Until recently, the first and last time I used Java was in the year 2000. Back then, it was a relatively young language, simple and inefficient, looking as some seriously bastardized C++ (regardless of what its authors had to say on the subject). I shrugged and moved away from Java.

Fast forward to 2019: I am writing in Java again, and of course it has a different feel to it now, neither simple nor slow anymore. Somehow, I got especially fascinated by its metaprogramming capabilities, or the lack thereof. Here is a story of what I found—but first, some personal history.

Doing It By Hand

A long time ago, in the midst of spending a few happy years writing C++ code, I happened to work on a project in plain C. It felt awkward after C++, but ended up being somewhat tolerable, until I needed a couple of hash tables for objects of different types. In my mind, it was a simple, innocent request, but this is where my foray back to the C land hit a brick wall.

Dismayed that there was no standard solution to this simple problem, and having similar issues with code duplication elsewhere on the project, I ended up implementing a simple templating system, and a Perl script that would generate C files from the templates.

Originally I thought it was a rather heavy-handed, desperate approach, but it worked remarkably well, and later it grew on me. All it required was an extra line in the makefile; the intermediate C file were readily available for inspection and debugging; and of course the power of Perl was far greater than any other solution available.

Since then, I became a big proponent of code writing code. Essentially, you construct a higher-level language for yourself. A higher language always means greater expressive power and programmer productivity.

Let’s talk, for a second, about other options available to me back then. Of course, C does have a built-in solution for code generation I was “supposed” to use: the preprocessor. Alas, the C preprocessor is a sad joke (beyond a few simple tasks like conditional compilation); it’s not even Turing-complete. Which is kind of disappointing, historically speaking, since there were industrial languages before C with vastly superior preprocessing capabilities.

Take, for example, PL/I, developed by IBM in the 1960s. I remember very well learning how to program it in school. It had a full-featured preprocessor with typed variables, loops and subroutines, not to mention the syntax being a subset of the PL/I language itself.

With the rise of minicomputers and PCs all that somehow got forgotten. The history of computing made one of its many resets of complex to simple. One can easily argue that even in 2019 the modern mainstream languages still haven’t caught up with the 1960s state of the art.

If I were writing in C++ instead of C, I of course would have easily solved my hash table problem with templates. However, C++ templates make a poor general solution for metaprogramming, even though they were shown to be Turing-complete. I still remember reading the famous Modern C++ Design by Andrei Alexandrescu, and briefly considering checking myself into a mental asylum after finishing it. Of course, it was the same Alexandrescu who wrote a multi-page paper exploring different implementation options for max(a, b) in templated C++ and coming to the conclusion that no satisfactory solution exists (or, at least, didn’t exist in 2001 vintage C++).

All That Is Gold Doesn’t Glitter

After my happy C++ years, I happen to work for a company that was doing enterprise software development in Lisp—an endangered species, indeed!

My time there was an eye-opener. Lisp, the second oldest high-level programming language (1958; FORTRAN was released in 1957), for decades was ahead of much more recent languages, that only recently caught up with it—mostly.

One of the areas where Lisp was far ahead of its time was its unmatched preprocessing capability (not explicitly existing in the original version of Lisp, but introduced soon thereafter, in the early 1960s). In Lisp’s macro system, the preprocessor is not just a subset of the source language, as in PL/I; it is the source language, just applied to the source code at compile time. Here is a very short example of a unit test framework definition in Common Lisp (from Practical Common Lisp by Peter Seibel):

(defun report-result (result form)
    (format t "~:[FAIL~;pass~] ... ~a~%" result form))

(defmacro check (&body forms)
    `(progn
        ,@(loop for f in forms collect `(report-result ,f ',f))))

Given that, you can now start testing something, like arithmetic expressions:

> (check
      (= 3 (+ 1 2))
      (= 5 (* 2 2)))

pass ... (= 3 (+ 1 2))
FAIL ... (= 5 (* 2 2))

Again, I don’t know any commonly used language that can accomplish the same as elegantly and with such a pleasing end result. And it is conceivable that no such language exits: the power of Lisp macros comes from the ability to treat the program source as data, which in turn comes from Lisp the language having essentially no syntax.

Very few programming languages have trivial syntax. Try to manipulate C++ or Perl sources programmatically!

Another powerful application of Lisp macros is defining new languages. For example, in CLSQL (a Common Lisp library providing interface to SQL databases) you can write something like this:

(select [email] :from [users]
    :where [= ["lower(name)"] (string-downcase customer)])

—not only protecting yourself from SQL injection, but also getting compile-time syntax checking for SQL!

But probably even more impressive use of macros is in Common Lisp itself, to extend the basic underlying Lisp language constructs. You might have noticed a for-each loop in the code above: (loop for f in forms collect ...). When every other language on the planet wants to have a for-each loop, they have to add it to the basic language definition (thank you C++ and Java for finally doing that after so many years of pain). In Common Lisp, this loop is just a part of the standard library: yet another macro defined in terms of much more basic language constructs.

The ease and the natural way of defining macros in Lisp have a very interesting practical effect. Whereas writing macros in C is (rightfully) frown upon, the Lisp programmer has no such inhibitions. From what I could see, real-world Lisp programmers would write macros literally every day; for every bit of functionality you need to implement, you just make a decision whether it is better executed at run time (then it’s a function) or at compile time (then it’s a macro).

Dynamic All The Way

Eventually, I had to keep up with the times, and move to a dynamic language. It happened to be Python, because pragmatically it’s the best choice these days, much better than my beloved Perl. I have no idea why a language created in 1991 feels modern—maybe it is just a function of me being old.

Metaprogramming in Python differs from all the other languages we’ve explored so far, because it happens at run time. And where else? To be sure, Python does have a compiler, which even produces artifacts on hard disk (the infamous *.pyc files), but Python compilation is so fast and boring that nobody really cares about it. Everything interesting in the Python land happens at run time.

Let’s say we want to implement a facility that would add getter and setter methods for class member variables (called in Python attributes). This is not a common Python pattern, but if were insisting on it, we would probably use Python decorator. Here is one way to do it:

def getter_setter(*args):
    def getter(member): return lambda self: getattr(self, member)
    def setter(member): return lambda self, val: setattr(self, member, val)
    def decorator_getter_setter(cls):
        for member in args:
            setattr (cls, 'get_' + member, getter(member))
            setattr (cls, 'set_' + member, setter(member))
        return cls
    return decorator_getter_setter

@getter_setter('x', 'y')
class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

Now you can do:

p = Point(1, 2)

p.set_x(11)
print(p.get_x())

By the way: only when I tried to implement it, I realized that unlike in C++ and Java, the problem statement is ambiguous. How do you even know what attributes your class has? They can be added and deleted dynamically, at run time.

In fact, that’s the only way they are created (in the case of our Point, in the constructor); there are no capabilities in Python to pre-define instance attributes. In this example, we solve this problem by explicitly telling the decorator for which attributes to define getters and setters; not an unreasonable choice, since it gives full control to the programmer while still keeping the code short and clean.

Note that even though the code mutation happens at run time, it is only done once for the given program run (at import time). The performance implications are thus almost certainly negligible.

Of course, decorators can be used in an even simpler fashion: to define pre- and post-actions for functions, for example if we wanted to time them:

def timeit(func):
    def wrapped(*args, **kwargs):
        start = time.time()
        func(*args, **kwargs)
        print("%s took %f s." % (func.__name__, time.time() - start))
    return wrapped

@timeit
def foo(...):
    ...

This is quite similar to aspect-oriented programming in Java, only so much simpler. There is no magic involved; after all, decorators don’t give you any special power; they are just a syntactic sugar for func = decorator(func).

If they are just some syntactic sugar, why do we even need them? Here is an important part: because they are concise, and make the programmer’s intention clear. In essence, they make the code more declarative and less procedural. They help us to define a higher level language.

On the other hand, just willy-nilly reassigning functions on the fly is the opposite: confusing, error-prone, hard to inspect and to reason about. It feels similar to the (in)famous #define TRUE FALSE in C: a good reason why the preprocessor usage is frowned upon in that language.

(On yet another hand, Lisp macros are totally fine: their definitions may be slightly harder to read, but their usage is as readable as regular functions.)

Beautiful Hacky Island

And now we come to my latest foray into Java. Last time I used it was a long, long time ago: in the year 2000. Back then it was a young and simple language, essentially C++ stripped of its perceived complexities, including its meager metaprogramming tools: preprocessing and templates. That was yet historical reset from complex to simple, not unlike Go in our days.

Sop, speaking about simple: data in Java data structures turned into a pumpkin Object references, and there was absolutely no way around it, short of falling back to generating code with hand-written Perl scripts.

I call this ostrich typing: bury your head in the sand and pretend that you have a strongly, statically-typed language.

Fast-forward 19 years: Java is no longer young not simple, and it got templates all right (called “generics”).

Now, while re-learning Java by programming in it, I was quite surprised to see code like this:

@Getter
@Setter
class Point {
    int x;
    int y;
}

It looked almost identical to the Python code we’ve written above, and achieved the same purpose: programmatically generate getter and setter functions for all the class attributes. Clearly, Java has come a long way since I touched it last time!

I was curious to learn what was that and how it worked. Here is what I learned.

The @-prefixed modifiers—annotations—have been a part of Java for quite some time. (Curiously, even though my first reaction was, “how cute, it looks almost like Python”, in reality it went the other way around: it was the Java annotations syntax that inspired decorators in Python.)

Annotations are widely used in modern Java—typically, for what their name suggest: to annotate, or mark, certain code elements for the run-time inspection via reflection. For example, the JUnit framework iterates over test class methods at run time, finds the ones marked with @Test, and executes them as test cases. This is fine, and clearly useful, but not particularly impressive, and can hardly be classified as code writing code. In more powerful languages, where functions/methods are first-class object, one can set their attributes directly, with no need for special syntax:

def foo ():
    ...

foo.test = True

Interestingly, one motivation for the unusual annotation syntax was the need to maintain backwards compatibility—in particular, not to introduce any new reserved keywords. (At this point, I can’t resist the temptation to mention the good old PL/I again. Somehow it managed to have no reserved keywords: you could name your variables “if” and “do” to your heart’s content, and the compiler was competent enough to tell apart statements from identifiers based on the grammar. How come we can’t do it anymore fifty years later?)

In any case, the language designers’ plan seems to have worked well enough. Different Java frameworks are using annotations to enrich the language, essentially defining a higher language on top of Java. One good example is the Spring framework, essentially built around annotations. They seem to be so pervasive that are naturally causing some thoughtful backlash. Anyhow, Spring annotations, like many others, seem to be based on the same run-time reflection behavior we just discussed for JUnit.

There is, however, a way to handle annotations at compile time. You can write your own annotation processor (a class implementing javax.annotation.processing.Processor—or, usually, extending AbstractProcessor) and hook it up into a compiler. You just place a jar file with a special processor description in your build path, and the compiler automatically picks it up an calls your annotation processor as needed.

This is where things get exciting. First of all, the Java compiler is actually running a JVM and executing Java code during the compilation process, and runs your code at compile time! Second, your annotation processor has access to AST (abstract syntax tree, the parsed representation of the source code being compiled) and can generate more source code, which would also be compiled during the same compilation process. This definitely introduces a completely new, powerful capability into the language, unlike anything it had before.

Unfortunately, the access to AST within an annotation processor is read-only. You can’t change the code being compiled, just generate more.

If an annotation processor cannot change the currently compiled class, then how that @Getter/@Setter business can possibly work? You will not find an answer in the Java language documentation, yet this is exactly what Project Lombok does.

Lombok is a beautiful Indonesian island not far from the island of Java (see the title image). Project Lombok defines a bunch of annotations that generate so-called boilerplate code: getters, setters, constructors, equals() and hashCode()—this kind of stuff. They do it by taking the next step in the annotation processor: after getting access to the program’s AST, they use undocumented, internal Sun compiler API to directly modify AST. Fortunately, most everyone happens to use Sun’s (now Oracle’s) javac, and the entire thing happens to work quite well in practice.

The Java community appears to be split on Project Lombok. Some rightfully consider it to be one giant hack: a cardinal sin for the language and the community obsessed with defining interfaces, separating access, and hiding private methods. Others applaud it as a great hack making source code shorter, cleaner, more declarative, more maintainable: in essence, improving the source language.

Though this obviously is not the only application of code writing code, to me it’s been a Holy Grail of all the techniques we reviewed so far: extend the language to make it more powerful, easier to use, more adopted to the problem domain.

And the fact is, Project Lombok manages to do just that. It is indeed code writing code, moreover, Java writing Java: using the same language for metaprogramming rightfully feels powerful. I am still excited by it.

Unlike most other annotations uses, that seem to be within the context of this or that framework, Lombok is not a framework. Its annotations do not add new functionality to the annotated code; they just make it more concise and maintainable.

For example, consider @Data, one of the most powerful annotations in Project Lombok.

@Data
class Point {
    int x;
    int y;
}

As its documentation states, “@Data generates all the boilerplate that is normally associated with simple POJOs (Plain Old Java Objects) and beans: getters for all fields, setters for all non-final fields, and appropriate toString, equals and hashCode implementations that involve the fields of the class, and a constructor that initializes all final fields”. This pretty mechanical code that you are somehow supposed to write yourself (if not for Project Lombok), tens of lines of it even for a trivial class like this, not only would completely obscure the meaningful parts of Point and make it next to impossible to read. It is also a few more functions to test. And if you don’t test them, because they are boring and trivial,—well, then don’t be surprised when you decide to make your Point three-dimensional and add a z coordinate that you forget to update hashCode() and get some weird behavior of your Points in a HashMap.

Every time you have to write lines of code irrelevant to the task at hand, it is a sign of a low-level language. By saving you from having to write it, Project Lombok elevates Java, makes it higher-level and better language.

However, there a huge “but”, of course,—even a few. Even though Project Lombok is implemented in Java, the actual language of defining and specifying annotations is rather weak, with poor type system no loops and conditionals, and no ability for different annotations to inter-operate.

Lombok’s annotation implementations live in a completely separate project, very far from the source code being modified. And boy, aren’t they complex! Just take a quick look at this blog post describing how to add a @HelloWorld annotation to Lombok. Count how many screens takes something that would only require a few lines in Lisp or Python.

The complexity is partially driven by the hackish nature of the project, but partially it is also the nature of the beast: compiler plugins are hard, and Java syntax and its AST are not trivial, either. Not to mention the need for Project Lombok to inter-operate with IDE (somehow, Java programming is unthinkable without an IDE these days).

Either way, the takeaway is the same: even though Lombok’s annotations are super-useful (I firmly belong to that camp), their implementation is not for the faint of heart. Developers are unlikely to casually add them on the day to day basis the way they do in Lisp, Python—even in C preprocessor, for that matter. Lombok will remain its own beautiful island, separate from the island of Java.

Interestingly enough, good ideas keep cross-pollinating languages. Just as the Python decorator syntax was influenced by Java, Lombok’s annotations seem to be influenced by Python decorators. And now, Python 3.7 (the latest version of Python) introduced a standard @dataclass decoration, very similar to Project Lombok’s @Data annotation we have just discussed.

This proves once again the broad cross-language appeal of using code writing code to get read of mundane, “boilerplate” code. However some languages—and language cultures—clearly produce much more of that than the others. The language that produced Project Lombok is probably one of the worst offenders. Some of it is cultural, and some is caused by the lack of language power. For example, coming back to the getter and setter functions: I mentioned that they are not used in a properly Pythonic code, and the reason is that the language i more powerful. It has the concept of properties, which allow to define getters and setters completely transparently, and only when they do something nontrivial: that is, when they are not boilerplate.

* * *

And here, on this unsatisfactory note, I have to end my story. Yes, Java has come a long way, but in terms of code writing code it not just haven’t caught up with the Lisp of fifty years ago: it is still light years behind. For me, I’ll hold my enthusiasm for some younger and bolder languages, like Rust, that appears to have a well-developed macro capability.

What are your favorite examples of metaprogramming? Let me know! And as I said, my C++ and Lisp are somewhat rusty at this point, and I am just starting to re-learn Java. If I got something about these languages wrong, let me know too.