OSGi Blueprint visualization

What is blueprint?Blueprint is a dependency injection framework for OSGi bundles. It could be written by hand or generated using Blueprint Maven Plugin. Blueprint file is only an XML describing beans, services and references. Each OSGi bundle could hav…

What is blueprint?

Blueprint is a dependency injection framework for OSGi bundles. It could be written by hand or generated using Blueprint Maven Plugin. Blueprint file is only an XML describing beans, services and references. Each OSGi bundle could have one or more blueprint files.

Blueprint files represent architecture of our bundle. Let’s visualize it using groovy script and graphviz available in my github repository and analyze.

Example generation

Pre: All you need is groovy and graphviz installed on your OS

I am working mostly with bundles with generated blueprint, so I will use blueprint file generated from Blueprint Maven Plugin tests as example. All examples are included in github repository.

Generation could be invoked by running run.sh script with given destination file prefix (png extension will be added to it) and path to blueprint file:

mkdir -p target

./run.sh target/fullBlueprint fullBlueprint.xml

Visualization is available here.

Separating domains

First if you look at the image, you see that some beans are grouped. You could easily extract such domains with tree roots: beanWithConfigurationProperties and beanWithCallbackMethods to separate blueprint files and bundles in future and generate images from them:

./run.sh target/beanWithCallbackMethods example/firstCut/beanWithCallbackMethods.xml
./run.sh target/beanWithConfigurationProperties example/firstCut/beanWithConfigurationProperties.xml
./run.sh target/otherStuff example/firstCut/otherStuff.xml

Now we have three, a bit cleaner, images: beanWithConfigurationProperties.png, beanWithCallbackMethods.png and otherStuff.png.

We also could generate image from more than one blueprint:

./run.sh target/joinFirstCut example/firstCut/otherStuff.xml example/firstCut/beanWithConfigurationProperties.xml example/firstCut/beanWithCallbackMethods.xml

And the result is here. The image contains beans grouped by file, but if you do not like it, you could force generation without such separation using option --no-group-by-file:

./run.sh target/joinFirstCutGrouped example/firstCut/otherStuff.xml example/firstCut/beanWithConfigurationProperties.xml example/firstCut/beanWithCallbackMethods.xml --no-group-by-file

It will generate image with all beans from all files.

Exclusion

Sometimes it is difficult to spot and extract other domains. It will be easier to do some experiments on blueprint. For example, bean my1 is a dependency for too many other beans. You could consider converting my1 bean to OSGi service and extracting it to another bundle.

Let’s exclude my1 bean from generation via -e option and see what happens:

./run.sh target/otherStuffWithoutMy example/firstCut/otherStuff.xml -e my1

Result is available here. Now we see, that tree with root bean myFactoryBeanAsService could be separated and my1 could be inject to it as osgi service in another bundle.

You could exclude more than one bean adding -e switch for each of them, e. g. -e my1 -e m2 -e myBean123.

Conclusion

Blueprint is great for dependency injection for OSGi bundles, but it is easy to create quite big context containing many domains. It is much easier to recognize or search for such domains using blueprint visualizer script.

 

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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)

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  • 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 '''       
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if __name__ == "__main__":
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  • 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.

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  • 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.