Classloader problem with Java 7 and WebServices in Grails

Our Grails 2.1 application communicates with external SOAP WebServices. It worked fine as we follow Software Guy’s advices from this blog post. Recently, our client required new functionality – export to Excel. We’ve used Apache POI libraries for expor…

Our Grails 2.1 application communicates with external SOAP WebServices. It worked fine as we follow Software Guy’s advices from this blog post.

Recently, our client required new functionality – export to Excel. We’ve used Apache POI libraries for export. And our web service communication died. All it gave us was:

Caused by: java.lang.LinkageError: loader constraint violation: when resolving field "DATETIME" the class loader (instance of org/codehaus/groovy/grails/cli/support/GrailsRootLoader) of the referring class, javax/xml/datatype/DatatypeConstants, and the class loader (instance of <bootloader>) for the field's resolved type, ants, have different Class objects for that type</bootloader>

This message is strange, LinkageError can give you creeps. But read carefully: resolved type, ants? Something is definitely wrong here. After many searches it turned out that Java 7 already contains some conflicting classes from stax-api.jar. To solve this problem there are two thing you need to do:

  1. Ensure that your jaxws-rt dependency is runtime, not compile! // http://asoftwareguy.com/2012/02/25/web-service-clients-where-grails-lost-its-mojo/
    // Do not remove this dependency. Web services need this to work flawlessly.
    runtime (‘com.sun.xml.ws:jaxws-rt:2.1.4’)
  2. Create dependency report (grails dependency-report), search and exclude all stax-api dependencies other than jaxws-rt like this example:
    compile ('org.apache.poi:poi-ooxml-3.7') {
        excludes 'stax-api'
    }
    compile ('org.apache.poi:poi-ooxml-schemas:3.7') {
        excludes 'stax-api'
    }
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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!

Log4j and MDC in Grails

Log4j provides very useful feature: MDC - mapped diagnostic context. It can be used to store data in context of current thread. It may sound scary a bit but idea is simple.

My post is based on post http://burtbeckwith.com/blog/?p=521 from Burt Beckwith's excellent blog, it's definitely worth checking if you are interested in Grails.

Short background story...


Suppose we want to do logging our brand new shopping system and we want to have in each log customer's shopping basket number. And our system can be used at once by many users who can perform many transactions, actions like adding items and so on. How can we achieve that? Of course we can add basket number in every place where we do some logging but this task would be boring and error-prone. 

Instead of this we can use MDC to store variable with basket number in map. 

In fact MDC can be treated as map of custom values for current thread that can be used by logger. 


How to do that with Grails?


Using MDC with Grails is quite simple. All we need to do is to create our own custom filter which works for given urls and puts our data in MDC.

Filters in Grails are classes in directory grails-app/conf/* which names end with *Filters.groovy postfix. We can create this class manually or use Grails command: 
grails create-filters info.rnowak.App.Basket

In result class named BasketFilters will be created in grails-app/conf/info/rnowak/UberApp.

Initially filter class looks a little bit empty:
class BasketFilters {
def filters = {
all(controller:'*', action:'*') {
before = {

}
after = { Map model ->

}
afterView = { Exception e ->

}
}
}
}
All we need to do is fill empty closures, modify filter properties and put some data into MDC.

all is the general name of our filter, as class BasketFilters (plural!) can contain many various filters. You can name it whatever you want, for this post let assume it will be named basketFilter

Another thing is change of filter parameters. According to official documentation (link) we can customize our filter in many ways. You can specify controller to be filtered, its actions, filtered urls and so on. In our example you can stay with default option where filter is applied to every action of every controller. If you are interested in filtering only some urls, use uri parameter with expression describing desired urls to be filtered.

Three closures that are already defined in template have their function and they are started in these conditions:

  • before - as name says, it is executed before filtered action takes place
  • after - similarly, it is called after the action
  • afterView - called after rendering of the actions view
Ok, so now we know what are these mysterious methods and when they are called. But what can be done within them? In official Grails docs (link again) under section 7.6.3 there is a list of properties that are available to use in filter.

With that knowledge, we can proceed to implementing filter.

Putting something into MDC in filter


What we want to do is quite easy: we want to retrieve basket number from parameters and put it into MDC in our filter:
class BasketFilters {
def filters = {
basketFilter(controller:'*', action:'*') {
before = {
MDC.put("basketNumber", params.basketNumber ?: "")
}
after = { Map model ->
MDC.remove("basketNumber")
}
}
}
}

We retrieve basket number from Grails params map and then we put in map under specified key ("basketNumber" in this case), which will be later used in logger conversion pattern. It is important to remove custom value after processing of action to avoid leaks.

So we are putting something into MDC. But how make use of it in logs?


We can refer to custom data in MDC in conversion patter using syntax: %X{key}, where key is our key we used in filter to put data, like:
def conversionPattern = "%d{yyyy-MM-dd HH:mm:ss} %-5p %t [%c{1}] %X{basketNumber} - %m%n"


And that's it :) We've put custom data in log4j MDC and successfully used it in logs to display interesting values.