DDevelopment & Design

Spring Boot and AngularJS quick start

In this post I am going to show very simple and quick example of web application using Spring Boot with AngularJS. This app contains simple functionality of sending and storing imaginary messages. I’ve also used gradle for build management. All code is…
In this post I am going to show very simple and quick example of web application using Spring Boot with AngularJS. This app contains simple functionality of sending and storing imaginary messages. I’ve also used gradle for build management. All code is public and it is available on my github: https://github.com/rafalnowak/spring-boot-fun

Introduction to Spring Boot

Spring Boot is quite new project created under Spring Source umbrella. It was very few months ago when it reached version 1.0 and status of general availability.
Most important and prominent goals of this projects are:
  • providing ability to create simple web apps very quickly
  • minimizing amount of XML codebloat which is usually necessary to configure every Spring application
  • most of app configuration is automatical
  • simplify running and deployment process by using embedded Tomcat or Jetty servers that can run our applications without special effort and deploy process
  • there are lot of so called spring boot starters which are packages containing default configuration for various fields of Spring like database access by JPA, aspect oriented programming or security
As we can see, it looks promising. In this post I’ll show few basic steps necessary to create and boot simple Spring Boot web application.

First steps

Although Spring Boot can be used with special command line interface tools, I’ve decided to use it with very popular gradle build system.
Spring Boot comes with plugins to integrate with maven or gradle. They allow us to easily run application in embedded server. Necessary instructions to include these plugin are shown on snippet below: 
buildscript {
    repositories {
        mavenCentral()
    }

    dependencies {
        classpath("org.springframework.boot:spring-boot-gradle-plugin:1.0.1.RELEASE")
    }
}
With this basic config we can proceed to next steps. In my sample project I’ve divided application into two modules: one contains persistence layer with domain object and JPA repositories and another contains presentation layer with controllers. Of course this completely optional and in such simple project it does not add any benefits. But it can show how to create multi module project in gradle. Next code fragment contains common configuration for all modules in our gradle build: 
allprojects {
    apply plugin: "java"

    version = '1.0-SNAPSHOT'
    group = "info.rnowak.springBootFun"

    repositories {
        mavenLocal()
        mavenCentral()
    }

    dependencies {
        compile "org.springframework.boot:spring-boot-starter-test:1.0.1.RELEASE"
        compile "com.google.guava:guava:16.0.1"
        compile "com.h2database:h2:1.3.175"

        testCompile "junit:junit:4.11"
        testCompile "org.mockito:mockito-all:1.9.5"
        testCompile "org.assertj:assertj-core:1.5.0"
    }
}

Now when we have common configuration, we can declare basic modules of application: 

project(":persistence") {
    dependencies {
        compile "org.springframework.boot:spring-boot-starter-data-jpa:1.0.1.RELEASE"

        testCompile project(":webapp")
    }
}

project(":webapp") {
    apply plugin: "spring-boot"

    dependencies {
        compile project(":persistence")
        compile "org.springframework.boot:spring-boot-starter-web:1.0.1.RELEASE"
    }
}
Most important parts are including special Spring Boot Starter packages and declaring usage of spring-boot plugin in one of subprojects.
Every starter packet contains dependencies for all necessary libraries used on given feature. For example, JPA starter has Hibernate dependencies and AOP starter contains spring-aop and AspectJ libraries. What is more, with this libraries Spring Boot provides also default configuration.
It is simple quick start configuration but it is enough for some starter applications.

Let’s start fun with Spring!

Our next step should be creating of starting point of application. With Spring Boot it can be done by writing regular main method in some class. Now you only need to annotate this class with special Spring Boot auto configuration annotations and application is ready to run! Example of start class is shown below: 
package info.rnowak.springFun;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.EnableAutoConfiguration;
import org.springframework.context.annotation.ComponentScan;

@ComponentScan
@EnableAutoConfiguration
public class SpringFun {
    public static void main(String[] args) {
        SpringApplication app = new SpringApplication(SpringFun.class);
        app.setShowBanner(false);
        app.run(args);
    }
}
Well, this step look simple but it has few interesting implications for all application.
Firstly, this class enables component scan for Spring managed beans with root package info.rnowak.springFun because it is placed in this package.
Another thing is that this main method allows to run application using command gradle run. By default it uses embedded Tomcat running on port 8080. Of course this behaviour can be changed and it is very well described in project documentation. It is also possible to create runnable jar from our application.
With main class defined we can create all other classes in our application like controllers, repositories, domain classes or services. But I won’t show exact examples of such classes because they do not differ in any way from the same classes in old classic Spring. If you are interesed in my example, please take a look at the repository Spring Boot Fun repo.

Add some AngularJS

One of another “side effect” of Spring Boot main configuration class is that we get few default view resolvers. View resolver, in short version, is Spring feature, which maps names of view to specific view files.
Spring Boot with its default configuration sets lookup path for index.html file which will be served by default controller. Framework looks for this file in public/, webapp/ or resources/ directory on classpath. So you can just put index.html file in one of these locations and Spring Boot will create controller serving this view. And this is the way we can use AngularJS in our project. Of course it’s not the only way but it is the simplest method for using AngularJS with Spring Boot application.
In our example application index.html file was placed in webapp/ directory and it looks like this: 
<!DOCTYPE html>

<html ng-app="springFun">

<head>
    <link rel="stylesheet" href="//netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css">

    <script src="//ajax.googleapis.com/ajax/libs/jquery/2.1.0/jquery.min.js"></script>
    <script src="//netdna.bootstrapcdn.com/bootstrap/3.1.1/js/bootstrap.min.js"></script>

    <script src="https://ajax.googleapis.com/ajax/libs/angularjs/1.3.0-beta.4/angular.min.js"></script>
    <script src="https://ajax.googleapis.com/ajax/libs/angularjs/1.3.0-beta.4/angular-route.min.js"></script>
    <script src="js/application.js"></script>
    <script src="js/controllers.js"></script>
</head>

<body>

    <nav class="navbar navbar-default" role="navigation">
        <div class="container-fluid">
            <div class="navbar-header">
                <a class="navbar-brand" href="#/index">Spring Boot Fun</a>
            </div>
            <div class="collapse navbar-collapse">
                <ul class="nav navbar-nav">
                    <li><a href="#/list">Messages list</a></li>
                    <li><a href="#/about">About</a></li>
                </ul>
            </div>
        </div>
    </nav>

    <div ng-view></div>

    <footer class="text-center">
        Spring Boot Fun
    </footer>

</body>

</html>
This file includes all angular libraries used in project, controllers definition and main application module with routing defined.
The rest of files is available in repository mentioned earlier in post so I will not provide all listings here as it would be just waste of virtual space in post :)

Summary

As we can see, Spring Boot greatly decreases time needed to write and run simple Java web application. It reduces amount of XML configuration and provieds a lot of default values and conventions. But if we want to precisely set some settings, Spring Boot does not forbid it and programmer can manually set all the settings.
Also deploy of application is simplified because Spring Boot with gradle or maven plugin allows to run application in place with these tools. We can also create runnable jar that contains embedded Tomcat or Jetty. And if it is not desired by us, we can always use war plugin and create regular, traditional war and deploy it in classical way.
Spring Boot has also great documentation and I strongly encourage to read it by everybody interested in this tool: Spring Boot Docs

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DDevelopment & Design

Recently at storm-users

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

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.