DDevelopment & Design

Writing JAXB in Groovy

Suppose you want write a jaxb class in groovy. Why? Because you do not have to write these all getters, setters and other methods. You only have to write your fields down.@XmlRootElement@HashCodeAndEquals@ToStringclass Person { String firstName String …

Suppose you want write a jaxb class in groovy. Why? Because you do not have to write these all getters, setters and other methods. You only have to write your fields down.

@XmlRootElement 
@HashCodeAndEquals 
@ToString 
class Person {
    String firstName String lastName Integer age
}

 

Lets check if we could unmarshal xml to Person class:

def 'should unmarshall person xml to object'(){
    given:
        JAXBContext jc = JAXBContext.newInstance(Person)
        String xml = 'JohnSmith20' 
    expect:
        jc.createUnmarshaller().unmarshal(new StringReader(xml)) == new Person(firstName: 'John', lastName: 'Smith', age: 20)
}

 

When we try this, then we obtain an eception:

com.sun.xml.internal.bind.v2.runtime.IllegalAnnotationsException: 1 counts of IllegalAnnotationExceptions
groovy.lang.MetaClass is an interface, and JAXB can't handle interfaces.
 this problem is related to the following location:
  at groovy.lang.MetaClass
  at public groovy.lang.MetaClass com.blogspot.przybyszd.jaxbingroovy.Person.getMetaClass()
  at com.blogspot.przybyszd.jaxbingroovy.Person

 at com.sun.xml.internal.bind.v2.runtime.IllegalAnnotationsException$Builder.check(IllegalAnnotationsException.java:91)
 at com.sun.xml.internal.bind.v2.runtime.JAXBContextImpl.getTypeInfoSet(JAXBContextImpl.java:445)
 at com.sun.xml.internal.bind.v2.runtime.JAXBContextImpl.(JAXBContextImpl.java:277)
 at com.sun.xml.internal.bind.v2.runtime.JAXBContextImpl.(JAXBContextImpl.java:124)
 at com.sun.xml.internal.bind.v2.runtime.JAXBContextImpl$JAXBContextBuilder.build(JAXBContextImpl.java:1123)
 at com.sun.xml.internal.bind.v2.ContextFactory.createContext(ContextFactory.java:147)
 at javax.xml.bind.ContextFinder.newInstance(ContextFinder.java:247)
 at javax.xml.bind.ContextFinder.newInstance(ContextFinder.java:234)
 at javax.xml.bind.ContextFinder.find(ContextFinder.java:462)
 at javax.xml.bind.JAXBContext.newInstance(JAXBContext.java:641)
 at javax.xml.bind.JAXBContext.newInstance(JAXBContext.java:584)
 at com.blogspot.przybyszd.jaxbingroovy.PersonJaxbTest.should unmarshall person xml to object(PersonJaxbTest.groovy:10)

It is because groovy defines getMetaClass method for us. Marshaller and Unmarshaller use by default

XmlAccessType.PUBLIC_MEMBER what means that public getters and setters should be used during marshalling/unmarshalling. To solve this just add XmlAccessorType annotatnio with XmlAccessType.FIELD on jaxb class:

@XmlRootElement @EqualsAndHashCode @XmlAccessorType(XmlAccessType.FIELD) class Person {
    String firstName String lastName Integer age
}

 

Of course if you want to apply this rule for each jaxb class in package, then you could put XmlAccessorType in pacakge-info.java file.

@XmlAccessorType(XmlAccessType.FIELD) package com.blogspot.przybyszd.jaxbingroovy;
import javax.xml.bind.annotation.XmlAccessType;
import javax.xml.bind.annotation.XmlAccessorType;

 

Great, it works. Now let’s check out marshaller:

def 'should marshall person'() {
    given:
        JAXBContext jc = JAXBContext.newInstance(Person)
        Person p = new Person(firstName: 'John', lastName: 'Smith', age: 20)
        StringWriter sw = new StringWriter()
    when:
        jc.createMarshaller().marshal(p, sw)
    then:
        String xml = sw.toString()
        GPathResult gPathResult = new XmlSlurper().parseText(xml)
        gPathResult.name() == 'person'
        gPathResult.firstName == 'John'
        gPathResult.lastName == 'Smith'
        gPathResult.age == '20'
}

 

And it also works. Source is available here.

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DDevelopment & Design

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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):
    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.