EQ2EMu/EQ2/source/depends/boost_1_72_0/libs/fusion/doc/introduction.qbk

[/==============================================================================
    Copyright (C) 2001-2011 Joel de Guzman
    Copyright (C) 2006 Dan Marsden

    Use, modification and distribution is subject to the Boost Software
    License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
    http://www.boost.org/LICENSE_1_0.txt)
===============================================================================/]
[section Introduction]

An advantage other languages such as Python and Lisp/ Scheme, ML and
Haskell, etc., over C++ is the ability to have heterogeneous containers
that can hold arbitrary element types. All the containers in the standard
library can only hold a specific type. A `vector<int>` can only hold
`int`s. A `list<X>` can only hold elements of type `X`, and so on.

True, you can use inheritance to make the containers hold different types,
related through subclassing. However, you have to hold the objects through
a pointer or smart reference of some sort. Doing this, you'll have to rely
on virtual functions to provide polymorphic behavior since the actual type
is erased as soon as you store a pointer to a derived class to a pointer to
its base. The held objects must be related: you cannot hold objects of
unrelated types such as `char`, `int`, `class X`, `float`, etc. Oh sure you
can use something like __boost_any__ to hold arbitrary types, but then you
pay more in terms of runtime costs and due to the fact that you practically
erased all type information, you'll have to perform dangerous casts to get
back the original type.

The __tuple__ library written by __jaakko_jarvi__ provides heterogeneous
containers in C++. The `tuple` is a basic data structure that can hold
heterogeneous types. It's a good first step, but it's not complete. What's
missing are the algorithms. It's nice that we can store and retrieve data
to and from tuples, pass them around as arguments and return types. As it
is, the __tuple__ facility is already very useful. Yet, as soon as you use
it more often, usage patterns emerge. Eventually, you collect these
patterns into algorithm libraries.

Hmmm, kinda reminds us of STL right? Right! Can you imagine how it would be
like if you used STL without the algorithms? Everyone will have to reinvent
their own /algorithm/ wheels.

Fusion is a library and a framework similar to both __stl__ and the boost
__mpl__. The structure is modeled after __mpl__, which is modeled
after __stl__. It is named "fusion" because the library is reminiscent of
the "fusion" of compile time meta-programming with runtime programming. The
library inherently has some interesting flavors and characteristics of both
__mpl__ and __stl__. It lives in the twilight zone between compile time
meta-programming and run time programming. __stl__ containers work on
values. MPL containers work on types. Fusion containers work on both types
and values.

Unlike __mpl__, Fusion algorithms are lazy and non sequence-type
preserving. What does that mean? It means that when you operate on a
sequence through a Fusion algorithm that returns a sequence, the sequence
returned may not be of the same class as the original. This is by design.
Runtime efficiency is given a high priority. Like __mpl__, and unlike
__stl__, fusion algorithms are functional in nature such that algorithms
are non mutating (no side effects). However, due to the high cost of
returning full sequences such as vectors and lists, /Views/ are returned
from Fusion algorithms instead. For example, the __transform__ algorithm
does not actually return a transformed version of the original sequence.
__transform__ returns a __transform_view__. This view holds a reference to
the original sequence plus the transform function. Iteration over the
__transform_view__ will apply the transform function over the sequence
elements on demand. This /lazy/ evaluation scheme allows us to chain as
many algorithms as we want without incurring a high runtime penalty.

The /lazy/ evaluation scheme where algorithms return views allows
operations such as __push_back__ to be totally generic. In Fusion,
__push_back__ is actually a generic algorithm that works on all sequences.
Given an input sequence `s` and a value `x`, Fusion's __push_back__
algorithm simply returns a __joint_view__: a view that holds a reference to
the original sequence `s` and the value `x`. Functions that were once
sequence specific and need to be implemented N times over N different
sequences are now implemented only once.

Fusion provides full round compatibility with __mpl__. Fusion sequences are
fully conforming __mpl__ sequences and __mpl__ sequences are fully compatible
with Fusion. You can work with Fusion sequences on __mpl__ if you wish to work
solely on types [footnote Choose __mpl__ over fusion when doing pure type
calculations. Once the static type calculation is finished, you can instantiate
a fusion sequence (see __conversion__) for the runtime part.]. In __mpl__,
Fusion sequences follow __mpl__'s sequence-type preserving semantics (i.e.
algorithms preserve the original sequence type. e.g. transforming a vector
returns a vector). You can also convert from an __mpl__ sequence to a Fusion
sequence. For example, there are times when it is convenient to work solely on
__mpl__ using pure __mpl__ sequences, then, convert them to Fusion sequences as
a final step before actual instantiation of real runtime objects with data. You
have the best of both worlds.

[endsect]