Entity framework 7 in memory provider test

A while ago I wrote an article that http://www.codeproject.com/Articles/875165/To-Repository-Or-NOT which talked about how to test repository classes using Entity Framework and not using Entity Framework.

It has been a while and I am just about to start a small project for one of the Admin staff at work, to aid her in her day to day activities.

As always there will be a database involved.

I will likely be using Owin and OR MVC5 with Aurelia.IO for Client side.

Not sure about DB, so I decided to try out the In Memory support in the yet to be released Entity Framework 7.

Grabbing The Nuget Package

So lets have a look. The first thing you will need to do is grab the Nuget package which for me was as easy as using the Nuget package window in Visual Studio 2015.

image

The package name is “EntityFramework.InMemory” this will bring in the other bits and pieces you need.

NOTE : This is a pre-release NuGet package so you will need to include prelease packages.

 

The Model

So now that I have the correct packages in place its just a question of crafting some model classes. I am using the following 2

Person

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

namespace EF7_InMemoryProviderTest
{
    public class Person
    {
        public Person()
        {
            Qualifications = new List<Qualification>();
        }

        public int Id { get; set; }

        public string FirstName { get; set; }

        public string LastName { get; set; }

        public ICollection<Qualification> Qualifications { get; set; }

        public override string ToString()
        {
            string qualifications = Qualifications.Any() ?
                    Qualifications.Select(x => x.Description)
                        .Aggregate((x, y) => string.Format("{0} {1}", x, y)) :
                    string.Empty;

            return string.Format("Id : {0}, FirstName : {1}, LastName : {2}, \r\nQualifications : {3}\r\n",
                        Id, FirstName, LastName, qualifications);
        }
    }
}

Qualification

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

namespace EF7_InMemoryProviderTest
{
    public class Qualification
    {
       
        public int Id { get; set; }

        public string Description { get; set; }

        public override string ToString()
        {
            return string.Format("Id : {0}, Description : {1}",
                        Id, Description);
        }

    }
}

 

Custom DbContext

Nothing more to it than that. So now lets look at creating a DbContext which has our stuff in it. For me this again is very simple, I just do this:

using Microsoft.Data.Entity;
using Microsoft.Data.Entity.Infrastructure;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

namespace EF7_InMemoryProviderTest
{
    public class ClassDbContext : DbContext
    {
        public ClassDbContext(DbContextOptions options)
            : base(options)
        {
        }

        public DbSet<Person> Members { get; set; }
        public DbSet<Qualification> Qualifications { get; set; }
    }
}

Writing Some Test Code Using The InMemory Provider

So now that we have all the pieces in place, lets run some code to do a few things

  1. Seed some data
  2. Obtain a Person
  3. Add a Qualification to the Person obtained

Here is all the code to do this

using Microsoft.Data.Entity;
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;

namespace EF7_InMemoryProviderTest
{
    class Program
    {
        static void Main(string[] args)
        {
            var optionsBuilder = new DbContextOptionsBuilder<ClassDbContext>();
            optionsBuilder.UseInMemoryDatabase();

            using (var classDbContext = new ClassDbContext(optionsBuilder.Options))
            {
                SeedData(classDbContext);

                var personId1 = GetMember(classDbContext, 1);
                Console.WriteLine("> Adding a qualication\r\n");
                personId1.Qualifications.Add(classDbContext.Qualifications.First());
                classDbContext.SaveChanges();

                personId1 = GetMember(classDbContext, 1);


                Console.ReadLine();

            }
        }

        private static Person GetMember(ClassDbContext classDbContext, int id)
        {
            var person = classDbContext.Members.FirstOrDefault(x => x.Id == id);
            Console.WriteLine(person);
            return person;
        }


        private static void SeedData(ClassDbContext classDbContext)
        {
            classDbContext.Members.Add(new Person()
                {
                    Id = 1,
                    FirstName = "Sacha",
                    LastName = "Barber"
                });
            classDbContext.Members.Add(new Person()
                {
                    Id = 2,
                    FirstName = "Sarah",
                    LastName = "Barber"
                });

            classDbContext.Qualifications.Add(new Qualification()
                {
                    Id = 1,
                    Description = "Bsc Hons : Computer Science"
                });
            classDbContext.Qualifications.Add(new Qualification()
                {
                    Id = 2,
                    Description = "Msc : Computer Science"
                });
            classDbContext.Qualifications.Add(new Qualification()
                {
                    Id = 3,
                    Description = "Bsc Hons : Naturapathic medicine"
                });

            classDbContext.SaveChanges();
        }
    }
}

And this is the results

image

Closing Note

Quite happy with how easy this was, and I think I would definitely try this out for real.

If you want to play along, I have a demo project (for Visual Studio 2015) here:

https://github.com/sachabarber/EF7_InMemoryTest

Apache Kafka 0.9 Scala Producer/Consumer

For my job at the moment, I am roughly spending 50% of my time working on .NET and the other 50% of the time working with Scala. As such a lot of Scala/JVM toys have spiked my interest of late. My latest quest was to try and learn Apache Kafka, well enough that I at least understood the core concepts. I have even read a book or two on Apache Kafka, now, so feel I am at least talking partial sense in this article.

So what is Apache Kafka, exactly?

Here is what the Apache Kafka folks have to say about their own tool.

Apache Kafka is publish-subscribe messaging rethought as a distributed commit log.
Fast
A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients.

Scalable
Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers

Durable
Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact.

Distributed by Design
Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.

Taken from http://kafka.apache.org/ up on date 11/03/16

Apache Kafka was designed and built by a team of engineers at LinkedIn, where I am sure you will agree they probably had to deal with quite a bit of data.

 

I decided to learn a bit more about all this and have written an article on this over at code project :

 

http://www.codeproject.com/Articles/1085758/Apache-Kafka-Scala-Producer-Consumer-With-Some-RxS

 

In this article I will talk you through some of the core Apache Kafka concepts, and will also show how to create a Scala Apache Kafka Producer and a Scala Apache Kafka Consumer. I will also sprinkle some RxScala pixie dust on top of the Apache Kafka Consumer code such that the RX operators to be applied to the incoming Apache Kafka messages.

CASSANDRA + SPARK 2 OF 2

Last time I walked you through how to install Cassandra in the simplest manner possible. Which was as a single node installation using the DataStax community edition.

All good stuff. So I have also just written up how you might use Scala/DataStax Cassandra/Spark connector, to allow you to retrieve data from Cassandra into Spark RDDs and Cassandra tables to be hydrated into Spark RDDs.

These 2 Cassandra articles and the 1st Spark one kind of form a series of articles, which you can find using the series links at the top of the articles.

Anyway here is the latest installment

Apache Spark/Cassandra 2 of 2

CASSANDRA + SPARk 1 of 2

A while ago I wrote about using Apache Spark, which is a great tool. I have been using Cassandra for a bit at work now, so thought it might be nice to revisit that artilcle and talk through how to use Spark with Cassandra.

Here is the 1st part of that ; http://www.codeproject.com/Articles/1073158/Apache-Spark-Cassandra-of

Scala : multi project sbt setup

A while ago I wrote a post about how to use SBT (Scala Build Tool):

https://sachabarbs.wordpress.com/2015/10/13/sbt-wheres-my-nuget/

In that post I showed simple usages of SBT. Thing is that was not really that realistic, so I wanted to have a go at a more real world example of this. One where we might have multiple projects, say like this:

 

SachasSBTDemo-App which depends on 2 sub projects

  • SachasSBTDemo-Server
  • SachasSBTDemo-Common

So how do we go about doing this with SBT?

There are 5 main steps to do this. Which we look at in turn.

SBT Directory Structure

The first that we need to do is create a new project folder (if you are from Visual Studio / .NET background think of this as the solution folder) called “project”

In here we will create 2 files

build.properties which just lists the version of SBT we will use. It looks like this

sbt.version=0.13.8

SachaSBTDemo.scala is what I have called the other file, but you can call it what you like. Here is the contents of that file, this is the main SBT file that governs how it all hangs together. I will be explaining each of these parts as we go.

  import sbt._
  import Keys._

object BuildSettings {


  val buildOrganization = "sas"
  val buildVersion      = "1.0"
  val buildScalaVersion = "2.11.5"

  val buildSettings = Defaults.defaultSettings ++ Seq (
    organization := buildOrganization,
    version      := buildVersion,
    scalaVersion := buildScalaVersion
  )
}


object Dependencies {
  val jacksonjson = "org.codehaus.jackson" % "jackson-core-lgpl" % "1.7.2"
  val scalatest = "org.scalatest" % "scalatest_2.9.0" % "1.4.1" % "test"
}


object SachasSBTDemo extends Build {

  import Dependencies._
  import BuildSettings._

  // Sub-project specific dependencies
  val commonDeps = Seq (
     jacksonjson,
     scalatest
  )

  val serverDeps = Seq (
     scalatest
  )


  lazy val demoApp = Project (
    "SachasSBTDemo-App",
    file ("SachasSBTDemo-App"),
    settings = buildSettings
  )
  //build these projects when main App project gets built
  .aggregate(common, server)
  .dependsOn(common, server)

  lazy val common = Project (
    "common",
    file ("SachasSBTDemo-Common"),
    settings = buildSettings ++ Seq (libraryDependencies ++= commonDeps)
  )

  lazy val server = Project (
    "server",
    file ("SachasSBTDemo-Server"),
    settings = buildSettings ++ Seq (libraryDependencies ++= serverDeps)
  ) dependsOn (common)
  
}

 

Projects

In order to have separate project we need to use the Project item from the SBT library JARs. A minimal Project setup will tell SBT where to create the new Project. Here is an example of a Project, where the folder we expect SBT to create will be called “SachasSBTDemo-App”.

lazy val demoApp = Project (
    "SachasSBTDemo-App",
    file ("SachasSBTDemo-App"),
    settings = buildSettings
  )

Project Dependencies

We can also specify Project dependencies using “dependsOn” which takes a Seq of other projects that this Project depends on.

That means that when we apply an action to the Project that is depended on, the Project that has the dependency will also have the action applied.

lazy val demoApp = Project (
    "SachasSBTDemo-App",
    file ("SachasSBTDemo-App"),
    settings = buildSettings
  )
  //build these projects when main App project gets built
  .aggregate(common, server)
  .dependsOn(common, server)

Project Aggregation

We can also specify Project aggregates results from other projects, using “aggregate” which takes a Seq of other projects that this Project aggregates.

What “aggregate” means is that whenever we apply an action on the aggregating Project we should also see the same action applied to the aggregated Projects.

lazy val demoApp = Project (
    "SachasSBTDemo-App",
    file ("SachasSBTDemo-App"),
    settings = buildSettings
  )
  //build these projects when main App project gets built
  .aggregate(common, server)
  .dependsOn(common, server)

Library Dependencies

Just like the simple post I did before, we still need to bring in our JAR files using SBT. But this time we come up with a nicer way to manage them. We simply wrap them all up in a simple object, and then use the object to satisfy the various dependencies of the Projects. Much neater.

import sbt._
import Keys._


object Dependencies {
  val jacksonjson = "org.codehaus.jackson" % "jackson-core-lgpl" % "1.7.2"
  val scalatest = "org.scalatest" % "scalatest_2.9.0" % "1.4.1" % "test"
}

  // Sub-project specific dependencies
  val serverDeps = Seq (
     scalatest
  )

  .....
  .....
  lazy val server = Project (
    "server",
    file ("SachasSBTDemo-Server"),
    //bring in the library dependencies
    settings = buildSettings ++ Seq (libraryDependencies ++= serverDeps)
  ) dependsOn (common)

The Finished Product

The final product once run through SBT should be something like this if viewed in IntelliJ IDEA:

image

 

Or like on the file system

image

If you want to grab my source files, they are available here at GitHub : https://github.com/sachabarber/SBT_MultiProject_Demo

MVP For 2016

Well I just got the email from the big house. I am an MVP for 2016 for “Visual Studio and Development Technologies”. This will be the 9th time I have been awarded the MVP award. Neato

Interesting thing is I spent 1/2 of last year working with the JVM / open source (few Apache projects) which I started to blog about. Whilst I spent the other 1/2 on .NET, so I honestly did not think I would be receiving the MVP award this time around, so it was a nice surprise. Awards are always nice.

That said I have never tried to “GET” the MVP award. It is nice when you get recognized for your efforts, but right now I am really enjoying the open source stuff, and I will be continuing to work with that for sure. I will ALWAYS have time for .NET, I love it. My current role has me spending 50% of my time in .NET land, and the other 50% in Scala and open source, so I am a happy camper right now.

I will continue to blog about stuff that I personally find interesting, and if that is .NET / Scala / open source stuff, so be it. Hopefully that will cover .NET and the other stuff of interest that I am digging lately. Only time will tell.

 

Anyway thanks Microsoft, and thanks to all the readers of my blog. Happy new year to you all

 

 

 

 

 

SCALA mocking

 

Last time we looked at writing unit tests for our code, where we looked at using ScalaTest. This time we will be looking at mocking.

In .NET there are several choices available that I like (and a couple that I don’t), such as :

  • Moq
  • FakeItEasy
  • RhinoMocks (this is one I am not keen on)

I personally am most familiar with Moq, so when I started looking at JVM based mocking frameworks I kind of wanted one that used roughly the same syntax as the ones that I had used in .NET land.

There are several choices available that I think are quite nicely, namely :

  • ScalaMock
  • EasyMock
  • JMock
  • Mockito

Which all play nicely with ScalaTest (which I am sure you are all very pleased to here).

So with that list what did I decide upon. I personally opted for Mockito, as I liked the syntax the best, that is not to say the others are not fine and dandy, it is just that I personally liked Mockito and it seemed to have good documentation and favorable Google search results, so Mockito it is.

So for the rest of this post I will talk about how to use Mockito to write our mocks. I will be used Mockito along side ScalaTest which we looked at last time.

SBT Requirements

As with most of the previous posts you will need to grab the libraries using SBT. As such your SBT file will need to use the following:

libraryDependencies ++= Seq(
  "org.mockito" % "mockito-core" % "1.8.5",
  "org.scalatest" %% "scalatest" % "2.2.5" % "test"
)

 

Our First Example

So with all that stated above. Lets have a look at a simple example. This trivial example mocks out a java.util.ArrayList[String]. And also sets up a few verifications

class FlatSpec_Mocking_Tests extends FlatSpec with Matchers with MockitoSugar {


  "Testing using Mockito " should "be easy" in {


    //mock creation
    val mockedList = mock[java.util.ArrayList[String]]

    //using mock object
    mockedList.add("one");
    mockedList.clear

    //verification
    verify(mockedList).add("one")
    verify(mockedList).clear

  }
}

One thing you may notice straight away is how the F*k am I able to mock a ArrayList[T], which is a class which is not abstract by the way. This is pretty cool.

 

Stubbing

Using Mockito we can also stub out things just as you would expect with any 1/2 decent mocking framework. Here is an example where we try and mock out a simple trait.

import java.util.Date
import org.scalatest._
import org.scalatest.mock._
import org.mockito.Mockito._


trait DumbFormatter {

  def formatWithDataTimePrefix(inputString : String, date : Date) : String = {
    s"date : $date : $inputString"
  }

  def getDate() : String = {
    new Date().toString
  }
}



class FlatSpec_Mocking_Tests extends FlatSpec with Matchers with MockitoSugar {

  "Stubbing using Mockito " should "be easy" in {

    var mockDumbFormatter = mock[DumbFormatter]
    when(mockDumbFormatter.getDate()).thenReturn("01/01/2015")
    assert("01/01/2015" === mockDumbFormatter.getDate())
  }
}

It can be seen above that it is quite easy to mock a trait. You can also see how we stub the mock out using the  Mockito functions

  • when
  • thenReturn

 

Return Values

We just saw an example above of how to use the “thenReturn” Mockito function, which is what you would use to setup your return value. If you want a dynamic return value this could quite easily call some other function which deals with creating the return values. Kind of a return value factory method.

 

Argument Matching

Mockito comes with something that allows you to match against any argument value. It also comes with regex matchers, and allows you to write custom matchers if the ones out of the box don’t quite fit your needs.

Here is an example of writing a mock where we use the standard argument matchers:

import java.util.Date
import org.scalatest._
import org.scalatest.mock._
import org.mockito.Mockito._
import org.mockito.Matchers._


trait DumbFormatter {

  def formatWithDataTimePrefix(inputString : String, date : Date) : String = {
    s"date : $date : $inputString"
  }

  def getDate() : String = {
    new Date().toString
  }
}



class FlatSpec_Mocking_Tests extends FlatSpec with Matchers with MockitoSugar {

  "Stubbing using Mockito " should "be easy" in {

    var mockDumbFormatter = mock[DumbFormatter]
    when(mockDumbFormatter.formatWithDataTimePrefix(anyString(),any[Date]())).thenReturn("01/01/2015 Something")
    assert("01/01/2015 Something" === mockDumbFormatter.formatWithDataTimePrefix("blah blah blah", new Date()))
  }
}

Exceptions

To throw exceptions with Mockito we simply need to use the “thenThrow(….) function. Here is how.

import java.util.Date
import org.scalatest._
import org.scalatest.mock._
import org.mockito.Mockito._
import org.mockito.Matchers._


trait DumbFormatter {

  def formatWithDataTimePrefix(inputString : String, date : Date) : String = {
    s"date : $date : $inputString"
  }

  def getDate() : String = {
    new Date().toString
  }
}



class FlatSpec_Mocking_Tests extends FlatSpec with Matchers with MockitoSugar {

  "Stubbing using Mockito " should "be easy" in {

    var mockDumbFormatter = mock[DumbFormatter]
    when(mockDumbFormatter.formatWithDataTimePrefix(anyString(),any[Date]()))
	.thenThrow(new RuntimeException())

    //use the ScalaTest intercept to test for exceptions
    intercept[RuntimeException] {
      mockDumbFormatter.formatWithDataTimePrefix("blah blah blah", new Date())
    }
  }
}

See how we also have to use the ScalaTest “intercept” for the actually testing

 

CallBacks

Callbacks are useful when you want to see what a method was called with and then you can make informed decisions about what you could possibly return.

Here is how you do callbacks in Mockito, note the use of the “thenAnswer” function, and how we use an anonymous Answer object.

import java.util.Date
import org.mockito.invocation.InvocationOnMock
import org.mockito.stubbing.Answer
import org.scalatest._
import org.scalatest.mock._
import org.mockito.Mockito._
import org.mockito.Matchers._


trait DumbFormatter {

  def formatWithDataTimePrefix(inputString : String, date : Date) : String = {
    s"date : $date : $inputString"
  }

  def getDate() : String = {
    new Date().toString
  }
}



class FlatSpec_Mocking_Tests extends FlatSpec with Matchers with MockitoSugar {

  "Stubbing using Mockito " should "be easy" in {

    var mockDumbFormatter = mock[DumbFormatter]
    when(mockDumbFormatter.formatWithDataTimePrefix(anyString(),any[Date]()))
      .thenAnswer(new Answer[String] {
        override def answer(invocation: InvocationOnMock): String = {
          val result = "called back nicely sir"
          println(result)
          result
        }
      })

    assert("called back nicely sir" === mockDumbFormatter.formatWithDataTimePrefix("blah blah blah", new Date()))



  }
}

 

Verification

The last thing I wanted to talk about was verification. Which may include verifying functions got called, and were called the right number of times.

Here is a simple example of this:

import java.util.Date
import org.mockito.invocation.InvocationOnMock
import org.mockito.stubbing.Answer
import org.scalatest._
import org.scalatest.mock._
import org.mockito.Mockito._
import org.mockito.Matchers._


trait DumbFormatter {

  def formatWithDataTimePrefix(inputString : String, date : Date) : String = {
    s"date : $date : $inputString"
  }

  def getDate() : String = {
    new Date().toString
  }
}



class FlatSpec_Mocking_Tests extends FlatSpec with Matchers with MockitoSugar {

  "Stubbing using Mockito " should "be easy" in {

    var mockDumbFormatter = mock[DumbFormatter]
    when(mockDumbFormatter.formatWithDataTimePrefix(anyString(),any[Date]()))
      .thenReturn("someString")

    val theDate = new Date()
    val theResult = mockDumbFormatter.formatWithDataTimePrefix("blah blah blah", theDate)
    val theResult2 = mockDumbFormatter.formatWithDataTimePrefix("no no no", theDate)

    verify(mockDumbFormatter, atLeastOnce()).formatWithDataTimePrefix("blah blah blah", theDate)
    verify(mockDumbFormatter, times(1)).formatWithDataTimePrefix("no no no", theDate)


  }
}

 

 

Further Reading

You can read more about how to use Mockito from the docs : https://docs.google.com/document/d/15mJ2Qrldx-J14ubTEnBj7nYN2FB8ap7xOn8GRAi24_A/edit

 

 

End Of The Line

Personally my quest goes on, I am going to keep going until I consider myself  good at Scala (which probably means I know nothing).

Anyway behind the scenes I will be studying more and more stuff about how to get myself to that point. As such I guess it is only natural that I may post some more stuff about Scala in the future.

But for now this it it, this is the end of the line for this brief series of posts on Scala. I hope you have all enjoyed the posts, and if you have please feel free to leave a comment, they are always appreciated.

 

SCALA : TESTING OUR CODE

 

So last time we looked at how to use Slick to connect to a SQL server database.

This time we look at how to use one of the 2 popular Scala testing frameworks.

The 2 big names when it comes to Scala testing are

  • ScalaTest
  • Specs2

I have chosen to use ScalaTest as it seems slightly more popular, when you do a Google search, and I quite liked the syntax. That said Specs2 is also very good. so if you fancy having a look at that you should.

SBT for ScalaTest

So what do we need to get started with ScalaTest. As always we need to grab the JAR, which we do using SBT.

At time of writing this was accomplished using this SBT entry:

name := "ClassesDemo"

version := "1.0"

scalaVersion := "2.11.7"

libraryDependencies ++= Seq(
  "org.scalatest" %% "scalatest" % "2.2.5" % "test"
)

With that in place, SBT should pull down the JAR from Maven Central for you. So once you are happy that you have the ScalaTest JAR installed, we can not proceed to write some tests.

 

Writing Some Tests

I come from a .NET background, and as such I am using to working with tools such as

  • NUnit
    • TestFixture
    • Setup : To setup the test
    • TearDown : To teardown the test
  • Moq / FakeItEasy : Mocking frameworks

As such I wanted to make sure I could do everything that I was used to in .NET using ScalaTest.

This article will concentrate on the testing side of things, while the next post will be on the mocking side of things.

So let’s carry on for now shall we.

Choosing You Test Style

ScalaTest allows you to use 2 different styles of writing tests.

  • FunSuite : This is more in line with what you get with NUnit say. We would write something like Test(“testing should be easy”)
  • FlatSpec : This is more of a BDD style test declaration, where we would write something like this: “Testing” should “be easy”
     

We will see an examples of both of these styles in just a minute, but before that lets carry on and looks at some of the common things you may want to do with your tests

Setup / TearDown

You may want to run some startup/teardown code that is run. Typically startup would be used to setup mocks for your test cases, and that sort of thing.

In things like NUnit this would simply be done by creating a method and attributing it to say it is the Setup/TearDown methods.

In ScalaTest things are slightly different in that we need to mixin the “BeforeAndAfter”  trait to do this. Lets see an example:

import org.scalatest.{FunSuite, BeforeAndAfter}
import scala.collection.mutable.ListBuffer

class FunSuite_Example_Tests extends FunSuite with BeforeAndAfter {

  val builder = new StringBuilder
  val buffer = new ListBuffer[String]

  before {
    builder.append("ScalaTest is ")
  }

  after {
    builder.clear()
    buffer.clear()
  }
}

It can be seen in this example that the BeforeAndAfter trait, gives you 2 additional functions

  • before
  • after

You can use these to perform your startup/teardown logic.

This example uses the FunSuite style, but the “BeforeAndAfter”  trait mixin is done exactly the same for the FlatSpec style testing.

 

Writing A Test Using FunSuite

I think if you have come from a NUnit / XUnit type of background you will probably identify more with the FunSuite style of testing.

Here is an example of a set of FunSuite tests.

import org.scalatest.{FunSuite, BeforeAndAfter}

import scala.collection.mutable.ListBuffer

class FunSuite_Example_Tests extends FunSuite with BeforeAndAfter {

  val builder = new StringBuilder
  val buffer = new ListBuffer[String]

  before {
    builder.append("ScalaTest is ")
  }

  after {
    builder.clear()
    buffer.clear()
  }

  test("Testing should be easy") {
    builder.append("easy!")
    assert(builder.toString === "ScalaTest is easy!")
    assert(buffer.isEmpty)
    buffer += "sweet"
  }

  test("Testing should be fun") {
    builder.append("fun!")
    assert(builder.toString === "ScalaTest is fun!")
    assert(buffer.isEmpty)
  }
}

It can be see that they follow the very tried and tested approach of tools like NUnit, where you have a test(…) function, where “…” is the text that describes your testcase.

Nothing much more to say there apart from to make sure you mixin the FunSuite trait.

 

Writing A Test Using FlatSpec

ScalaTest also supports another way of writing your tests, which is to use the FlatSpec trait, which you would mixin instead of the FunSuite trait.

When you use FlatSpec you would be writing your tests more like this:

  • “Testing” should “be easy” in {…}
  • it should “be fun” in {…}

Its more of a BDD style way of creating your test cases.

Here is the exact same test suite that we saw above but this time written using the FlatSpec instead of FunSuite.

import scala.collection.mutable.ListBuffer
 
class FlatSpec_Example_Tests extends FlatSpec with BeforeAndAfter {
 
    val builder = new StringBuilder
    val buffer = new ListBuffer[String]
 
     before {
         builder.append("ScalaTest is ")
       }
 
     after {
         builder.clear()
         buffer.clear()
       }
 
    "Testing" should "be easy" in {
         builder.append("easy!")
         assert(builder.toString === "ScalaTest is easy!")
         assert(buffer.isEmpty)
         buffer += "sweet"
       }
 
     it should "be fun" in {
         builder.append("fun!")
         assert(builder.toString === "ScalaTest is fun!")
         assert(buffer.isEmpty)
       }
}

I don’t mind either, I guess it’s down to personal choice/taste at the end of the day.

Using Matchers

Matchers are ScalaTest’s way of providing additonal constraints to assert against. In some testing frameworks we would just use the Assert class for that along with things like

  • Assert.AreEqual(..)
  • Assert.IsNotNull(..)

In ScalaTest you can still use the assert(..) function, but matchers are also a good way of expressing your test conditional.

So what exactly are matchers?

In the words of the ScalaTest creators:

ScalaTest provides a domain specific language (DSL) for expressing assertions in tests using the word should.

So what do we need to do to use these ScalaTest matchers? Well quite simply we need to just mix in Matchers, like this:

import org.scalatest._

class ExampleSpec extends FlatSpec with Matchers { ...}

You can alternatively import the members of the trait, a technique particularly useful when you want to try out matcher expressions in the Scala interpeter. Here’s an example where the members of Matchers are imported:

import org.scalatest._
import Matchers._

class ExampleSpec extends FlatSpec { // Can use matchers here ...

So that give us the ability to use the ScalaTest matchers DSL. So what do these things look like. Lets see a couple of examples:

import org.scalatest._


class FlatSpec_Example_Tests extends FlatSpec with Matchers {

    "Testing" should "probably use some matchers" in {

          //equality examples
          Array(1, 2) should equal (Array(1, 2))
          val resultInt = 23
          resultInt should equal (3) // can customize equality
          resultInt should === (3)   // can customize equality and enforce type constraints
          resultInt should be (3)    // cannot customize equality, so fastest to compile
          resultInt shouldEqual 3    // can customize equality, no parentheses required
          resultInt shouldBe 3       // cannot customize equality, so fastest to compile, no parentheses required

          //length examples
          List(1,2) should have length 2
          "cat" should have length 3

          //string examples
          val helloWorld = "Hello worlld"
          helloWorld should startWith ("Hello")
          helloWorld should endWith ("world")

          val sevenString ="six seven eight"
          sevenString should include ("seven")

          //greater than / less than
          val one = 1
          val zero = 0
          val seven = 7
          one should be < seven
          one should be > zero
          one should be <= seven
          one should be >= zero

          //emptiness
          List() shouldBe empty
          List(1,2) should not be empty
       }

}

 

 

For more information on using matchers, you should consult this documentation, which you can find here:

http://www.scalatest.org/user_guide/using_matchers

 

 

SCALA : Connecting to a database

 

This time we will proceed to look at using Scala to connect to SQL server.

In .NET we have quite a few ORM choices available, as well as standard ADO.NET. For example we could use any of the following quite easily

  • Linq to SQL
  • Entity Framework
  • Dapper
  • NHibernate
  • ADO .NET

In Scala things are a bit more tame on the ORM front. We basically only have one player, which is called “Slick”. The rest of this post will be about how to use Slick.

 

Slick

The good thing about Slick is that it works with a wide range of SQL dialects. For this post I will be using what I know which is MS SQL server. As such I will be using a MS SQL server driver, and there may be differences between the driver I use and other Slick drivers, but hopefully you will get the idea.

 

Notes on MS SQL Server

The following notes assume you are install

I found that I had to do the following to get Slick to work with MS SQL Server

  • Turn on the TCP/IP
  • Insure that the full set of SQL server services were running for the Slick Extension SQL Server driver to work.

Demo IntelliJ IDEA Project

As this one is quite a lot bigger than the previous Scala posts. I have decided to upload this one to GitHub.

You can grab the project from here :

https://github.com/sachabarber/ScalaSlickTest

But before you try and run it you should make sure you have done the following :

  • Created a MS SQL Server DB
  • Run  the schema creation scripts included in the IntelliJ IDEA project
  • Changed the “application.conf” file to point to YOUR SQL Server installation

 

The rest of this post will deal with how to do various things using Slick such as:

  • Use direct SQL commands (sql strings)
  • Use the slick ORM for CRUD
  • Use a store procedure with Slick

But before we get on to any of that lets just outline the schema we will be working with. The one and only table we will be using is this one :

image

So now that we know what the single (I know lame we should have had more, but meh) table looks like lets crack on

NOTE : In the examples shown in this post I am using the Scala Async Library that I have talked about before.

 

Using Direct SQL Commands

In this section we will see how we can use Slick to run arbitrary SQL commands. Lets see some examples

Return a Scalar value

Say we only want 1 value back. Perhaps count of the rows. We can just do this:

def selectScalarObject(db:Database) : Unit = {

  val action = sql"""Select count(*) as 'sysobjectsCount'  from sysobjects""".as[Int]
  val futureDB : Future[Vector[Int]] = db.run(action)

  async {
    val sqlData = await(futureDB)
    val count = sqlData.head
    println(s"PlainSQLHelper.selectScalarObject() sysobjectsCount: $count")
  } onFailure {
    case e => {
      println(s"ERROR : $e")
    }
  }
}

Return more than 1 value

We may of course want a couple of values, but we are not quite ready to return a brand new entity. So we can use a Tuple.

Here is an example:

def selectTupleObject(db: Database) : Unit = {

  val action = sql"""Select count(*)  as 'sysobjectsCount', count(*)/10  as 'sysobjectsCountDiv10' from sysobjects""".as[(Int,Int)]
  val futureDB : Future[Vector[(Int,Int)]] = db.run(action)

  async {
    val sqlData = await(futureDB)
    val (x,y) = sqlData.head
    println(s"PlainSQLHelper.selectTupleObject() sysobjectsCount: $x, sysobjectsCountDiv10: $y")
  } onFailure {
    case e => {
      println(s"ERROR : $e")
    }
  }
}

Return a case class

We can obviously make things more formal, and be nice and return  a nice case class. Here is an example of that:

def selectRawTableObject(db: Database) : Unit = {

  val action = sql"""Select * from Items""".as[(Int,String, Double, Int)]
  val futureDB : Future[Vector[(Int,String, Double, Int)]] = db.run(action)

  async {
    val sqlData = await(futureDB)
    val (id,desc, cost, location) = sqlData.head
    val item = RawSQLItem(id,desc, cost, location)
    println(s"PlainSQLHelper.selectRawTableObject() Id: ${item.id}, Description: ${item.description}, Cost: ${item.cost}, WarehouseLocation: ${item.warehouseLocationId}")
  } onFailure {
    case e => {
      println(s"ERROR : $e")
    }
  }
}


case class RawSQLItem(id: Int, description: String, cost: Double,  warehouseLocationId: Int)

 

 

Using The Slick ORM For CRUD

These examples show how you can do the basic CRUD operations with Slick.

However before we start to look at the CRUD operations, lets just see a bit of basic Slick code. Slick uses a trait called Table which you MUST mixin. It is also common practice that we use a companion object to create a TableQuery[T]. Here is the one for the CRUD operations we will be looking at next

package org.com.barbers.slicktest

import com.typesafe.slick.driver.ms.SQLServerDriver.api._

object Items {
  val items = TableQuery[Items]
}

case class DBItem(id: Int, description: String, cost: Double,  warehouseLocationId: Int)

class Items(tag: Tag) extends Table[DBItem](tag, "Products") {
  def id = column[Int]("Id", O.PrimaryKey, O.AutoInc)
  def description = column[String]("Description")
  def cost = column[Double]("Cost")
  def warehouseLocationId = column[Int]("WarehouseLocationId")
  def * = (id, description, cost, warehouseLocationId) <> (DBItem.tupled, DBItem.unapply)
}

Create

Ok so now we have seen that Slick uses a Table mixin, and that there is a TableQuery[T] at play. Let’s move on to see how we can create some data.

This is quite weird to do. Normally what we want from a INSERT is an Id. How Slick does that is a bit strange. We need to use the Slick DSL to say what we would like returned (the “Id”), which we do using the “returning” followed by the map of the Items table. This may sound weird but the example below may help to illustrate this a bit. Here is how we do it:

def saveItem(db: Database, item: DBItem) = {

  val action =(Items.items returning Items.items.map(_.id)) +=
    DBItem(-1, item.description, item.cost, item.warehouseLocationId)
  val futureDB : Future[Int] = db.run(action)

  async {
    val savedItemId = await(futureDB)
    println(s"TableResultRunner.saveItem() savedItem.Id ${savedItemId}")
  } onFailure {
    case e => {
      println(s"ERROR : $e")
    }
  }
}

And here is how we store several items.For a bulk insert, we can’t really get the inserted Ids. But we can add all Items in on go using the standard Scala collection operator ++=, which appends a new collection to the current collection.

Again an example will make this clearer

def insertSeveralItems(db: Database, items : List[DBItem]) : Unit = {

  implicit val session: Session = db.createSession()
  val insertActions = DBIO.seq(
    (Items.items ++= items.toSeq).transactionally
  )
  val sql = Items.items.insertStatement
  val futureDB : Future[Unit] = db.run(insertActions)

  async {
    await(futureDB)
    println(s"TableResultRunner.insertSeveralItems() DONE")
  } onFailure {
    case e => {
      println(s"ERROR : $e")
    }
  }
}

 

Retrieve

So we now have some Items, so how do we get them back from the DB?

There are many ways to do this with Slick. Let’s use a simple Take(2) operation to start with

def selectTwoItems(db: Database) : Unit = {

  implicit val session: Session = db.createSession()
  val q =  Items.items.take(2)
  val futureDB : Future[Seq[DBItem]] = db.run(q.result)

  async {
    val sqlData = await(futureDB)
    val item = sqlData.head
    println(s"TableResultRunner.selectTwoItems()[0] " +
      s"Id: ${item.id}, Description: ${item.description}, " +
      s"Cost: ${item.cost}, WarehouseLocationId: ${item.warehouseLocationId}")
  } onFailure {
    case e => {
      println(s"ERROR : $e")
    }
  }
}

We can also use Queries to filter out what we want from the DB. Here is an example of using a Query, where we use a filter to get all Items that have a Id that matches a Id

def findItemById(db: Database,id : Int) = {

  async {
    val q = for { p <- Items.items if p.id === id } yield p
    val futureDBQuery : Future[Option[DBItem]] = db.run(q.result.headOption)
    val item : Option[DBItem] = await(futureDBQuery)
    println(s"OPTION ${item}")
    item match {
      case Some(x) =>  println(s"TableResultRunner.findItemById The item is $x")
      case _ => ()
    }
  } onFailure {
    case e => {
      println(s"ERROR : $e")
    }
  }
}

 

Update

Update is a stranger on. Where we get out only the attributes we want from the DB using a query, and then use Slicks inbuilt update(..) function to perform the update on the columns we want. This is clearer with an example.

In this example we want to update ONLY the “cost” column of an Item.

def updateItemCost(db: Database, description : String, cost : Double) = {

  async {
    val q = Items.items
      .filter(_.description === description)
      .map(_.cost)
      .update(cost)

    val futureDB = db.run(q)
    val done = await(futureDB)
    println(s"Update cost of ${description}, to ${cost}")

    val q2 = for { p <- Items.items if p.description === description } yield p
    val futureDBQuery : Future[Seq[DBItem]] = db.run(q2.result)
    val items = await(futureDBQuery)
    items.map(item => println(s"TableResultRunner.updateItemCost The item is now $item") )
  } onFailure {
    case e => {
      println(s"ERROR : $e")
    }
  }
}

Delete

Lastly we would like to delete an Item. So let’ see how we can do that. Again we use some Slick magic for this, where we use the .delete() function. Here is an example where I delete a random Item from the DB.

def deleteRandomItem(db: Database) = {

  async {
    val q =  Items.items.take(1)
    val futureDB : Future[Seq[DBItem]] = db.run(q.result)
    val sqlData = await(futureDB)
    val item = sqlData.head
    val deleteFuture : Future[Unit] = db.run(
      Items.items.filter(_.id === item.id).delete).map(_ => ())
    await(deleteFuture)
    println(s"TableResultRunner.deleteRandomItem() deleted item.Id ${item.id}")
  } onFailure {
    case e => {
      println(s"ERROR : $e")
    }
  }
}

 

Calling A Stored Procedure

To call a stored procedure is a as simple as using the db session, and building out the call to the right stored procedure:

Say we have this stored procedure:

USE [SLICKTEST]
GO

SET ANSI_NULLS ON
GO

SET QUOTED_IDENTIFIER ON
GO

CREATE PROCEDURE [dbo].[sp_SelectItemsByDescription]
    (
      @description NVARCHAR(MAX)
    )
AS
BEGIN
	SET NOCOUNT ON;

	select * from Items i where i.[Description] LIKE '%' + @description + '%'

END

GO


This is how we would call it using slick

def selectItems(db: Database, description: String): Unit = {

  val sqlStatement = db.source.createConnection().prepareCall(
    "{ call [dbo].[sp_SelectItemsByDescription](?) }",
    ResultSet.TYPE_FORWARD_ONLY, ResultSet.CONCUR_READ_ONLY)

  sqlStatement.setFetchDirection(ResultSet.FETCH_FORWARD);
  sqlStatement.setString("@desc", description)

  val rs = sqlStatement.executeQuery()

  while (rs.next()) {
    val item = new DBItem(
      rs.getInt("Id"),
      rs.getString("Description"),
      rs.getDouble("Cost"),
      rs.getInt("WarehouseLocationId"))

    println(s"StoredProcedureHelper.selectProducts " +
      "using description set to ${desc} got this result : " +
      s"Id: ${item.id}, Description: ${item.description}, " +
      s"Cost: ${item.cost}, WarehouseLocationId: ${item.warehouseLocationId}")
  }

  rs.close()
  sqlStatement.close()
}

 

 

scala : dependency injection / ioc

In software engineering, dependency injection is a software design pattern that implements inversion of control for resolving dependencies. Dependency injection means giving an object its instance variables. Really. That’s it.

However there are several ways of doing this, and as such it is a fairly big topic, and I will not be able to go into the very specific details of DI/ IOC in one post.

Instead I shall attempt to outline some of the ways you could do DI / IOC in Scala (and like I say there are a few).

I will play nice though, and will try and point out good resources along the way, that you can follow for more information

 

Factories

One way of doing simple poor mans DI is to use factories, which decouple the client from the actual instance class that it may need to fulfill its role.

Here is an example of a factory and a class that needs a service inside it. We simply use the factory to get the service we need.



import com.typesafe.config.{ConfigObject, ConfigValue, ConfigFactory, Config}
import scala.collection.JavaConverters._
import java.net.URI
import java.util.Map.Entry


trait Processor {
  def Process() : Unit
}

class ActualProcessor() extends Processor {
  override def Process(): Unit = {
      println("ActualProcessor")
  }
}


object ProcessorFactory {

  var _processor: Processor = new ActualProcessor()

  // Getter
  def processor = _processor

  // Setter
  def processor_=(newProcessor: Processor): Unit = _processor = newProcessor
}


class OrderService {

  def processOrder(): Unit = {
    val processor = ProcessorFactory.processor
    processor.Process
  }
  
}



object ClassesDemo {

  def main(args: Array[String]) : Unit =
  {
    new OrderService().processOrder();
    System.in.read()
  }
}


Factories typically use static methods, such that they act as singletons and can be used from anywhere, and have a new instance set from anywhere (which is typically at the start of the app, or a test case)

Here is how we might change the factory to use a mock/test double before the testing starts. I am using ScalaTest in this example

package org.scalatest.examples.flatspec.beforeandafter

import org.scalatest._


class ExampleSpec extends FlatSpec with BeforeAndAfter {

  before {
    //for the tests we could use a Mock, or a Test double
    ProcessorFactory._processor = new MockProcessor()
  }
}

Google Guice

Google Guice is a DI library primarily for Java. However since Scala is a JVM language we may use it from Scala.

You can read more about Google Guice here : https://github.com/google/guice/wiki up on date 17/11/15

You willl need the following SBT libraryDependencies

 "com.google.inject" % "guice" % "3.0"

Typical usage can be thought of as 4 separate things

  • Defining an abstraction, that our client code will depend on
  • Stating that the client code wants a dependency injected. This is done with annotations in Java/Scala using the @Inject annotation
  • Providing the wire up code to wire the abstraction that the client code wanted satisfied with the actual implementation instance that the client code will get at runtime
  • Get the item from the Google Guice DI framework

Let’s see an example of these 4 points

import com.google.inject.{ Inject, Module, Binder, Guice }

//The abstraction
trait Processor {
  def Process() : Unit
}

class ActualProcessor() extends Processor {
  override def Process(): Unit = {
      println("ActualProcessor")
  }
}


// OrderService needs a Processor abstraction
class OrderService @Inject()(processor : Processor) {

  def processOrder(): Unit = {
    processor.Process
  }

}


//Declare a Google guice module that provides the wire up code
class DependencyModule extends Module {
  def configure(binder: Binder) = {
    binder.bind(classOf[Processor]).to(classOf[ActualProcessor])
  }
}


object ClassesDemo {

  def main(args: Array[String]) : Unit =
  {
    //get the item from the DI framework
    val injector = Guice.createInjector(new DependencyModule)
    val orderService = injector.getInstance(classOf[OrderService])
    orderService.processOrder()
    System.in.read()
  }
}


 

This is a very very quick introduction to DI using Google Guice, but as you can see it is quite similar to other DI frameworks such as Spring (or Castle, Autofac, Unity in the .NET world). You should certainly read the wiki a bit more on this one.

 

 

MacWire

We will now spend a bit more time looking at another framework called “macwire” which you can read more about at this GitHub project :

https://github.com/adamw/macwire up on date 17/11/15

So how do we use this MacWire framework. Well to be honest it is not that different from Google Guice in the code you wrte, but it uses the idea of Scala Macros under the hood. Though you don’t really need to get involved with that to use it.

We need to include the following SBT libraryDependencies before we start

libraryDependencies ++= Seq(
  "com.softwaremill.macwire" %% "macros" % "2.1.0" % "provided",
  "com.softwaremill.macwire" %% "util" % "2.1.0",
  "com.softwaremill.macwire" %% "proxy" % "2.1.0"
)

So lets see an example usage shall we:

package com.barbersDemo

import com.softwaremill.macwire._

//The abstraction
trait Processor {
  def Process() : Unit
}

class ActualProcessor() extends Processor {
  override def Process(): Unit = {
      println("ActualProcessor")
  }
}


class MyApp {
  val processor = new ActualProcessor()
}


// OrderService needs a Processor abstraction
class OrderService(processor : Processor) {

  def processOrder(): Unit = {
    processor.Process
  }

}

object ClassesDemo {

  def main(args: Array[String]) : Unit =
  {

    // we would substitute this line for a line that loads a Test
    // module with a set of test services services instead if we
    // were interested in testing/mocking
    val wired = wiredInModule(new MyApp)

    val orderService = wired.wireClassInstance[OrderService](classOf[OrderService])
    orderService.processOrder()
    System.in.read()
  }
}


 

As you can see from a usability point of view, it is not that different from using Google Guice. What is different is that we DO NOT have to use the @Inject annotation 

 

Cake Pattern

The cake pattern for me is the hardest one to get out of the lot, but seems to be the defacto way of doing DI in Scala.

You do get used to it. I managed to do this without the internet to refer to with a colleague today, so it is something that comes with time.

So here is the example:

package com.barbersDemo


// This trait is how you would express a dependency
// Any class that needs a Processor would mix in this trait
// along with using a self type to allow us to mixin either
// a mock / test double
trait ProcessorComponent {

  //abstract implementation, inheritors provide implementation
  val processor : Processor

  trait Processor {
    def Process() : Unit
  }
}


// An actual Processor
trait ActualProcessorComponent extends ProcessorComponent {

  val processor = new ActualProcessor()

  class ActualProcessor() extends Processor {
    def Process(): Unit = {
      println("ActualProcessor")
    }
  }
}


// An test double Processor
trait TestProcessorComponent extends ProcessorComponent {

  val processor = new TestProcessor()

  class TestProcessor() extends Processor {
    def Process(): Unit = {
      println("TestProcessor")
    }
  }
}



// The service that needs the Processor dependency
// satisfied.Which happens via the use of mixins
// and the use of a self type
class OrderService {

  // NOTE : The self type that allows to
  // mixin and use a ProcessorComponent
  this: ProcessorComponent =>

  def ProcessOrder() {
    processor.Process()
  }

}


object ClassesDemo {

  def main(args: Array[String]) : Unit =
  {
    //val defaultOrderServiceComponent = new DefaultOrderServiceComponent with ActualProcessorComponent

    // To use the test double or mock we would use a line similar to this
    val defaultOrderServiceComponent = new OrderService with TestProcessorComponent

    defaultOrderServiceComponent.ProcessOrder()
    System.in.read()
  }
}


 

There are a couple of things to not there

  • We want to make use of a trait (abstract class) called “Processor” which others may extend to do something, or provide a mock/test implementation
  • We wrap the trait we want to inject in a xxxComponent (this appears to be some sort of convention), and we also have an abstract val that the inheritor of the trait will provide an implementation for. You can see this in the ProcessorComponent trait (which is abstract)
  • We then have an ActualProcessorComponent / TestProcessorComponent which implement the trait ProcessorComponent
  • The place where we want to make use of the service, we make use of the self type within the OrderService which is this part “this: ProcessorComponent =>”. What this really means is that the OrderService NEEDS a ProcessorComponent  mixed in to work correctly. But since we know we will have a ProcessorComponent  mixed in (eithe real implementation or mock / test double) we can make use of it in the OrderService class.
  • All that is left is to wire up the OrderService with either a real implementation or mock / test double. This is done in the ClassesDemo.main(..) method shown above

 

Some further “Cake Pattern” blogs

 

 

Structural Typing

The last example I wanted to look at was using structural typing. To my mind this is kind of like duck typing, if you are expecting something that has a Print method, and you get something that has a Print method you should be able to use it.

NOTE : this approach USES reflection so will have a performance impact if used a lot

Here is an example of using structural typing

package com.barbersDemo

import com.softwaremill.macwire._

//The abstraction
trait Processor {
  def Process() : Unit
}

class ActualProcessor() extends Processor {
  override def Process(): Unit = {
      println("ActualProcessor")
  }
}


class TestProcessor() extends Processor {
  override def Process(): Unit = {
    println("TestProcessor")
  }
}



// OrderService needs a Processor abstraction
// but this tim we use structural typing, if it looks like
// a duck and quakes like a duck its a duck kind of thing
class OrderService(env: { val processor: Processor }) {

  def processOrder(): Unit = {
    //this time we use the env parameter to obtain the dependency
    env.processor.Process
  }

}



object Config {
  lazy val processor = new ActualProcessor() // this is where injection happens
}

object TestConfig {
  lazy val processor = new TestProcessor() // this is where injection happens
}

object ClassesDemo {

  def main(args: Array[String]) : Unit =
  {
    new OrderService(Config).processOrder()
    new OrderService(TestConfig).processOrder()
    System.in.read()
  }
}


As this is a bit stranger I have included, a call which uses the actual implementation and also a call that uses a test implementation.

The good thing about this is there there is no extra libraries, it is all standard Scala, and it is immutable and type safe.

A nice way to go about things if you ask me

 

 

Follow

Get every new post delivered to your Inbox.

Join 164 other followers