How to test Spring session scoped beans

byJakub Nabrdalik
April 15, 2011
1 minute read

I wanted to use the http session just as a repository (database/files), to keep facebook access token for currently logged user. While I can manipulate session directly, another option is to declare the class as a session scoped bean in Spring. Somet…

public class RepositoryOnHttpSession {
    private String facebookAccessToken;

    public FacebookTemplate getFacebookTemplate() {
        return new FacebookTemplate(facebookAccessToken);
    }

    public void setFacebookAccessToken(String facebookAccessToken) {
        this.facebookAccessToken = facebookAccessToken;
    }    
}

makes Spring IoC container create a cglib proxy, and inject that to other singleton type beans instead. All nice and cool, except integration tests (which get Spring IoC container to inject all the dependencies) are blowing up with:

java.lang.IllegalStateException: No Scope registered for scope ‘session’

While there’s a lot of solutions to be googled (including redeclaring the object as a prototype/sinlgeton for test context, injecting mock http session and request), the easiest way to have a simple thread-bound session scope is just to declare it in the TEST IoC configuration, like below. Just keep in mind that junit fires all tests in a single thread by default, so the state is persisted between tests. You may need to clean it up in @After.

Tags:
java

Jakub Nabrdalik

Solution Architect @ TouK

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Simple trick to DRY your Grails controller

byTomasz Kalkosiński
June 5, 2013

Grails controllers are not very DRY. It's easy to find duplicated code fragments in default generated controller. Take a look at code sample below. It is duplicated four times in show, edit, update and delete actions:

class BookController {
def show() {
def bookInstance = Book.get(params.id)
if (!bookInstance) {
flash.message = message(code: 'default.not.found.message', args: [message(code: 'book.label', default: 'Book'), params.id])
redirect(action: "list")
return
        }
        [bookInstance: bookInstance]
    }
}

Why is it duplicated?

There is a reason for that duplication, though. If you move this snippet to a method, it can redirect to "list" action, but it can't prevent controller from further execution. After you call redirect, response status changes to 302, but after method exits, controller still runs subsequent code.

Solution

At TouK we've implemented a simple trick to resolve that situation:

wrap everything with a simple withStoppingOnRender method,
whenever you want to render or redirect AND stop controller execution - throw EndRenderingException.

We call it Big Return - return from a method and return from a controller at once. Here is how it works:

class BookController {
def show(Long id) {
withStoppingOnRender {
Book bookInstance = Book.get(id)
validateInstanceExists(bookInstance)
            [bookInstance: bookInstance]
        }
    }

protected Object withStoppingOnRender(Closure closure) {
try {
return closure.call()
        } catch (EndRenderingException e) {}
    }

private void validateInstanceExists(Book instance) {
if (!instance) {
flash.message = message(code: 'default.not.found.message', args: [message(code: 'book.label', default: 'Book'), params.id])
redirect(action: "list")
throw new EndRenderingException()
        }
    }
}

class EndRenderingException extends RuntimeException {}

Example usage

For simple CRUD controllers, you can use this solution and create some BaseController class for your controllers. We use withStoppingOnRender in every controller so code doesn't look like a spaghetti, we follow DRY principle and code is self-documented. Win-win-win! Here is a more complex example:

class DealerController {
@Transactional
def update() {
withStoppingOnRender {
Dealer dealerInstance = Dealer.get(params.id)
validateInstanceExists(dealerInstance)
validateAccountInExternalService(dealerInstance)
checkIfInstanceWasConcurrentlyModified(dealerInstance, params.version)
            dealerInstance.properties = params
saveUpdatedInstance(dealerInstance)
redirectToAfterUpdate(dealerInstance)
        }
    }
}

DDevelopment & Design

Recently at storm-users

byMarcin Cylke
August 12, 2013

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.

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.

DDevelopment & Design

RTP/RTCP – Realtime Transport/Control Protocol

byTouK
July 31, 2008

Ponieważ ostatnio musiałem odświeżyc swoją wiedzę na temat RTP ponizej powtórka z przeszlosci. Poniższy post oparty jest bezpośrednio…