Greenlets, threads, and processes

It's very common in a program to want to do two things at once: repaginate a document while still responding to user input, or handle requests from two (or 10000) web browsers at the same time. In fact, pretty much any GUI application, network server, game, or simulator needs to do this.

It's possible to write your program to explicitly switch off between different tasks, and there are many higher-level approaches to this, which I've covered in previous posts. But an alternative is to have multiple "threads of control", each doing its own thing independently.

There are three ways to do this: processes, threads, or greenlets. How do you decide between them?

Processes are good for running tasks that need to use CPU in parallel and don't need to share state, like doing some complex mathematical calculation to hundreds of inputs.
Threads are good for running a small number of I/O-bound tasks, like a program to download hundreds of web pages.
Greenlets are good for running a huge number of simple I/O-bound tasks, like a web server. Update: See greenlets vs. explicit coroutines on deciding whether to use automatic greenlets or explicit ones.

If your program doesn't fit one of those three, you have to understand the tradeoffs.

Multiprocessing

Traditionally, the way to have separate threads of control was to have entirely independent programs. And often, this is still the best answer. Especially in Python, where you have helpers like multiprocessing.Process, multiprocessing.Pool, and concurrent.futures.ProcessPoolExecutor to wrap up most of the scaffolding for you.

Separate processes have one major advantage: They're completely independent of each other. They can't interfere with each others' global objects by accident. This can make it easier to design your program. It also means that if one program crashes, the others are unaffected.

Separate processes also have a major disadvantage: They're completely independent of each other. They can't share high-level objects. Processes can pass objects around—which is often a better solution. The standard library solutions do this by pickling the objects; this means that any object that can't be pickled (like a socket), or that would be too expensive to pickle and copy around (like a list of a billion numbers) won't work. Processes can also share buffers full of low-level data (like an array of a billion 32-bit C integers). In some cases, you can pass explicit requests and responses instead (e.g., if the background process is only going to need to get or set a few of those billion numbers, you can send get and set messages; the stdlib has Manager classes that do this automatically for simple lists and dicts). But sometimes, there's just no easy way to make this work.

As a more minor disadvantage, on many platforms (especially Windows), starting a new process is a pretty heavy thing to do. We're not talking minutes here, just milliseconds, but still, if you're kicking off jobs that may only take 5ms to finish, and you add 30ms of overhead to each one, that's not exactly an optimization. Usually, using a Pool or Executor is the easy way around this problem, but it's not always appropriate.

Finally, while modern OS's are pretty good at running, say, a couple dozen active processes and a couple hundred dormant ones, if you push things up to hundreds of active processes or thousands of dormant ones, you may end up spending more time in context-switching and scheduling overhead than doing actual work. If you know that your program is going to be using most of the machine's CPU, you generally want to try to use exactly as many processes as there are cores. (Again, using a Pool or Executor makes this easy, especially since they default to creating one process per core.)

Threading

Almost all modern operating systems have threads. These are like separate processes as far as the operating system's scheduler is concerned, but are still part of the same process in terms of the memory heap, open file table, etc. are concerned.

The advantage of threads over processes is that everything is shared. If you modify an object in one thread, another thread can see it.

The disadvantage of threads is that everything is shared. If you modify an object in two different threads, you've got a race condition. Even if you only modify it in one thread, it's not deterministic whether another thread sees the old value or the new one—which is especially bad for operations that aren't "atomic", where another thread could see some invalid intermediate value.

One way to solve this problem is to use locks and other synchronization objects. (You can also use low-level "interlocked" primitives, like "atomic compare and swap", to build your own synchronization objects or lock-free objects, but this is very tricky and easy to get wrong.)

The other way to solve this problem is to pretend you're using separate processes and pass around copies even though you don't have to.

Python adds another disadvantage to threads: Under the covers, the Python interpreter itself has a bunch of globals that it needs. The CPython implementation (the one you're using if you don't know otherwise) does this by protecting its global state with a Global Interpreter Lock (GIL). So, a single process running Python can only execute one instruction at a time. So, if you have 16 processes, your 16 core machine can execute 16 instructions at once, one per process. But if you have 16 threads, you'll only execute one instruction, while the other 15 cores sit around idle. Custom extensions can work around this by releasing the GIL when they're busy doing non-Python work (NumPy, for example, will often do this), but it's still a problem that you have to profile. Some other implementations (Jython, IronPython, and some non-default-as-of-early-2015 optional builds of PyPy) get by without a GIL, so it may be worth looking at those implementations. But for many Python applications, multithreading means single-core.

So, why ever use threads? Two reasons.

First, some designs are just much easier to think of in terms of shared-everything threading. (However, keep in mind that many designs look easier this way, until you try to get the synchronization right…)

Second, if your code is mostly I/O-bound (meaning you spend more time waiting on the network, the filesystem, the user, etc. than doing actual work—you can tell this because your CPU usage is nowhere near 100%), threads will usually be simpler and more efficient.

Greenlets

Greenlets—aka cooperative threads, user-level threads, green threads, or fibers—are similar to threads, but the application has to schedule them manually. Unlike a process or a thread, your greenlet function just keeps running until it decides to yield control to someone else.

Why would you want to use greenlets? Because in some cases, your application can schedule things much more efficiently than the general-purpose scheduler built into your OS kernel. In particular, if you're writing a server that's listening on thousands of sockets, and your greenlets spend most of their time waiting on a socket read, your greenlet can tell the scheduler "Wake me up when I've got something to read" and then yield to the scheduler, and then do the read when it's woken up. In some cases this can be an order of magnitude more scalable than letting the OS interrupt and awaken threads arbitrarily.

That can get a bit clunky to write, but third-party libraries like gevent and eventlet make it simple: you just call the recv method on a socket, and it automatically turns that into a "wake me up later, yield now, and recv once we're woken up". Then it looks exactly the same as the code you'd write using threads.

Another advantage of greenlets is that you know that your code will never be arbitrarily preempted. Every operation that doesn't yield control is guaranteed to be atomic. This makes certain kinds of race conditions impossible. You still need to think through your synchronization, but often the result is simpler and more efficient.

The big disadvantage is that if you accidentally write some CPU-bound code in a greenlet, it will block the entire program, preventing any other greenlets from running at all instead, whereas with threads it will just slow down the other threads a bit. (Of course sometimes this is a good thing—it makes it easier to reproduce and recognize the problem…)

Update: Greenlets can also be used with explicit waiting. While the older ways of doing this were a bit clumsy, with newer frameworks, the question really is as simple as whether you mark each wait with await (as in asyncio) vs. whether they happen automatically when you call magic functions like socket.recv (as in gevent). What happens under the covers is the same either way. The tl;dr is that there's usually more advantage than disadvantage in marking your yields explicitly, but read greenlets vs. explicit coroutines if you want (a few) more details.

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

Languages that enable Functional Addiction often feature Type Inference. This means that the compiler can infer your typing without you having to be explicit:

function getCustName(custID)
{
    // WARNING: Make sure the DB is locked here or
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

We didn't specify what will happen if the DB is not locked. And that's fine, because the compiler will figure it out and insert code that corrupts the data, without us needing to tell it to!

By contrast, most Abject-Oriented languages are either nominally typed—meaning that you give names to all of your types instead of meanings—or dynamically typed—meaning that your variables are all unique individuals that can accomplish anything if they try.

Memoization

Memoization means caching the results of a function call:

function getCustName(custID)
{
    if (custID == 3) { return "John Smith"; }
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

Non-Strictness

Non-Strictness is often confused with Laziness, but in fact Laziness is just one kind of Non-Strictness. Here's an example that compares two different forms of Non-Strictness:

/****************************************
*
* TO DO:
*
* get tax rate for the customer state
* eventually from some table
*
****************************************/
// function lazyTaxRate(custId) {}

function callByNameTextRate(custId)
{
    /****************************************
    *
    * TO DO:
    *
    * get tax rate for the customer state
    * eventually from some table
    *
    ****************************************/
}

Both are Non-Strict, but the second one forces the compiler to actually compile the function just so we can Call it By Name. This causes code bloat. The Lazy version will be smaller and faster. Plus, Lazy programming allows us to create infinite recursion without making the program hang:

/****************************************
*
* TO DO:
*
* get tax rate for the customer state
* eventually from some table
*
****************************************/
// function lazyTaxRateRecursive(custId) { lazyTaxRateRecursive(custId); }

Laziness is often combined with Memoization:

function getCustName(custID)
{
    // if (custID == 3) { return "John Smith"; }
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

Outside the world of Functional Addicts, this same technique is often called Test-Driven Development. If enough tests can be embedded in the code to achieve 100% coverage, or at least a decent amount, your code is guaranteed to be safe. But because the tests are not compiled and executed in the normal run, or indeed ever, they don't affect performance or correctness.

Conclusion

Many people claim that the days of Abject-Oriented Programming are over. But this is pure hype. Functional Addiction and Abject Orientation are not actually at odds with each other, but instead complement each other.