DDevelopment & Design

Modular Web Application using Eclipse Snaps

Few days ago, new milestone 3.0.0.M03 of Eclipse Virgo was released. Final release of this well prepared OSGi-based application Server getting closer so I decided to take a look at how to use it to write simple modular web application.

What does it mean a modular web application in OSGi environment? I imagine that this application should:

1) Change their look after installation of some extensions.

2) Also we should be able to write new request handlers for our application or modify flow control of existed.

First idea which occurred to me how to resolve first mentioned problem is to use fragment bundles mechanism. Fragment bundles give opportunity to create bundles with resources and classes which will be accessible from host bundle classloader. A key use case of its was “providing translation files for different locales“. So we can do few fragments with jsp files which should be able to include in index servlet page. Also we can provide some configuration files with class names of new request handlers which could be created using java reflection.

But fragment bundles have some disadvantages. How we can read in specification: “The new content of the updated fragment must not be allowed to attach to the host bundles until the Framework is restarted or the host bundle is refreshed“. This mean that we cannot do installation of extention without refreshing host bundle. Also new jsps must be compiled as a part of host application’s jsps so it is second reason why host bundle should be restarted. The last thing is that fragment bundles can’t have Bundle Activator which is also useful in many cases.

Eclipse Virgo gives component which resolves this problems – Virgo Snaps. It is easy in use extension which helps create modular web application framework. Sources are available here

After build of project and copy artifacts to virgo:

cd build-snaps

ant jar

cd ..

cp org.eclipse.virgo.snaps.api/target/artifacts/org.eclipse.virgo.snaps.api.jar ${virgo.home}/repository/usr/

cp org.eclipse.virgo.snaps.core/target/artifacts/org.eclipse.virgo.snaps.core.jar ${virgo.home}/repository/usr/

… and restart of server, we are ready to use snaps.

In samples dir there is example menu-bar showing idea of Snaps. In host bundle – animal.menu.bar we can see in top.jsp usage of taglib snaps:

  • “> ${snap.properties[‘link.text’]}

 

 

It is simple way to iterate through snaps associated with this host. Only thing which we must to do is to add this snippet to MANIFEST.MF:

Snap-Host: animal.menu.bar;version="[1.0, 2.0)"

Snap-ContextPath: /cat

In first line we are manifesting that this snap will be used as a part of host animal.menu.bar. In the second that all servlets will be deployed in this subcontext of context of host.

In this example also were used properties from file snap.properties which are available in session as attribute properties of snap.

After install host bundle at url: http://localhost:8080/animal-menu-bar we can see page without menu items. After installation of new items are become available.

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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.