It's been more than a decade since Typical Programmer Greg Jorgensen taught the word about Abject-Oriented Programming.

Much of what he said still applies, but other things have changed. Languages in the Abject-Oriented space have been borrowing ideas from another paradigm entirely—and then everyone realized that languages like Python, Ruby, and JavaScript had been doing it for years and just hadn't noticed (because these languages do not require you to declare what you're doing, or even to know what you're doing). Meanwhile, new hybrid languages borrow freely from both paradigms.

This other paradigm—which is actually older, but was largely constrained to university basements until recent years—is called Functional Addiction.

A Functional Addict is someone who regularly gets higher-order—sometimes they may even exhibit dependent types—but still manages to retain a job.

Retaining a job is of course the goal of all programming. This is why some of these new hybrid languages, like Rust, check all borrowing, from both paradigms, so extensively that you can make regular progress for months without ever successfully compiling your code, and your managers will appreciate that progress. After all, once it does compile, it will definitely work.

Closures

It's long been known that Closures are dual to Encapsulation.

As Abject-Oriented Programming explained, Encapsulation involves making all of your variables public, and ideally global, to let the rest of the code decide what should and shouldn't be private.

Closures, by contrast, are a way of referring to variables from outer scopes. And there is no scope more outer than global.

Immutability

One of the reasons Functional Addiction has become popular in recent years is that to truly take advantage of multi-core systems, you need immutable data, sometimes also called persistent data.

Instead of mutating a function to fix a bug, you should always make a new copy of that function. For example:

function getCustName(custID)
{
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

When you discover that you actually wanted fields 2 and 3 rather than 1 and 2, it might be tempting to mutate the state of this function. But doing so is dangerous. The right answer is to make a copy, and then try to remember to use the copy instead of the original:

function getCustName(custID)
{
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

function getCustName2(custID)
{
    custRec = readFromDB("customer", custID);
    fullname = custRec[2] + ' ' + custRec[3];
    return fullname;
}

This means anyone still using the original function can continue to reference the old code, but as soon as it's no longer needed, it will be automatically garbage collected. (Automatic garbage collection isn't free, but it can be outsourced cheaply.)

Higher-Order Functions

In traditional Abject-Oriented Programming, you are required to give each function a name. But over time, the name of the function may drift away from what it actually does, making it as misleading as comments. Experience has shown that people will only keep once copy of their information up to date, and the CHANGES.TXT file is the right place for that.

Higher-Order Functions can solve this problem:

function []Functions = [
    lambda(custID) {
        custRec = readFromDB("customer", custID);
        fullname = custRec[1] + ' ' + custRec[2];
        return fullname;
    },
    lambda(custID) {
        custRec = readFromDB("customer", custID);
        fullname = custRec[2] + ' ' + custRec[3];
        return fullname;
    },
]

Now you can refer to this functions by order, so there's no need for names.

Parametric Polymorphism

Traditional languages offer Abject-Oriented Polymorphism and Ad-Hoc Polymorphism (also known as Overloading), but better languages also offer Parametric Polymorphism.

The key to Parametric Polymorphism is that the type of the output can be determined from the type of the inputs via Algebra. For example:

function getCustData(custId, x)
{
    if (x == int(x)) {
        custRec = readFromDB("customer", custId);
        fullname = custRec[1] + ' ' + custRec[2];
        return int(fullname);
    } else if (x.real == 0) {
        custRec = readFromDB("customer", custId);
        fullname = custRec[1] + ' ' + custRec[2];
        return double(fullname);
    } else {
        custRec = readFromDB("customer", custId);
        fullname = custRec[1] + ' ' + custRec[2];
        return complex(fullname);
    }
}

Notice that we've called the variable x. This is how you know you're using Algebraic Data Types. The names y, z, and sometimes w are also Algebraic.

Type Inference

I haven't posted anything new in a couple years (partly because I attempted to move to a different blogging platform where I could write everything in markdown instead of HTML but got frustrated—which I may attempt again), but I've had a few private comments and emails on some of the old posts, so I decided to do some followups.

A couple years ago, I wrote a blog post on greenlets, threads, and processes. At that time, it was already possible to write things in terms of explicit coroutines (Greg Ewing's original yield from proposal already had a coroutine scheduler as an example, Twisted already had @inlineCallbacks, and asyncio had even been added to the stdlib), but it wasn't in heavy use yet. Things have changed since then, especially with the addition of the async and await keywords to the language (and the popularity of similar constructs in a wide variety of other languages). So, it's time to take a look back (and ahead).

Differences

Automatic waiting

Greenlets are the same thing as coroutines, but greenlet libraries like gevent are not just like coroutine libraries like asyncio. The key difference is that greenlet libraries do the switching magically, while coroutine libraries make you ask for it explicitly.

For example, with gevent, if you want to yield until a socket is ready to read from and then read from the socket when waking up, you write this:

    buf = sock.recv(4096)

To do the same thing with asyncio, you write this:

    buf = await loop.sock_recv(sock, 4096)

Forget (for now) the difference in whether recv is a socket method or a function that takes a socket; the key difference is that await. In asyncio, any time you're going to wait for a value, yielding the processor to other coroutines until you're ready to run, you always do this explicitly, with await. In gevent, you just call one of the functions that automatically does the waiting for you.

In practice, while marking waits explicitly is a little harder to write (especially during quick and dirty prototyping), it seems to be harder to get wrong, and a whole lot easier to debug. And the more complicated things get, the more important this is.

If you miss an await, or try to do it in a non-async function, your code will usually fail hard with a obvious error message, rather than silently doing something undesirable.

Meanwhile, let's say you're using some shared container, and you've got a race on it, or a lock that's being held too long. It's dead simple to tell at a glance whether you have an await between a read and a write to that container, while with automatic waiting, you have to read every line carefully. Being able to follow control flow at a glance is really one of the main reasons people use Python in the first place, and await extends that ability to concurrent code.

Serial-style APIs

Now it's time to come back to the difference between sock.recv and sock_recv(sock). The asyncio library doesn't expose a socket API, it exposes an API that looks sort of similar to the socket API. And, if you look around other languages and frameworks, from JavaScript to C#, you'll see the same thing.

It's hard to argue that the traditional socket API is in any objective sense better, but if you've been doing socket programming for a decade or four, it's certainly more familiar. And there's a lot more language-agnostic documentation on how it works, both tutorial and reference (e.g., if you need to look up the different quirks of a function on Linux vs. *BSD, the closer you are to the core syscall, the easier it will be to find and understand the docs).

In practice, however, the vast majority of code in a nontrivial server is going to work at a higher level of abstraction. Most often, that abstraction will be Streams or Protocols or something similar, and you'll never even see the sockets. If not, you'll probably be building your own abstraction, and only the code on the inside—a tiny fraction of your overall code—will ever see the sockets.

One case where using the serial-style APIs really does help, however, is when you've got a mess of already-written code that's either non-concurrent or using threads or processes, and you want to convert it to use coroutines. Rewriting all that code around asyncio (no matter which level you choose) is probably a non-trivial project; rewriting it around gevent, you just import all the monkeypatches and you're 90% done. (You still need to scan your code, and test the hell out of it, to make sure you're not doing anything that will break or become badly non-optimal, of course, but you don't need to rewrite everything.)

Conclusion

If I were writing the same blog post today, I wouldn't recommend magic greenlets for most massively-concurrent systems; I'd recommend explicit coroutines instead.

There is still a place for gevent. But that place is largely in migrating existing threading-based (or on-concurrent) codebases. If you (and your intended collaborators) are familiar enough with threading and traditional APIs, it may still be worth considering for simpler systems. But otherwise, I'd strongly consider asyncio (or some other explicit coroutine framework) instead.