Tips on how to parse custom file format

Question

Sorry about the vague title, but I really don't know how to describe this problem concisely.

I've created a (more or less) simple domain-specific language that I will to use to specify what validation rules to apply to different entities (generally forms submitted from a web page). I've included a sample at the bottom of this post of what the language looks like.

My problem is that I have no idea how to begin parsing this language into a form I can use (I will be using Python to do the parsing). My goal is to end up with a list of rules/filters (as strings, including arguments, e.g. 'cocoa(99)') that should be applied (in order) to each object/entity (also a string, e.g. 'chocolate', 'chocolate.lindt', etc.).

I'm not sure what technique to use to start with, or even what techniques exist for problems like this. What do you think is the best way of going about this? I'm not looking for a complete solution, just a general nudge in the right direction.

Thanks.

Sample file of language:

# Comments start with the '#' character and last until the end of the line
# Indentation is significant (as in Python)


constant NINETY_NINE = 99       # Defines the constant `NINETY_NINE` to have the value `99`


*:      # Applies to all data
    isYummy             # Everything must be yummy

chocolate:              # To validate, say `validate("chocolate", object)`
    sweet               # chocolate must be sweet (but not necessarily chocolate.*)

    lindt:              # To validate, say `validate("chocolate.lindt", object)`
        tasty           # Applies only to chocolate.lindt (and not to chocolate.lindt.dark, for e.g.)

        *:              # Applies to all data under chocolate.lindt
            smooth      # Could also be written smooth()
            creamy(1)   # Level 1 creamy
        dark:           # dark has no special validation rules
            extraDark:
                melt            # Filter that modifies the object being examined
                c:bitter        # Must be bitter, but only validated on client
                s:cocoa(NINETY_NINE)    # Must contain 99% cocoa, but only validated on server. Note constant
        milk:
            creamy(2)   # Level 2 creamy, overrides creamy(1) of chocolate.lindt.* for chocolate.lindt.milk
            creamy(3)   # Overrides creamy(2) of previous line (all but the last specification of a given rule are ignored)



ruleset food:       # To define a chunk of validation rules that can be expanded from the placeholder `food` (think macro)
    caloriesWithin(10, 2000)        # Unlimited parameters allowed
    edible
    leftovers:      # Nested rules allowed in rulesets
        stale

# Rulesets may be nested and/or include other rulesets in their definition



chocolate:              # Previously defined groups can be re-opened and expanded later
    ferrero:
        hasHazelnut



cake:
    tasty               # Same rule used for different data (see chocolate.lindt)
    isLie
    ruleset food        # Substitutes with rules defined for food; cake.leftovers must now be stale


pasta:
    ruleset food        # pasta.leftovers must also be stale




# Sample use (in JavaScript):

# var choc = {
#   lindt: {
#       cocoa: {
#           percent: 67,
#           mass:    '27g'
#       }
#   }
#   // Objects/groups that are ommitted (e.g. ferrro in this example) are not validated and raise no errors
#   // Objects that are not defined in the validation rules do not raise any errors (e.g. cocoa in this example)
# };
# validate('chocolate', choc);

# `validate` called isYummy(choc), sweet(choc), isYummy(choc.lindt), smooth(choc.lindt), creamy(choc.lindt, 1), and tasty(choc.lindt) in that order
# `validate` returned an array of any validation errors that were found

# Order of rule validation for objects:
# The current object is initially the object passed in to the validation function (second argument).
# The entry point in the rule group hierarchy is given by the first argument to the validation function.
# 1. First all rules that apply to all objects (defined using '*') are applied to the current object,
#    starting with the most global rules and ending with the most local ones.
# 2. Then all specific rules for the current object are applied.
# 3. Then a depth-first traversal of the current object is done, repeating steps 1 and 2 with each object found as the current object
# When two rules have equal priority, they are applied in the order they were defined in the file.



# No need to end on blank line

Answer 1

If your goal is to learn about parsing, I'd highly recommend an OO style library like PyParsing. They are not as fast as the more sophisticated antler, lex, yac options, but you get started with the parsing right away.

Answer 2

First off, if you want to learn about parsing, then write your own recursive descent parser. The language you've defined only requires a handful of productions. I suggest using Python's tokenize library to spare yourself the boring task of converting a stream of bytes into a stream of tokens.

For practical parsing options, read on...

A quick and dirty solution is to use python itself:

NINETY_NINE = 99       # Defines the constant `NINETY_NINE` to have the value `99`

rules = {
  '*': {     # Applies to all data
    'isYummy': {},      # Everything must be yummy

    'chocolate': {        # To validate, say `validate("chocolate", object)`
      'sweet': {},        # chocolate must be sweet (but not necessarily chocolate.*)

      'lindt': {          # To validate, say `validate("chocolate.lindt", object)`
        'tasty':{}        # Applies only to chocolate.lindt (and not to chocolate.lindt.dark, for e.g.)

        '*': {            # Applies to all data under chocolate.lindt
          'smooth': {}  # Could also be written smooth()
          'creamy': 1   # Level 1 creamy
        },
# ...
    }
  }
}

There are several ways to pull off this trick, e.g., here's a cleaner (albeit somewhat unusual) approach using classes:

class _:
    class isYummy: pass

    class chocolate:
        class sweet: pass

        class lindt:
            class tasty: pass

            class _:
                class smooth: pass
                class creamy: level = 1
# ...

As an intermediate step to a full parser, you can use the "batteries-included" Python parser, which parses Python syntax and returns an AST. The AST is very deep with lots of (IMO) unnecessary levels. You can filter these down to a much simpler structure by culling any nodes that have only one child. With this approach you can do something like this:

import parser, token, symbol, pprint

_map = dict(token.tok_name.items() + symbol.sym_name.items())

def clean_ast(ast):
    if not isinstance(ast, list):
        return ast
    elif len(ast) == 2: # Elide single-child nodes.
        return clean_ast(ast[1])
    else:
        return [_map[ast[0]]] + [clean_ast(a) for a in ast[1:]]

ast = parser.expr('''{

'*': {     # Applies to all data
  isYummy: _,    # Everything must be yummy

  chocolate: {        # To validate, say `validate("chocolate", object)`
    sweet: _,        # chocolate must be sweet (but not necessarily chocolate.*)

    lindt: {          # To validate, say `validate("chocolate.lindt", object)`
      tasty: _,        # Applies only to chocolate.lindt (and not to chocolate.lindt.dark, for e.g.)

      '*': {            # Applies to all data under chocolate.lindt
        smooth: _,  # Could also be written smooth()
        creamy: 1   # Level 1 creamy
      }
# ...
    }
  }
}

}''').tolist()
pprint.pprint(clean_ast(ast))

This approach does have its limitations. The final AST is still a bit noisy, and the language you define has to be interpretable as valid python code. For instance, you couldn't support this...

*:
    isYummy

...because this syntax doesn't parse as python code. Its big advantage, however, is that you control the AST conversion, so it is impossible to inject arbitrary Python code.

Answer 3

As 'Marcelo Cantos' suggested you can use python dict, benefit is that you do not have to parse any thing, you can use same rules on server side as python dict and on client side using javascript objects, and can pass them from server to client or viceversa as JSON.

If you really want to do parsing yourself see this http://nedbatchelder.com/text/python-parsers.html

but I am not sure you will be easily able to parse a indented language.

Answer 4

There are libraries and tools to make parsing easier. One of the more well known is lex / yacc. There's a python library called 'lex' and a tutorial on using it.

Answer 5

Again not teaching you about parsing, but your format is so close to legal YAML that you might want to just redefine your language as a subset of YAML and use a standard YAML parser.

Answer 6

what is the motivation for the customized file structure? Would it be possible to remodel your data into a better known structure like XML? If so you could use one of a multitude to parse your file. Using an accepted parsing tool may save you a lot of time debugging, and it may make your file more readable if that is a consideration