Sometimes, you need to split a program into two parts.

For many cases--one step in your process briefly uses a ton of memory, and you want that to be released to the system, or you want to parallelize part of the process to take advantage of multiple cores--you just use multiprocessing or concurrent.futures.

But sometimes that doesn't work. Maybe the reason you want to split your code is that you need Python 3.3 features for one part, but a 2.7-only module for another part. Or part of your program needs Java or .NET, but another part needs a C extension module. And so on.

Example

To make this concrete, let's take one example: You've written a cool GUI in Jython, but now you discover that you need to call a function out of a C library. The library is named mylibrary; it's at /usr/local/lib/libmylibrary.so.2, and it defines two functions:

    long unhex(const char *hexstr) {
        return strtol(hexstr, NULL, 16);
    }

    long parse_int(const char *intstr, int base) {
        return strtol(intstr, NULL, base);
    }

You could do this by using JNI to bridge C to Java and then Jython's Java API to bridge that through to your Jython, but let's say you already know how to use ctypes, and you want to use that.

If you were just using CPython or PyPy, you could call unhex like this:

    import ctypes

    mylibrary = ctypes.CDLL('/usr/local/lib/libmylibrary.so.2')
    mylibrary.unhex.argtypes = [c_char_p]
    mylibrary.unhex.restype = c_long

    if __name__ == '__main__':
        import sys
        for arg in sys.argv[1:]:
            print(unhex(arg))

But in Jython, you can't do that, because there's no ctypes.

Fun with subprocess

In this simple case, all you want to do is run one function in a different Python interpreter. As long as the input is just a few strings, and the output is just a string, all you need is subprocess.check_output:
    import subprocess

    def unhex(hexstr):
        return subprocess.check_output(['python', 'mylibrary_wrapper.py', hexstr])

Obviously you can use 'python3' or 'jython' or '/usr/local/bin/pypy' or '/opt/local/custompython/bin/python' or r'C:\CustomPython\Python.exe' or whatever in place of 'python' there.

If you need to get back more than one string as output, as long as you can easily encode it into a string, that's pretty easy. For example, let's say you wanted to unhex multiple strings:

   def unhex(*hexstrs):
        return subprocess.check_output(['python', 'mylibrary_wrapper.py', hexstrs]).splitlines()

You can also encode input this way. There are limitations on what you can pass in through command-line arguments, but you can always pass things through stdin. For example, change the above program to:
        for line in sys.stdin:
            print(unhex(line))

And now you can pass it a whole mess of strings without worrying about the command-line argument limits:

    def unhex(*hexstrs):
        with subprocess.Popen(['python', 'mylibrary_wrapper.py'], 
                              stdin=subprocess.PIPE, stdout=subprocess.PIPE) as p:
            return p.communicate('\n'.join(hexstrs)).splitlines()

But what if you need to call the function thousands of times, and not all at once? The cost of starting up and shutting down thousands of Python interpreters may be prohibitive.

In that case, the answer is some form of RPC. You kick off a background program that stays running in the background, listening on a socket (or pipe, or whatever). Then, whenever you need to call on it, you send it a message over that socket, and it replies.

Running a service

For really trivial cases, you can build a trivial protocol that runs directly over sockets. For really complicated cases, you may want to build a custom protocol around something like Twisted. But for everything in the middle, it may be simpler to just piggyback on a protocol that already exists and has ready-to-go implementations.

For example, let's use JSON-RPC directly over sockets, through the bjsonrpc library.

First, we need to build the server. Take the wrapper script above, leave the ctypes stuff alone, and replace the sys.argv or sys.stdin stuff with:
    import bjsonrpc
    from bjsonrpc.handlers import BaseHandler

    class MyLibraryHandler(BaseHandler):
        def unhex(self, hexstr):
            return mylibrary.unhex(hexstr)

    s = bjsonrpc.createserver(port=12345, handler_factory=MyLibraryHandler)
    s.serve()

Now, in your Jython code, you can do this:

    import subprocess
    import bjsonrpc

    class MyLibraryClient(object):
        def __init__(self):
            self.proc = subprocess.Popen(['python', 'mylibrary_wrapper.py'])
            self.conn = bjsonrpc.connect(port=12345)
        def close(self):
            self.conn.close()
            self.proc.kill()
        def unhex(self, hexstr):
            return self.conn.call.unhex(hexstr)

And that's it.

If you want to extend this to expose parse_int as well as unhex, you just need to wrap the ctypes function, add another method to the MyLibraryHandler and MyLibraryClient, and you can call it.

Automating the process

If you're wrapping up 78 functions in 5 different libraries that are under heavy development and keep changing, it will get very tedious (and error-prone and brittle) to add the same information in 3 places. You can make the ctypes stuff a lot easier by replacing it with a custom C extension module using, say, Cython, SWIG, SIP, or Boost.Python, or make it less brittle by using cffi. But what do you do about the server and client code?

Well, first, notice that you don't really need the wrappers in the client. self.conn.call is already a dynamic wrapper around whatever the server happens to export.

And on the server side, you're just delegating calls from self to mylibrary. You can build those delegating methods up at start time, or use your favorite other technique for delegation.

If you want to get really crazy, you can write the interface in an IDL dialect and generate the C headers, C implementation stubs, ctypes/cffi/SIP/whatever wrappers, server wrappers, and client wrappers all out of the same source.

Of course you probably don't want to get really crazy, but the point is that you can. You've built an RPC server, and all of the powerful features of RPC and network servers are available if you need them.
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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.
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