DDevelopment & Design

Announcing Krush – idiomatic persistence layer for Kotlin, based on Exposed

We’ve released a persistence library for Kotlin, you can find it on our Github. It’s a JPA-to-Exposed SQL DSL generator.

TL;DR

We’ve released a persistence library for Kotlin, you can find it on our Github. It’s a JPA-to-Exposed SQL DSL generator.

The state of persistence in Kotlin

One of the key decisions that helped Kotlin gain massive popularity was to reuse Java ecosystem instead of inventing it’s own. This means that you can safely use Kotlin as a primary language for a project developed using any popular Java stack like Spring Boot and built with Java build tool like Maven. What this also means is that natural choice for persistence layer in Kotlin is Spring Data with JPA 3 with Hibernate as an implementation.

However, JPA, which highly relies on mutable objects and dirty checking, may not look like pure Kotlin, which tries to embrace functional programming and immutability. The official Spring JPA guide for Kotlin uses mutable classes and properties which is not really idiomatic for Kotlin where you want to use immutable data classes whenever it’s possible.

There are some other options, which can be used safely with Kotlin and data classes, like Spring Data JDBC — interesting approach based on pure JDBC, embracing DDD and aggregate root concepts or Micronaut Data JDBC — if you’re not tied to Spring ecosystem. But they’re both relatively new, not mature yet and miss another idiomatic Kotlin feature — a DSL for making SQL queries.

DSL for SQL queries

Another thing that made Kotlin really powerful and popular is its ability to construct Domain Specific Languages using features like property reference, operators, infix and extension functions. For example, for Android development there is excellent anko library for constructing complex view layouts for Android apps. In Spring/JPA the default approach to SQL queries are query methods, where you use special naming convention of methods in repository interfaces. The method names are parsed at runtime to provide required SQL queries and mapping. The naming convention is supported by IntelliJ Idea and other IDEs and works well in simple cases, but may be not flexible enough when you want complex queries e.g. with some conditions based on dynamic filters. If you want to use a true, type-safe, composable and idiomatic Kotlin SQL DSL, you can try to use other libraries, like Requery or Exposed.

Requery

Requery is a lightweight persistence library for Java and Kotlin with RxJava and Java 8 streams support. It uses annotations (both custom and JPA) to process your entities and generate some infrastructure code called “model”.

So given a Book interface:

@Entity
@Table(name = "books")

interface Book : Persistable {
    @get:Key @get:Generated
    val id: Long

    val isbn: String
    val author: String
    val title: String

    val publishDate: LocalDate
}

 

You can instantiate and persist it by using generated BookEntity class:

//given
val book = BookEntity().apply {
    setIsbn("1449373321")
    setPublishDate(LocalDate.of(2017, Month.APRIL, 11))
    setTitle("Designing Data-Intensive Applications")
    setAutor("Martin Kleppmann")
}

// when
val persistedBook = dataStore.insert(book)

And the use SQL DSL to fetch data and map the results back to entities:

// then
val books = dataStore.select(Book::class).where(Book::id eq  book.id).get().toList()

assertThat(books).containsExactly(persistedBook)

This was really close to our needs! We like the idea of having annotations on the entities combined with the rich SQL DSL. Also the RxJava bindings and lazy Kotlin sequences support looks promising. On the other side, there are few minor issues related to immutable classes support:

  • immutable interface approach needs to be backed up with this generated, mutablexxxEntity class
  • there are some restrictions: e.g. you cannot use them to map relations to other entities (just foreign keys by ids)
  • @Generated also doesn’t work for ids in data classes.

You can check example project using Requery in requery branch of krush-example project on GitHub.

Exposed

Another approach which given you rich SQL DSL support is Exposed — a Kotlin-only persistence layer maintained by the JetBrains team. It comes in two flavors: active-record DAO and lightweight SQL DSL. As we are not the fans of active records, we tried the SQL DSL flavor. It works by creating additional mapping code using Kotlin objects and extension functions:

object  BookTable : Table("books") {
    val id: Column<Long> = long("id").promaryKey().autoIncrement()
    val isbn: Column<String> = varchar("isbn". 255)
    val autor: Column<String> = varchar("author". 255)
    val title: Column<String> = varchar("title". 255)
    val publishDate: Column<LocalDate> = date("publishDate")
}

Then you can refer to these Column properties to create type-safe queries and map results using Kotlin collections API:

val titles: List<String> = BookTable
        .select { BookTable.author like "Martin K%" }
        .map { it[BookTable.title] }

As you can see, Exposed is not a full-blown ORM — there is no direct mapping to/from your domain classes into these Table objects, but it’s not hard to write simple mapping functions for that. You can also benefit from Kotlin null-types support and write bindings for your own types by using Kotlin’s extension functions. We wrote some time ago this article about our approach to using Exposed in our projects.

Krush

We really like the Kotlin-first feeling combined with great flexibility of Exposed, but at some time we were tired of writing these table mappings manually. We thought that it would be nice to generate them from JPA-compatible annotations, in similar way it’s done in Requery. This ended with building a library called Krush, which we’re announcing today ;)

Krush consist of two components:

  • annotation-processor which generates Exposed mappings by reading (a subset of) standard JPA annotations found on entity classes
  • utility functions for persisting entities and mapping from/to Exposed objects

So given this entity:

@Entity
@Table(name = "books")

data class Book(
    @Id @GeneratedValue
    val id: Long? = null,

    val isbn: String,
    val author: String,
    val title: String,
    val publishDate: LocalDate
)

Krush will generate BookTable object which allows to persist it like this:

//given
val book = Book(
        isbn = "1449373321", publishDate = LocalDate.of(2017, Month.APRIL, 11),
        title = "Designing Data-Intensive Applications", author = "Martin Kleppmann"
)
val persistedBook = BookTable.insert(book)
assertThat(persistedBook.id).isNotNull()

And write queries using type-safe DSL just like you were using plain Exposed:

val bookId = book.id ?: throw IllegalargumentException( )
val fetchedBook = BookTable.select { BookTable.id eq bookId }.singleOrNull()?.toBook( )
assertThat(fetchedBook).isEqualTo(book)

val selectedBooks = BookTable
        .select { BookTable.author like "Martin Kx" }
        .toBookList()

assertThat(selectedBooks).containsOnly(book)

That’s it! You can find more details and supported features in the README of Krush repository or in some example projects.

Enjoy! Looking for feedback from the community!

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DDevelopment & Design

Spock basics

Spock (homepage) is like its authors say 'testing and specification framework'. Spock combines very elegant and natural syntax with the powerful capabilities. And what is most important it is easy to use.

One note at the very beginning: I assume that you are already familiar with principles of Test Driven Development and you know how to use testing framework like for example JUnit.

So how can I start?


Writing spock specifications is very easy. We need basic configuration of Spock and Groovy dependencies (if you are using mavenized project with Eclipse look to my previous post: Spock, Java and Maven). Once we have everything set up and running smooth we can write our first specs (spec or specification is equivalent for test class in other frameworks like JUnit of TestNG).

What is great with Spock is fact that we can use it to test both Groovy projects and pure Java projects or even mixed projects.


Let's go!


Every spec class must inherit from spock.lang.Specification class. Only then test runner will recognize it as test class and start tests. We will write few specs for this simple class: User class and few tests not connected with this particular class.

We start with defining our class:
import spock.lang.*

class UserSpec extends Specification {

}
Now we can proceed to defining test fixtures and test methods.

All activites we want to perform before each test method, are to be put in def setup() {...} method and everything we want to be run after each test should be put in def cleanup() {...} method (they are equivalents for JUnit methods with @Before and @After annotations).

It can look like this:
class UserSpec extends Specification {
    User user
    Document document

    def setup() {
        user = new User()
        document = DocumentTestFactory.createDocumentWithTitle("doc1")
    }

    def cleanup() {

    }
}
Of course we can use field initialization for instantiating test objects:
class UserSpec extends Specification {
    User user = new User()
    Document document = DocumentTestFactory.createDocumentWithTitle("doc1")

    def setup() {

    }

    def cleanup() {

    }
}

What is more readable or preferred? It is just a matter of taste because according to Spock docs behaviour is the same in these two cases.

It is worth mentioning that JUnit @BeforeClass/@AfterClass are also present in Spock as def setupSpec() {...} and def cleanupSpec() {...}. They will be runned before first test and after last test method.


First tests


In Spock every method in specification class, expect setup/cleanup, is treated by runner as a test method (unless you annotate it with @Ignore).

Very interesting feature of Spock and Groovy is ability to name methods with full sentences just like regular strings:
class UserSpec extends Specification {
    // ...

    def "should assign coment to user"() {
        // ...
    }
}
With such naming convention we can write real specification and include details about specified behaviour in method name, what is very convenient when reading test reports and analyzing errors.

Test method (also called feature method) is logically divided into few blocks, each with its own purpose. Blocks are defined like labels in Java (but they are transformed with Groovy AST transform features) and some of them must be put in code in specific order.

Most basic and common schema for Spock test is:
class UserSpec extends Specification {
    // ...

    def "should assign coment to user"() {
        given:
            // do initialization of test objects
        when:
            // perform actions to be tested
        then:
            // collect and analyze results
    }
}

But there are more blocks like:
  • setup
  • expect
  • where
  • cleanup
In next section I am going to describe each block shortly with little examples.

given block

This block is used to setup test objects and their state. It has to be first block in test and cannot be repeated. Below is little example how can it be used:
class UserSpec extends Specification {
    // ...

    def "should add project to user and mark user as project's owner"() {
        given:
            User user = new User()
            Project project = ProjectTestFactory.createProjectWithName("simple project")
        // ...
    }
}

In this code given block contains initialization of test objects and nothing more. We create simple user without any specified attributes and project with given name. In case when some of these objects could be reused in more feature methods, it could be worth putting initialization in setup method.

when and then blocks

When block contains action we want to test (Spock documentation calls it 'stimulus'). This block always occurs in pair with then block, where we are verifying response for satisfying certain conditions. Assume we have this simple test case:
class UserSpec extends Specification {
    // ...

    def "should assign user to comment when adding comment to user"() {
        given:
            User user = new User()
            Comment comment = new Comment()
        when:
            user.addComment(comment)
        then:
            comment.getUserWhoCreatedComment().equals(user)
    }

    // ...
}

In when block there is a call of tested method and nothing more. After we are sure our action was performed, we can check for desired conditions in then block.

Then block is very well structured and its every line is treated by Spock as boolean statement. That means, Spock expects that we write instructions containing comparisons and expressions returning true or false, so we can create then block with such statements:
user.getName() == "John"
user.getAge() == 40
!user.isEnabled()
Each of lines will be treated as single assertion and will be evaluated by Spock.

Sometimes we expect that our method throws an exception under given circumstances. We can write test for it with use of thrown method:
class CommentSpec extends Specification {
    def "should throw exception when adding null document to comment"() {
        given:
            Comment comment = new Comment()
        when:
            comment.setCommentedDocument(null)
        then:
            thrown(RuntimeException)
    }
}

In this test we want to make sure that passing incorrect parameters is correctly handled by tested method and that method throws an exception in response. In case you want to be certain that method does not throw particular exception, simply use notThrown method.


expect block

Expect block is primarily used when we do not want to separate when and then blocks because it is unnatural. It is especially useful for simple test (and according to TDD rules all test should be simple and short) with only one condition to check, like in this example (it is simple but should show the idea):
def "should create user with given name"() {
    given:
        User user = UserTestFactory.createUser("john doe")
    expect:
        user.getName() == "john doe"
}



More blocks!


That were very simple tests with standard Spock test layout and canonical divide into given/when/then parts. But Spock offers more possibilities in writing tests and provides more blocks.


setup/cleanup blocks

These two blocks have the very same functionality as the def setup and def cleanup methods in specification. They allow to perform some actions before test and after test. But unlike these methods (which are shared between all tests) blocks work only in methods they are defined in. 


where - easy way to create readable parameterized tests

Very often when we create unit tests there is a need to "feed" them with sample data to test various cases and border values. With Spock this task is very easy and straighforward. To provide test data to feature method, we need to use where block. Let's take a look at little the piece of code:

def "should successfully validate emails with valid syntax"() {
    expect:
        emailValidator.validate(email) == true
    where:
        email }

In this example, Spock creates variable called email which is used when calling method being tested. Internally feature method is called once, but framework iterates over given values and calls expect/when block as many times as there are values (however, if we use @Unroll annotation Spock can create separate run for each of given values, more about it in one of next examples).

Now, lets assume that we want our feature method to test both successful and failure validations. To achieve that goal we can create few parameterized variables for both input parameter and expected result. Here is a little example:

def "should perform validation of email addresses"() {
    expect:
        emailValidator.validate(email) == result
    where:
        email         result }
Well, it looks nice, but Spock can do much better. It offers tabular format of defining parameters for test what is much more readable and natural. Lets take a look:
def "should perform validation of email addresses"() {
    expect:
        emailValidator.validate(email) == result
    where:
        email           | result
        "WTF"           | false
        "@domain"       | false
        "foo@bar.com"   | true
        "a@test"        | false
}
In this code, each column of our "table" is treated as a separate variable and rows are values for subsequent test iterations.

Another useful feature of Spock during parameterizing test is its ability to "unroll" each parameterized test. Feature method from previous example could be defined as (the body stays the same, so I do not repeat it):
@Unroll("should validate email #email")
def "should perform validation of email addresses"() {
    // ...
}
With that annotation, Spock generate few methods each with its own name and run them separately. We can use symbols from where blocks in @Unroll argument by preceding it with '#' sign what is a signal to Spock to use it in generated method name.


What next?


Well, that was just quick and short journey  through Spock and its capabilities. However, with that basic tutorial you are ready to write many unit tests. In one of my future posts I am going to describe more features of Spock focusing especially on its mocking abilities.