Skip to main content

Model View Presenter pattern

Model View Presenter (MVP) is a software design pattern that brings a lot of benefits developing web applications having GWT as frontend framework. I am going to do a brief description of this pattern. I am currently applying it on a project I am working on and I have noticed that for people coming from MVC (Model View Controller) sometimes it's hard to understand the benefits of MVP (and how to implement it): so I decided to share my experience explaining this pattern from scratch.
MVP is a derivative of the MVC pattern and it's used mainly for building user interfaces. As well as the MVC pattern, MVP decouples the model from the views and the views from the logic that controls them. This way you really have the separation of concerns for the presentation logic. The three actors of this pattern are:


  • Model: an interface defining the data to be displayed.
  • View: a passive interface that displays the data of a model and routes user commands (typically events) to the presenter to act upon the data.
  • Presenter: it retrieves data from the model and then formats it to be displayed in the views.


The MVP pattern that best fits with GWT is more similar to the Model View Presenter Controller pattern (an extension of MVP). The Presenter is split into two components, the presenter (view control logic) and the controller (abstract purpose control logic). So the View, the Presenter and the Controller are really separated and then they can vary independently. MVPC improves MVP because:


  • controllers applying different control logics can be interchanged without impacting the presenter or the view;
  • completely different views can be applied to the same controller without impacting the underlying control logic;
  • multiple related views can be coordinated by a single controller. 


The following image shows an high level architectural diagram of the MVP implementation in GWT:



In the next post we will explore more in detail this diagram and then we will see how to code everything.
 

Comments

Popular posts from this blog

Shipping and analysing MongoDB logs using the Streamsets Data Collector, ElasticSearch and Kibana

In order to show that the considerations done in my last post are general for any log shipping purpose, let's see now how the same process applies to a more real use case scenario: the log shipping and analysis of a MongoDB database logs. MongoDB logs pattern Starting from the release 3.0 (I am considering the release 3.2 for this post) the MongoDB logs come with the following pattern: <timestamp> <severity> <component> [<context>] <message> where:     timestamp is in iso8601-local format.     severity is the level associated to each log message. It is a single character field. Possible values are F (Fatal), E (Error), W (Warning), I (Informational) and D (Debug).     component is for a functional categorization of the log message. Please refer to the specific release of MongoDB you're using to know the full list of possible values.     context is the specific context for a me...

Load testing MongoDB using JMeter

Apache JMeter ( http://jmeter.apache.org/ ) added support for MongoDB since its 2.10 release. In this post I am referring to the latest JMeter release (2.13). A preliminary JMeter setup is needed before starting your first test plan for MongoDB. It uses Groovy as scripting reference language, so Groovy needs to be set up for our favorite load testing tool. Follow these steps to complete the set up: Download Groovy from the official website ( http://www.groovy-lang.org/download.html ). In this post I am referring to the Groovy release 2.4.4, but using later versions is fine. Copy the groovy-all-2.4.4.jar to the $JMETER_HOME/lib folder. Restart JMeter if it was running while adding the Groovy JAR file. Now you can start creating a test plan for MongoDB load testing. From the UI select the MongoDB template ( File -> Templates... ). The new test plan has a MongoDB Source Config element. Here you have to setup the connection details for the database to be tested: The Threa...

Turning Python Scripts into Working Web Apps Quickly with Streamlit

 I just realized that I am using Streamlit since almost one year now, posted about in Twitter or LinkedIn several times, but never wrote a blog post about it before. Communication in Data Science and Machine Learning is the key. Being able to showcase work in progress and share results with the business makes the difference. Verbal and non-verbal communication skills are important. Having some tool that could support you in this kind of conversation with a mixed audience that couldn't have a technical background or would like to hear in terms of results and business value would be of great help. I found that Streamlit fits well this scenario. Streamlit is an Open Source (Apache License 2.0) Python framework that turns data or ML scripts into shareable web apps in minutes (no kidding). Python only: no front‑end experience required. To start with Streamlit, just install it through pip (it is available in Anaconda too): pip install streamlit and you are ready to execute the working de...