OVal – validate your models quickly and effortlessly!

Some time ago one of the projects at work required me to validate some Java POJOs. Theses were my model classes and I’ve been creating them from incoming WebService requests. One would say that XSD would be sufficient for the task, for parts of this va…Some time ago one of the projects at work required me to validate some Java POJOs. Theses were my model classes and I’ve been creating them from incoming WebService requests. One would say that XSD would be sufficient for the task, for parts of this va…

Some time ago one of the projects at work required me to validate some Java POJOs. Theses were my model classes and I’ve been creating them from incoming WebService requests. One would say that XSD would be sufficient for the task, for parts of this validations – sure, it would. But there were some advanced rules XSD would not handle, or would render the schema document very complicated.

Rules I needed to express were like:

  • person’s first_name and last_name should be of appropriate length – between 2 and 20, and additionally one could pass a zero-length string just to remove the previous value
  • state field should consist only defined values – as in dictionary value – this one would be completable with XSD’s enumerations, but would require often changing schema files and redistributing them to interested parties :(

The library I’ve decided to use for this task is OVal and it came out really nice! Read on to find out the details!

Oval is quite mature library that allows POJO validation, but is not JSR303 (bean validation) implementation. It has converters that enable it to understand those annotations, but I’m not sure about the compatibility.

I’ve tried only a subset of the available checks, among which were:

  • NotNull
  • NotEmpty
  • Length

There are many more, and their attributes give interesting ways to configure the validation process. But using them was rather easy and did not require to much brainstorming. What I really needed were custom checks. And in this area OVal shows it’s strength. Implementing a check is really easy.

I needed an annotation that would check a field against some values in a dictionary. If field’s value was in the given set, than the validation would succeed, if not, an exception would be thrown. To accomplish this task it is required to implement two classes: annotation class and check class – called by the validation engine on a given field.

Let’s start with our new annotation:

 

In the above snippet I’ve defined a check-annotation, that would be used like this:

 

You can pass file – containing dictionary values for this field. There is also message field in the annotation which is an error message returned by the validation engine of failed check – pretty handy. And can be expressed in .properties file as:

 

Placeholder, like context, will be replaced with correct values supplied by the validation engine.

Annotating a field is not enough. It is also needed to create a validator for this kind of check. The name of the class is already defined in DictionaryValue annotation, it is called DictionaryValueCheck and I’ve done this check this way:

 

What this basically does is:

  1. when file is set – read dictionary content from the file into map
  2. upon check request just lookup value in dictionary parsed from the input file

And that’s it!

For me Oval is really great tool. With it at ones disposal it is extremely easy to create any imaginable validation you need. This library is really easy to use and offers lots of handy features.

But perhaps I’m reinventing the wheel and all this can be done easily with some other library? Share Your opinion!

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Sample for lift-ng: Micro-burn 1.0.0 released

During a last few evenings in my free time I've worked on mini-application called micro-burn. The idea of it appear from work with Agile Jira in our commercial project. This is a great tool for agile projects management. It has inline tasks edition, drag & drop board, reports and many more, but it also have a few drawbacks that turn down our team motivation.

Motivation

From time to time our sprints scope is changing. It is not a big deal because we are trying to be agile :-) but Jira's burndowchart in this situation draw a peek. Because in fact that chart shows scope changes not a real burndown. It means, that chart cannot break down an x-axis if we really do more than we were planned – it always stop on at most zero.

Also for better progress monitoring we've started to split our user stories to technical tasks and estimating them. Original burndowchart doesn't show points from technical tasks. I can find motivation of this – user story almost finished isn't finished at all until user can use it. But in the other hand, if we know which tasks is problematic we can do some teamwork to move it on.

So I realize that it is a good opportunity to try some new approaches and tools.

Tools

I've started with lift framework. In the World of Single Page Applications, this framework has more than simple interface for serving REST services. It comes with awesome Comet support. Comet is a replacement for WebSockets that run on all browsers. It supports long polling and transparent fallback to short polling if limit of client connections exceed. In backend you can handle pushes in CometActor. For further reading take a look at Roundtrip promises

But lift framework is also a kind of framework of frameworks. You can handle own abstraction of CometActors and push to client javascript that shorten up your way from server to client. So it was the trigger for author of lift-ng to make a lift with Angular integration that is build on top of lift. It provides AngularActors from which you can emit/broadcast events to scope of controller. NgModelBinders that synchronize your backend model with client scope in a few lines! I've used them to send project state (all sprints and thier details) to client and notify him about scrum board changes. My actor doing all of this hard work looks pretty small:

Lift-ng also provides factories for creating of Angular services. Services could respond with futures that are transformed to Angular promises in-fly. This is all what was need to serve sprint history:

And on the client side - use of service:


In my opinion this two frameworks gives a huge boost in developing of web applications. You have the power of strongly typing with Scala, you can design your domain on Actors and all of this with simplicity of node.js – lack of json trasforming boilerplate and dynamic application reload.

DDD + Event Sourcing

I've also tried a few fresh approaches to DDD. I've organize domain objects in actors. There are SprintActors with encapsulate sprint aggregate root. Task changes are stored as events which are computed as a difference between two boards states. When it should be provided a history of sprint, next board states are computed from initial state and sequence of events. So I realize that the best way to keep this kind of event sourcing approach tested is to make random tests. This is a test doing random changes at board, calculating events and checking if initial state + events is equals to previously created state:



First look

Screenshot of first version:


If you want to look at this closer, check the source code or download ready to run fatjar on github.During a last few evenings in my free time I've worked on mini-application called micro-burn. The idea of it appear from work with Agile Jira in our commercial project. This is a great tool for agile projects management. It has inline tasks edition, drag & drop board, reports and many more, but it also have a few drawbacks that turn down our team motivation.