B] {

    def apply(a: A)
        (implicit s: Semigroup[E]): Validated[E, B] =
      func(a)
  }

  final case class PurePredicate[E, A](
    pred: Predicate[E, A]) extends Check[E, A, A] {

    def apply(a: A)
        (implicit s: Semigroup[E]): Validated[E, A] =
      pred(a)
  }

  def apply[E, A](pred: Predicate[E, A]): Check[E, A, A] =
    PurePredicate(pred)

  def apply[E, A, B]
      (func: A => Validated[E, B]): Check[E, A, B] =
    Pure(func)
}

We have a complete implementation of Check and Predicate that do most of what we originally set out to do. However, we are not finished yet. You have probably recognised structure in Predicate and Check that we can abstract over: Predicate has a monoid and Check has a monad. Furthermore, in implementing Check you might have felt the implementation doesn’t do much—all we do is call through to underlying methods on Predicate and Validated.

There are a lot of ways this library could be cleaned up. However, let’s implement some examples to prove to ourselves that our library really does work, and then we’ll turn to improving it.

Implement checks for some of the examples given in the introduction:

You might find the following predicates useful:

import cats.data.{NonEmptyList, Validated}

type Errors = NonEmptyList[String]

def error(s: String): NonEmptyList[String] =
  NonEmptyList(s, Nil)

def longerThan(n: Int): Predicate[Errors, String] =
  Predicate.lift(
    error(s"Must be longer than $n characters"),
    str => str.size > n)

val alphanumeric: Predicate[Errors, String] =
  Predicate.lift(
    error(s"Must be all alphanumeric characters"),
    str => str.forall(_.isLetterOrDigit))

def contains(char: Char): Predicate[Errors, String] =
  Predicate.lift(
    error(s"Must contain the character $char"),
    str => str.contains(char))

def containsOnce(char: Char): Predicate[Errors, String] =
  Predicate.lift(
    error(s"Must contain the character $char only once"),
    str => str.filter(c => c == char).size == 1)

Here’s our reference solution. Implementing this required more thought than we expected. Switching between Check and Predicate at appropriate places felt a bit like guesswork till we got the rule into our heads that Predicate doesn’t transform its input. With this rule in mind things went fairly smoothly. In later sections we’ll make some changes that make the library easier to use.

import cats.syntax.apply._     // for mapN
import cats.syntax.validated._ // for valid and invalid

Here’s the implementation of checkUsername:

// A username must contain at least four characters
// and consist entirely of alphanumeric characters

val checkUsername: Check[Errors, String, String] =
  Check(longerThan(3) and alphanumeric)

And here’s the implementation of checkEmail, built up from a number of smaller components:

// An email address must contain a single `@` sign.
// Split the string at the `@`.
// The string to the left must not be empty.
// The string to the right must be
// at least three characters long and contain a dot.

val splitEmail: Check[Errors, String, (String, String)] =
  Check(_.split('@') match {
    case Array(name, domain) =>
      (name, domain).validNel[String]

    case _ =>
      "Must contain a single @ character".
        invalidNel[(String, String)]
  })

val checkLeft: Check[Errors, String, String] =
  Check(longerThan(0))

val checkRight: Check[Errors, String, String] =
  Check(longerThan(3) and contains('.'))

val joinEmail: Check[Errors, (String, String), String] =
  Check { case (l, r) =>
    (checkLeft(l), checkRight(r)).mapN(_ + "@" + _)
  }

val checkEmail: Check[Errors, String, String] =
  splitEmail andThen joinEmail

Finally, here’s a check for a User that depends on checkUsername and checkEmail:

final case class User(username: String, email: String)

def createUser(
      username: String,
      email: String): Validated[Errors, User] =
  (checkUsername(username), checkEmail(email)).mapN(User)

We can check our work by creating a couple of example users:

createUser("Noel", "noel@underscore.io")
// res5: Validated[Errors, User] = Valid(User("Noel", "noel@underscore.io"))
createUser("", "dave@underscore.io@io")
// res6: Validated[Errors, User] = Invalid(
//   NonEmptyList(
//     "Must be longer than 3 characters",
//     List("Must contain a single @ character")
//   )
// )

One distinct disadvantage of our example is that it doesn’t tell us where the errors came from. We can either achieve that through judicious manipulation of error messages, or we can modify our library to track error locations as well as messages. Tracking error locations is outside the scope of this case study, so we’ll leave this as an exercise to the reader.

10.5 Kleislis

We’ll finish off this case study by cleaning up the implementation of Check. A justifiable criticism of our approach is that we’ve written a lot of code to do very little. A Predicate is essentially a function A => Validated[E, A], and a Check is basically a wrapper that lets us compose these functions.

We can abstract A => Validated[E, A] to A => F[B], which you’ll recognise as the type of function you pass to the flatMap method on a monad. Imagine we have the following sequence of operations:

We can illustrate this as shown in Figure 24. We can also write out this example using the monad API as follows:

val aToB: A => F[B] = ???
val bToC: B => F[C] = ???

def example[A, C](a: A): F[C] =
  aToB(a).flatMap(bToC)
Figure 24: Sequencing monadic transforms

Figure 24: Sequencing monadic transforms

Recall that Check is, in the abstract, allowing us to compose functions of type A => F[B]. We can write the above in terms of andThen as:

val aToC = aToB andThen bToC

The result is a (wrapped) function aToC of type A => F[C] that we can subsequently apply to a value of type A.

We have achieved the same thing as the example method without having to reference an argument of type A. The andThen method on Check is analogous to function composition, but is composing function A => F[B] instead of A => B.

The abstract concept of composing functions of type A => F[B] has a name: a Kleisli.

Cats contains a data type cats.data.Kleisli that wraps a function just as Check does. Kleisli has all the methods of Check plus some additional ones. If Kleisli seems familiar to you, then congratulations. You’ve seen through its disguise and recognised it as another concept from earlier in the book: Kleisli is just another name for ReaderT.

Here is a simple example using Kleisli to transform an integer into a list of integers through three steps:

import cats.data.Kleisli
import cats.instances.list._ // for Monad

These steps each transform an input Int into an output of type List[Int]:

val step1: Kleisli[List, Int, Int] =
  Kleisli(x => List(x + 1, x - 1))

val step2: Kleisli[List, Int, Int] =
  Kleisli(x => List(x, -x))

val step3: Kleisli[List, Int, Int] =
  Kleisli(x => List(x * 2, x / 2))

We can combine the steps into a single pipeline that combines the underlying Lists using flatMap:

val pipeline = step1 andThen step2 andThen step3

The result is a function that consumes a single Int and returns eight outputs, each produced by a different combination of transformations from step1, step2, and step3:

pipeline.run(20)
// res0: List[Int] = List(42, 10, -42, -10, 38, 9, -38, -9)

The only notable difference between Kleisli and Check in terms of API is that Kleisli renames our apply method to run.

Let’s replace Check with Kleisli in our validation examples. To do so we need to make a few changes to Predicate. We must be able to convert a Predicate to a function, as Kleisli only works with functions. Somewhat more subtly, when we convert a Predicate to a function, it should have type A => Either[E, A] rather than A => Validated[E, A] because Kleisli relies on the wrapped function returning a monad.

Add a method to Predicate called run that returns a function of the correct type. Leave the rest of the code in Predicate the same.

Here’s an abbreviated definition of run. Like apply, the method must accept an implicit Semigroup:

import cats.Semigroup
import cats.data.Validated

sealed trait Predicate[E, A] {
  def run(implicit s: Semigroup[E]): A => Either[E, A] =
    (a: A) => this(a).toEither

  def apply(a: A): Validated[E, A] =
    ??? // etc...

  // other methods...
}

Now rewrite our username and email validation example in terms of Kleisli and Predicate. Here are few tips in case you get stuck:

First, remember that the run method on Predicate takes an implicit parameter. If you call aPredicate.run(a) it will try to pass the implicit parameter explicitly. If you want to create a function from a Predicate and immediately apply that function, use aPredicate.run.apply(a)

Second, type inference can be tricky in this exercise. We found that the following definitions helped us to write code with fewer type declarations.

type Result[A] = Either[Errors, A]

type Check[A, B] = Kleisli[Result, A, B]

// Create a check from a function:
def check[A, B](func: A => Result[B]): Check[A, B] =
  Kleisli(func)

// Create a check from a Predicate:
def checkPred[A](pred: Predicate[Errors, A]): Check[A, A] =
  Kleisli[Result, A, A](pred.run)

Working around limitations of type inference can be quite frustrating when writing this code, Working out when to convert between Predicates, functions, and Validated, and Either simplifies things, but the process is still complex:

import cats.data.{Kleisli, NonEmptyList}
import cats.instances.either._   // for Semigroupal

Here is the preamble we suggested in the main text of the case study:

type Errors = NonEmptyList[String]

def error(s: String): NonEmptyList[String] =
  NonEmptyList(s, Nil)

type Result[A] = Either[Errors, A]

type Check[A, B] = Kleisli[Result, A, B]

def check[A, B](func: A => Result[B]): Check[A, B] =
  Kleisli(func)

def checkPred[A](pred: Predicate[Errors, A]): Check[A, A] =
  Kleisli[Result, A, A](pred.run)

Our base predicate definitions are essenitally unchanged:

def longerThan(n: Int): Predicate[Errors, String] =
  Predicate.lift(
    error(s"Must be longer than $n characters"),
    str => str.size > n)

val alphanumeric: Predicate[Errors, String] =
  Predicate.lift(
    error(s"Must be all alphanumeric characters"),
    str => str.forall(_.isLetterOrDigit))

def contains(char: Char): Predicate[Errors, String] =
  Predicate.lift(
    error(s"Must contain the character $char"),
    str => str.contains(char))

def containsOnce(char: Char): Predicate[Errors, String] =
  Predicate.lift(
    error(s"Must contain the character $char only once"),
    str => str.filter(c => c == char).size == 1)

Our username and email examples are slightly different in that we make use of check() and checkPred() in different situations:

val checkUsername: Check[String, String] =
  checkPred(longerThan(3) and alphanumeric)

val splitEmail: Check[String, (String, String)] =
  check(_.split('@') match {
    case Array(name, domain) =>
      Right((name, domain))

    case _ =>
      Left(error("Must contain a single @ character"))
  })

val checkLeft: Check[String, String] =
  checkPred(longerThan(0))

val checkRight: Check[String, String] =
  checkPred(longerThan(3) and contains('.'))

val joinEmail: Check[(String, String), String] =
  check {
    case (l, r) =>
      (checkLeft(l), checkRight(r)).mapN(_ + "@" + _)
  }

val checkEmail: Check[String, String] =
  splitEmail andThen joinEmail

Finally, we can see that our createUser example works as expected using Kleisli:

final case class User(username: String, email: String)

def createUser(
      username: String,
      email: String): Either[Errors, User] = (
  checkUsername.run(username),
  checkEmail.run(email)
).mapN(User)

createUser("Noel", "noel@underscore.io")
// res2: Either[Errors, User] = Right(User("Noel", "noel@underscore.io"))
createUser("", "dave@underscore.io@io")
// res3: Either[Errors, User] = Left(
//   NonEmptyList("Must be longer than 3 characters", List())
// )

We have now written our code entirely in terms of Kleisli and Predicate, completely removing Check. This is a good first step to simplifying our library. There’s still plenty more to do, but we have a sophisticated building block from Cats to work with. We’ll leave further improvements up to the reader.

10.6 Summary

This case study has been an exercise in removing rather than building abstractions. We started with a fairly complex Check type. Once we realised we were conflating two concepts, we separated out Predicate leaving us with something that could be implemented with Kleisli.

We made several design choices above that reasonable developers may disagree with. Should the method that converts a Predicate to a function really be called run instead of, say, toFunction? Should Predicate be a subtype of Function to begin with? Many functional programmers prefer to avoid subtyping because it plays poorly with implicit resolution and type inference, but there could be an argument to use it here. As always the best decisions depend on the context in which the library will be used.

11 Case Study: CRDTs

In this case study we will explore Commutative Replicated Data Types (CRDTs), a family of data structures that can be used to reconcile eventually consistent data.

We’ll start by describing the utility and difficulty of eventually consistent systems, then show how we can use monoids and their extensions to solve the issues that arise. Finally, we will model the solutions in Scala.

Our goal here is to focus on the implementation in Scala of a particular type of CRDT. We’re not aiming at a comprehensive survey of all CRDTs. CRDTs are a fast-moving field and we advise you to read the literature to learn about more.

11.1 Eventual Consistency

As soon as a system scales beyond a single machine we have to make a fundamental choice about how we manage data.

One approach is to build a system that is consistent, meaning that all machines have the same view of data. For example, if a user changes their password then all machines that store a copy of that password must accept the change before we consider the operation to have completed successfully.

Consistent systems are easy to work with but they have their disadvantages. They tend to have high latency because a single change can result in many messages being sent between machines. They also tend to have relatively low uptime because outages can cut communications between machines creating a network partition. When there is a network partition, a consistent system may refuse further updates to prevent inconsistencies across machines.

An alternative approach is an eventually consistent system. This means that at any particular point in time machines are allowed to have differing views of data. However, if all machines can communicate and there are no further updates they will eventually all have the same view of data.

Eventually consistent systems require less communication between machines so latency can be lower. A partitioned machine can still accept updates and reconcile its changes when the network is fixed, so systems can also have better uptime.

The big question is: how do we do this reconciliation between machines? CRDTs provide one approach to the problem.

11.2 The GCounter

Let’s look at one particular CRDT implementation. Then we’ll attempt to generalise properties to see if we can find a general pattern.

The data structure we will look at is called a GCounter. It is a distributed increment-only counter that can be used, for example, to count the number of visitors to a web site where requests are served by many web servers.

11.2.1 Simple Counters

To see why a straightforward counter won’t work, imagine we have two servers storing a simple count of visitors. Let’s call the machines A and B. Each machine is storing an integer counter and the counters all start at zero as shown in Figure 25.

Figure 25: Simple counters: initial state

Figure 25: Simple counters: initial state

Now imagine we receive some web traffic. Our load balancer distributes five incoming requests to A and B, A serving three visitors and B two. The machines have inconsistent views of the system state that they need to reconcile to achieve consistency. One reconciliation strategy with simple counters is to exchange counts and add them as shown in Figure 26.

Figure 26: Simple counters: first round of requests and reconciliation

Figure 26: Simple counters: first round of requests and reconciliation

So far so good, but things will start to fall apart shortly. Suppose A serves a single visitor, which means we’ve seen six visitors in total. The machines attempt to reconcile state again using addition leading to the answer shown in Figure 27.

Figure 27: Simple counters: second round of requests and (incorrect) reconciliation

Figure 27: Simple counters: second round of requests and (incorrect) reconciliation

This is clearly wrong! The problem is that simple counters don’t give us enough information about the history of interactions between the machines. Fortunately we don’t need to store the complete history to get the correct answer—just a summary of it. Let’s look at how the GCounter solves this problem.

11.2.2 GCounters

The first clever idea in the GCounter is to have each machine storing a separate counter for every machine it knows about (including itself). In the previous example we had two machines, A and B. In this situation both machines would store a counter for A and a counter for B as shown in Figure 28.

Figure 28: GCounter: initial state

Figure 28: GCounter: initial state

The rule with GCounters is that a given machine is only allowed to increment its own counter. If A serves three visitors and B serves two visitors the counters look as shown in Figure 29.

Figure 29: GCounter: first round of web requests

Figure 29: GCounter: first round of web requests

When two machines reconcile their counters the rule is to take the largest value stored for each machine. In our example, the result of the first merge will be as shown in Figure 30.

Figure 30: GCounter: first reconciliation

Figure 30: GCounter: first reconciliation

Subsequent incoming web requests are handled using the increment-own-counter rule and subsequent merges are handled using the take-maximum-value rule, producing the same correct values for each machine as shown in Figure 31.

Figure 31: GCounter: second reconciliation

Figure 31: GCounter: second reconciliation

GCounters allow each machine to keep an accurate account of the state of the whole system without storing the complete history of interactions. If a machine wants to calculate the total traffic for the whole web site, it sums up all the per-machine counters. The result is accurate or near-accurate depending on how recently we performed a reconciliation. Eventually, regardless of network outages, the system will always converge on a consistent state.

11.2.3 Exercise: GCounter Implementation

We can implement a GCounter with the following interface, where we represent machine IDs as Strings.

final case class GCounter(counters: Map[String, Int]) {
  def increment(machine: String, amount: Int) =
    ???

  def merge(that: GCounter): GCounter =
    ???

  def total: Int =
    ???
}

Finish the implementation!

Hopefully the description above was clear enough that you can get to an implementation like the one below.

final case class GCounter(counters: Map[String, Int]) {
  def increment(machine: String, amount: Int) = {
    val value = amount + counters.getOrElse(machine, 0)
    GCounter(counters + (machine -> value))
  }

  def merge(that: GCounter): GCounter =
    GCounter(that.counters ++ this.counters.map {
      case (k, v) =>
        k -> (v max that.counters.getOrElse(k, 0))
    })

  def total: Int =
    counters.values.sum
}

11.3 Generalisation

We’ve now created a distributed, eventually consistent, increment-only counter. This is a useful achievement but we don’t want to stop here. In this section we will attempt to abstract the operations in the GCounter so it will work with more data types than just natural numbers.

The GCounter uses the following operations on natural numbers:

You can probably guess that there’s a monoid in here somewhere, but let’s look in more detail at the properties we’re relying on.

As a refresher, in Chapter 2 we saw that monoids must satisfy two laws. The binary operation + must be associative:

(a + b) + c == a + (b + c)

and the empty element must be an identity:

0 + a == a + 0 == a

We need an identity in increment to initialise the counter. We also rely on associativity to ensure the specific sequence of merges gives the correct value.

In total we implicitly rely on associativity and commutativity to ensure we get the correct value no matter what arbitrary order we choose to sum the per-machine counters. We also implicitly assume an identity, which allows us to skip machines for which we do not store a counter.

The properties of merge are a bit more interesting. We rely on commutativity to ensure that machine A merging with machine B yields the same result as machine B merging with machine A. We need associativity to ensure we obtain the correct result when three or more machines are merging data. We need an identity element to initialise empty counters. Finally, we need an additional property, called idempotency, to ensure that if two machines hold the same data in a per-machine counter, merging data will not lead to an incorrect result. Idempotent operations are ones that return the same result again and again if they are executed multiple times. Formally, a binary operation max is idempotent if the following relationship holds:

a max a = a

Written more compactly, we have:

Method Identity Commutative Associative Idempotent
increment Y N Y N
merge Y Y Y Y
total Y Y Y N

From this we can see that

Since increment and get both use the same binary operation (addition) it’s usual to require the same commutative monoid for both.

This investigation demonstrates the powers of thinking about properties or laws of abstractions. Now we have identified these properties we can substitute the natural numbers used in our GCounter with any data type with operations satisfying these properties. A simple example is a set, with the binary operation being union and the identity element the empty set. With this simple substitution of Int for Set[A] we can create a GSet type.

11.3.1 Implementation

Let’s implement this generalisation in code. Remember increment and total require a commutative monoid and merge requires a bounded semilattice (or idempotent commutative monoid).

Cats provides a type class for both Monoid and CommutativeMonoid, but doesn’t provide one for bounded semilattice14. That’s why we’re going to implement our own BoundedSemiLattice type class.

import cats.kernel.CommutativeMonoid

trait BoundedSemiLattice[A] extends CommutativeMonoid[A] {
  def combine(a1: A, a2: A): A
  def empty: A
}

In the implementation above, BoundedSemiLattice[A] extends CommutativeMonoid[A] because a bounded semilattice is a commutative monoid (a commutative idempotent one, to be exact).

11.3.2 Exercise: BoundedSemiLattice Instances

Implement BoundedSemiLattice type class instances for Ints and for Sets. The instance for Int will technically only hold for non-negative numbers, but you don’t need to model non-negativity explicitly in the types.

It’s common to place the instances in the companion object of BoundedSemiLattice so they are in the implicit scope without importing them.

Implementing the instance for Set provides good practice with implicit methods.

trait BoundedSemiLattice[A] extends CommutativeMonoid[A] {
  def combine(a1: A, a2: A): A
  def empty: A
}

object BoundedSemiLattice {
  implicit val intInstance: BoundedSemiLattice[Int] =
    new BoundedSemiLattice[Int] {
      def combine(a1: Int, a2: Int): Int =
        a1 max a2

      val empty: Int =
        0
    }

  implicit def setInstance[A]: BoundedSemiLattice[Set[A]] =
    new BoundedSemiLattice[Set[A]]{
      def combine(a1: Set[A], a2: Set[A]): Set[A] =
        a1 union a2

      val empty: Set[A] =
        Set.empty[A]
    }
}

11.3.3 Exercise: Generic GCounter

Using CommutativeMonoid and BoundedSemiLattice, generalise GCounter.

When you implement this, look for opportunities to use methods and syntax on Monoid to simplify your implementation. This is a good example of how type class abstractions work at multiple levels in our code. We’re using monoids to design a large component—our CRDTs—but they are also useful in the small, simplifying our code and making it shorter and clearer.

Here’s a working implementation. Note the use of |+| in the definition of merge, which significantly simplifies the process of merging and maximising counters:

import cats.instances.list._   // for Monoid
import cats.instances.map._    // for Monoid
import cats.syntax.semigroup._ // for |+|
import cats.syntax.foldable._  // for combineAll

final case class GCounter[A](counters: Map[String,A]) {
  def increment(machine: String, amount: A)
        (implicit m: CommutativeMonoid[A]): GCounter[A] = {
    val value = amount |+| counters.getOrElse(machine, m.empty)
    GCounter(counters + (machine -> value))
  }

  def merge(that: GCounter[A])
        (implicit b: BoundedSemiLattice[A]): GCounter[A] =
    GCounter(this.counters |+| that.counters)

  def total(implicit m: CommutativeMonoid[A]): A =
    this.counters.values.toList.combineAll
}

11.4 Abstracting GCounter to a Type Class

We’ve created a generic GCounter that works with any value that has instances of BoundedSemiLattice and CommutativeMonoid. However we’re still tied to a particular representation of the map from machine IDs to values. There is no need to have this restriction, and indeed it can be useful to abstract away from it. There are many key-value stores that we want to work with, from a simple Map to a relational database.

If we define a GCounter type class we can abstract over different concrete implementations. This allows us to, for example, seamlessly substitute an in-memory store for a persistent store when we want to change performance and durability tradeoffs.

There are a number of ways we can implement this. One approach is to define a GCounter type class with dependencies on CommutativeMonoid and BoundedSemiLattice. We define this as a type class that takes a type constructor with two type parameters represent the key and value types of the map abstraction.

trait GCounter[F[_,_],K, V] {
  def increment(f: F[K, V])(k: K, v: V)
        (implicit m: CommutativeMonoid[V]): F[K, V]

  def merge(f1: F[K, V], f2: F[K, V])
        (implicit b: BoundedSemiLattice[V]): F[K, V]

  def total(f: F[K, V])
        (implicit m: CommutativeMonoid[V]): V
}

object GCounter {
  def apply[F[_,_], K, V]
        (implicit counter: GCounter[F, K, V]) =
    counter
}

Try defining an instance of this type class for Map. You should be able to reuse your code from the case class version of GCounter with some minor modifications.

Here’s the complete code for the instance. Write this definition in the companion object for GCounter to place it in global implicit scope:

import cats.instances.list._   // for Monoid
import cats.instances.map._    // for Monoid
import cats.syntax.semigroup._ // for |+|
import cats.syntax.foldable._  // for combineAll

implicit def mapGCounterInstance[K, V]: GCounter[Map, K, V] =
  new GCounter[Map, K, V] {
    def increment(map: Map[K, V])(key: K, value: V)
          (implicit m: CommutativeMonoid[V]): Map[K, V] = {
      val total = map.getOrElse(key, m.empty) |+| value
      map + (key -> total)
    }

    def merge(map1: Map[K, V], map2: Map[K, V])
          (implicit b: BoundedSemiLattice[V]): Map[K, V] =
      map1 |+| map2

    def total(map: Map[K, V])
        (implicit m: CommutativeMonoid[V]): V =
      map.values.toList.combineAll
  }

You should be able to use your instance as follows:

import cats.instances.int._ // for Monoid

val g1 = Map("a" -> 7, "b" -> 3)
val g2 = Map("a" -> 2, "b" -> 5)

val counter = GCounter[Map, String, Int]

val merged = counter.merge(g1, g2)
// merged: Map[String, Int] = Map("a" -> 7, "b" -> 5)
val total  = counter.total(merged)
// total: Int = 12

The implementation strategy for the type class instance is a bit unsatisfying. Although the structure of the implementation will be the same for most instances we define, we won’t get any code reuse.

11.5 Abstracting a Key Value Store

One solution is to capture the idea of a key-value store within a type class, and then generate GCounter instances for any type that has a KeyValueStore instance. Here’s the code for such a type class:

trait KeyValueStore[F[_,_]] {
  def put[K, V](f: F[K, V])(k: K, v: V): F[K, V]

  def get[K, V](f: F[K, V])(k: K): Option[V]

  def getOrElse[K, V](f: F[K, V])(k: K, default: V): V =
    get(f)(k).getOrElse(default)

  def values[K, V](f: F[K, V]): List[V]
}

Implement your own instance for Map.

Here’s the code for the instance. Write the definition in the companion object for KeyValueStore to place it in global implicit scope:

implicit val mapKeyValueStoreInstance: KeyValueStore[Map] =
  new KeyValueStore[Map] {
    def put[K, V](f: Map[K, V])(k: K, v: V): Map[K, V] =
      f + (k -> v)

    def get[K, V](f: Map[K, V])(k: K): Option[V] =
      f.get(k)

    override def getOrElse[K, V](f: Map[K, V])
        (k: K, default: V): V =
      f.getOrElse(k, default)

    def values[K, V](f: Map[K, V]): List[V] =
      f.values.toList
  }

With our type class in place we can implement syntax to enhance data types for which we have instances:

implicit class KvsOps[F[_,_], K, V](f: F[K, V]) {
  def put(key: K, value: V)
        (implicit kvs: KeyValueStore[F]): F[K, V] =
    kvs.put(f)(key, value)

  def get(key: K)(implicit kvs: KeyValueStore[F]): Option[V] =
    kvs.get(f)(key)

  def getOrElse(key: K, default: V)
        (implicit kvs: KeyValueStore[F]): V =
    kvs.getOrElse(f)(key, default)

  def values(implicit kvs: KeyValueStore[F]): List[V] =
    kvs.values(f)
}

Now we can generate GCounter instances for any data type that has instances of KeyValueStore and CommutativeMonoid using an implicit def:

implicit def gcounterInstance[F[_,_], K, V]
    (implicit kvs: KeyValueStore[F], km: CommutativeMonoid[F[K, V]]) =
  new GCounter[F, K, V] {
    def increment(f: F[K, V])(key: K, value: V)
          (implicit m: CommutativeMonoid[V]): F[K, V] = {
      val total = f.getOrElse(key, m.empty) |+| value
      f.put(key, total)
    }

    def merge(f1: F[K, V], f2: F[K, V])
          (implicit b: BoundedSemiLattice[V]): F[K, V] =
      f1 |+| f2

    def total(f: F[K, V])(implicit m: CommutativeMonoid[V]): V =
      f.values.combineAll
  }

The complete code for this case study is quite long, but most of it is boilerplate setting up syntax for operations on the type class. We can cut down on this using compiler plugins such as Simulacrum and Kind Projector.

11.6 Summary

In this case study we’ve seen how we can use type classes to model a simple CRDT, the GCounter, in Scala. Our implementation gives us a lot of flexibility and code reuse: we aren’t tied to the data type we “count”, nor to the data type that maps machine IDs to counters.

The focus in this case study has been on using the tools that Scala provides, not on exploring CRDTs. There are many other CRDTs, some of which operate in a similar manner to the GCounter, and some of which have very different implementations. A fairly recent survey gives a good overview of many of the basic CRDTs. However this is an active area of research and we encourage you to read the recent publications in the field if CRDTs and eventually consistency interest you.


  1. We assume you are using SBT 1.0.0 or newer.

  2. The word “class” doesn’t strictly mean class in the Scala or Java sense.

  3. You may occasionally see extension methods referred to as “type enrichment” or “pimping”. These are older terms that we don’t use anymore.

  4. If you’re interested in the finer rules of implicit resolution in Scala, start by taking a look at this Stack Overflow post on implicit scope and this blog post on implicit priority.

  5. We can also use an implicit object, which provides the same thing as an implicit val.

  6. In some libraries and languages, notably Scalaz and Haskell, pure is referred to as point or return and flatMap is referred to as bind or >>=. This is purely a difference in terminology. We’ll use the term flatMap for compatibility with Cats and the Scala standard library.

  7. Cats provides an instance of MonadError for EitherT, allowing us to create instances using raiseError as well as pure.

  8. It is a well known fact that Autobot neural nets are implemented in Scala. Decepticon brains are, of course, dynamically typed.

  9. It is also the winner of Underscore’s 2017 award for the most difficult functional programming term to work into a coherent English sentence.

  10. Semigroupal is referred to as “monoidal” in the paper.

  11. See Rob Norris’ infographic for a the complete picture.

  12. Technically this is a warning not an error. It has been promoted to an error in our case because we’re using the -Xfatal-warnings flag on scalac.

  13. In Hadoop there is also a shuffle phase that we will ignore here.

  14. A closely related library called Spire already provides that abstractions.