DDevelopment & Design

Enums for scala

Scala has very limited implementation of Enumeration. Enumerated objects can’t extends other classes. Partial replacement for it is to use sealed classes. You can do pattern matching on them. When you ommit some possible value you will get compiler wa…

Scala has very limited implementation of Enumeration. Enumerated objects can’t extends other classes. Partial replacement for it is to use sealed classes. You can do pattern matching on them. When you ommit some possible value you will get compiler warning for not exhaustive pattern matching. One missing feature is that you can’t get sorted values of all objects extending them. You can simple got it using my (40-lines) EnumOf class from scala-enum. Examples below.

Declaration

sealed abstract class Color(red: Double, green: Double, blue: Double)

object Color extends EnumOf[Color] {
  case object Red   extends Color(1, 0, 0)
  case object Green extends Color(0, 1, 0)
  case object Blue  extends Color(0, 0, 1)
  case object White extends Color(0, 0, 0)
  case object Black extends Color(1, 1, 1)
}

Usage

Color.values shouldEqual List(Red, Green, Blue, White, Black)

Color.valueOfOpt("Blue").value shouldEqual Blue
Color.valueOfOpt("NotExisiting").isEmpty shouldBe true

You can also enumerate on objects nested in instances

Declaration

case class DistanceFrom(srcCity: String, srcCoordinates: Coordinate) extends EnumOf[DistanceBetween] {

    case object ToBerlin extends DistanceFromSrcCityTo("Berlin", Coordinate(52.5075419, 13.4251364))
    case object ToNewYork extends DistanceFromSrcCityTo("New York", Coordinate(40.7033127, -73.979681))
    abstract class DistanceFromSrcCityTo(val destCity: String, val destCoordinates: Coordinate) extends DistanceBetween {
        override def srcCoordinates: Coordinate = DistanceFrom.this.srcCoordinates
    } 
}

sealed abstract class DistanceBetween {
    def srcCoordinates: Coordinate
    def destCity: String
    def destCoordinates: Coordinate
    def inKm: Int = Coordinate.distanceInKm(srcCoordinates, destCoordinates).toInt
}

Usage

val DistanceFromWarsaw = DistanceFrom("Warsaw", Coordinate(52.232938, 21.0611941))
DistanceFromWarsaw.ToBerlin.inKm shouldEqual 519
DistanceFromWarsaw.ToNewYork.inKm shouldEqual 6856
DistanceFromWarsaw.values.map(_.inKm) shouldEqual List(519, 6856)

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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

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Retrofit is one of the most popular REST client for Android, if you never use it, it is high time to start. There are a lot of articles and tutorial talking about Retrofit. I just would like to show how to mock a REST server during develop of app and i...Retrofit is one of the most popular REST client for Android, if you never use it, it is high time to start. There are a lot of articles and tutorial talking about Retrofit. I just would like to show how to mock a REST server during develop of app and i...