[/==============================================================================
    Copyright (C) 2001-2010 Joel de Guzman
    Copyright (C) 2001-2010 Hartmut Kaiser

    Distributed under 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:lexer_introduction Introduction to __lex__]

Lexical scanning is the process of analyzing the stream of input characters and 
separating it into strings called tokens, separated by whitespace. 
Most compiler texts start here, and devote several chapters to discussing 
various ways to build scanners. __lex__ is a library built to take care of the 
complexities of creating a lexer for your grammar (in this documentation we 
will use the terms 'lexical analyzer', 'lexer' and 'scanner' interchangeably). 
All that is needed to create a lexer is to know the set of patterns describing 
the different tokens you want to recognize in the input. To make this a bit more 
formal, here are some definitions:

* A token is a sequence of consecutive characters having a collective meaning.
  Tokens may have attributes specific to the token type, carrying additional
  information about the matched character sequence.
* A pattern is a rule expressed as a regular expression and describing how a 
  particular token can be formed. For example, [^\[A-Za-z\]\[A-Za-z_0-9\]*] is 
  a pattern for a rule matching C++ identifiers.
* Characters between tokens are called whitespace; these include spaces, tabs, 
  newlines, and formfeeds. Many people also count comments as whitespace, 
  though since some tools such as lint look at comments, this method is not 
  perfect.

[heading Why Use a Separate Lexer?]

Typically, lexical scanning is done in a separate module from the parser, 
feeding the parser with a stream of input tokens only. Theoretically it is 
not necessary implement this separation as in the end there is only one set of 
syntactical rules defining the language, so in theory we could write the whole 
parser in one module. In fact, __qi__ allows you to write parsers without using a 
lexer, parsing the input character stream directly, and for the most part this 
is the way __spirit__ has been used since its invention.

However, this separation has both practical and theoretical basis, and proves to 
be very useful in practical applications. In 1956, Noam Chomsky defined the 
"Chomsky Hierarchy" of grammars:

* Type 0:  Unrestricted grammars (e.g., natural languages)
* Type 1:  Context-Sensitive grammars
* Type 2:  Context-Free grammars
* Type 3:  Regular grammars

The complexity of these grammars increases from regular grammars being the 
simplest to unrestricted grammars being the most complex. Similarly, the 
complexity of pattern recognition for these grammars increases. Although, a few 
features of some programming languages (such as C++) are Type 1, fortunately 
for the most part programming languages can be described using only the Types 2 
and 3. The neat part about these two types is that they are well known and the 
ways to parse them are well understood. It has been shown that any regular 
grammar can be parsed using a state machine (finite automaton). Similarly, 
context-free grammars can always be parsed using a push-down automaton 
(essentially a state machine augmented by a stack). 

In real programming languages and practical grammars, the parts that can be 
handled as regular expressions tend to be the lower-level pieces, such as the 
definition of an identifier or of an integer value:

    letter     := [a-zA-Z]
    digit      := [0-9]

    identifier := letter [ letter | digit ]*
    integer    := digit+

Higher level parts of practical grammars tend to be more complex and can't be
implemented using plain regular expressions. We need to store 
information on the built-in hardware stack while recursing the grammar 
hierarchy, and that is the preferred approach used for top-down 
parsing. Since it takes a different kind of abstract machine to parse the two 
types of grammars, it proved to be efficient to separate the lexical scanner 
into a separate module which is built around the idea of a state machine. The 
goal here is to use the simplest parsing technique needed for the job.

Another, more practical, reason for separating the scanner from the parser is
the need for backtracking during parsing. The input data is a stream of 
characters, which is often thought to be processed left to right without any 
backtracking. Unfortunately, in practice most of the time that isn't possible.
Almost every language has certain keywords such as IF, FOR, and WHILE. The 
decision if a certain character sequence actually comprises a keyword or just 
an identifier often can be made only after seeing the first delimiter /after/
it. In fact, this makes the process backtracking, since we need to store the 
string long enough to be able to make the decision. The same is true for more 
coarse grained language features such as nested IF/ELSE statements, where the 
decision about to which IF belongs the last ELSE statement can be made only
after seeing the whole construct.

So the structure of a conventional compiler often involves splitting up the 
functions of the lower-level and higher-level parsing. The lexical scanner 
deals with things at the character level, collecting characters into strings, 
converting character sequence into different representations as integers, etc., 
and passing them along to the parser proper as indivisible tokens. It's also 
considered normal to let the scanner do additional jobs, such as identifying 
keywords, storing identifiers in tables, etc. 

Now, __spirit__ follows this structure, where __lex__ can be used to implement 
state machine based recognizers, while __qi__ can be used to build recognizers 
for context free grammars. Since both modules are seamlessly integrated with 
each other and with the C++ target language it is even possible to use the 
provided functionality to build more complex grammar recognizers.

[heading Advantages of using __lex__]

The advantage of using __lex__ to create the lexical analyzer over using more 
traditional tools such as __flex__ is its carefully crafted integration with 
the __spirit__ library and the C++ host language. You don't need any external 
tools to generate the code, your lexer will be perfectly integrated with the 
rest of your program, making it possible to freely access any context 
information and data structure. Since the C++ compiler sees all the code, it
will generate optimal code no matter what configuration options have been chosen
by the user. __lex__ gives you the vast majority of features you could get from 
a similar __flex__ program without the need to leave C++ as a host language:

* The definition of tokens is done using regular expressions (patterns)
* The token definitions can refer to special substitution strings (pattern 
  macros) simplifying pattern definitions
* The generated lexical scanner may have multiple start states
* It is possible to attach code to any of the token definitions; this code gets
  executed whenever the corresponding token pattern has been matched

Even if it is possible to use __lex__ to generate C++ code representing 
the lexical analyzer (we will refer to that as the /static/ model, described in 
more detail in the section __sec_lex_static_model__) - a model
very similar to the way __flex__ operates - we will mainly focus on the 
opposite, the /dynamic/ model. You can directly integrate the token definitions 
into your C++ program, building the lexical analyzer dynamically at runtime. The 
dynamic model is something not supported by __flex__ or other lexical scanner 
generators (such as __re2c__, __ragel__, etc.). This dynamic flexibility allows 
you to speed up the development of your application.

[heading The Library Structure of __lex__]

The [link spirit.lexerflow figure] below shows a high level overview of how the 
__lex__ library might be used in an application. __lex__ allows to create 
lexical analyzers based on patterns. These patterns are regular expression 
based rules used to define the different tokens to be recognized in the 
character input sequence. The input sequence is expected to be provided to the
lexical analyzer as an arbitrary standard forward iterator. The lexical 
analyzer itself exposes a standard forward iterator as well. The difference 
here is that the exposed iterator provides access to the token sequence instead
of to the character sequence. The tokens in this sequence are constructed on 
the fly by analyzing the underlying character sequence and matching this to the 
patterns as defined by the application.

[fig lexerflow.png..The Library structure and Common Flow of Information while using __lex__ in an application..spirit.lexerflow]


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