Using Kotlin extensions in Groovy

Extensions in Kotlin and GroovyKotlin and Groovy have mechanisms for extending existing classes without using inheritance or decorators. In both languages, the mechanisms are called extension methods. Their source code looks different, but generated by…

Using Kotlin extensions in Groovy

Extensions in Kotlin and Groovy

Kotlin and Groovy have mechanisms for extending existing classes without using inheritance or decorators. In both languages, the mechanisms are called extension methods. Their source code looks different, but generated bytecode is quite similar. Thanks to that, Groovy is able to use Kotlin extensions just like its own.

Why would I want to use such extensions in Groovy? The main reason is that I want to test my extensions using the best testing framework available for the JVM – Spock Framework.

Code is available here.

Extensions in Kotlin

There are many types of extensions in Kotlin. I decided to focus only on extension functions and properties.

As an example, I extend the java.lang.String class. First, I create an extension function skipFirst, which skips first N characters:

fun String.skipFirst(n: Int) = if (length > n) this.substring(n) else ""

 

Next, I create an extension property answer, which is the Answer to the Ultimate Question of Life, the Universe, and Everything:

val String.answer
    get() = 42

Both extensions are declared in package com.github.alien11689.extensions, in file called StringExtensions. However, the generated class in target directory is named StringExtensionsKt and this is the name that must be used when accessing from other languages. Specific class name can be forced by annotation @file:JvmName.

Using Kotlin extensions in Groovy

There are two ways for using extensions in Groovy that are supported by good IDEs. First, you can declare scope where the extensions are available by use method:

def "should use extension method"() {
    expect:
        use(StringExtensionsKt) {
            input.skipFirst(n) == expected
        }
    where:
        input  | n | expected
        "abcd" | 3 | "d"
        "abcd" | 6 | ""
        ""     | 3 | ""
}

def "should use extension property"() {
    expect:
        use(StringExtensionsKt) {
            "abcd".answer == 42
        }
}

It is acceptable, but is not very convenient. The second and much better way is to use an extension module definition. The extension module is defined in file org.codehaus.groovy.runtime.ExtensionModule in directory src/main/resources/META-INF/services/. The same directory is monitored by ServiceLoader, but the file format is completely different:

moduleName=string-extension-module
moduleVersion=1.0.0
extensionClasses=com.github.alien11689.extensions.StringExtensionsKt

The tests look much better now:

def "should use extension method"() {
    expect:
        input.skipFirst(n) == expected
    where:
        input  | n | expected
        "abcd" | 3 | "d"
        "abcd" | 6 | ""
        ""     | 3 | ""
}

def "should use extension property"() {
    expect:
        "abcd".answer == 42
}
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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()
  • 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.