Charming Python #b6:

Parsing in Python with Spark.


David Mertz, Ph.D.
Analyzer, Gnosis Software, Inc.
December, 2001

Spark is a powerful and general parser/compiler framework written in Python. In some respects, Spark offers more than SimpleParse or other Python parsers. Being pure Python, however, it is also slower. This article discusses the Spark module, with code samples, and explanation of usage, and suggestions for its areas of applicability.

Preface

In this article, which follows on an earlier installment of Charming Python devoted to SimpleParse, I introduce some basic concepts in parsing, and discuss the Spark module. Parsing frameworks are a rich topic that warrant quite a bit of study to get a full sense of--these two articles make a good start, for both readers and myself.

In my programming life, I have frequently needed to identify parts and structures that exist inside textual documents: log files, configuration files, delimited data, and more free-form (but still semi-structured) report formats. All of these documents have their own "little languages" for what can occur within them. The way I have programmed these informal parsing tasks has always been somewhat of a hodgepodge of custom state-machines, regular expressions, and context driven string tests. The pattern in these programs was always, roughly, "read a bit of text, figure out if we can make something of it, maybe read a bit more text afterwards, keep trying."

Parsers of the formal sort distill descriptions the parts and structures in documents into concise, clear, and declarative rules for how to identify what makes up a document. Most formal parsers use variants on Extended Backus-Naur Form (EBNF) to describe the "grammars" of the languages they describe. Basically, an EBNF grammar gives names to the parts one might find in a document; additionally, larger parts are frequently composed of smaller parts. The frequency and order in which small parts may occur in larger parts is specified by operators. For example, this is the EBNF grammar typographify.def that we saw in the SimpleParse installment. (other tools spell things slightly differently):

typographify.def

para        := (plain / markup)+
plain       := (word / whitespace / punctuation)+
whitespace  := [ \t\r\n]+
alphanums   := [a-zA-Z0-9]+
word        := alphanums, (wordpunct, alphanums)*, contraction?
wordpunct   := [-_]
contraction := "'", ('am'/'clock'/'d'/'ll'/'m'/'re'/'s'/'t'/'ve')
markup      := emph / strong / module / code / title
emph        := '-', plain, '-'
strong      := '*', plain, '*'
module      := '[', plain, ']'
code        := "'", plain, "'"
title       := '_', plain, '_'
punctuation := (safepunct / mdash)
mdash       := '--'
safepunct   := [!@#$%^&()+=|\{}:;<>,.?/"]


Introduction To Spark

The Spark parser has a bit in common with EBNF grammars, but breaks the parsing/processing process into smaller components than a traditional EBNF grammar allows. The advantage Spark has is fine-tuned control of the operations at each step along the way, along with the capability of inserting custom code into the process. Readers of the SimpleParse installment will recall that our process was a rough scale one: (1) Generate and entire taglist from the grammar (and from a source document); (2) Use the taglist as data for custom-programmed actions.

The disadvantages Spark has over a standard EBNF-based tool is that it is more verbose, and that it lacks direct occurrence quantifiers (i.e., existential "+", possible "*", limited "?"). Quantifiers can be used within the regular expressions of the Spark tokenizer, and can be simulated by recursion in parse expression grammars. But it would be nice if Spark allowed quantification in its grammar expressions. Another disadvantage worth mentioning is that Spark's speed suffers compared to the underlying C-based mxTextTools engine SimpleParse uses.

In Compiling Little Languages in Python, Spark creator John Aycock breaks a compiler into four stages. The problem this article presents will only use the first two-and-a-half stages, both out of length limits and because we will take on the same comparatively simple "text markup" problem that previous installments looked at. Spark can potentially be used for full-cycle code compilers/interpreters, not only for the "parse and process" job I look at. Let us look at Aycock's four stages (quoted in abridged form):

1. Scanning, or lexical analysis. Breaks the input stream into a list of tokens.
2. Parsing, or syntax analysis. Ensures that a list of tokens has valid syntax according to a grammar.
3. Semantic analysis. Traverses the abstract syntax tree (AST) on or more times, collecting information and checking that the input program makes sense.
4. Code generation. Again traversing the AST, this phase may directly interpret the program, our output code in C or assembly.

For each stage, Spark provides one or more abstract classes for performing the step, and a rather unusual protocol for specializing these classes. Rather than merely redefine or add specific methods as in most inheritance patterns, Spark concrete classes have two features (the general pattern is common to the stages and various parents). The first unusual protocol is that much of the work done by a concrete class is specified in the docstrings of methods. The second special protocol is that sets of methods describing patterns are given distinct names indicating their role. The parent classes in turn contain introspective methods that look for features of the instance to operate. This is more clear once we look at examples.

Recognizing Text Markup

The problem this installment looks at is one we have solved in several other ways already. I use a format I call "smart ASCII" for various purposes. This format looks a lot like the conventions that have developed for email and newsgroup communications. For various purposes, I automatically convert this format to other formats like HTML, XML, LaTeX. That is what we will do again. To see informally what I mean, here is a short sample that will be used in this article:

Smart ASCII sample text ('p.txt')

Text with *bold*, and -itals phrase-, and [module]--this
should be a good 'practice run'.

There is a little more to the format than in the sample, but not too much (but there are some subtleties to how markup and punctuation interact).

Generating Tokens

The first thing our Spark "smart ASCII" parser needs to do is to break an input text into its relevant parts. At the level of tokenizing, we are not yet interested in how the tokens are structured, just what they are. Later on will combine token sequences into parse trees.

The grammar shown above in typographify.def provides guidance for the design of a Spark lexer/scanner. The trick is that we can only use those names that are "primitive" at the scanner stage. That is, those (compound) patterns that include other named patterns must be postponed for the parsing stage. Other than that, we can really just copy our old grammar:

Abridged 'wordscanner.py' [Spark] script

class WordScanner(GenericScanner):
    "Tokenize words, punctuation and markup"
    def tokenize(self, input):
        self.rv = []
        GenericScanner.tokenize(self, input)
        return self.rv
    def t_whitespace(self, s):
        r" [ \t\r\n]+ "
        self.rv.append(Token('whitespace', ' '))
    def t_alphanums(self, s):
        r" [a-zA-Z0-9]+ "
        print "{word}",
        self.rv.append(Token('alphanums', s))
    def t_safepunct(self, s): ...
    def t_bracket(self, s): ...
    def t_asterisk(self, s): ...
    def t_underscore(self, s): ...
    def t_apostrophe(self, s): ...
    def t_dash(self, s): ...

class WordPlusScanner(WordScanner):
    "Enhance word/markup tokenization"
    def t_contraction(self, s):
        r" (?<=[a-zA-Z])'(am|clock|d|ll|m|re|s|t|ve) "
        self.rv.append(Token('contraction', s))
    def t_mdash(self, s):
        r' -- '
        self.rv.append(Token('mdash', s))
    def t_wordpunct(self, s): ...

There is an interesting trick here. WordScanner is a perfectly good scanner class by itself. But a Spark scanner class can itself be further specialized by inheritance--child regular expression patterns are matched before parents, and child methods/regex's may overrid parents if desired. So the specializations in WordPlusScanner are matched before those in WordScanner are attempted (possibly thereby grabbing some bytes first). Any regular expressions are allowed in pattern docstrings--the method .t_contraction(), for example, contains a "lookbehind assertion" in the pattern.

The scanner inheritance logic is somewhat broken by Python 2.2, unfortunately. In Python 2.2, all of the defined patterns are matched in alphabetical order (by their name), regardless of where they are defined in the inheritance chain. The fix for this problem is a one line change in in the Spark function _namelist():

Corrected reflective function for 'spark.py'

def _namelist(instance):
    namelist, namedict, classlist = [], {}, [instance.__class__]
    for c in classlist:
        for b in c.__bases__:
            classlist.append(b)
        # for name in dir(c):   # dir() behavior changed in 2.2
        for name in c.__dict__.keys():  # <-- USE THIS
            if not namedict.has_key(name):
                namelist.append(name)
                namedict[name] = 1
    return namelist

I have informed Spark creator John Aycock of the problem, and future versions will fix this. In the meanwhile, make this change in your own copy.

Let us take a look at what the WordPlusScanner does when applied to the above smart ASCII sample. The created list is actually a list of Token instances, but they contain a .__repr__ method that makes them display nicely:

Tokenizing smart ASCII with 'WordPlusScanner'

>>> from wordscanner import WordPlusScanner
>>> tokens = WordPlusScanner().tokenize(open('p.txt').read())
>>> filter(lambda s: s<>'whitespace', tokens)
[Text, with, *, bold, *, ,, and, -, itals, phrase, -, ,, and, [,
module, ], --, this, should, be, a, good, ', practice, run, ', .]

It is worth noting that although methods .t_alphanums() are recognized by Spark introspection based on their "t_" prefix, they are also regular methods. Any extra code put into a method will execute whenever the corresponding token is encountered--the method .t_alphanums() contains a trivial example of this with a print statement.

Generating Abstract Syntax Trees

Finding tokens is a bit interesting, but the real work comes with applying grammars to the token lists. The parsing stage creates arbitrary tree structures on the bases of token lists. It is just a matter of specifying the expression grammar.

Spark has several ways to create AST's. The "manual" way is to specialize the GenericParser class. In this case, the concrete child parser should provide a number of methods with names in the form p_foobar(self, args). The docstring of each such method contains one or several assigments of patterns to names. Each method can contain arbitrary code to execute whenever its grammar expression(s) are matched.

However, Spark also offers an "automatic" way of generating AST's. This style inherits from the GenericASTBuilder class. All the grammar expression are listed in a single top-level method, and the .terminal() and .nonterminal() methods may be specialized to manipulate subtrees during generation (or to perform any other arbitrary actions, if desired). The result is still an AST, but the parent class does most of the work for you. My grammar class looks like this:

Abridged 'markupbuilder.py' [Spark] script

class MarkupBuilder(GenericASTBuilder):
    "Write out HTML markup based on matched markup"
    def p_para(self, args):
        '''
        para   ::= plain
        para   ::= markup
        para   ::= para plain
        para   ::= para emph
        para   ::= para strong
        para   ::= para module
        para   ::= para code
        para   ::= para title
        plain  ::= whitespace
        plain  ::= alphanums
        plain  ::= contraction
        plain  ::= safepunct
        plain  ::= mdash
        plain  ::= wordpunct
        plain  ::= plain plain
        emph   ::= dash plain dash
        strong ::= asterisk plain asterisk
        module ::= bracket plain bracket
        code   ::= apostrophe plain apostrophe
        title  ::= underscore plain underscore
        '''
    def nonterminal(self, type_, args):
        #  Flatten AST a bit by not making nodes if only one child.
        if len(args)==1:  return args[0]
        if type_=='para': return nonterminal(self, type_, args)
        if type_=='plain':
            args[0].attr = foldtree(args[0])+foldtree(args[1])
            args[0].type = type_
            return nonterminal(self, type_, args[:1])
        phrase_node = AST(type_)
        phrase_node.attr = foldtree(args[1])
        return phrase_node

My .p_para() should contain only a set of grammar rules in its docstring (no code). I decided to specialize the .nonterminal()' method to flatten my AST a bit. "plain" nodes that consist of a family of "plain" subtrees compact the subtrees into one longer string. Likewise, markup subtrees (i.e. "emph", "strong", "module", "code", "title") are collapsed to a single node of the right type, and containing a compound string.

One thing that is less than ideal in the Spark grammar has been mentioned: no quantifiers. By having one rule be:

plain ::= plain plain

We can aggregate subtrees of the "plain" type pairwise. But I would prefer if Spark allowed a more EBNF-style grammar expression like:

plain ::= plain+

We might then more simply create n-ary subtrees of "as many plain's as possible." In this case, our tree would start out much flatter, even without the massaging in .nonterminal().

Working With Trees

The Spark module provides several classes for working with AST's. For my purposes, these are heavier weight than I need. If you want them, GenericASTTraversal and GenericASTMatcher provide ways of walking trees, using similar inheritance protocols to those presented for the scanner and parser.

But walking a tree is not all that difficult just using recursive functions. I have created a few such examples in the article archive file prettyprint.py. One of these is showtree(). This function displays an AST with a couple conventions: Each line shows the descent depth; Nodes with only children (no content) have leading dashes; Node types are double angle-bracketed. Let us take a look at the AST generated in the above examples:

Tokenizing smart ASCII with 'WordPlusScanner'

>>> from wordscanner import tokensFromFname
>>> from markupbuilder import treeFromTokens
>>> from prettyprint import showtree
>>> showtree(treeFromTokens(tokensFromFname('p.txt')))
 0  <<para>>
 1 - <<para>>
 2 -- <<para>>
 3 --- <<para>>
 4 ---- <<para>>
 5 ----- <<para>>
 6 ------ <<para>>
 7 ------- <<para>>
 8 -------- <<plain>>
 9           <<plain>>  Text with
 8          <<strong>> bold
 7 ------- <<plain>>
 8          <<plain>> , and
 6        <<emph>> itals phrase
 5 ----- <<plain>>
 6        <<plain>> , and
 4      <<module>> module
 3 --- <<plain>>
 4      <<plain>> --this should be a good
 2    <<code>> practice run
 1 - <<plain>>
 2    <<plain>> .

Understanding the tree structure is illustrative, but what about the actual modified markup we are aiming for? Fortunately, it takes just a few lines to traverse the tree and produce it:

Outputting markup from AST (prettyprint.py)

def emitHTML(node):
    from typo_html import codes
    if hasattr(node, 'attr'):
        beg, end = codes[node.type]
        sys.stdout.write(beg+node.attr+end)
    else: map(emitHTML, node._kids)

The file typo_html.py is the same file from the SimpleParse installment--it just contains a dictionary mapping names to begintag/endtag pairs. Obviously, we could use the same approach for markup other than HTML. For the curious, here is what it produces for our example:

The HTML output of the whole process

Text with <strong>bold</strong>, and <em>itals phrase</em>,
and <em><code>module</code></em>--this should be a good
<code>practice run</code>.


Conclusion

Quite a number of Python programmers have recommended Spark to me. While the unusual conventions Spark uses take a little bit of getting used to--and while the documentation could probably be a little more explicit on certain points--the power of Spark is really quite amazing. The style of programming Spark implements allows an end-programmer to insert code blocks everywhere within a scanning/parsing process--something that is usually a "black box" to end users.

For all its strengths, the real drawback I found with Spark is its speed. Spark is the first Python program I've used where I really found the speed penalty of an interpreted language to be an major issue. But Spark is slow; not "wish it were a second faster" slow, but "take a long lunch and hope it finishes" slow. In my experiments, the tokenizer is plenty fast; but the parsing bogs down, even with quite small test cases. It is possible that in my inexperience I have designed inefficient grammars; but if so, most users would do likewise.

Resources

This article builds on the earlier discussion in my Charming Python installment "Parsing in Python with SimpleParse". Consult that article at:

http://gnosis.cx/publish/programming/charming_python_b4.html

John Aycock's Spark has a homepage at:

http://pages.cpsc.ucalgary.ca/~aycock/spark/

On the Spark homepage, the most important document to read is the original presentation of the Spark framework, Compiling Little Languages in Python, by John Aycock. You can download it from:

http://www.foretec.com/python/workshops/1998-11/proceedings/papers/aycock-little/aycock-little.ps

Mike Fletcher's SimpleParse can be found, along with a brief introduction to its usage, at:

http://members.rogers.com/mcfletch/programming/simpleparse/simpleparse.html

mxTextTools is now part of the larger eGenix package of extensions. Information can be found at:

http://www.lemburg.com/files/python/mxTextTools.html

Information on the ISO 14977 standard for EBNF syntax can be found at:

http://www.cl.cam.ac.uk/~mgk25/iso-ebnf.html

The files mentioned in this article can be found in an archive at:

http://gnosis.cx/download/charming_python_b6.zip

Picture of Author David Mertz would like to write, with Nietzsche, that these are the musings of an old philologist, but that untruth would unmask itself. But perhaps his (right here gratuitously plugged) forthcoming book, Text Processing in Python, will someday be mistaken for a cybernetic variant of philology. David may be reached at mertz@gnosis.cx; his life pored over at http://gnosis.cx/publish/. Suggestions and recommendations on this, past, or future, columns are welcomed.