Kotlin’s extensions for each class

Extensions in Kotlin are very powerful mechanism. It allows for add any method to any of existing classes. Each instance has (as in Java) equals, toString and hashCode methods, but there is much more in Kotlin.Example classesLet’s define some simple cl…

Extensions in Kotlin are very powerful mechanism. It allows for add any method to any of existing classes. Each instance has (as in Java) equals, toString and hashCode methods, but there is much more in Kotlin.

Example classes

Let’s define some simple classes describing person: normal class and data class.

class PersonJaxb {
    var firstName: String? = null
    var lastName: String? = null
    var age: Int? = null
}

data class Person(val firstName: String, val lastName: String, val age: Int)

 

Normal class extensions

All instances have methods described below.

apply method

I often work with jaxb classes similar to PersonJaxb, which has not all arg constructor and all fields must be set via setters. Kotlin helps to deal with it via apply method. Target instance is provided as delagate to closure so we could define all fields values in it and returns this. The signature is T.apply(f: T.() -> Unit): T.

@Test
fun applyTest() {
    //when
    val person = PersonJaxb().apply {
        firstName = "John"
        lastName = "Smith"
        age = 20
    }

//then
assertEquals(20, person.age)
assertEquals(“John”, person.firstName)
assertEquals(“Smith”, person.lastName)
}

 

let method

Another extension is let method which is similar to map operation for collections. It has signature T.let(f: (T) -> R): R. this is passed as parameter to given closure/function.

@Test
fun letTest() {
    //when
    val fullName = Person("John", "Smith", 20).let {
        "${it.firstName} ${it.lastName}"
    }

//then
assertEquals(“John Smith”, fullName)
}

 

run method

run method looks like merge of apply and let methods: access to this is via delegate as in apply, but it also returns value as in let method. It has signature T.run(f: T.() -> R): R.

@Test
fun runTest() {
    //when
    val fullName = Person("John", "Smith", 20).run {
        "$firstName $lastName"
    }

//then
assertEquals(“John Smith”, fullName)
}

 

to method

Each instance has also defined to infix operator, which is used to create Pair. Pairs is helpful to create map entries. It has signature A.to(that: B): Pair<A, B>.

@Test
fun toTest() {
    //when
    val pair = Person("John", "Smith", 20) to 5

//then
assertEquals(Person(“John”, “Smith”, 20), pair.first)
assertEquals(5, pair.second)
}

 

Data class methods

Data class instances have also some other helpful methods (which are not extensions, but are generated for us).

componentX methods

Data class Person has three fields and it has component method generated for each of them: component1 for firstName, component2 for lastName and component3 for age.

@Test
fun componentsTest() {
    //when
    val p = Person("John", "Smith", 20)

//then
assertEquals(“John”, p.component1())
assertEquals(“Smith”, p.component2())
assertEquals(20, p.component3())
}

Why is it helpful? componentX methods are used in extracting (similar to Scala case classes extracting mechanism), e. g.:

@Test
fun extractingTest() {
    //when
    val (first, last, age) = Person("John", "Smith", 20)

//then
assertEquals(20, age)
assertEquals(“John”, first)
assertEquals(“Smith”, last)
}

 

copy method

copy method allows to create new instance based on current instance.

@Test
fun copyTest() {
    //when
    val person = Person("John", "Smith", 20).copy(lastName = "Kowalski", firstName = "Jan")

//then
assertEquals(Person(“Jan”, “Kowalski”, 20), person)
}

 

Summary

Kotlin’s extensions for each instances are very simple and help to solve many problems. The code written with these extensions is much more readable and concise than written in Java.

Sources are available here.

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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.

33rd Degree day 1 review

33rd Degree is over. After the one last year, my expectations were very high, but Grzegorz Duda once again proved he's more than able to deliver. With up to five tracks (most of the time: four presentations + one workshop), and ~650 attendees,  there was a lot to see and a lot to do, thus everyone will probably have a little bit different story to tell. Here is mine.

Twitter: From Ruby on Rails to the JVM

Raffi Krikorian talking about Twitter and JVM
The conference started with  Raffi Krikorian from Twitter, talking about their use for JVM. Twitter was build with Ruby but with their performance management a lot of the backend was moved to Scala, Java and Closure. Raffi noted, that for Ruby programmers Scala was easier to grasp than Java, more natural, which is quite interesting considering how many PHP guys move to Ruby these days because of the same reasons. Perhaps the path of learning Jacek Laskowski once described (Java -> Groovy -> Scala/Closure) may be on par with PHP -> Ruby -> Scala. It definitely feels like Scala is the holy grail of languages these days.

Raffi also noted, that while JVM delivered speed and a concurrency model to Twitter stack, it wasn't enough, and they've build/customized their own Garbage Collector. My guess is that Scala/Closure could also be used because of a nice concurrency solutions (STM, immutables and so on).

Raffi pointed out, that with the scale of Twitter, you easily get 3 million hits per second, and that means you probably have 3 edge cases every second. I'd love to learn listen to lessons they've learned from this.

 

Complexity of Complexity


The second keynote of the first day, was Ken Sipe talking about complexity. He made a good point that there is a difference between complex and complicated, and that we often recognize things as complex only because we are less familiar with them. This goes more interesting the moment you realize that the shift in last 20 years of computer languages, from the "Less is more" paradigm (think Java, ASM) to "More is better" (Groovy/Scala/Closure), where you have more complex language, with more powerful and less verbose syntax, that is actually not more complicated, it just looks less familiar.

So while 10 years ago, I really liked Java as a general purpose language for it's small set of rules that could get you everywhere, it turned out that to do most of the real world stuff, a lot of code had to be written. The situation got better thanks to libraries/frameworks and so on, but it's just patching. New languages have a lot of stuff build into, which makes their set of rules and syntax much more complex, but once you get familiar, the real world usage is simple, faster, better, with less traps laying around, waiting for you to fall.

Ken also pointed out, that while Entity Service Bus looks really simple on diagrams, it's usually very difficult and complicated to use from the perspective of the programmer. And that's probably why it gets chosen so often - the guys selling/buying it, look no deeper than on the diagram.

 

Pointy haired bosses and pragmatic programmers: Facts and Fallacies of Software Development

Venkat Subramaniam with Dima
Dima got lucky. Or maybe not.

Venkat Subramaniam is the kind of a speaker that talk about very simple things in a way, which makes everyone either laugh or reflect. Yes, he is a showman, but hey, that's actually good, because even if you know the subject quite well, his talks are still very entertaining.
This talk was very generic (here's my thesis: the longer the title, the more generic the talk will be), interesting and fun, but at the end I'm unable to see anything new I'd have learned, apart from the distinction between Dynamic vs Static and Strong vs Weak typing, which I've seen the last year, but managed to forgot. This may be a very interesting argument for all those who are afraid of Groovy/Ruby, after bad experience with PHP or Perl.

Build Trust in Your Build to Deployment Flow!


Frederic Simon talked about DevOps and deployment, and that was a miss in my  schedule, because of two reasons. First, the talk was aimed at DevOps specifically, and while the subject is trendy lately, without big-scale problems, deployment is a process I usually set up and forget about. It just works, mostly because I only have to deal with one (current) project at a time. 
Not much love for Dart.
Second, while Frederic has a fabulous accent and a nice, loud voice, he tends to start each sentence loud and fade the sound at the end. This, together with mics failing him badly, made half of the presentation hard to grasp unless you were sitting in the first row.
I'm not saying the presentation was bad, far from it, it just clearly wasn't for me.
I've left a few minutes before the end, to see how many people came to Dart presentation by Mike West. I was kind of interested, since I'm following Warsaw Google Technology User Group and heard a few voices about why I should pay attentions to that new Google language. As you can see from the picture on the right, the majority tends to disagree with that opinion.

 

Non blocking, composable reactive web programming with Iteratees

Sadek Drobi's talk about Iteratees in Play 2.0 was very refreshing. Perhaps because I've never used Play before, but the presentation was flawless, with well explained problems, concepts and solutions.
Sadek started with a reflection on how much CPU we waste waiting for IO in web development, then moved to Play's Iteratees, to explain the concept and implementation, which while very different from the that overused Request/Servlet model, looked really nice and simple. I'm not sure though, how much the problem is present when you have a simple service, serving static content before your app server. Think apache (and faster) before tomcat. That won't fix the upload/download issue though, which is beautifully solved in Play 2.0

The Future of the Java Platform: Java SE 8 & Beyond


Simon Ritter is an intriguing fellow. If you take a glance at his work history (AT&T UNIX System Labs -> Novell -> Sun -> Oracle), you can easily see, he's a heavy weight player.
His presentation was rich in content, no corpo-bullshit. He started with a bit of history of JCP and how it looks like right now, then moved to the most interesting stuff, changes. Now I could give you a summary here, but there is really no point: you'd be much better taking look at the slides. There are only 48 of them, but everything is self-explanatory.
While I'm very disappointed with the speed of changes, especially when compared to the C# world, I'm glad with the direction and the fact that they finally want to BREAK the compatibility with the broken stuff (generics, etc.).  Moving to other languages I guess I won't be the one to scream "My god, finally!" somewhere in 2017, though. All the changes together look very promising, it's just that I'd like to have them like... now? Next year max, not near the heat death of the universe.

Simon also revealed one of the great mysteries of Java, to me:
The original idea behind JNI was to make it hard to write, to discourage people form using it.
On a side note, did you know Tegra3 has actually 5 cores? You use 4 of them, and then switch to the other one, when you battery gets low.

BOF: Spring and CloudFoundry


Having most of my folks moved to see "Typesafe stack 2.0" fabulously organized by Rafał Wasilewski and  Wojtek Erbetowski (with both of whom I had a pleasure to travel to the conference) and knowing it will be recorded, I've decided to see what Josh Long has to say about CloudFoundry, a subject I find very intriguing after the de facto fiasco of Google App Engine.

The audience was small but vibrant, mostly users of Amazon EC2, and while it turned out that Josh didn't have much, with pricing and details not yet public, the fact that Spring Source has already created their own competition (Could Foundry is both an Open Source app and a service), takes a lot from my anxiety.

For the review of the second day of the conference, go here.

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