DDevelopment & Design

Sonar Gerrit Plugin Release

I am happy to announce a first release of my Sonar Gerrit plugin. This plugin reports Sonar violations on your patchsets to your Gerrit server. Sonar analyses full project, but only files included in patchset are commented on Gerrit. Please forward to project page for installation instructions. This plugin is intended to use with Gerrit Trigger plugin for Jenkins CI server. Together they provide a great tool for automatic static code analysis.I am happy to announce a first release of my Sonar Gerrit plugin. This plugin reports Sonar violations on your patchsets to your Gerrit server. Sonar analyses full project, but only files included in patchset are commented on Gerrit. Please forward to project page for installation instructions. This plugin is intended to use with Gerrit Trigger plugin for Jenkins CI server. Together they provide a great tool for automatic static code analysis.

Initial release

I am happy to announce a first release of my Sonar Gerrit plugin. This plugin reports Sonar violations on your patchsets to your Gerrit server. Sonar analyses full project, but only files included in patchset are commented on Gerrit. Please forward to project page for installation instructions.

This plugin is intended to use with Gerrit Trigger plugin for Jenkins CI server. Together they provide a great tool for automatic static code analysis.

How does it work?

At the moment you push a patchset to Gerrit, Jenkins is notified with a ssh event. It fetches a code with a patchset and it builds your changes. It quits when build or tests fail.

But if it succeeds, Sonar analase your project in a post-build action. This is a place where my Sonar Gerrit plugin shines. It asks Gerrit for changed files before analysis and after Sonar analysis is finished, plugin reports comments on these files as a Gerrit reviewer. Currently plugin always reports +1 for Code Review, as it’s still in development. However, you should always treat these comments as hints to improve, not as direct errors.

Extras

I’ve released also a second plugin: Sonar File Alerts plugin. This plugin raises alerts on file level in Sonar. It extends default behaviour, which raises alerts only at root project level. It is useful when you create alert rules in Sonar like "Code Coverage < 60". Each file is checked against this rule!

If you use Sonar File Alerts plugin and an alert will be generated on some file, then a comment will be published on this file on Gerrit.

Feedback

Please provide a feedback on these plugins. Feel free to submit issues on github or comment. It’s still an early stage so your input is very welcome!

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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.