Often, you have an algorithm that just screams out to use a dict for storage, but your data set is just too big to hold in memory. Or you need to keep the data persistently, but pickling or JSON-ing takes way too long.

A database like sqlite3 (built into the stdlib for almost all builds of Python) is a possible solution, but it's a pretty major change. Instead of just writing this:

    dial(phone_numbers[person])

… you have to write this:

    cur = db.execute('SELECT number FROM phone_numbers WHERE person=?',
                     (person_id,))
    dial(cur.fetchone()[0])    

Fortunately, there's a much simpler answer: the dbm family of modules. It gives you a simple dict-like interface, built on top of a simple key-value database. The data are kept on disk, using a hash table implementation optimized for on-disk storage (with in-memory caching), instead of an in-memory hash table. There are limits to it (see below), but in many cases it just works.

Unfortunately, it scares a lot of people away. If you search for "python dbm", the first thing you'll find is probably the docs for the Python 2.7 dbm module, which it says only supports Unix.

This is because all the modules shuffled around between 2.x and 3.x. Rest assured, you can use the dbm family on Windows, you just can't (usually) use the specific module that's called dbm on Python 2.7.

Limitations

A dbm database can only store strings, both as keys and values. (In fact, it only stores bytes, and by default uses your default encoding if you give it Unicode strings.)

If you want to store arbitrary (pickleable) Python object as value, but can live with strings for keys, see the shelve module.

If you need your keys to be something other than strings, then dbm may not be the answer. If you have a way to decorate and undecorate your values as strings that's unambiguous and efficient, you can write a wrapper… but often if you're getting that fancy, you need something better than dbm.

Generally, only one program can use a dbm database file at the same time. If you want to share data between processes, dbm is not the answer.

Despite all having the same API, the different implementations have different storage formats. Some of them may also use incompatible storage on different platforms. So, a dbm database is generally not portable between machines.

What does "dbm" mean, and is it Unix-only?

The actual library named dbm is Unix-only—as in real, licensed, 1970s-style AT&T Unix; not linux or even OS X. Nobody uses it today, but it's the ancestor of a whole family of simple key-value databases used today, like ndbm, gdbm, and Berkeley DB, and that family is often collectively called dbm.

In Python 3.x, dbm is a package in the standard library that includes and wraps up all of the database modules.

In Python 2.x, those modules were all scattered around the stdlib. The top-level wrapper module is named anydbm, and the name dbm is used for the specific ndbm implementation.

Berkeley DB

Berkeley DB is a more powerful replacement for dbm. It has a lot of features dbm doesn't, but also a completely different API. Versions up to 1.x were BSD-licensed; later versions were Sleepycat-licensed, and then dual-licensed as AGPL or commercial. Also, each major version has changed the API considerably. So, many people have stuck with older versions. 1.85 is still in use today—it's available for Windows, and built-in on Macs.

What does Python support?

If you use the wrappers, your code will work on every platform. But it may be using the "dumb" implementation on some. If this matters to you, you'll have to know what's available so you can decide what you want to use.

Python 2.7 and 3.4 mostly support the same libraries, but under different names. I'll give the 3.x names first, separated by a slash where relevant.

In general, the wrappers around C libraries are present if the library was present when you built Python. But of course you probably don't build Python, you just install it from a binary, or it comes with your OS. The official Windows installers don't contain any of the implementations except dumb. The official Mac installers contain dumb and ndbm. Apple's pre-installed Python versions contain dumb, ndbm, and bsddb185. Linux distros may include any of dumb, ndbm, gdbm, bsddb185, and bsddb, and will usually have packages for any they don't include; you'll have to check your distro. Similarly for FreeBSD, Solaris, etc. And if you use a third-party installation like ActiveState or Enthought, you'll have to check the documentation.
  • dbm / anydbm (and whichdb). Wrapper that uses the appropriate module for existing files, and the best available module for new ones. 
  • dbm.dumbdumbdbm. Simple, pure-Python implementation that's always available.
  • dbm.ndbm / dbm. Wrapper around ndbm, or around gdbm's ndbm-compat mode.
  • dbm.gnu / gdbm. Wrapper around gdbm.
  • bsddb: Obsolete wrapper around Berkeley DB 1.x-4.x, deprecated since 2.6. Does not provide a dbm-style API, but the dbhash module wraps it with one. You probably don't want this.
  • bsddb185: Third-party (but you may have it pre-installed) wrapper around Berkeley DB 1.85. Includes the dbhash-style wrapper to provide a dbm-style API.
  • pybsddb: Third-party wrapper around Berkeley DB 5.0+. Includes the dbhash-style wrapper to provide a dbm-style API.
So, what should you use? That depends on a whole lot of factors, but here are some rough rules of thumb:
  • If you don't need to support Windows, or dumb is fast enough, just use dbm/anydbm's generic wrappers and don't worry about it.
  • If you aren't distributing your project, or are willing to dual-license your open source project as AGPL, or to buy a license for your commercial project, consider Berkeley DB 5.0+ and pybsddb.
  • If you don't mind compiling Python yourself on Windows, consider ndbm.
  • Otherwise, consider Berkeley DB 1.85 and bsddb185.
You might also want to look at third-party Python bundles like ActiveState to see if they can guarantee a better-than-dumb dbm on every platform you care about.

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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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