DDevelopment & Design

33rd Degree day 3 review

At the last day of the conference, I’ve decided to skip the first presentations, and get some sleep instead. I was afraid that Venkat’s show is going to be too basic, I will see Jacek Laskowski talking about closure at 4Developers, which I’m kind of s…
At the last day of the conference, I’ve decided to skip the first presentations, and get some sleep instead. I was afraid that Venkat’s show is going to be too basic, I will see Jacek Laskowski talking about closure at 4Developers, which I’m kind of supervising from the Java perspective. As an afterthought, I should have gone to see “Static Code Analysis and AST Transformations” by Hamlet D’Arcy, as I really like this guy and the topic is intriguing at least, but I didn’t want to intoxicate myself more with coffee, and really needed to get some sleep.

MongoDB: Scaling Web Application

The first talk I’ve seen was by Ken Sipe. It was a very interesting case study, and I love case studies. They give you a real perspective, unlike the product-half-marketing that some authors are doing, and unlike self-marketing that some “professional” speakers perform. 
Ken had an interesting project at hand: a Facebook like social service, that deals with politic, named GoVote. Half of the presentation was a bit basic, with simple CRUD syntax, but when Ken started to talk about modeling, it got interesting.
Ken used Grails as the web framework and went through several plugins/libs to get him to the performance and efficiency he needed. Every one of them failed, except for GSP, which is a thing worth reflecting about. His lesson was not really surprising: you should not use any abstracts (ORM?), when playing with document databases. He sticked to GORM because of how fast his problems were solved on the mailing list and bug tracker, but GORM doesn’t really give you the resolution you want, when dealing with tree structures composed of documents.
My favourite part, albeit short, was about modeling. Ken managed to get ONE hit to DB per dynamic web page, something I’ve always dreamed of. His advice was to focus on use cases, and design document model with use cases in mind. You get a sign of bad design, when you find nested joins (hitting more than once for the data), and you can usually get away with tags, instead. Interesting stuff, I hope to verify that soon.

HTML5 For Developers

Nathaniel Schutta had a large presentation at hand, nothing he kind fit into only one hour, so he gave us a choice: what do we want to hear about. It turned out, the audience was most interested into Web Workers, local web storage and Canvas. Each had their caveats. 
Web workers allow to get some much needed concurrency  into heavy GUI (or game engines, I guess), work with a simple API where you can handle errors and spawn new workers.
Local storage is unfortunately limited by default to 5MB, and you may get unsuccessful persuading the user to change it. Apart from that, it’s a simple key-value store (both Strings), with storage events (think on-insert etc.), but current implementations handle those not without some problems.
Canvas was quite interesting. Though it has a very simple API, and only basic primitives implemented, I’ve already bought a 500 pages long book about it (and that’s without any info on all the libs build on top of it).

Ending keynotes

There were three ending keynotes, one from Nathaniel Schutta, Jurgen Appelo and the last from Robert C. Martin. As expected, those were entertaining, though unless you’ve been sleeping through the last three years in a cave, you would not be surprised by both craftsman. Jurgen, however, was quite a different animal.
His talk, titled “How to Change the World”, was based on his book “Management 3.0” and his work afterwards. I’ve been interested in the mechanics of change, since I’ve been trying to change with more or less luck, all the companies I’ve worked for. Jurgen has a lot to say in that matter, and I’ve been wrong on so many levels in my attitude towards change, that the talk was very refreshing.
You can take a look at his presentation here:
How to Change the World
Overall, while it’s too early to say whether that was the best JVM conference in Poland this year, I can recommend 33rd on all grounds. I feel I can bet blindly on it next year. Grzegorz Duda delivers enormous knowledge in so little time and so little money.

Solution Architect @ TouK

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DDevelopment & Design

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!

DDevelopment & Design

Need to make a quick json fixes – JSONPath for rescue

From time to time I have a need to do some fixes in my json data. In a world of flat files I do this with grep/sed/awk tool chain. How to handle it for JSON? Searching for a solution I came across the JSONPath. It quite mature tool (from 2007) but I haven't hear about it so I decided to share my experience with others.

First of all you can try it without pain online: http://jsonpath.curiousconcept.com/. Full syntax is described at http://goessner.net/articles/JsonPath/



But also you can download python binding and run it from command line:
$ sudo apt-get install python-jsonpath-rw
$ sudo apt-get install python-setuptools
$ sudo easy_install -U jsonpath

After that you can use inside python or with simple cli wrapper:
#!/usr/bin/python
import sys, json, jsonpath

path = sys.argv[1]

result = jsonpath.jsonpath(json.load(sys.stdin), path)
print json.dumps(result, indent=2)

… you can use it in your shell e.g. for json:
{
"store": {
"book": [
      {
"category": "reference",
"author": "Nigel Rees",
"title": "Sayings of the Century",
"price": 8.95
      },
      {
"category": "fiction",
"author": "Evelyn Waugh",
"title": "Sword of Honour",
"price": 12.99
      },
      {
"category": "fiction",
"author": "Herman Melville",
"title": "Moby Dick",
"isbn": "0-553-21311-3",
"price": 8.99
      },
      {
"category": "fiction",
"author": "J. R. R. Tolkien",
"title": "The Lord of the Rings",
"isbn": "0-395-19395-8",
"price": 22.99
      }
    ],
"bicycle": {
"color": "red",
"price": 19.95
    }
  }
}

You can print only book nodes with price lower than 10 by:
$ jsonpath '$..book[?(@.price

Result:
[
  {
"category": "reference",
"price": 8.95,
"title": "Sayings of the Century",
"author": "Nigel Rees"
  },
  {
"category": "fiction",
"price": 8.99,
"title": "Moby Dick",
"isbn": "0-553-21311-3",
"author": "Herman Melville"
  }
]

Have a nice JSON hacking!From time to time I have a need to do some fixes in my json data. In a world of flat files I do this with grep/sed/awk tool chain. How to handle it for JSON? Searching for a solution I came across the JSONPath. It quite mature tool (from 2007) but I haven't hear about it so I decided to share my experience with others.