Testing Kotlin with Spock Part 1 – Object

The object keyword in Kotlin creates singleton in a very convenient way. It can be used for example as a state of an operation. Spock Framework is one of the most expressive and readable test frameworks available in the Java ecosystem. Let’s see how Kotlin object can be used in the Spock tests.

What do we want to test?

We have a single method validate in Validator interface which returns validation status: Ok or Error.

sealed class ValidationStatus
object Ok : ValidationStatus()
object Error : ValidationStatus()


interface Validator<T> {
    fun validate(value: T): ValidationStatus
}

We also provide a simple implementation of this interface:

class AdultValidator : Validator<Int> {
    override fun validate(value: Int) = if (value >= 18) Ok else Error
}

 

How to test it with Spock?

First – silly approach

First, let’s write a parameterized test for the validator:

AdultValidator sut = new AdultValidator()

def 'should validate age #age'() {
    expect:
        sut.validate(age) == result
    where:
        age | result
        0   | Error
        17  | Error
        18  | Ok
        19  | Ok
}

 

We expect it to pass, but it fails… Error and Ok are classes in the code above.

Second – naive approach

We need instances instead, so we modify the test a little:

def 'should validate age #age'() {
    expect:
        sut.validate(age) == result
    where:
        age | result
        0   | new Error()
        17  | new Error()
        18  | new Ok()
        19  | new Ok()
}

 

And again, this one fails as well. Why? It is because Error and Ok classes do not have overridden equals method. But why? We expects Kotlin objects (those created with object keyword, not plain object) to have it implemented correctly. What is more, it works correctly in Kotlin:

fun isOk(status:ValidationStatus) = status == Ok

Third – correct approach

Let’s look into the class file:

$ javap com/github/alien11689/testingkotlinwithspock/Ok.class
Compiled from "Validator.kt"
public final class com.github.alien11689.testingkotlinwithspock.Ok extends com.github.alien11689.testingkotlinwithspock.ValidationStatus {
  public static final com.github.alien11689.testingkotlinwithspock.Ok INSTANCE;
  static {};
}

If we want to access the real object that Kotlin uses in such comparisson, then we should access the class static property called INSTANCE:

def 'should validate age #age'() {
    expect:
        sut.validate(age) == result
    where:
        age | result
        0   | Error.INSTANCE
        17  | Error.INSTANCE
        18  | Ok.INSTANCE
        19  | Ok.INSTANCE
}

Now the test passes.

Fourth – alternative approach

We can also check the method result without specific instance of the object class and use instanceof or Class#isAssignableFrom instead.

Show me the code

Code is available here.

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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):
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       ''' 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()
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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.