ceph/src/boost/libs/fusion/doc/introduction.qbk

   1 [/==============================================================================
   2     Copyright (C) 2001-2011 Joel de Guzman
   3     Copyright (C) 2006 Dan Marsden
   4
   5     Use, modification and distribution is subject to the Boost Software
   6     License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at
   7     http://www.boost.org/LICENSE_1_0.txt)
   8 ===============================================================================/]
   9 [section Introduction]
  10
  11 An advantage other languages such as Python and Lisp/ Scheme, ML and
  12 Haskell, etc., over C++ is the ability to have heterogeneous containers
  13 that can hold arbitrary element types. All the containers in the standard
  14 library can only hold a specific type. A `vector<int>` can only hold
  15 `int`s. A `list<X>` can only hold elements of type `X`, and so on.
  16
  17 True, you can use inheritance to make the containers hold different types,
  18 related through subclassing. However, you have to hold the objects through
  19 a pointer or smart reference of some sort. Doing this, you'll have to rely
  20 on virtual functions to provide polymorphic behavior since the actual type
  21 is erased as soon as you store a pointer to a derived class to a pointer to
  22 its base. The held objects must be related: you cannot hold objects of
  23 unrelated types such as `char`, `int`, `class X`, `float`, etc. Oh sure you
  24 can use something like __boost_any__ to hold arbitrary types, but then you
  25 pay more in terms of runtime costs and due to the fact that you practically
  26 erased all type information, you'll have to perform dangerous casts to get
  27 back the original type.
  28
  29 The __tuple__ library written by __jaakko_jarvi__ provides heterogeneous
  30 containers in C++. The `tuple` is a basic data structure that can hold
  31 heterogeneous types. It's a good first step, but it's not complete. What's
  32 missing are the algorithms. It's nice that we can store and retrieve data
  33 to and from tuples, pass them around as arguments and return types. As it
  34 is, the __tuple__ facility is already very useful. Yet, as soon as you use
  35 it more often, usage patterns emerge. Eventually, you collect these
  36 patterns into algorithm libraries.
  37
  38 Hmmm, kinda reminds us of STL right? Right! Can you imagine how it would be
  39 like if you used STL without the algorithms? Everyone will have to reinvent
  40 their own /algorithm/ wheels.
  41
  42 Fusion is a library and a framework similar to both __stl__ and the boost
  43 __mpl__. The structure is modeled after __mpl__, which is modeled
  44 after __stl__. It is named "fusion" because the library is reminiscent of
  45 the "fusion" of compile time meta-programming with runtime programming. The
  46 library inherently has some interesting flavors and characteristics of both
  47 __mpl__ and __stl__. It lives in the twilight zone between compile time
  48 meta-programming and run time programming. __stl__ containers work on
  49 values. MPL containers work on types. Fusion containers work on both types
  50 and values.
  51
  52 Unlike __mpl__, Fusion algorithms are lazy and non sequence-type
  53 preserving. What does that mean? It means that when you operate on a
  54 sequence through a Fusion algorithm that returns a sequence, the sequence
  55 returned may not be of the same class as the original. This is by design.
  56 Runtime efficiency is given a high priority. Like __mpl__, and unlike
  57 __stl__, fusion algorithms are functional in nature such that algorithms
  58 are non mutating (no side effects). However, due to the high cost of
  59 returning full sequences such as vectors and lists, /Views/ are returned
  60 from Fusion algorithms instead. For example, the __transform__ algorithm
  61 does not actually return a transformed version of the original sequence.
  62 __transform__ returns a __transform_view__. This view holds a reference to
  63 the original sequence plus the transform function. Iteration over the
  64 __transform_view__ will apply the transform function over the sequence
  65 elements on demand. This /lazy/ evaluation scheme allows us to chain as
  66 many algorithms as we want without incurring a high runtime penalty.
  67
  68 The /lazy/ evaluation scheme where algorithms return views allows
  69 operations such as __push_back__ to be totally generic. In Fusion,
  70 __push_back__ is actually a generic algorithm that works on all sequences.
  71 Given an input sequence `s` and a value `x`, Fusion's __push_back__
  72 algorithm simply returns a __joint_view__: a view that holds a reference to
  73 the original sequence `s` and the value `x`. Functions that were once
  74 sequence specific and need to be implemented N times over N different
  75 sequences are now implemented only once.
  76
  77 Fusion provides full round compatibility with __mpl__. Fusion sequences are
  78 fully conforming __mpl__ sequences and __mpl__ sequences are fully compatible
  79 with Fusion. You can work with Fusion sequences on __mpl__ if you wish to work
  80 solely on types [footnote Choose __mpl__ over fusion when doing pure type
  81 calculations. Once the static type calculation is finished, you can instantiate
  82 a fusion sequence (see __conversion__) for the runtime part.]. In __mpl__,
  83 Fusion sequences follow __mpl__'s sequence-type preserving semantics (i.e.
  84 algorithms preserve the original sequence type. e.g. transforming a vector
  85 returns a vector). You can also convert from an __mpl__ sequence to a Fusion
  86 sequence. For example, there are times when it is convenient to work solely on
  87 __mpl__ using pure __mpl__ sequences, then, convert them to Fusion sequences as
  88 a final step before actual instantiation of real runtime objects with data. You
  89 have the best of both worlds.
  90
  91 [endsect]