DDevelopment & Design

Mój wykład na Warszawa JUG

We wtorek (29.10.2012) pokażę podstawy budowania Front Endu przy użyciu Twitter Bootstrap i jQuery. Zbudujemy razem aplikację do zarządzania biblioteką Warszawa JUG. Dlaczego warto przyjść? Bo będzie niedużo, ale powoli i ze zrozumieniem. Będzie to dobry fundament do dalszego rozwijania umiejętności związanych z budowanie FE.

Przeklejam zapowiedź z WJUG:

W najbliższą sobotę 100. spotkanie warszawskiego JUGa w postaci warsztatowej,
ale grupa nie zwalnia tempa i miło będzie nam gościć jednego z liderów grupy – Bartka Zdanowskiego!
Gorąco zapraszamy w najbliższy wtorek, 30 października o godzinie 18:00,
na Wydziale Matematyki Informatyki i Mechaniki UW (Banacha 2), w sali 5440 (IV piętro).
Temat: Budowanie frontendu przy użyciu TwitterBootstrap i jQuery – Bartek Zdanowski

Bartek o wykładzie:
Podczas wykładu zrobię mały wstęp do JavaScriptu (niezbędne minimum),
pokaże jak używać TwitterBootstrap[1], aby zbudować layout i jak to
ożywić przy użyciu jQuery[2]. W przypadku jQ zobaczymy też jak
komunikować się z backendem. Postaramy się razem zbudować długo
oczekiwaną aplikację do zarządzania biblioteką WJUG. Pokażę Wam rapid
development przy użyciu liveview, czy automatycznego odświeżania
przeglądarki w miarę powstawiania layoutu.
Backend zapewni nam Grails[3], którego nie będę pokazywał, chyba, że
starczy nam czasu i będą chętni.
Poziom wykładu: początkujący.

*Uwaga*: Jeśli pobijemy rekord frekwencji w październiku, to wśród
zebranych rozlosujemy licencję IntelliJ Idea lub dwie, jeśli przyjdzie
dostatecznie dużo ludzi! Na pewno do rozlosowania będzie roczna
licencja JRebel, bardzo dobrego narzędzia.

O Bartku:

Bartek Zdanowski na co dzień pracuje jako developer w TouK[4], jest
tatą dzieci, mężem żony oraz panem psa. Żonę wspiera w Fundacji
Artystycznej Młyn[5], która wystawia spektakle dla dorosłych, na które
bardzo serdecznie zaprasza ;-) Nie wypada nie mieć bloga, więc ma [6].
Od jakiegoś czasu jest współorganizatorem największej społecznościowej
konferencji Confitura[7], a ostatnio po godzinach jest szalonym
naukowcem[8].

Planowany czas prezentacji wraz z dyskusją to 120 min.

Informacje o spotkaniach zawsze widoczne w kalendarzu grupy oraz na Twitterze.

Zapraszamy!

PS. Yeah! Pobiłem rekord ilości linków w mojej zapowiedzi!

— Previous article

Inconsistent Dependency Injection to domains with Grails

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Udana Warsjawa V - 100. spotkanie WJUG

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DDevelopment & Design

Private fields and methods are not private in groovy

I used to code in Java before I met groovy. Like most of you, groovy attracted me with many enhancements. This was to my surprise to discover that method visibility in groovy is handled different than Java!

Consider this example:

class Person {
private String name
public  String surname

private Person() {}

private String signature() { "${name?.substring(0, 1)}. $surname" }

public String toString() { "I am $name $surname" }
}

How is this class interpreted with Java?

  1. Person has private constructor that cannot be accessed
  2. Field "name" is private and cannot be accessed
  3. Method signature() is private and cannot be accessed

Let's see how groovy interpretes Person:

public static void main(String[] args) {
def person = new Person()   // constructor is private - compilation error in Java
println(person.toString())

    person.@name = 'Mike'       // access name field directly - compilation error in Java
println(person.toString())

    person.name = 'John'        // there is a setter generated by groovy
println(person.toString())

    person.@surname = 'Foo'     // access surname field directly
println(person.toString())

    person.surname = 'Bar'      // access auto-generated setter
println(person.toString())

println(person.signature()) // call private method - compilation error in Java
}

I was really astonished by its output:

I am null null
I am Mike null
I am John null
I am John Foo
I am John Bar
J. Bar

As you can see, groovy does not follow visibility directives at all! It treats them as non-existing. Code compiles and executes fine. It's contrary to Java. In Java this code has several errors, pointed out in comments.

I've searched a bit on this topic and it seems that this behaviour is known since version 1.1 and there is a bug report on that: http://jira.codehaus.org/browse/GROOVY-1875. It is not resolved even with groovy 2 release. As Tim Yates mentioned in this Stackoverflow question: "It's not clear if it is a bug or by design". Groovy treats visibility keywords as a hint for a programmer.

I need to keep that lesson in mind next time I want to make some field or method private!

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.