At least two trends are making paralell and distributed programming come to focus: computers with multiple cores getting cheaper and getting more cores, and websites leveraging terabytes worth of of user content. There are several services, tools and programming models around to help people cope with such trends, such as Amazon EC2, Hadoop, Fork Join, Actor Models, Non relational Databases, and so on. A couple of things to bear in mind when using multiple cores or multiple computers (with or without these tools):
- Amdahl’s law: It states that, with a fixed problem size, there is a point for every program after which adding more computational units do not give you improved performance. By computational units, you can take either cores (for paralell programming) or hosts (for distributed programming). Gustafson’s law tackles the issue when you do not fix the problem size, but this does not really help when you need faster response for your current problem size.
- Brewer’s CAP Theorem: This has to do with distributed programming only, and many people said a lot of about this. But essentially it means that a distributed system can only have at most two out of the three following properties: consistency, availability and partition tolerance (a kind of fault tolerance).
Amdahl’s law is quite troublesome: you cannot really do anything about it, but changing the algorithms involved. But CAP Theorem allows you to trade off one property for another. You can relax consistency into eventual consistency, you can relax on fault tolerance against partition tolerance, or you may live with less nines of up-time. It all depends on your application’s profile which one you will have to abandon. Dealing with this means no longer looking for ACID (Atomicity, Consistency, Isolation, Durability) systems, where consistency is very important, but looking for BASE (Basic available, soft-state or scalable, and eventually consistent) systems. It means listening to Gregor Hohpe’s suggestion and accepting that Two Phase Commit may not be the right way to go in detriment to pursuing the new ACID properties (Associative, Commutative, Idempotent and Distributed).
Not every system requires looking into these trends and thinking about such limitations and trade-offs. But if yours does, then keeping these in mind might come in handy.
It has been a while since Java was the sole language running over a JVM. Scala is another such language which gained a lot attention recently for being used to scale Twitter‘s backend. Scala differs from most other languages that run on the JVM, such as Groovy, JRuby and Jython, as it is statically typed. This means that, similar to Java and C#, the types must be known at compile time. Scala is usually introduced as being both OO and functional. While this statement is true (and daunting, as many people are uncomfortable with the f*** word), it fails to grasp the important aspects of Scala.
Among the most direct benefits of using Scala feature:
- Compatible with Java. Kinda obvious (as so are all the other 200+ languages over the JVM), but it is such an important feature that should not be overlooked. This means that Scala can use all Java libraries and frameworks. Which shows respect for people’s and companies investment on the technology.
- Joint Compilation. This means that, like Groovy, Scala classes are compiled to Java classes, and therefore can be used on Java projects (even by java classes on the same project they are defined). Even if your team decides to make the complete move towards Scala, this can be useful integrating with dynamic languages via JSR 223.
- Type Inference. If the compiler can guess the type (and it usually can), you don’t have to tell it. This allows Scala code to be as concise as dynamic languages, while still being type safe.
- Implicit conversion allows you to achieve in a type safe way what extension methods do for C# and open classes (mostly) do for ruby. That is: add methods to types you might not have not defined yourself (such as strings, lists, integers). This is one of the features that make Scala DSL friendly.
- Object immutability is encouraged and easy to accomplish. Scala even comes with immutable collections built-in.
- Getters and Setters are automatically generated for you. If you don’t want them (if you only want setters for example), you have to explicitly make them private. Which is not a problem, as the common case is to want them.
- Scala has first-order functions and implements an enumeration protocol (with the iterable trait), which helps keeping code clearer, more concise, and brings several other benefits.
- The Actor programming model eases up the development of highly concurrent applications.
- Exceptions don’t have to be explictly caught or thrown. It can be argued that having checked exceptions does more harm than good.
These features alone would be enough to make Scala a very interesting language, and worth being heralded as the current heir apparent to the Java throne by one of JRuby’s creator, Charles Nutter (a view somewhat shared by Neal Gafter). Or even worth of being endorsed both by Groovy’s creator, James Strachan, and by the inventor of Java, James Gosling. Nonetheless Scala is deep, and there are several exciting advanced features that allow developers to be more productive. But learning such features before getting a good grasp the basics can be quite frustrating, more so without a good supporting literature (such as IBM‘s, Aritma‘s, Jonas Bonér‘s, Daniel Spiewak‘s, Sven Efftinge‘s, the official one, and several others). However it quite is feasible, not only encouraged, to delve into deeper concepts as you need them.
Even though Scala has academic roots (as it shows on its papers page, and some advanced concepts these tackle), Scala has been successfully used on enterprise projects, besides Twitter, such as Siemens, Électricité de France Trading and WattzOn.
Besides all the good points, Scala does have some rough edges. Even though many people are working on overcoming them, they are likely to be relevant on the short term:
- Incipient IDE support. As Lift‘s author expressed, IDEs for Scala, while undergoing a lot of development, are not what they are for Java. There is poor refactoring support, code completion and unit test integration. Not to mention the fact that most framework support tools will not play nicely with Scala. This can also put off some newcomers, as an IDE can help people learn the language. On the other hand, Martin Folwer relativizes this IDE situation, as a language that allows you to be more productive can more than make up for the lack of sophisticated tools.
- Joint Compilation is not supported by most IDEs as well. Again, likely to change as Scala grows in popularity.
- Immutability on a class is not really immutability, since referring objects may not be immutable themselves. And there is no way at the moment to ensure the whole object graph is immutable.
- Making JSR 223 work perfectly with Scala can be challenging. On the other hand, making it work good enough is quite attainable.
- Scala doesn’t support metaprogramming. This can be worked around by combining it with dynamic languages, such as Ruby (following a polyglot programming approach), but if you are going to do heavy use of metaprogramming, than using a whole different language may be a better solution (Fan is another static type language that runs over the JVM, similar to Scala, that has metaprogramming support).
- Frameworks that expect Java source, such as the client-side GWT, will not play nicely with Scala (note that people have made Scala work with GWT on the server-side though). However there is an ongoing project that will translate Scala into Java source.
- The syntax and some concepts are bit different from Java, such as: inverted type declaration order, underscore being used instead of wildcards, asterisks and default values, many kinds of nothing, no static methods (you need to use singleton objects instead) and other minor things. The documentation walks through this quite nicely though, but keep in mind that it is not an automatic transition from writing Java to writing Scala code.
As Joe Armstrong said, the need for languages that allow developers to easily make use of CPUs with multiple cores will only increase as such CPUs become cheaper and gain more and more cores. Scala is quite suited for such task, while Java’s development is stuck dealing with issues that come from being widely deployed, uncertanties of how open it will be in the future and political issues with some of its main contributors. Given the situation, Scala seems to fit quite nicely the role of the successor to Java’s throne.