Bio Howard Lewis Ship is the creator of the Apache Tapestry project, and is a noted expert on Java framework design and developer productivity. He has over twenty years of full-time software development under his belt, with over ten years of Java. Howard is a frequent speaker at JavaOne, NoFluffJustStuff, ApacheCon and other conferences, and the author of "Tapestry in Action". Twitter: @hlship.
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Sure. Obviously everything I do, pretty much, is Tapestry, so I have been working with various clients, supporting them with Tapestry, dealing with some performance issues at the high end, that type of thing. I’ve been working with other clients to develop Tapestry applications, and of course I’ve been getting Tapestry 5.3, the latest release, out the door. In fact the vote is going on right now - it's the Apache way, there is a vote. So that’s been a lot of fun; putting in a lot of new features improving Tapestry in a number of ways, improving performance, decreasing memory utilization, making it easier to extend and adapt. I am very excited about that. And I am really excited about what is going to be coming next.
So, Tapestry has always been built with certain concerns built in from the ground up. And I call these simplicity, consistency, efficiency, and feedback. This has really come to the fore with Tapestry 5.
Simplicity - it’s designed to be that the classes you write, the page classes, the component classes are all very bare bones, very simple, no inheritance, no interfaces to implement, just your bare POJO class.
Consistency - things that work at the page level, work at the component level and vice versa, which makes it very reasonable to assemble very complicated functionality very easily, and Tapestry picks up all the details about wiring everything together.
Efficiency - that is something that got renewed focus for 5.3. It’s very efficient; it doesn’t use reflection at runtime, it does a lot of work with bytecode, and a lot of hidden things, to optimize and really deliver terrific performance.
And lastly feedback, which is one of the areas I think Tapestry does best against anybody - it’s just the emphasis Tapestry has on supporting the developer; telling them what’s going on. This is reflected by the really exceptionally useful exception report page, that really digs out a lot of information and helps you focus directly on what’s wrong, but also a lot of other things throughout the framework. For instance, Tapestry keeps a track of what operation it’s doing, so that if an exception occurs deep down it can tell you why it was doing that, not merely a stack trace. Something like: I am processing this event for this page; I am creating the page; I am creating the component for this page; I am parsing the template; and I hit something that I don’t understand. It gives you that really drill down view of what’s going on; something very missing.
And other things, in terms of feedback, such as, any time you are choosing an item from a list in your code by its name, if you got the name wrong, it doesn’t just MPE it produces an exception report that includes a list of all the names that would have been valid; that type of thing. So there is an emphasis on that, start to finish, throughout the entire framework.
So, Tapestry really is fundamentally built not to care where the data it uses comes from or goes to; just POJOs - objects with properties that can be read and updated. So if they are coming from J2EE or from Guice, or from a database, or via Hibernate, it doesn’t matter. It’s quite happy to pull that data in, allow components to edit them, push that data out. So although there's lots of changes in JEE and it’s getting simpler, with each release, those things are sort of orthogonal to Tapestry; it’s just a source and sink of data.
Yes. Tapestry has always had a really credible dependency injection container. So this is something that was built with Tapestry 5; it’s very powerful, it’s drawn ideas from Spring, it’s drawn ideas from Guice, and maybe influenced some ideas back at both of them. But it used its own annotations, and its own general approach. So it had an annotation @inject that was specific to Tapestry. What JSR303 provides is standard annotations that can be used by any of the major containers: Guice, Spring, Tapestry. All that was really necessary was to treat those alternate annotations, the javax version of inject, as the same as the Tapestry version of inject, and keep compatibility with both. The theory is that you can start writing code that really doesn’t care which container it’s injected into, and that’s probably a good thing.
6. You said, I think it was about 2008, 2009 that you felt that the Java language was kind of done; that it should be frozen. I was interested in whether that was still your opinion or whether you are pleased to see things like Lambda being added to the language in Java 8?
Yes, back then I remember, on expert panels, saying really freeze the language. We were still sort of in the after shocks of generics, and all the problems and pain that generics had given us. We were in an uncertain future; that was a little earlier than getting into the whole, "Who will buy Java? Oh it turns out it’s Oracle". The point is, at that time it really looked like Sun was not in a position to drive any kind of innovation, and there was tonnes of innovation going on at that time: Scala, Groovy, Clojure, JRuby. All the stuff people wanted was already available in these other languages. So I was thinking, "Yes, let’s freeze Java" - as if I have a voice - "Let’s freeze Java; let it be the language of contracts and interfaces, and let all the innovation happen everywhere else".
But a lot of things have happened. Certainly Oracle has a better idea: they can keep their eyes on the prize, they can drive change to the language. And they put people in charge of this, people I really respect. I am thinking specifically Brian Goetz. So here is someone who really understands things front to back, from the metal all the way up. And now I am really seeing that the way they are driving the change for Lambda is going to be a good thing, because pretty much whatever is in Java is now the de facto mainstream. And the drive for Lambda that they are looking for, is really to introduce significant functional features into Java the language. And if that means lots of people are going to take functional seriously, as something they really need to learn and understand and embrace, yes that’s a great thing.
Let me see. I haven’t really thought about what I want to add to Java; mostly I think about what I want to remove from Java. Certainly Java has this huge legacy of fits and starts, ideas that seemed great at the time, but have turned into quite a bit of weight. Just I’d love to see the removal of so many deprecated classes and methods that get in the way. I’d like to see the Java language streamlined down; I’d like to see it reduced. Get rid of things like CORBA, all those CORBA support that really aren't used by the majority of people; make those things optional. I know project Jigsaw is all about this, but even so....
I would like to see Java be a first class citizen on the desktop. I think it’s so tragic that we are so many years into the life of Java, I mean pretty much 1995 up to now, and we are still reliant on shell scripts and Windows bat files to get our applications to launch. I mean, probably that’s the reason why it went onto the server; it was just easier to start up your apps if they were server side; never got the client side really figured out. And I still think there is room for Java to be a good language for developing tools that live on the desktop or at the command line.
So there aren’t particular things I think are missing from the language per se. There are things I would love to see removed, like Checked Exceptions, and a lot of the type stuff I think gets in the way, but what I really would like to see is faster start up. It’s just so painful for me to launch a simple command and see Java, and maybe Groovy on top of that, launch up, eat fifty megabytes of RAM, start up twenty five threads, and by the time it’s printing out its first bit of text an equivalent tool in all of these supposedly weaker languages, like Ruby or Python, would be done.
Clojure is LISP reborn for the Java Virtual Machine. So Rich Hickey, who's the creator of the language, did something really brave. He said, "You know, I'm going to take about three years off; start working on my vision of what it should be to develop software today using the best of the newest thing in the world, which is the JVM HotSpot, the Java Virtual Machine, which is this incredible execution platform, except maybe for its startup time, and LISP which has this tremendous history".
People talk about LISP as a language that wasn’t created so much as it was discovered, and it’s been around since the 60’s. I’m always amused when I’m watching episodes of Mad Men, I’m thinking, "Somewhere out there John McCarthy is figuring out how to make parentheses and S-expressions work". The point is, LISP has always been known as a language that had tremendous symbolic power.
In fact it has always been a language that was as much a language toolkit as a language unto itself. It's a language for building the language that describes the solution to your problem. But it had its own set of problems; it had sort of this kitchen sink approach to dealing with all the different versions. So the common LISP became this incredibly complicated concept. It didn’t have the enterprise reach of the Java Virtual Machine; it didn’t have an environment that could run in so many different places. I mean I have a version of Clojure that runs on my Android phone. It’s something of a toy, but that’s pretty telling. So at the same time, Rich, who has a background in a lot of real time computation, was identifying some of the issues with doing that even in Java. The primary one being, as he says, "State: you’re doing it wrong". Mutable state is just the death of anything real time, because anything real time wants to be spread across many threads cooperating, and as soon as any of the data is mutable you are going to have a program whose behavior is unknown, or a program that runs slow or dead locks trying to deal with all the locking that’s necessary.
So one of the main focuses of Clojure is this idea of immutable data; that every object or collection that you create in Clojure is created in its final state and can never change, except for a couple of special reference types that are allowed to change under very carefully controlled circumstances. And you’d think this would be like putting on heavyweight chains and locking yourself down, but it’s actually incredible liberating to know that you never have to worry about an object that you might want to share across threads ever being corrupted, ever being walked on, ever being changed in a way that makes it invalid.
The other aspect is he has such good collections; they're very performant collections, the persistent data types, you know, vectors and maps and sets and lists, partly because they are very smart; if you introduce a change into one of them it doesn’t have to make a deep copy of the whole thing, it can copy just certain structural pieces from the old version into the new version. As your data types get larger, they’re not really consuming that much more space; you're not churning up the GC quite that badly. And these things all sort of work together in the language; every aspect supports every other aspect in a really cool way.
One of the Alan Perlis epigrams is it’s better to have 100 operations on a single data type than to have ten operations on ten data types. And that’s really a major part of Clojure; that it has this consistent view of data. And the data is dumb; there is no place to attach a method to a Clojure map or a Clojure list; you always operate on the data using functions. But the end result is that you can compose things really nicely. So by comparison, Java seems like a complicated recipe where every bit of code you write has to do little increments of work and little fits and starts. And Clojure is like this flow of transformations where you say how to start from one piece of data and what you need to do to get to what you want at the end, and it takes care of a lot of the details. On top of that, the other aspect that is really intriguing, is how it really embraces the concept of lazy evaluation; something that’s common in the functional world, because mathematical functions always return the same value for the same inputs, and that frees you from the constraint of time.
It means that whether you invoke a function now or next week or next year, you’re going to get the same results if you have the same parameters passed in. So the core of Clojure is all the major functions are lazy, which means that you never evaluate more than you need to. So you can create these elegant chains of transformations, but if you only need the first two or three or ten values from one side of the chain to the other, most of the work never actually happens.
Clojure's paradigms and Java's paradigms; obviously quite different. Java has the concept of classes and inheritance and Clojure, technically, just has functions. In terms of the interop, Clojure is really strongly designed to work well with its host platform, so it has excellent ability to call into Java code, whether you pass objects to it in some way or whether it’s using static methods to locate things. It has a really good syntax for making method invocations on Java objects look like function calls. So when you define a function in Clojure, it’s designed partly to interoperate with the Java world. So although it’s a Clojure function it’s still fundamentally an object, and that object implements not just in a Clojure function interface but Java interfaces such as Runnable, Callable and Comparable; so that, from the Clojure side, you might create a function and pass it directly in as a parameter into some Java side code.
So that really is excellent; the interop between the two is great. And increasingly you can define very efficient types inside the Clojure world, this is defrecord and deftype, where you say, "Here is a batch of Clojure functions. Make them look just like a Java object with a particular interface," and then that object can again be released into the Java world to act as the bridge between the two. So really on both sides of the equations it’s very easy for Clojure to reach into the Java world and pass to the Java world the objects and values that it expects, but it is also very reasonable to do the flip side; have Java code go into the Clojure runtime, find Clojure namespaces and functions within those namespaces and invoke them.
The syntax is something a lot of people are simply never going to get through, and that’s really a shame, because it’s so pure. If you’ve ever studied compilers, which I did way back in college (I read the Dragon Book and wrote things like that), you know that one of the intermediate stages between program code and your ability to generate something - so source code and something you can turn into executable code - is the abstract syntax tree. One of the neat things about LISPs, and Clojure, is that you pretty much code the AST directly; that’s what all those parentheses are doing, they're providing all the grouping that would be an abstract syntax tree.
So it’s as if you are doing the first step. But what it means is everything is so uniform; that’s that term you see floating around, homoiconicity, data and code look exactly the same. And that’s part of that concept that in Clojure you can create the language that solves your problem, inside of Clojure. And then ultimately, even when you are coding really Java code, there are so many conveniences and aspects of Clojure in it that you could actually write clearer, more maintainable, code with fewer parentheses from the Clojure side than you can from the Java side.
Yes, I’ve looked. In terms of web frameworks for Clojure, probably the main one is now Composure. Originally Composure was sort of a soup to nuts kind of approach, where it would do the interfacing to a servlet container such as Jetty on the one side, and all the way to templating on the other side to generate markup, along with dispatch in the middle. And those things kind of broke up. So now there is a small library called Ring and it’s the part, based on Ruby Sinatra I believe, of dealing with the Jetty or servlet container, and ultimately pulling out information - the path, the request parameters - and then delegating all the real application behavior down the road. And Composure split the other way as well, so its templating language, where you do the templating right in Clojure code, is called Hiccup, so that’s a separate piece; you’ve got to love all the names.
And then Composure sits in the middle, binding those two pieces together, and also providing an easier way to generate these complicated ... not complicated, but generate these Ring routes, which are the way you map from an incoming request to a particular function. And, because of that design, it’s really easy to plug in alternatives, so there are several different templating options that you can just plug right into Composure. I’ve even been working on one, called Cascade, which tries to bring into the Clojure world some of the better ideas from Tapestry; the ones that fit.
I actually have no plans to use Clojure code within Tapestry; just like I have no plans to use Groovy code in Tapestry or Scala code within Tapestry. Yes, there would be a lot of things that would code up much cleaner, or at least more concisely, if I would do those things. But I really have made a concerted effort to reduce or eliminate external dependencies from Tapestry and so, because of that, as tempting as it might be, it’s going to stay pure Java for the foreseeable future.