I am writing a toy language for curiosity's sake. I have written a functioning lexer to create tokens using a number of regular expressions.
From a performance point of view, is it faster/more efficient to loop through the source code character by character and figure out the tokens or should I stick with regex? Just curious how "proper" lexers are implemented.
Given the architecture of the computers we use, a regex ends up being implemented with loops.
If the code is structured, it will be a combination of something like a switch statement within a while statement, with the cases in the switch representing the states of the Deterministic Finite Automaton that recognizes the same language as the regex.
If goto is allowed, the implementation can be much more efficient than what a generic regex library can do.
Unless you have specific efficiency needs, sticking to a regex library should be efficient enough, and it will save you lots of programming (debugging) time.
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I tried really hard to find answer to this question on google engine.
But I wonder how these high level programming languages are created in principle of automata or is automata theory not included in defining the languages?
Language design tends to have two important levels:
Lexical analysis - the definition of what tokens look like. What is a string literal, what is a number, what are valid names for variables, functions, etc.
Syntactic analysis - the definition of how tokens work together to make meaningful statements. Can you assign a value to a literal, what does a block look like, what does an if statement look like, etc.
The lexical analysis is done using regular languages, and generally tokens are defined using regular expressions. It's not that a DFA is used (most regex implementations are not DFAs in practice), but that regular expressions tend to line up well with what most languages consider tokens. If, for example, you wanted a language where all variable names had to be palindromes, then your language's token specification would have to be context-free instead.
The input to the lexing stage is the raw characters of the source code. The alphabet would therefore be ASCII or Unicode or whatever input your compiler is expecting. The output is a stream of tokens with metadata, such as string-literal (value: hello world) which might represent "hello world" in the source code.
The syntactic analysis is typically done using a subset of context-free languages called LL or LR parsers. This is because the implementation of CFG (PDAs) are nondeterministic. LL and LR parsing are ways to make deterministic decisions with respect to how to parse a given expression.
We use CFGs for code because this is the level on the Chomsky hierarchy where nesting occurs (where you can express the idea of "depth", such as with an if within an if). Higher or lower levels on the hierarchy are possible, but a regular syntax would not be able to express nesting easily, and context-sensitive syntax would probably cause confusion (but it's not unheard of).
The input to the syntactic analysis step is the token stream, and the output is some form of executable structure, typically a parse tree that is either executed immediately (as in interpretted languages) or stored for later optimization and/or execution (as in compiled languages) or something else (as in intermediate-compiled languages like Java). The alphabet of the CFG is therefore the possible tokens specified by the lexical analysis step.
So this whole thing is a long-winded way of saying that it's not so much the automata theory that's important, but rather the formal languages. We typically want to have the simplest language class that meets our needs. That typically means regular tokens and context-free syntax, but not always.
The implementation of the regular expression need not be an automaton, and the implementation of the CFG cannot be a PDA, because PDAs are nondeterministic, so we define deterministic parsers on reasonable subsets of the CFG class instead.
More generally we talk about Theory of computation.
What has happened through the history of programming languages is that it has been formally proven that higher-level constructs are equivalent to the constructs in the abstract machines of the theory.
We prefer the higher-level constructs in modern languages because they make programs easier to write, and easier to understand by other people. That in turn leads to easier peer-review and team-play, and thus better programs with less bugs.
The Wikipedia article about Structured programming tells part of the history.
As to Automata theory, it is still present in the implementation of regular expression engines, and in most programming situations in which a good solution consists in transitioning through a set of possible states.
I'm trying to write a lexer rule that would match following strings
a
aa
aaa
bbbb
the requirement here is all characters must be the same
I tried to use this rule:
REPEAT_CHARS: ([a-z])(\1)*
But \1 is not valid in antlr4. is it possible to come up with a pattern for this?
You can’t do that in an ANTLR lexer. At least, not without target specific code inside your grammar. And placing code in your grammar is something you should not do (it makes it hard to read, and the grammar is tied to that language). It is better to do those kind of checks/validations inside a listener or visitor.
Things like back-references and look-arounds are features that krept in regex-engines of programming languages. The regular expression syntax available in ANTLR (and all parser generators I know of) do not support those features, but are true regular languages.
Many features found in virtually all modern regular expression libraries provide an expressive power that far exceeds the regular languages. For example, many implementations allow grouping subexpressions with parentheses and recalling the value they match in the same expression (backreferences). This means that, among other things, a pattern can match strings of repeated words like "papa" or "WikiWiki", called squares in formal language theory.
-- https://en.wikipedia.org/wiki/Regular_expression#Patterns_for_non-regular_languages
Is there any difference in how Data.Text and Data.Vector.Unboxed Char work internally? Why would I choose one over the other?
I always thought it was cool that Haskell defines String as [Char]. Is there a reason that something analagous wasn't done for Text and Vector Char?
There certainly would be an advantage to making them the same.... Text-y and Vector-y tools could be written to be used in both camps. Imagine Ropes of Ints, or Regexes on strings of poker cards.
Of course, I understand that there were probably historical reasons and I understand that most current libraries use Data.Text, not Vector Char, so there are many practical reasons to favor one over the other. But I am more interested in learning about the abstract qualities, not the current state that we happen to be in.... If the whole thing were rewritten tomorrow, would it be better to unify the two?
Edit, with more info-
To put stuff into perspective-
According to this page, http://www.haskell.org/haskellwiki/GHC/Memory_Footprint, GHC uses 16 bytes for each Char in your program!
Data.Text is not O(1) index'able, it is O(n).
Ropes (binary trees wrapped around text) can also hold strings.... They have better complexity for index/insert/delete, although depending on the number of nodes and balance of the tree, index could be close to that of Text.
This is my takeaway from this-
Text and Vector Char are different internally....
Use String if you don't care about performance.
If performance is important, default to using Text.
If fast indexing of chars is necessary, and you don't mind a lot of memory overhead (up to 16x), use Vector Char.
If you want to insert/delete a lot of data, use Ropes.
It's a fairly bad idea to think of Text as being a list of characters. Text is designed to be thought of as an opaque, user-readable blob of Unicode text. Character boundaries might be defined based on encoding, locale, language, time of month, phase of the moon, coin flips performed by a blinded participant, and migratory patterns of Venezuela's national bird whatever it may be. The same story happens with sorting, up-casing, reversing, etc.
Which is a long way of saying that Text is an abstract type representing human language and goes far out of its way to not behave just the same way as its implementation, be it a ByteString, a Vector UTF16CodePoint, or something totally unique (which is the case).
To clarify this distinction take note that there's no guarantee that unpack . pack witnesses an isomorphism, that the preferred ways of converting from Text to ByteString are in Data.Text.Encoding and are partial, and that there's a whole sophisticated plug-in module text-icu littered with complex ways of handling human language strings.
You absolutely should use Text if you're dealing with a human language string. You should also be really careful to treat it with care since human language strings are not easily amenable to computer processing. If your string is better thought of as a machine string, you probably should use ByteString.
The pedagogical advantages of type String = [Char] are high, but the practical advantages are quite low.
To add to what J. Abrahamson said, it's also worth making the distinction between iterating over runes (roughly character by character, but really could be ideograms too) as opposed to unitary logical unicode code points. Sometimes you need to know if you're looking at a code point that has been "decorated" by a previous code point.
In the case of the latter, you then have to make the distinction between code points that stand alone (such as letters, ideograms) and those that modify the text that follows (right-to-left code point, diacritics, etc).
Well implemented unicode libraries will typically abstract these details away and let you process the text in a more or less character-by-character fashion but you have to drop certain assumptions that come from thinking in terms of ASCII.
A byte is not a character. A logical unit of text isn't necessarily a "character". Not every code point stands alone, some decorate/annotate the following code point or even the rest of the byte stream until invalidated (right-to-left).
Unicode is hard. There is no one true encoding that will eliminate the difficulty of encapsulating the variety inherent in human language. Data.Text does a respectable job of it though.
To summarize:
The methods of processing are:
byte-by-byte - totally invalid for unicode, only applicable to latin-1/ASCII
code point by code point - works for processing unicode, but is lower-level than people realize
logical rune-by-rune - what you actually want
The types are:
String (aka [Char]) - has a limited scope. Best used for teaching Haskell or for legacy use-cases.
Text - the preferred way to handle "human" text.
Bytestring - for byte streams, raw data, binary etc.
While the general opinion of the Haskell community seems to be that it's always better to use Text instead of String, the fact that still the APIs of most of maintained libraries are String-oriented confuses the hell out of me. On the other hand, there are notable projects, which consider String as a mistake altogether and provide a Prelude with all instances of String-oriented functions replaced with their Text-counterparts.
So are there any reasons for people to keep writing String-oriented APIs except backwards- and standard Prelude-compatibility and the "switch-making inertia"?
Are there possibly any other drawbacks to Text as compared to String?
Particularly, I'm interested in this because I'm designing a library and trying to decide which type to use to express error messages.
My unqualified guess is that most library writers don't want to add more dependencies than necessary. Since strings are part of literally every Haskell distribution (it's part of the language standard!), it is a lot easier to get adopted if you use strings and don't require your users to sort out Text distributions from hackage.
It's one of those "design mistakes" that you just have to live with unless you can convince most of the community to switch over night. Just look at how long it has taken to get Applicative to be a superclass of Monad – a relatively minor but much wanted change – and imagine how long it would take to replace all the String things with Text.
To answer your more specific question: I would go with String unless you get noticeable performance benefits by using Text. Error messages are usually rather small one-off things so it shouldn't be a big problem to use String.
On the other hand, if you are the kind of ideological purist that eschews pragmatism for idealism, go with Text.
* I put design mistakes in scare quotes because strings as a list-of-chars is a neat property that makes them easy to reason about and integrate with other existing list-operating functions.
If your API is targeted at processing large amounts of character oriented data and/or various encodings, then your API should use Text.
If your API is primarily for dealing with small one-off strings, then using the built-in String type should be fine.
Using String for large amounts of text will make applications using your API consume significantly more memory. Using it with foreign encodings could seriously complicate usage depending on how your API works.
String is quite expensive (at least 5N words where N is the number of Char in the String). A word is same number of bits as the processor architecture (ex. 32 bits or 64 bits):
http://blog.johantibell.com/2011/06/memory-footprints-of-some-common-data.html
There are at least three reasons to use [Char] in small projects.
[Char] does not rely on any arcane staff, like foreign pointers, raw memory, raw arrays, etc that may work differently on different platforms or even be unavailable altogether
[Char] is the lingua franka in haskell. There are at least three 'efficient' ways to handle unicode data in haskell: utf8-bytestring, Data.Text.Text and Data.Vector.Unboxed.Vector Char, each requiring dealing with extra package.
by using [Char] one gains access to all power of [] monad, including many specific functions (alternative string packages do try to help with it, but still)
Personally, I consider utf16-based Data.Text one of the most questionable desicions of the haskell community, since utf16 combines flaws of both utf8 and utf32 encoding while having none of their benefits.
I wonder if Data.Text is always more efficient than Data.String???
"cons" for instance is O(1) for Strings and O(n) for Text. Append is O(n) for Strings and O(n+m) for strict Text's. Likewise,
let foo = "foo" ++ bigchunk
bar = "bar" ++ bigchunk
is more space efficient for Strings than for strict Texts.
Other issue not related to efficiency is pattern matching (perspicuous code) and lazyness (predictably per-character in Strings, somehow implementation dependent in lazy Text).
Text's are obviously good for static character sequences and for in-place modification. For other forms of structural editing, Data.String might have advantages.
I do not think there is a single technical reason for String to remain.
And I can see several ones for it to go.
Overall I would first argue that in the Text/String case there is only one best solution :
String performances are bad, everyone agrees on that
Text is not difficult to use. All functions commonly used on String are available on Text, plus some useful more in the context of strings (substitution, padding, encoding)
having two solutions creates unnecessary complexity unless all base functions are made polymorphic. Proof : there are SO questions on the subject of automatic conversions. So this is a problem.
So one solution is less complex than two, and the shortcomings of String will make it disappear eventually. The sooner the better !
My company maintains a domain-specific language that syntactically resembles the Excel formula language. We're considering adding new builtins to the language. One way to do this is to identify verbose commands that are repeatedly used in our codebase. For example, if we see people always write the same 100-character command to trim whitespace from the beginning and end of a string, that suggests we should add a trim function.
Seeing a list of frequent substrings in the codebase would be a good start (though sometimes the frequently used commands differ by a few characters because of different variable names used).
I know there are well-established algorithms for doing this, but first I want to see if I can avoid reinventing the wheel. For example, I know this concept is the basis of many compression algorithms, so is there a compression module that lets me retrieve the dictionary of frequent substrings? Any other ideas would be appreciated.
The string matching is just the low hanging fruit, the obvious cases. The harder cases are where you're doing similar things but in different order. For example suppose you have:
X+Y
Y+X
Your string matching approach won't realize that those are effectively the same. If you want to go a bit deeper I think you need to parse the formulas into an AST and actually compare the AST's. If you did that you could see that the tree's are actually the same since the binary operator '+' is commutative.
You could also apply reduction rules so you could evaluate complex functions into simpler ones, for example:
(X * A) + ( X * B)
X * ( A + B )
Those are also the same! String matching won't help you there.
Parse into AST
Reduce and Optimize the functions
Compare the resulting AST to other ASTs
If you find a match then replace them with a call to a shared function.
I would think you could use an existing full-text indexer like Lucene, and implement your own Analyzer and Tokenizer that is specific to your formula language.
You then would be able to run queries, and be able to see the most used formulas, which ones appear next to each other, etc.
Here's a quick article to get you started:
Lucene Analyzer, Tokenizer and TokenFilter
You might want to look into tag-cloud generators. I couldn't find any source in the minute that I spent looking, but here's an online one:
http://tagcloud.oclc.org/tagcloud/TagCloudDemo which probably won't work since it uses spaces as delimiters.