I've trying to find out a way to construct string data from a compiled *regexp.Regexp with given interface{} array. For example:
re := regexp.MustCompile(`(?P<word>\w+)\s*(?P<num>\d+)`)
I want to construct a string from the structure found in re by a string and a int data which may be received as interface{}.
Can't figure out how I can do that in Go. Please help me out.
Thanks in advance.
Such a library, often called Xeger, exists for many languages, including go. However, this one is called regen: It's a tool to generate random strings from Go/RE2 regular expressions.
Here's an example:
$ regen -n 3 '[a-z]{6,12}(\+[a-z]{6,12})?#[a-z]{6,16}(\.[a-z]{2,3}){1,2}'
iprbph+gqastu#regegzqa.msp
abxfcomj#uyzxrgj.kld.pp
vzqdrmiz#ewdhsdzshvvxjk.pi
Essentially, all regen does is parse the regular expressions it's
given and iterate over the tree produced by regexp/syntax and attempt
to generate strings based on the ops described by its results. This
could probably be optimized further by compiling the resulting Regexp
into a Prog, but I didn't feel like this was worthwhile when it's a
very small tool.
Some additional information can be
found at https://godoc.org/go.spiff.io/regen.
While technically not impossible this is a huge amount of work.
Go regexp package compiles regular expressions into bytecode programs that are then executed to search strings. De-compiling this bytecode back into a regular expression is not impossible but quite difficult and moreover you cannot be sure that the regexp expression you get is the exact same that was used originally... for example
/(?:a+)+/
and
/a+/
will be compiled to the same code for matching (expressions are "simplified" before being passed to the code generator).
Most probably you want to find another solution, like saving the original strings before compiling them into regular expression objects.
First of all, I use C# 4.0 to parse the code of a VB6 application.
I have some old VB6 code and about 500+ copies of it. And I use a regular expression to grab all kinds of global variables from the code. The code is described as "Yuck" and some poor victim still has to support this. So I'm hoping to help this poor sucker a bit by generating overviews of specific constants. (And yes, it should be rewritten but it ain't broke, so...)
This is a sample of a code line I need to match, in this case all boolean constants:
Public Const gDemo = False 'Is this a demo version
And this is the regular expression I use at this moment:
Public\s+Const\s+g(?'Name'[a-zA-Z][a-zA-Z0-9]*)\s+=\s+(?'Value'[0-9]*)
And I think it too is yuckie, since the * at the end of the boolean group. But if I don't use it, it will only return 'T' or 'F'. I want the whole word.
Is this the proper RegEx to use as solution or is there an even nicer-looking option?
FYI, I use similar regexs to find all string constants and all numeric constants. Those work just fine. And basically the same .BAS file is used for all 50 copies but with different values for all these variables. By parsing all files, we have a good overview of how every version is configured.
And again, yes, we need to rebuild the whole project from scratch since it becomes harder to maintain these days. But it works and we need the manpower for other tasks. It just needs the occasional tweaks...
You can use: Public\s+Const\s+g(?<Name>[a-zA-Z][a-zA-Z0-9]*)\s+=\s+(?<Value>False|True)
demo
(EDITED both title and main text and created a spin-off question that arose)
For our application it would be ideal to parse a simple DSL of logical expressions. However the way I'd like to do this is to parse (at runtime) the input text which gives the expressions into some lazily evaluated structure (an expression template) which can then be later used within more performance sensitive code.
Ideally the evaluation is as fast as possible using this technique as it will be used a large number of times with different values substituting into the placeholders each time. I'm not expecting the expression template to be equivalent in performance to say a hardcoded function that models the same function as the given input text string i.e. there is no need to go down a route of actually compiling say, c++, in situ of a running program (I believe other questions cover dynamic library compiling/loading).
My own thoughts reading examples from boost is that I can use boost::spirit to do the parsing of the input text and I'm confident I can develop the grammar I need. However, I'm not sure how I can combine the parser with boost::proto to build an executable expression template. Most examples of spirit that I've seen are merely interpreters or end up building some kind of syntax tree but go no further. Most examples of proto that I've seen assume the DSL is embedded in the host source code and does not need to be initially interpreted from a string. I'm aware that boost::spirit is actually implemented with boost::proto but not sure if this is relevant to the problem or whether that fact will suggest a convenient solution.
To re-iterate, I need to be able to make real the something like following:
const std::string input_text("a && b || c");
// const std::string input_text(get_dsl_string_from_file("expression1.dsl"));
Expression expr(input_text);
while(keep_intensively_processing) {
...
Context context(…);
// e.g. context.a = false; context.b=false; context.c=true;
bool result(evaluate(expr, context));
...
}
I would really appreciate a minimal example or even just a small kernel that I can build upon that creates an expression from input text which is evaluated later in context.
I don't think this is exactly the same question as posted here: parsing boolean expressions with boost spirit
as I'm not convinced this is necessarily the quickest executing way of doing this, even though it looks very clever. In time I'll try to do a benchmark of all answers posted.
My impulse is to say yes, especially if I'm using the same regex in multiple code locations, but this article indicates that the library will cache the compilation for me (which I'm not even sure how it would do):
There’s normally no need to compile regular expression patterns. A pattern will be compiled the first time it’s used, and your Haskell runtime should memoise the compiled representation for you.
If you reuse a regular expression then it is worthwhile to use the RegexMaker type class to define the "compiled" regular expression. It has the ability to take additional options and the ability to report compilation failure in your choice of Monad.
To use the "compiled" form you can use 'match' or 'matchM' from RegexLike which gives you the equivalent to =~ or ==~ operators.
GHC (as of 7.8.4/regex-tdfa-1.2.0) does not memoize regular expressions matched with (=~) or (=~~). I see an order of magnitude performance improvement using compile and execute with a large number of potential matches.
compile and execute
(=~)
Lets say that I have 10,000 regexes and one string and I want to find out if the string matches any of them and get all the matches.
The trivial way to do it would be to just query the string one by one against all regexes. Is there a faster,more efficient way to do it?
EDIT:
I have tried substituting it with DFA's (lex)
The problem here is that it would only give you one single pattern. If I have a string "hello" and patterns "[H|h]ello" and ".{0,20}ello", DFA will only match one of them, but I want both of them to hit.
This is the way lexers work.
The regular expressions are converted into a single non deterministic automata (NFA) and possibily transformed in a deterministic automata (DFA).
The resulting automaton will try to match all the regular expressions at once and will succeed on one of them.
There are many tools that can help you here, they are called "lexer generator" and there are solutions that work with most of the languages.
You don't say which language are you using. For C programmers I would suggest to have a look at the re2c tool. Of course the traditional (f)lex is always an option.
I've come across a similar problem in the past. I used a solution similar to the one suggested by akdom.
I was lucky in that my regular expressions usually had some substring that must appear in every string it matches. I was able to extract these substrings using a simple parser and index them in an FSA using the Aho-Corasick algorithms. The index was then used to quickly eliminate all the regular expressions that trivially don't match a given string, leaving only a few regular expressions to check.
I released the code under the LGPL as a Python/C module. See esmre on Google code hosting.
We had to do this on a product I worked on once. The answer was to compile all your regexes together into a Deterministic Finite State Machine (also known as a deterministic finite automaton or DFA). The DFA could then be walked character by character over your string and would fire a "match" event whenever one of the expressions matched.
Advantages are it runs fast (each character is compared only once) and does not get any slower if you add more expressions.
Disadvantages are that it requires a huge data table for the automaton, and there are many types of regular expressions that are not supported (for instance, back-references).
The one we used was hand-coded by a C++ template nut in our company at the time, so unfortunately I don't have any FOSS solutions to point you toward. But if you google regex or regular expression with "DFA" you'll find stuff that will point you in the right direction.
Martin Sulzmann Has done quite a bit of work in this field.
He has a HackageDB project explained breifly here which use partial derivatives seems to be tailor made for this.
The language used is Haskell and thus will be very hard to translate to a non functional language if that is the desire (I would think translation to many other FP languages would still be quite hard).
The code is not based on converting to a series of automata and then combining them, instead it is based on symbolic manipulation of the regexes themselves.
Also the code is very much experimental and Martin is no longer a professor but is in 'gainful employment'(1) so may be uninterested/unable to supply any help or input.
this is a joke - I like professors, the less the smart ones try to work the more chance I have of getting paid!
10,000 regexen eh? Eric Wendelin's suggestion of a hierarchy seems to be a good idea. Have you thought of reducing the enormity of these regexen to something like a tree structure?
As a simple example: All regexen requiring a number could branch off of one regex checking for such, all regexen not requiring one down another branch. In this fashion you could reduce the number of actual comparisons down to a path along the tree instead of doing every single comparison in 10,000.
This would require decomposing the regexen provided into genres, each genre having a shared test which would rule them out if it fails. In this way you could theoretically reduce the number of actual comparisons dramatically.
If you had to do this at run time you could parse through your given regular expressions and "file" them into either predefined genres (easiest to do) or comparative genres generated at that moment (not as easy to do).
Your example of comparing "hello" to "[H|h]ello" and ".{0,20}ello" won't really be helped by this solution. A simple case where this could be useful would be: if you had 1000 tests that would only return true if "ello" exists somewhere in the string and your test string is "goodbye;" you would only have to do the one test on "ello" and know that the 1000 tests requiring it won't work, and because of this, you won't have to do them.
If you're thinking in terms of "10,000 regexes" you need to shift your though processes. If nothing else, think in terms of "10,000 target strings to match". Then look for non-regex methods built to deal with "boatloads of target strings" situations, like Aho-Corasick machines. Frankly, though, it seems like somethings gone off the rails much earlier in the process than which machine to use, since 10,000 target strings sounds a lot more like a database lookup than a string match.
Aho-Corasick was the answer for me.
I had 2000 categories of things that each had lists of patterns to match against. String length averaged about 100,000 characters.
Main Caveat: The patters to match were all language patters not regex patterns e.g. 'cat' vs r'\w+'.
I was using python and so used https://pypi.python.org/pypi/pyahocorasick/.
import ahocorasick
A = ahocorasick.Automaton()
patterns = [
[['cat','dog'],'mammals'],
[['bass','tuna','trout'],'fish'],
[['toad','crocodile'],'amphibians'],
]
for row in patterns:
vals = row[0]
for val in vals:
A.add_word(val, (row[1], val))
A.make_automaton()
_string = 'tom loves lions tigers cats and bass'
def test():
vals = []
for item in A.iter(_string):
vals.append(item)
return vals
Running %timeit test() on my 2000 categories with about 2-3 traces per category and a _string length of about 100,000 got me 2.09 ms vs 631 ms doing sequential re.search() 315x faster!.
You'd need to have some way of determining if a given regex was "additive" compared to another one. Creating a regex "hierarchy" of sorts allowing you to determine that all regexs of a certain branch did not match
You could combine them in groups of maybe 20.
(?=(regex1)?)(?=(regex2)?)(?=(regex3)?)...(?=(regex20)?)
As long as each regex has zero (or at least the same number of) capture groups, you can look at what what captured to see which pattern(s) matched.
If regex1 matched, capture group 1 would have it's matched text. If not, it would be undefined/None/null/...
If you're using real regular expressions (the ones that correspond to regular languages from formal language theory, and not some Perl-like non-regular thing), then you're in luck, because regular languages are closed under union. In most regex languages, pipe (|) is union. So you should be able to construct a string (representing the regular expression you want) as follows:
(r1)|(r2)|(r3)|...|(r10000)
where parentheses are for grouping, not matching. Anything that matches this regular expression matches at least one of your original regular expressions.
I would recommend using Intel's Hyperscan if all you need is to know which regular expressions match. It is built for this purpose. If the actions you need to take are more sophisticated, you can also use ragel. Although it produces a single DFA and can result in many states, and consequently a very large executable program. Hyperscan takes a hybrid NFA/DFA/custom approach to matching that handles large numbers of expressions well.
I'd say that it's a job for a real parser. A midpoint might be a Parsing Expression Grammar (PEG). It's a higher-level abstraction of pattern matching, one feature is that you can define a whole grammar instead of a single pattern. There are some high-performance implementations that work by compiling your grammar into a bytecode and running it in a specialized VM.
disclaimer: the only one i know is LPEG, a library for Lua, and it wasn't easy (for me) to grasp the base concepts.
I'd almost suggest writing an "inside-out" regex engine - one where the 'target' was the regex, and the 'term' was the string.
However, it seems that your solution of trying each one iteratively is going to be far easier.
You could compile the regex into a hybrid DFA/Bucchi automata where each time the BA enters an accept state you flag which regex rule "hit".
Bucchi is a bit of overkill for this, but modifying the way your DFA works could do the trick.
I use Ragel with a leaving action:
action hello {...}
action ello {...}
action ello2 {...}
main := /[Hh]ello/ % hello |
/.+ello/ % ello |
any{0,20} "ello" % ello2 ;
The string "hello" would call the code in the action hello block, then in the action ello block and lastly in the action ello2 block.
Their regular expressions are quite limited and the machine language is preferred instead, the braces from your example only work with the more general language.
Try combining them into one big regex?
I think that the short answer is that yes, there is a way to do this, and that it is well known to computer science, and that I can't remember what it is.
The short answer is that you might find that your regex interpreter already deals with all of these efficiently when |'d together, or you might find one that does. If not, it's time for you to google string-matching and searching algorithms.
The fastest way to do it seems to be something like this (code is C#):
public static List<Regex> FindAllMatches(string s, List<Regex> regexes)
{
List<Regex> matches = new List<Regex>();
foreach (Regex r in regexes)
{
if (r.IsMatch(string))
{
matches.Add(r);
}
}
return matches;
}
Oh, you meant the fastest code? i don't know then....