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(Soon to be) the fastest pure-Python PEG parser I could muster

Project description

Parsimonious aims to be the fastest PEG parser written in pure Python. It was designed to undergird a MediaWiki parser that wouldn’t take 5 seconds or a GB of RAM to do one page.

Beyond speed, secondary goals include…

  • Frugal RAM use

  • Minimalistic, understandable, idiomatic Python code

  • Readable grammars

  • Extensible grammars

  • Complete test coverage

  • Separation of concerns. Some Python parsing kits mix recognition with instructions about how to turn the resulting tree into some kind of other representation. This is limiting when you want to do several different things with a tree: for example, render wiki markup to HTML or to text.

  • Good error reporting. I want the parser to work with me as I develop a grammar.

Example

Here’s how to build a simple grammar:

>>> from parsimonious.grammar import Grammar
>>> grammar = Grammar(
...     """
...     bold_text  = bold_open text bold_close
...     text       = ~"[A-Z 0-9]*"i
...     bold_open  = "(("
...     bold_close = "))"
...     """)

You can have forward references or anything you want; it’s all taken care of by the grammar compiler. The first rule is taken to be the default start symbol, but you can override that.

Next, let’s parse something and get an abstract syntax tree:

>>> grammar.parse('((bold stuff))')
<Node called "bold_text" matching "((bold stuff))">
    <Node called "bold_open" matching "((">
    <RegexNode called "text" matching "bold stuff">
    <Node called "bold_close" matching "))">

You’d typically then use a nodes.NodeVisitor subclass (see below) to walk the tree and do something useful with it.

But first, lots of scary warnings…

Status

Take it easy, because 0.1 is a useable but rough preview release.

  • Everything that exists works. Test coverage is good.

  • I don’t plan on making any backward-incompatible changes to the grammar DSL in the future, so you can write grammars without fear.

  • It may be slow and use a lot of RAM; I haven’t measured either yet. However, I have several macro- and micro-optimizations in mind.

  • Error reporting and debugging are nonexistent, though repr methods of things are often helpful and informative. I have big things planned here.

  • Grammar extensibility story is underdeveloped at the moment. You should be able to extend a grammar by simply concatening more rules onto its textual (DSL) representation; later rules of the same name should override previous ones. However, this is untested and may not be the final story.

  • I’m not in love with the Grammar API, so it may change. ExpressionFlattener is probably going to move or get absorbed by something else.

  • No Sphinx docs yet, but the docstrings are pretty good

Next, I’ll do some optimization and see if I can make Parsimonious worthy of its name. RAM use and better thought-out grammar extensibility come after that.

A Little About PEG Parsers

PEG parsers don’t draw a distinction between lexing and parsing; everything’s done at once. As a result, there is no lookahead limit, as there is with, for instance, Yacc. And, due to both of these properties, PEG grammars are easier to write: they’re basically just EBNF. With caching, they take O(grammar size * text length) memory (though I plan to do better), but they run in O(text length) time.

More Technically

PEGs can describe a superset of LL(k) languages, any deterministic LR(k) language, and many others, including some that aren’t context-free (http://www.brynosaurus.com/pub/lang/peg.pdf). They can also deal with what would be ambiguous languages if described in canonical EBNF. They do this by trading the | alternation operator for the / operator, which works the same except that it makes priority explicit: a / b / c first tries matching a. If that fails, it tries b, and, failing that, moves on to c. Thus, ambiguity is resolved by always yielding the first successful recognition.

Writing Grammars

They’re basically just extended EBNF syntax:

"some literal"

Used to quote literals. Backslash escaping and Python conventions for “raw” and Unicode strings help support fiddly characters.

space

Sequences are made out of space- or tab-delimited things. a b c matches spots where those 3 terms appear in that order.

a / b

Alternatives. The first to succeed of a / b / c wins.

a & b

A lookahead assertion followed by a normal, consumed symbol. a & b & c means that a, b, and c all have to match at the current position. c, however, is the only bit that’s actually consumed.

thing?

An optional expression

!thing

(Not implemented yet.) Matches if thing isn’t found here. Doesn’t consume any text.

things*

Zero or more things

things+

One or more things

~r"regex"ilmsux

Regexes have ~ in front and are quoted like literals. Any flags follow the end quotes as single chars. Regexes are good for representing character classes ([a-z0-9]) and optimizing for speed. The downside is that they won’t be able to take advantage of our fancy debugging, once we get that working. Ultimately, I’d like to deprecate explicit regexes and instead have Parsimonious build them dynamically out of simpler primitives.

We might implement parentheses in the future for anonymous grouping. For now, just break up complex rules instead.

We shouldn’t need to represent Empty; the quantifiers should suffice.

Optimizing Grammars

Don’t repeat expressions. If you need a Regex('such-and-such') at some point in your grammar, don’t type it twice; make it a rule of its own, and reference it from wherever you need it. You’ll get the most out of the caching this way, since cache lookups are by expression object identity (for speed). Even if you have an expression that’s very simple, not repeating it will save RAM, as there can, at worst, be a cached int for every char in the text you’re parsing. But hmm, maybe I can identify repeated subexpressions automatically and factor that up while building the grammar….

How much should you shove into one Regex, versus how much should you break them up to not repeat yourself? That’s a fine balance and worthy of benchmarking. More stuff jammed into a regex will execute faster, because it doesn’t have to run any Python between pieces, but a broken-up one will give better cache performance if the individual pieces are re-used elsewhere. If the pieces of a regex aren’t used anywhere else, by all means keep the whole thing together.

Quantifiers: bring your ? and * quantifiers up to the highest level you can. Otherwise, lower-level patterns could succeed but be empty and put a bunch of useless nodes in your tree that didn’t really match anything.

Dealing With Parse Trees

A parse tree has a node for each expression matched, even if it matched a zero-length string, like "thing"? might do.

The NodeVisitor class provides an inversion of control framework for walking a tree and returning a new construct (tree, string, or whatever) based on it. For now, have a look at its docstrings for more detail. There’s also a good example in grammar.DslVisitor. Notice how we take advantage of nodes’ iterability by using tuple unpacks in the formal parameter lists:

def visit_or_term(self, or_term, (_, slash, term)):
    ...

When something goes wrong in your visitor, you get a nice error like this:

[normal traceback here...]
VisitationException: 'Node' object has no attribute 'foo'

Parse tree:
<Node called "rules" matching "number = ~"[0-9]+"">  <-- *** We were here. ***
    <Node matching "number = ~"[0-9]+"">
        <Node called "rule" matching "number = ~"[0-9]+"">
            <Node matching "">
            <Node called "label" matching "number">
            <Node matching " ">
                <Node called "_" matching " ">
            <Node matching "=">
            <Node matching " ">
                <Node called "_" matching " ">
            <Node called "rhs" matching "~"[0-9]+"">
                <Node called "term" matching "~"[0-9]+"">
                    <Node called "atom" matching "~"[0-9]+"">
                        <Node called "regex" matching "~"[0-9]+"">
                            <Node matching "~">
                            <Node called "literal" matching ""[0-9]+"">
                            <Node matching "">
            <Node matching "">
            <Node called "eol" matching "
            ">
    <Node matching "">

The parse tree tacked onto the exception, and the node whose visitor method raised the error is pointed out.

Why No Streaming Tree Processing?

Some have asked why we don’t process the tree as we go, SAX-style. There are two main reasons:

  1. It wouldn’t work. With a PEG parser, no parsing decision is final until the whole text is parsed. If we had to change a decision, we’d have to backtrack and redo the SAX-style interpretation as well, which would involve reconstituting part of the AST and quite possibly scuttling whatever you were doing with the streaming output. (Note that some bursty SAX-style processing may be possible in the future if we use cuts.)

  2. It interferes with the ability to derive multiple representations from the AST: for example, first HTML and then text from wiki markup.

Future Directions

DSL Changes

  • Do we need a LookAhead? It might be slightly faster, but A Lookahead(B) is equivalent to AB & A, which, while more verbose, takes full advantage of packratting.

  • Maybe support left-recursive rules like PyMeta, if anybody cares.

  • The ability to mark certain nodes as undesired, so we don’t bother constructing them and cluttering the tree with them. For example, we might only care to see the OneOf node in the final tree, not the boring Literals inside it:

    greeting = "hi" / "hello" / "bonjour"

    Perhaps we could express it like this:

    greeting = -"hi" / -"hello" / -"bonjour"

    On the other hand, parentheses for anonymous subexpressions could largely solve this problem–and in a more familiar way–if we implicitly omitted their nodes. (The exception would be subexpressions that you end up having to repeat several times in the grammar.) On the third hand, I don’t really care to clutter grammar definitions up like this. It makes them less readable and conflates recognition with tree processing. I’ll most likely just focus on making NodeVisitor subclasses as easy as possible to write.

  • Pijnu has a raft of tree manipulators. I don’t think I want all of them, but a judicious subset might be nice. Don’t get into mixing formatting with tree manipulation. https://github.com/erikrose/pijnu/blob/master/library/node.py#L333

Optimizations

  • Make RAM use almost constant by automatically inserting “cuts”, as described in http://ialab.cs.tsukuba.ac.jp/~mizusima/publications/paste513-mizushima.pdf. This would also improve error reporting, as we wouldn’t backtrack out of everything informative before finally failing.

  • Think about having the user (optionally) provide some representative input along with a grammar. We can then profile against it, see which expressions are worth caching, and annotate the grammar. Perhaps there will even be positions at which a given expression is more worth caching. Or we could keep a count of how many times each cache entry has been used and evict the most useless ones as RAM use grows.

  • We could possibly compile the grammar into VM instructions, like in “A parsing machine for PEGs” by Medeiros.

  • If the recursion gets too deep in practice, use trampolining to dodge it.

Version History

0.1
  • A rough but useable preview release

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