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Scala Pet Store

An implementation of the Java Pet Store using FP techniques in Scala
An analysis of, explained step by step


Onion (or Hexagonal) Architecture

Onion architecture is defined by four tenets:

  • The application is built around an independent object model
  • Inner layers define interfaces, outer layers implement interfaces
  • Direction of coupling is toward the center
  • All application core code can be compiled and run separate from infrastructure

Hexagonal architecture has the intent to allow an application to equally be driven by users, programs, automated test or batch scripts, and to be developed and tested in isolation from its eventual run-time devices and databases.

The domain package constitutes the things inside our domain. It is deliberately free of the ugliness of JDBC, JSON, HTTP, and the rest.

  1. Service – the coarse grained use cases that work with other domain concepts to realize your use cases
  2. Repository – ways to get data into and out of persistent storage. Important: Repositories do not have any business logic in them, they should not know about the context in which they are used, and should not leak details of their implementations into the world.
  3. Models – things like Pet, Order, and User are all domain objects. We keep these lean (i.e. free of behavior). All of the behavior comes via Validations and Services.

Note that Repository is kind of like an interface in Java. It is a trait that is to be implemented elsewhere.

The infrastructure package is where the ugliness lives. It has HTTP things, JDBC things, and the like.

  1. Endpoint – contains the HTTP endpoints that we surface via http4s. You will also typically see JSON things in here via circe.
  2. Repository – contains the JDBC code, implementations of our Repositories. We have 2 implementations, an in-memory version as well as a doobie version.

The config package could be considered infrastructure, as it has nothing to do with the domain.

Fitting it all together

The idea with FP in general is to keep your domain pure, and to push the ugliness to the edges (which we achieve in part via DDD and Hexagonal Architecture). The way the application is bootstrapped is via the Server class. It’s job is to make sure that all the parts are configured and available so that our application can actually start up.

The Server will:

  1. Load the configuration. If the user has not properly configured the app, it will not start.
  2. Connect to the database. Here, we also run Flyway migrations to make sure that the database is in good order. If the database cannot be connected to, the app will not start.
  3. Create our Repositories and Services. This wires together our domain. We do not use any kind of dependency injection framework, rather we pass instances where needed using Constructors.
  4. Bind to our port and expose our services. If the port is unavailable, the app will not start.

What is with this F thing?

You see in most of the core domain that we use F[_] in a lot of places. This is called a higher kinded type, and simply represents a type that holds (or works with) another type. For example, List and Option are examples of types that hold other types, like List[Int] or Option[String].

We use F[_] to mean “some effect type”. We can leave this abstract, and bind to it “at the end of the world” in the Server when we bootstrap the application. This demonstrates the idea of late binding, leave your code abstract and only bind to it when absolutely necessary.

When you see a signature like def update(pet: Pet)(implicit M: Monad[F]), we are saying that the F[_] thing must have a Monad type class available at the call site.

In this application, we use cats effect IO as our effect type, and use cats for Monads and other FP type classes and data types. We could just as easily use scalazIO and scalaz in an alternative implementation without changing the code dramatically.


Every step is accessible via a respective tag, e.g. step-2, like this:

$ git checkout tags/step-2

1. Create the skeleton

We just use sbt new m99coder/cats-minimal.g8 to create a minimal project skeleton with Cats Core 1.6.0 and Cats Effect 1.2.0. The project is based on this template.

2. Adjust the dependencies

We now add Circe (for JSON serialization) and Http4s (as the webserver) to our /build.sbt.

name := "scala-pet-store"
version := "0.0.1-SNAPSHOT"

scalaVersion := "2.12.8"

scalacOptions ++= Seq("-Ypartial-unification")

resolvers += Resolver.sonatypeRepo("snapshots")

val CatsVersion        = "1.6.0"
val CatsEffectVersion  = "1.2.0"
val CirceVersion       = "0.11.1"
val CirceConfigVersion = "0.6.1"
val Http4sVersion      = "0.20.0-RC1"

libraryDependencies ++= Seq(
  "org.typelevel" %% "cats-core"           % CatsVersion,
  "org.typelevel" %% "cats-effect"         % CatsEffectVersion,
  "io.circe"      %% "circe-generic"       % CirceVersion,
  "io.circe"      %% "circe-config"        % CirceConfigVersion,
  "org.http4s"    %% "http4s-blaze-server" % Http4sVersion

addCompilerPlugin("org.spire-math" %% "kind-projector" % "0.9.9")

3. Add database support

To utilize a database in a functional way we use Doobie and Hikari as transactor, which is transforming programs (descriptions of computations requiring a database connection) into computations that actually can be executed. First we extend our /build.sbt by adding Doobie, H2 and Hikari.

name := "scala-pet-store"
version := "0.0.1-SNAPSHOT"

scalaVersion := "2.12.8"

scalacOptions ++= Seq("-Ypartial-unification")

resolvers += Resolver.sonatypeRepo("snapshots")

val CatsVersion        = "1.6.0"
val CatsEffectVersion  = "1.2.0"
val CirceVersion       = "0.11.1"
val CirceConfigVersion = "0.6.1"
val DoobieVersion      = "0.6.0"
val H2Version          = "1.4.199"
val Http4sVersion      = "0.20.0-RC1"
val LogbackVersion     = "1.2.3"

libraryDependencies ++= Seq(
  "org.typelevel"  %% "cats-core"           % CatsVersion,
  "org.typelevel"  %% "cats-effect"         % CatsEffectVersion,
  "io.circe"       %% "circe-generic"       % CirceVersion,
  "io.circe"       %% "circe-config"        % CirceConfigVersion,
  "org.http4s"     %% "http4s-blaze-server" % Http4sVersion,
  "org.tpolecat"   %% "doobie-core"         % DoobieVersion,
  "org.tpolecat"   %% "doobie-hikari"       % DoobieVersion,
  "com.h2database" %  "h2"                  % H2Version,
  "ch.qos.logback" %  "logback-classic"     % LogbackVersion

addCompilerPlugin("org.spire-math" %% "kind-projector" % "0.9.9")

Now we add the database configuration to /src/main/resources/application.conf.

petstore {
  database {
    url = "jdbc:h2:mem:test;DB_CLOSE_DELAY=-1"
    user = "sa"
    password = ""
    driver = "org.h2.Driver"
    connections = {
      poolSize = 10
  server {
    host = "localhost"
    port = 8080

To reflect it using Circe Config, we

  • add config/DatabaseConfig.scala,
  • extend config/PetStoreConfig.scala, and
  • extend config/package.scala.

Finally we can use the configuration to create a database transactor in Server.scala.

package io.m99.petstore

import cats.effect._
import cats.syntax.functor._
import doobie.util.ExecutionContexts
import io.circe.config.parser
import io.m99.petstore.config.{DatabaseConfig, PetStoreConfig}
import org.http4s.server.blaze.BlazeServerBuilder
import org.http4s.server.{Server => H4Server}

object Server extends IOApp {

  def createServer[F[_]: ContextShift: ConcurrentEffect: Timer]: Resource[F, H4Server[F]] =
    for {
      conf             <- Resource.liftF(parser.decodePathF[F, PetStoreConfig]("petstore"))
      fixedThreadPool  <- ExecutionContexts.fixedThreadPool[F](conf.database.connections.poolSize)
      cachedThreadPool <- ExecutionContexts.cachedThreadPool[F]
      _                <- DatabaseConfig.transactor(conf.database, fixedThreadPool, cachedThreadPool)
      server <- BlazeServerBuilder[F]
    } yield server

  def run(args: List[String]): IO[ExitCode] =
    createServer.use(_ => IO.never).as(ExitCode.Success)

4. Apply database migrations

Database migrations are driven by Flyway, so we add flyway-core version 5.2.4 to our /build.sbt. The migrations itself are created within the /src/main/resources/db/migration folder and follow a certain versioning schema. To actually run the migrations we add the initializeDb method to config/DatabaseConfig.scala.

def initializeDb[F[_]](config: DatabaseConfig)(implicit S: Sync[F]): F[Unit] =
  S.delay {
      val fw: Flyway = {
        Flyway.configure().dataSource(config.url, config.user, config.password).load()

Finally we execute the migrations in the for-comprehension of our createServer method in Server.scala.

_ <- Resource.liftF(DatabaseConfig.initializeDb(conf.database))

5. Add domain object, algebra and interpreter

First we add our domain object Pet in domain/pets/Pet.scala and an Algebraic Data Type (ADT) for the status property in domain/pets/PetStatus.scala. For the ADT we use a library called enumeratum, which we need to add to /build.sbt.

val EnumeratumCirceVersion = "1.5.20"

libraryDependencies ++= Seq(
  "com.beachape" %% "enumeratum-circe" % EnumeratumCirceVersion

The algebra defines the API we offer to interact with our domain object Pet. We define it in the tagless final manner using F in domain/pets/PetRepositoryAlgebra.scala.

package io.m99.petstore.domain.pets

trait PetRepositoryAlgebra[F[_]] {
  def create(pet: Pet): F[Pet]
  def update(pet: Pet): F[Option[Pet]]
  def get(id: Long): F[Option[Pet]]
  def delete(id: Long): F[Option[Pet]]

To connect the algebra with a concrete interpreter we create infrastructure/repository/doobie/DoobiePetRepositoryInterpreter.scala and use this one in our Server.scala.

transactor <- DatabaseConfig.transactor(conf.database, fixedThreadPool, cachedThreadPool)
_          = DoobiePetRepositoryInterpreter[F](transactor)

6. Validation

We want to separate logical errors from business errors, which are modeled with their own ADT. Therefore we create domain/ValidationError.scala.

package io.m99.petstore.domain

sealed trait ValidationError extends Product with Serializable

case object PetNotFoundError extends ValidationError

Now we create an algebra in domain/pets/PetValidationAlgebra.scala and an interpreter of this algebra in domain/pets/PetValidationInterpreter.scala for handling our business errors. As with the previous steps we finally include our validation interpreter into the for-comprehension of Server.scala.

petRepository = DoobiePetRepositoryInterpreter[F](transactor)
_             = PetValidationInterpreter[F](petRepository)

7. Service

The service is the entry point to our domain. It works with the provided repository and validation algebras to implement behavior. The only file we add is domain/pets/PetService.scala.

package io.m99.petstore.domain.pets

import cats.Monad
import cats.syntax.functor._
import io.m99.petstore.domain.PetNotFoundError

class PetService[F[_]](repositoryAlgebra: PetRepositoryAlgebra[F],
                       validationAlgebra: PetValidationAlgebra[F]) {
  def create(pet: Pet): F[Pet] = repositoryAlgebra.create(pet)
  def update(pet: Pet)(implicit M: Monad[F]): EitherT[F, PetNotFoundError.type, Pet] =
    for {
      _     <- validationAlgebra.doesNotExist(
      saved <- EitherT.fromOptionF(repositoryAlgebra.update(pet), PetNotFoundError)
    } yield saved
  def get(id: Long)(implicit M: Monad[F]): EitherT[F, PetNotFoundError.type, Pet] =
    EitherT.fromOptionF(repositoryAlgebra.get(id), PetNotFoundError)
  def delete(id: Long)(implicit M: Monad[F]): F[Unit] = repositoryAlgebra.delete(id).as(())

object PetService {
  def apply[F[_]: Monad](repositoryAlgebra: PetRepositoryAlgebra[F],
                         validationAlgebra: PetValidationAlgebra[F]) =
    new PetService[F](repositoryAlgebra, validationAlgebra)

Now we can use it again in the for-comprehension of our Server.scala.

petRepository = DoobiePetRepositoryInterpreter[F](transactor)
petValidation = PetValidationInterpreter[F](petRepository)
_             = PetService[F](petRepository, petValidation)

8. Endpoint

The most important part of creating the endpoint is located in infrastructure/endpoint/PetEndpoints.scala, which contains endpoint for different HTTP methods utilizing the provided PetService. To connect all the dots, change Server.scala like this.

services = PetEndpoints.endpoints[F](petService)
httpApp  = Router("/" -> services).orNotFound
_ <- Resource.liftF(DatabaseConfig.initializeDb(conf.database))
server <- BlazeServerBuilder[F]

Now you can interact with the application using curl like shown below.

$ # Creating a pet
$ curl -i \
    -H "Content-Type: application/json" \
    -d '{
          "category": "Cat",
          "bio": "I am fuzzy",
          "name": "Harry",
          "tags": [],
          "photoUrls": [],
          "status": "Available"
        }' \
    -X POST http://localhost:8080/pets
$ # Getting a pet
$ curl -i http://localhost:8080/pets/1

$ # Updating a pet
$ curl -i \
    -H "Content-Type: application/json" \
    -d '{
          "category": "Cat",
          "bio": "I am fuzzy",
          "name": "Harry",
          "tags": [],
          "photoUrls": [],
          "status": "Pending"
        }' \
    -X PUT http://localhost:8080/pets/1
$ # Deleting a pet
$ curl -i -X DELETE http://localhost:8080/pets/1

9. Extend algebra and validations

Now we extend our logic by three different parts:

  1. We add a new validation for an already existing pet based on name and category to domain/ValidationError.scala and adjust our validation algebra and interpreter accordingly
  2. We extend our algebra with a couple of convenience methods which will be used by our service and adjust the Doobie interpreter accordingly
  3. We finally also adjust our endpoint for creating a pet and add a list endpoint

You can easily demo that the added logic works for case 1 by creating the same pet twice, which now results in a 409 Conflict response and for case 3 by calling http://localhost:8080/pets before and after having created a pet.

10. Pagination and query parameters

We extend the listing endpoint of pets with optional query parameters for limiting the number of retrieved pets and specifying the offset as well. Therefore infrastructure/repository/doobie/SQLPagination.scala was added and incorporated into the domain logic. Additionally infrastructure/endpoint/Pagination.scala defines the query parameter decoders used in the endpoint. Finally we add two new endpoints which can be used to retrieve pets by status and tag. Both support multiple occurrences of the defined query parameter identifiers, status and tag.

$ # query pets with custom limit and offset
$ curl -i http://localhost:8080/pets\?limit\=2\&offset\=2

$ # query pets by status
$ curl -i http://localhost:8080/pets/findByStatus\?status\=Pending\&status\=Available

$ # query pets by tag
$ curl -i http://localhost:8080/pets/findByTag\?tag\=goldie\&tag\=labrador

11. Orders

Now we add our second domain object Order with all the components required. Be aware that the pet needs to exist in the database before you create an order referencing it, otherwise your query will result in an Internal Server Error.

$ # Creating an order
$ curl -i \
    -H "Content-Type: application/json" \
    -d '{
          "petId": 1,
          "status": "Placed",
          "complete": false
        }' \
    -X POST http://localhost:8080/orders
$ # Getting an order
$ curl -i http://localhost:8080/orders/1

$ # Deleting an order
$ curl -i -X DELETE http://localhost:8080/orders/1

12. Users

Now we add our third and last domain object User with all the components required. For now we don’t care about security, in the sense of storing the password in plain text. We will fix that in the next step.

$ # Creating a user
$ curl -i \
    -H "Content-Type: application/json" \
    -d '{
          "userName": "johndoe",
          "firstName": "John",
          "lastName": "Doe",
          "email": "",
          "password": "s3cr3t"
        }' \
    -X POST http://localhost:8080/users
$ # Getting a user
$ curl -i http://localhost:8080/users/johndoe

$ # Deleting a user
$ curl -i -X DELETE http://localhost:8080/users/johndoe

13. Security and Authentication

To apply proper security we hash the provided password using the tsec library. If you re-run the curl command to create a user you will recognize the hash in the response.

$ # Login with correct credentials
$ curl -i \
    -H "Content-Type: application/json" \
    -d '{
          "userName": "johndoe",
          "password": "s3cr3t"
        }' \
    -X POST http://localhost:8080/login
$ # Login with incorrect credentials
$ curl -i \
    -H "Content-Type: application/json" \
    -d '{
          "userName": "johndoe",
          "password": "foobar"
        }' \
    -X POST http://localhost:8080/login

14. Testing

For testing purposes we complement our Doobie based repository code with an in-memory variant in the folder infrastructure/repository/inmemory. Now we move to /src/test/scala/io/m99/petstore as the root folder. Next step is to define so called “arbitraries” for our property-based tests in PetStoreArbitraries.scala. After that we add tests for the endpoints to infrastructure/endpoint. The last thing we add are query type checks for the Doobie based repositories to infrastructure/repository/doobie.

To make it work, we need to add some dependencies to our /build.sbt.

val ScalaCheckVersion = "1.14.0"
val ScalaTestVersion  = "3.0.7"

libraryDependencies ++= Seq(
  "org.tpolecat"   %% "doobie-scalatest"    % DoobieVersion % Test,
  "org.http4s"     %% "http4s-blaze-client" % Http4sVersion % Test,
  "org.scalacheck" %% "scalacheck"          % ScalaCheckVersion % Test,
  "org.scalatest"  %% "scalatest"           % ScalaTestVersion % Test

Finally, we can run our tests.

$ sbt test
[info] OrderEndpointsSpec:
[info] - place order
[info] PetEndpointsSpec:
[info] - create pet
[info] - update pet
[info] UserQueryTypeCheckSpec:
[info] - Type check user queries
[info] OrderQueryTypeCheckSpec:
[info] - Type check order queries
[info] PetQueryTypeCheckSpec:
[info] - Type check pet queries
[info] UserEndpointsSpec:
[info] - create user
[info] - update user
[info] - get user by userName
[info] - delete user by userName
[info] ScalaTest
[info] Run completed in 4 seconds, 832 milliseconds.
[info] Total number of tests run: 10
[info] Suites: completed 6, aborted 0
[info] Tests: succeeded 10, failed 0, canceled 0, ignored 0, pending 0
[info] All tests passed.
[info] Passed: Total 10, Failed 0, Errors 0, Passed 10
[success] Total time: 7 s, completed May 6, 2019 12:02:38 PM

We are done :) Thanks for reading.

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