A KangaRoo on the JVM
On a previous project, I used Spring Roo to jumpstart the software development process. Spring Roo was created by Ben Alex, an Australian engineer who is also the creator of Spring Security. Spring Roo was a big productivity boost and generated a significant amount of code and configuration based on the specification of the domain model. Spring Roo automatically generated the following:
- The domain entities with support for JPA annotations.
- Repository and service layers. In addition to JPA, Spring Roo also supports NoSQL persistence for MongoDB based on the Spring Data repository abstraction.
- A web layer with Spring MVC controllers and JSP views with support for Tiles-based layout, theming, and localization. The JSP views were subsequently replaced with a combination of Thymeleaf (a next generation server-side HTML5 template engine) and Twitter Boostrap to support a Responsive Web Design (RWD) approach. Roo also supports GWT and JSF.
- REST and JSON remoting for all domain types.
- Basic configuration for Spring Security, Spring Web Flow, Spring Integration, JMS, Email, and Apache Solr.
- Entity mocking, automatic generation of test data ("Data on Demand"), in-container integration testing, and end-to-end Selenium integration tests.
- A Maven build file for the project and full integration with Spring STS.
- Deployment to Cloud Foundry.
For my future projects, I am looking forward to taking developer's productivity and innovation to the next level. There are several criteria in my mind:
- Being able to do more with less. This means being able to write code that is concise, expressive, requires less configuration and boilerplate coding, and is easier to understand and maintain (particularly for difficult concerns like concurrency which is a key factor in scalability).
- Interoperability with the Java language and being able to run on the JVM, so that I can take advantage of the larger and rich Java ecosystem of tools and frameworks.
- Lastly, my interest in responsive, massively scalable, and fault-tolerant systems has picked up recently.
Maven has been a very powerful build system for several projects that I have worked on. My goal now is to support continuous delivery pipelines as a pattern for achieving high quality software. Large open source projects like Hibernate, Spring, and Android have already moved to Gradle. Gradle builds are written in a Groovy DSL and are more concise than Maven POM files which are based on a more verbose XML syntax. Gradle supports Java, Groovy, and Scala out-of-the box. It also has other benefits like incremental builds, multi-project builds, and plugins for other essential development tools like Eclipse, Jenkins, SonarQube, Ivy, and Artifactory.
Grails is a full-stack framework based on Groovy, leveraging its concise syntax (which includes Closures), dynamic language programming, metaprogramming, and DSL support. The core principle of Grails is "convention over configuration". Grails also integrates well with existing and popular Java projects like Spring Security, Hibernate, and Sitemesh. Roo generates code at development time and makes use of AOP. Grails on the other hand generates code at run-time, allowing the developer to do more with less code. The scaffolding mechanism is very similar in Roo and Grails.
There are many factors that can influence the decision to use Roo vs. Grails (e.g., the learning curve associated with Groovy and Grails for a traditional Java team).
I am also interested in massively scalable and fault-tolerant systems. This is no longer a requirement solely for big internet players like Google, Twitter, Yahoo, and LinkedIn that need to scale to millions of users. These requirements (including response time and up time) are also essential in mission-critical applications such as healthcare.
The recently published "Reactive Manifesto" makes the case for a new breed of applications called "Reactive Applications". According to the manifesto, the Reactive Application architecture allows developers to build "systems that are event-driven, scalable, resilient, and responsive." That is the premise of the other two prominent languages on the JVM: Scala and Clojure. They are based on a different programming paradigm (than traditional OOP) called Functional Programming.
Twitter uses Scala and has open-sourced some of their internal Scala resources like "Effective Scala" and "Scala School". One interesting framework based on Scala is Akka, a concurrency framework built on the Actor Model.
The Play Framework 2 is a full-stack web application framework based on Scala which is currently used by LinkedIn (which has over 225 millions registered users worldwide). In addition to its elegant design, Play's unique benefits include:
- An embedded Java NIO (New I/O) non-blocking server based on JBoss Netty, providing the ability to call collaborating services asynchronously without relying on thread pools to handle I/O. This new breed of servers is called "Evented Servers" (NodeJS is another implementation) as opposed to the old "Threaded Servers". Older frameworks like Spring MVC use a threaded and synchronous approach which is more difficult to scale.
- The ability to make changes to the source code and just refresh the browser page to see the changes (this is called hot reload).
- Type-safe Scala templates (errors are displayed in the browser during development).
- Integrated support for Akka which provides (among other benefits) fault-tolerance, the ability to quickly recover from failure.
- Asynchronous responses (based on the concepts of "Future" and "Promise" also found in AngularJS), caching, iteratees (for processing large streams of data), and support for real-time push-based technologies like WebSockets and Server-Sent Events.
Bringing Clojure to the JVM
Clojure is a dialect of LISP and a dynamically-type functional programming language which compiles to JVM bytecode. Clojure supports multithreaded programming and immutable data structures. One interesting application of Clojure is Incanter, a statistical computing and data visualization environment enabling big data analysis on the JVM.