Parsing Part 3: Attoparsec
In part 2 of this series we looked at the Applicative Parsing library. We took a lot of smaller combinators and put them together to parse our Gherkin syntax (check out part 1 for a quick refresher on that).
This week, we’ll look at a new library: Attoparsec. Instead of trying to do everything with a purely applicative structure, this library uses a monadic approach. This approach is much more common. It results in syntax that is simpler to read and understand. It will also make it easier for us to add certain features.
To follow along with the code for this article, take a look at the
attoparsec branch on Github! For some more excellent ideas about useful libraries, download our Production Checklist! It includes material on libraries for everything from data structures to machine learning!
Finally, if you already know about Attoparsec, feel free to move onto part 4 and learn about Megaparsec!
In applicative parsing, all our parsers had the type
RE Char. This type belonged to the
Applicative typeclass but was not a
Monad. For Attoparsec, we’ll instead be using the
Parser type, a full monad. So in general we’ll be writing parsers with the following types:
featureParser :: Parser Feature scenarioParser :: Parser Scenario statementParser :: Parser Statement exampleTableParser :: Parser ExampleTable valueParser :: Parser Value
The first thing we should realize though is that our parser is still an
Applicative! So not everything needs to change! We can still make use of operators like
<|>. In fact, we can leave our value parsing code almost exactly the same! For instance, the
boolParser expressions can remain the same:
valueParser :: Parser Value valueParser = nullParser <|> boolParser <|> numberParser <|> stringParser nullParser :: Parser Value nullParser = (string "null" <|> string "NULL" <|> string "Null") *> pure ValueNull boolParser :: Parser Value boolParser = (trueParser *> pure (ValueBool True)) <|> (falseParser *> pure (ValueBool False)) where trueParser = string "True" <|> string "true" <|> string "TRUE" falseParser = string "False" <|> string "false" <|> string "FALSE"
If we wanted, we could make these more "monadic" without changing their structure. For instance, we can use
return instead of
pure (since they are identical). We can also use
>> instead of
*> to perform monadic actions while discarding a result. Our value parser for numbers changes a bit, but it gets simpler! The authors of Attoparsec provide a convenient parser for reading scientific numbers:
numberParser :: Parser Value numberParser = ValueNumber <$> scientific
Then for string values, we’ll use the
takeTill combinator to read all the characters until a vertical bar or newline. Then we’ll apply a few text functions to remove the whitespace and get it back to a
Parser monad we’re using parses things as
Text rather than
stringParser :: Parser Value stringParser = (ValueString . unpack . strip) <$> takeTill (\c -> c == '|' || c == '\n')
As we parse the example table, we’ll switch to a more monadic approach by using do-syntax. First, we establish a
cellParser that will read a value within a cell.
cellParser = do skipWhile nonNewlineSpace val <- valueParser skipWhile (not . barOrNewline) char '|' return val
Each line in our statement refers to a step of the parsing process. So first we skip all the leading whitespace. Then we parse our value. Then we skip the remaining space, and parse the final vertical bar to end the cell. Then we’ll return the value we parsed.
It’s a lot easier to keep track of what’s going on here compared to applicative syntax. It’s not hard to see which parts of the input we discard and which we use. If we don’t assign the value with
<- within do-syntax, we discard the value. If we retrieve it, we’ll use it. To complete the
exampleLineParser, we parse the initial bar, get many values, close out the line, and then return them:
exampleLineParser :: Parser [Value] exampleLineParser = do char '|' cells <- many cellParser char '\n' return cells where cellParser = ...
Reading the keys for the table is almost identical. All that changes is that our
many letter instead of
valueParser. So now we can put these pieces together for our
exampleTableParser :: Parser ExampleTable exampleTableParser = do string "Examples:" consumeLine keys <- exampleColumnTitleLineParser valueLists <- many exampleLineParser return $ ExampleTable keys (map (zip keys) valueLists)
We read the signal string "Examples:", followed by consuming the line. Then we get our keys and values, and build the table with them. Again, this is much simpler than mapping a function like
buildExampleTable like in applicative syntax.
Statement parser is another area where we can improve the clarity of our code. Once again, we’ll define two helper parsers. These will fetch the portions outside brackets and then inside brackets, respectively:
nonBrackets :: Parser String nonBrackets = many (satisfy (\c -> c /= '\n' && c /= '<')) insideBrackets :: Parser String insideBrackets = do char '<' key <- many letter char '>' return key
Now when we put these together, we can more clearly see the steps of the process outlined in do-syntax. First we parse the “signal” word, then a space. Then we get the “pairs” of non-bracketed and bracketed portions. Finally, we’ll get one last non-bracketed part:
parseStatementLine :: Text -> Parser Statement parseStatementLine signal = do string signal char ' ' pairs <- many ((,) <$> nonBrackets <*> insideBrackets) finalString <- nonBrackets ...
Now we can define our helper function
buildStatement and call it on its own line in do-syntax. Then we’ll return the resulting
Statement. This is much easier to read than tracking which functions we map over which sections of the parser:
parseStatementLine :: Text -> Parser Statement parseStatementLine signal = do string signal char ' ' pairs <- many ((,) <$> nonBrackets <*> insideBrackets) finalString <- nonBrackets let (fullString, keys) = buildStatement pairs finalString return $ Statement fullString keys where buildStatement :: [(String, String)] -> String -> (String, [String]) buildStatement  last = (last, ) buildStatement ((str, key) : rest) rem = let (str', keys) = buildStatement rest rem in (str <> "<" <> key <> ">" <> str', key : keys)
Scenarios and Features
As with applicative parsing, it’s now straightforward for us to finish everything off. To parse a scenario, we read the keyword, consume the line to read the title, and read the statements and examples:
scenarioParser :: Parser Scenario scenarioParser = do string "Scenario: " title <- consumeLine statements <- many (parseStatement <* char '\n') examples <- (exampleTableParser <|> return (ExampleTable  )) return $ Scenario title statements examples
Again, we provide an empty
ExampleTable as an alternative if there are no examples. The parser for Background looks very similar. The only difference is we ignore the result of the line and instead use
Background as the title string.
backgroundParser :: Parser Scenario backgroundParser = do string "Background:" consumeLine statements <- many (parseStatement <* char '\n') examples <- (exampleTableParser <|> return (ExampleTable  )) return $ Scenario "Background" statements examples
Finally, we’ll put all this together as a feature. We read the title, get the background if it exists, and read our scenarios:
featureParser :: Parser Feature featureParser = do string "Feature: " title <- consumeLine maybeBackground <- optional backgroundParser scenarios <- many scenarioParser return $ Feature title maybeBackground scenarios
One extra feature we’ll add now is that we can more easily parse the “description” of a feature. We omitted them in applicative parsing, as it’s a real pain to implement. It becomes much simpler when using a monadic approach. The first step we have to take though is to make one parser for all the main elements of our feature. This approach looks like this:
featureParser :: Parser Feature featureParser = do string "Feature: " title <- consumeLine (description, maybeBackground, scenarios) <- parseRestOfFeature return $ Feature title description maybeBackground scenarios parseRestOfFeature :: Parser ([String], Maybe Scenario, [Scenario]) parseRestOfFeature = ...
Now we’ll use a recursive function that reads one line of the description at a time and adds to a growing list. The trick is that we’ll use the
choice combinator offered by Attoparsec.
We’ll create two parsers. The first assumes there are no further lines of description. It attempts to parse the background and scenario list. The second reads a line of description, adds it to our growing list, and recurses:
parseRestOfFeature :: Parser ([String], Maybe Scenario, [Scenario]) parseRestOfFeature = parseRestOfFeatureTail  where parseRestOfFeatureTail prevDesc = do (fullDesc, maybeBG, scenarios) <- choice [noDescriptionLine prevDesc, descriptionLine prevDesc] return (fullDesc, maybeBG, scenarios)
So we’ll first try to run this
noDescriptionLineParser. It will try to read the background and then the scenarios as we’ve always done. If it succeeds, we know we’re done. The argument we passed is the full description:
where noDescriptionLine prevDesc = do maybeBackground <- optional backgroundParser scenarios <- some scenarioParser return (prevDesc, maybeBackground, scenarios)
Now if this parser fails, we know that it means the next line is actually part of the description. So we’ll write a parser to consume a full line, and then recurse:
descriptionLine prevDesc = do nextLine <- consumeLine parseRestOfFeatureTail (prevDesc ++ [nextLine])
And now we’re done! We can parse descriptions!
That wraps up our exploration of Attoparsec. Now you can move on to the fourth and final part of this series where we'll learn about Megaparsec. We’ll find that it’s syntactically very similar to Attoparsec with a few small exceptions. We’ll see how we can use some of the added power of monadic parsing to enrich our syntax.
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