How we use Kotlin with Exposed at TouK

Why Kotlin? At TouK, we try to early adopt technologies. We don’t have a starter project skeleton that is reused in every new project, we want to try something that fits the project needs, even if it’s not that popular yet. We tried Kotlin first it mid 2016, right after reaching 1.0.2 version

Why Kotlin?

At TouK, we try to early adopt technologies. We don’t have a starter project skeleton reused in every new project; we want to try something that fits the project’s needs, even if it’s not that popular yet. We tried Kotlin first in mid-2016, right after reaching the 1.0.2 version. It was getting really popular in Android development, but almost nobody used it on the backend, especially — with production deployment. After reading some “hello world” examples, including this great article by Sebastien Deleuze we decided to try Kotlin as main language for a new MVNO project. The project was mainly a backend for a mobile app, with some integrations with external services (chat, sms, payments) and background tasks regarding customer subscriptions. We felt that Kotlin would be something fresh and more pleasant for developers, but we also liked the “not reinventing the wheel” approach — reusing large parts of the Java/JVM ecosystem we were happy with for existing projects (Spring, Gradle, JUnit, Mockito).

Why Exposed?

We initially felt that Kotlin + JPA/Hibernate is not a perfect match. Kotlin’s functional nature with first-class immutability support was not something that could seemly integrate with full-blown ORM started in the pre-Java8 era. But Sebastien’s article led us to try Exposed — a SQL access library maintained by JetBrains. From the beginning, we really liked the main assumptions of Exposed:

  • not trying to be full ORM framework
  • two flavors — typesafe SQL DSL and DAO/ActiveRecord style
  • lightweight, no reflection
  • no code generation
  • Spring integration
  • no annotations on your domain classes (in SQL DSL flavor)
  • open for extension (e.g. PostGIS and new DB dialects)

TL;DR

If you want to see how we use Kotlin + Exposed duo in our projects, check out this Github repo. It’s a Spring Boot app exposing REST API with the implementation of Medium clone as specified in http://realworld.io (“The mother of all demo apps”).

Another nice example is this repo by Seb Schmidt.

SQL DSL

In our projects we decided to try the “typesafe SQL DSL” flavor of Exposed. In this approach you don’t have to add anything to your domain classes, just need to write a simple schema mapping using Kotlin in configuration-as-code manner:

data class User(
  val username: Username,
  val password: String,
  val email: String
)

object UserTable : Table("users") {
    val username = text("username")
    val email = text("email")
    val password = text("password")
}

And then you can write type/null-safe queries with direct mapping to your domain classes:

UserTable.select { UserTable.username eq username }?.toUser()

// or 

UserTable.select { UserTable.username like username }.map { it.toUser() }

fun ResultRow.toUser() = User(
       username = this[UserTable.username],
       email = this[UserTable.email],
       password = this[UserTable.password]
)

RefIds

We like type-safe RefIds in our domain code. This is particularly useful in DDD-ish architectures, where you can keep those RefIds in a shared domain and use them to communicate between contexts.

So we wrap plan ids (longs, strings) into simple wrapper classes (e.g. UserId, ArticleId, Username, Slug). Exposed allows to easily register your own column types or even generic WrapperColumnType implementation that you can find in our repo.

Using this technique you can rewrite this mapping to something like this:

sealed class UserId : RefId<Long>() {
  object New : UserId() {
    override val value: Long by IdNotPersistedDelegate<Long>()
  }
  data class Persisted(override val value: Long) : UserId() {
    override fun toString() = "UserId(value=$value)"
  }
}

data class User(
   val id: UserId = UserId.New,
   //...
)

fun Table.userId(name: String) = longWrapper<UserId>(name, UserId::Persisted, UserId::value)

object UserTable : Table("users") {
  val id = userId("id").primaryKey().autoIncrement()
//...
}

And now we can query by type-safe RefIds:

override fun findBy(userId: UserId) =
  UserTable.select { UserTable.id eq userId }?.toUser()

Relationship mapping

One of the most significant selling points of ORMs is how easy it is to deal with relations. You just annotate the related field/collection with OneToOne or OneToMany and then can fetch the whole graph of objects at once. In theory — quite a nice idea, but in practice, things often go wrong. I’m not going to dig into details, instead, I recommend you read e.g. these fragments of “Opinionated JPA with Querydsl” book:

In Exposed SQL DSL approach you have to do relationship mapping by yourself — if you need to. Let’s consider Article and Tag case from our project’s domain. We have a many-to-many relation here, so we need additional “article_tags” table:

object ArticleTagTable : Table("article_tags") {
   val tagId = tagId("tag_id").references(TagTable.id)
   val articleId = articleId("article_id").references(ArticleTable.id)
}

When creating an Article, we have to attach all the associated Tags by populating Article’s generated id into ArticleTabTable entries:

override fun create(article: Article): Article {
   val savedArticle = ArticleTable.insert { it.from(article) }
           .getOrThrow(ArticleTable.id)
           .let { article.copy(id = it) }
   savedArticle.tags.forEach { tag ->
       ArticleTagTable.insert {
           it[ArticleTagTable.tagId] = tag.id
           it[ArticleTagTable.articleId] = savedArticle.id
       }
   }
   return savedArticle
}

The funny part is the mapping of Article with Tags in query methods — in API specification Tags are always returned with the Article — so we need to eagerly fetch tags by using leftJoin:

val ArticleWithTags = (ArticleTable leftJoin ArticleTagTable leftJoin TagTable)

override fun findBy(articleId: ArticleId) =
  ArticleWithTags
    .select { ArticleTable.id eq articleId }
    .toArticles()
    .singleOrNull()

After joining, we have then one ResultRow per one Article-Tag pair, so we have to group them by ArticleId, and build the correct Article object by adding Tags for each matching resultRow:

fun Iterable<ResultRow>.toArticles(): List<ResultRow> {
   return fold(mutableMapOf<ArticleId, Article>()) { map, resultRow ->
       val article = resultRow.toArticle()
       val tagId = resultRow.tryGet(ArticleTagTable.tagId)
       val tag = tagId?.let { resultRow.toTag() }
       val current = map.getOrDefault(article.id, article)
       map[article.id] = current.copy(tags = current.tags + listOfNotNull(tag))
       map
   }.values.toList()
}

This implementation allows us to solve all the possible cases:

  • no articles (fold just returns empty map)
  • articles with no tags (tag is null, so listOfNotNull(tag) is empty)
  • articles with many tags (an article with a single tag is inserted into the map, then other tags are added in copy method)

However, consider when you need to fetch the dependent structure with the root object? For tags it makes sense since you always want the tags with the article, and the count of tags for any article should not be that huge. What about the comments? You definitely don’t want all the comments each time you fetch the article, instead you’ll need some kind of paging or even making a parent-child hierarchy for comments for the article. That’s why we recommend having this relationship mapped indirectly — every Comment should have ArticleId property, and the CommentRepository could have methods like:

fun findAllBy(articleId: ArticleId): List<Comment>
// or
fun findAllByPaged(articleId: ArticleId, pageRequest: PageRequest): Page<Comment>

Extendibility

Exposed is by design open for extension, making it even easier with Kotlin’s support for extension methods. You can define your own column type or expressions, e.g. for PostGIS point type support as Sebastian showed in his article. We used similar PostGIS extension in our project too. We were also able to implement a simple support for Java8 DateTime column type — for now, Exposed has Joda-time support, a generic approach for various date/time libraries is planned in the roadmap.

The bigger thing was Oracle DB dialect — we were forced to migrate to Oracle at some time in our project. We submitted a pull-request with foundations of Oracle 12 support, being tested in production for a while (then, we moved back to PostgreSQL…). The implementation was rather straightforward, with DataType- and FunctionProvider interfaces to provide and just a few tweaks in batch insert support.

Final thoughts

Our developer experience with Kotlin+Exposed duo was really pleasant. If you don’t plan to map many relations directly, just use simple data classes, connected by RefIds, it works really well. The Exposed library itself may need more exhaustive documentation and removing some annoying details (e.g. transaction management via thread-local — which is already on the roadmap), but we definitely recommend you give it a try in your project!

You May Also Like

Integration testing custom validation constraints in Jersey 2

I recently joined a team trying to switch a monolithic legacy system into set of RESTful services in Java. They decided to use latest 2.x version of Jersey as a REST container which was not a first choice for me, since I’m not a big fan of JSR-* specs. But now I must admit that JAX-RS 2.x is doing things right: requires almost zero boilerplate code, support auto-discovery of features and prefers convention over configuration like other modern frameworks. Since the spec is still young, it’s hard to find good tutorials and kick-off projects with some working code. I created jersey2-starter project on GitHub which can be used as starting point for your own production-ready RESTful service. In this post I’d like to cover how to implement and integration test your own validation constraints of REST resources.

Custom constraints

One of the issues which bothers me when coding REST in Java is littering your class model with annotations. Suppose you want to build a simple Todo list REST service, when using Jackson, validation and Spring Data, you can easily end up with this as your entity class:

@Document
public class Todo {
    private Long id;
    @NotNull
    private String description;
    @NotNull
    private Boolean completed;
    @NotNull
    private DateTime dueDate;

    @JsonCreator
    public Todo(@JsonProperty("description") String description, @JsonProperty("dueDate") DateTime dueDate) {
        this.description = description;
        this.dueDate = dueDate;
        this.completed = false;
    }
    // getters and setters
}

Your domain model is now effectively blured by messy annotations almost everywhere. Let’s see what we can do with validation constraints (@NotNulls). Some may say that you could introduce some DTO layer with own validation rules, but it conflicts for me with pure REST API design, which stands that you operate on resources which should map to your domain classes. On the other hand - what does it mean that Todo object is valid? When you create a Todo you should provide a description and due date, but what when you’re updating? You should be able to change any of description, due date (postponing) and completion flag (marking as done) - but you should provide at least one of these as valid modification. So my idea is to introduce custom validation constraints, different ones for creation and modification:

@Target({TYPE, PARAMETER})
@Retention(RUNTIME)
@Constraint(validatedBy = ValidForCreation.Validator.class)
public @interface ValidForCreation {
    //...
    class Validator implements ConstraintValidator<ValidForCreation, Todo> {
    /...
        @Override
        public boolean isValid(Todo todo, ConstraintValidatorContext constraintValidatorContext) {
            return todo != null
                && todo.getId() == null
                && todo.getDescription() != null
                && todo.getDueDate() != null;
        }
    }
}

@Target({TYPE, PARAMETER})
@Retention(RUNTIME)
@Constraint(validatedBy = ValidForModification.Validator.class)
public @interface ValidForModification {
    //...
    class Validator implements ConstraintValidator<ValidForModification, Todo> {
    /...
        @Override
        public boolean isValid(Todo todo, ConstraintValidatorContext constraintValidatorContext) {
            return todo != null
                && todo.getId() == null
                && (todo.getDescription() != null || todo.getDueDate() != null || todo.isCompleted() != null);
        }
    }
}

And now you can move validation annotations to the definition of a REST endpoint:

@POST
@Consumes(APPLICATION_JSON)
public Response create(@ValidForCreation Todo todo) {...}

@PUT
@Consumes(APPLICATION_JSON)
public Response update(@ValidForModification Todo todo) {...}

And now you can remove those NotNulls from your model.

Integration testing

There are in general two approaches to integration testing:

  • test is being run on separate JVM than the app, which is deployed on some other integration environment
  • test deploys the application programmatically in the setup block.

Both of these have their pros and cons, but for small enough servoces, I personally prefer the second approach. It’s much easier to setup and you have only one JVM started, which makes debugging really easy. You can use a generic framework like Arquillian for starting your application in a container environment, but I prefer simple solutions and just use emdedded Jetty. To make test setup 100% production equivalent, I’m creating full Jetty’s WebAppContext and have to resolve all runtime dependencies for Jersey auto-discovery to work. This can be simply achieved with Maven resolved from Shrinkwrap - an Arquillian subproject:

    WebAppContext webAppContext = new WebAppContext();
    webAppContext.setResourceBase("src/main/webapp");
    webAppContext.setContextPath("/");
    File[] mavenLibs = Maven.resolver().loadPomFromFile("pom.xml")
                .importCompileAndRuntimeDependencies()
                .resolve().withTransitivity().asFile();
    for (File file: mavenLibs) {
        webAppContext.getMetaData().addWebInfJar(new FileResource(file.toURI()));
    }
    webAppContext.getMetaData().addContainerResource(new FileResource(new File("./target/classes").toURI()));

    webAppContext.setConfigurations(new Configuration[] {
        new AnnotationConfiguration(),
        new WebXmlConfiguration(),
        new WebInfConfiguration()
    });
    server.setHandler(webAppContext);

(this Stackoverflow thread inspired me a lot here)

Now it’s time for the last part of the post: parametrizing our integration tests. Since we want to test validation constraints, there are many edge paths to check (and make your code coverage close to 100%). Writing one test per each case could be a bad idea. Among the many solutions for JUnit I’m most convinced to the Junit Params by Pragmatists team. It’s really simple and have nice concept of JQuery-like helper for creating providers. Here is my tests code (I’m also using builder pattern here to create various kinds of Todos):

@Test
@Parameters(method = "provideInvalidTodosForCreation")
public void shouldRejectInvalidTodoWhenCreate(Todo todo) {
    Response response = createTarget().request().post(Entity.json(todo));

    assertThat(response.getStatus()).isEqualTo(BAD_REQUEST.getStatusCode());
}

private static Object[] provideInvalidTodosForCreation() {
    return $(
        new TodoBuilder().withDescription("test").build(),
        new TodoBuilder().withDueDate(DateTime.now()).build(),
        new TodoBuilder().withId(123L).build(),
        new TodoBuilder().build()
    );
}

OK, enough of reading, feel free to clone the project and start writing your REST services!

I recently joined a team trying to switch a monolithic legacy system into set of RESTful services in Java. They decided to use latest 2.x version of Jersey as a REST container which was not a first choice for me, since I’m not a big fan of JSR-* specs. But now I must admit that JAX-RS 2.x is doing things right: requires almost zero boilerplate code, support auto-discovery of features and prefers convention over configuration like other modern frameworks. Since the spec is still young, it’s hard to find good tutorials and kick-off projects with some working code. I created jersey2-starter project on GitHub which can be used as starting point for your own production-ready RESTful service. In this post I’d like to cover how to implement and integration test your own validation constraints of REST resources.

Custom constraints

One of the issues which bothers me when coding REST in Java is littering your class model with annotations. Suppose you want to build a simple Todo list REST service, when using Jackson, validation and Spring Data, you can easily end up with this as your entity class:

@Document
public class Todo {
    private Long id;
    @NotNull
    private String description;
    @NotNull
    private Boolean completed;
    @NotNull
    private DateTime dueDate;

    @JsonCreator
    public Todo(@JsonProperty("description") String description, @JsonProperty("dueDate") DateTime dueDate) {
        this.description = description;
        this.dueDate = dueDate;
        this.completed = false;
    }
    // getters and setters
}

Your domain model is now effectively blured by messy annotations almost everywhere. Let’s see what we can do with validation constraints (@NotNulls). Some may say that you could introduce some DTO layer with own validation rules, but it conflicts for me with pure REST API design, which stands that you operate on resources which should map to your domain classes. On the other hand - what does it mean that Todo object is valid? When you create a Todo you should provide a description and due date, but what when you’re updating? You should be able to change any of description, due date (postponing) and completion flag (marking as done) - but you should provide at least one of these as valid modification. So my idea is to introduce custom validation constraints, different ones for creation and modification:

@Target({TYPE, PARAMETER})
@Retention(RUNTIME)
@Constraint(validatedBy = ValidForCreation.Validator.class)
public @interface ValidForCreation {
    //...
    class Validator implements ConstraintValidator<ValidForCreation, Todo> {
    /...
        @Override
        public boolean isValid(Todo todo, ConstraintValidatorContext constraintValidatorContext) {
            return todo != null
                && todo.getId() == null
                && todo.getDescription() != null
                && todo.getDueDate() != null;
        }
    }
}

@Target({TYPE, PARAMETER})
@Retention(RUNTIME)
@Constraint(validatedBy = ValidForModification.Validator.class)
public @interface ValidForModification {
    //...
    class Validator implements ConstraintValidator<ValidForModification, Todo> {
    /...
        @Override
        public boolean isValid(Todo todo, ConstraintValidatorContext constraintValidatorContext) {
            return todo != null
                && todo.getId() == null
                && (todo.getDescription() != null || todo.getDueDate() != null || todo.isCompleted() != null);
        }
    }
}

And now you can move validation annotations to the definition of a REST endpoint:

@POST
@Consumes(APPLICATION_JSON)
public Response create(@ValidForCreation Todo todo) {...}

@PUT
@Consumes(APPLICATION_JSON)
public Response update(@ValidForModification Todo todo) {...}

And now you can remove those NotNulls from your model.

Integration testing

There are in general two approaches to integration testing:

  • test is being run on separate JVM than the app, which is deployed on some other integration environment
  • test deploys the application programmatically in the setup block.

Both of these have their pros and cons, but for small enough servoces, I personally prefer the second approach. It’s much easier to setup and you have only one JVM started, which makes debugging really easy. You can use a generic framework like Arquillian for starting your application in a container environment, but I prefer simple solutions and just use emdedded Jetty. To make test setup 100% production equivalent, I’m creating full Jetty’s WebAppContext and have to resolve all runtime dependencies for Jersey auto-discovery to work. This can be simply achieved with Maven resolved from Shrinkwrap - an Arquillian subproject:

    WebAppContext webAppContext = new WebAppContext();
    webAppContext.setResourceBase("src/main/webapp");
    webAppContext.setContextPath("/");
    File[] mavenLibs = Maven.resolver().loadPomFromFile("pom.xml")
                .importCompileAndRuntimeDependencies()
                .resolve().withTransitivity().asFile();
    for (File file: mavenLibs) {
        webAppContext.getMetaData().addWebInfJar(new FileResource(file.toURI()));
    }
    webAppContext.getMetaData().addContainerResource(new FileResource(new File("./target/classes").toURI()));

    webAppContext.setConfigurations(new Configuration[] {
        new AnnotationConfiguration(),
        new WebXmlConfiguration(),
        new WebInfConfiguration()
    });
    server.setHandler(webAppContext);

(this Stackoverflow thread inspired me a lot here)

Now it’s time for the last part of the post: parametrizing our integration tests. Since we want to test validation constraints, there are many edge paths to check (and make your code coverage close to 100%). Writing one test per each case could be a bad idea. Among the many solutions for JUnit I’m most convinced to the Junit Params by Pragmatists team. It’s really simple and have nice concept of JQuery-like helper for creating providers. Here is my tests code (I’m also using builder pattern here to create various kinds of Todos):

@Test
@Parameters(method = "provideInvalidTodosForCreation")
public void shouldRejectInvalidTodoWhenCreate(Todo todo) {
    Response response = createTarget().request().post(Entity.json(todo));

    assertThat(response.getStatus()).isEqualTo(BAD_REQUEST.getStatusCode());
}

private static Object[] provideInvalidTodosForCreation() {
    return $(
        new TodoBuilder().withDescription("test").build(),
        new TodoBuilder().withDueDate(DateTime.now()).build(),
        new TodoBuilder().withId(123L).build(),
        new TodoBuilder().build()
    );
}

OK, enough of reading, feel free to clone the project and start writing your REST services!