In we completed our look at the library. We took all our smaller combinators and put them together to parse our Gherkin syntax. This week, we’ll look at a new library: . Instead of trying to do everything using 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. last week’s article Applicative Parsing Attoparsec To follow along with the code for this article, take a look at the branch on Github! For some more excellent ideas about useful libraries, download our ! It includes material on libraries for everything from data structures to machine learning! [attoparsec](https://github.com/jhb563/GherkinParsing/tree/attoparsec) Production Checklist If you’re new to Haskell, make sure you download our ! It’ll tell you about all the steps you need to take to get started on your Haskell journey! Beginner’s Checklist The Parser Type In applicative parsing, all our parsers had the type . This type belonged to the typeclass but was not a . For Attoparsec, we’ll instead be using the type, a full monad. So in general we’ll be writing parsers with the following types: RE Char Applicative Monad Parser featureParser :: Parser FeaturescenarioParser :: Parser ScenariostatementParser :: Parser StatementexampleTableParser :: Parser ExampleTablevalueParser :: Parser Value Parsing Values The first thing we should realize though is that our parser is still an ! So not everything needs to change! We can still make use of operators like and . In fact, we can leave our value parsing code almost exactly the same! For instance, the , , and expressions can remain the same: Applicative *> <|> valueParser nullParser boolParser valueParser :: Parser ValuevalueParser = nullParser <|> boolParser <|> numberParser <|> stringParser nullParser :: Parser ValuenullParser = (string "null" <|> string "NULL" <|> string "Null") *> pure ValueNull boolParser :: Parser ValueboolParser = (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 instead of (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: return pure >> *> numberParser :: Parser ValuenumberParser = ValueNumber <$> scientific Then for string values, we’ll use the combinator to read all the characters until a vertical bar or newline. Then we’ll apply a few text functions to remove the whitespace and get it back to a . (The monad we’re using parses things as rather than ). takeTill String Parser Text String stringParser :: Parser ValuestringParser = (ValueString . unpack . strip) <$> takeTill (\c -> c == '|' || c == '\n') Parsing Examples As we parse the example table, we’ll switch to a more monadic approach by using do-syntax. First, we establish a that will read a value within a cell. cellParser 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 , we parse the initial bar, get many values, close out the line, and then return them: <- exampleLineParser 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 uses instead of . So now we can put these pieces together for our : cellParser many letter valueParser exampleTableParser exampleTableParser :: Parser ExampleTableexampleTableParser = 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 like in applicative syntax. buildExampleTable Statements The 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: Statement nonBrackets :: Parser StringnonBrackets = many (satisfy (\c -> c /= '\n' && c /= '<')) insideBrackets :: Parser StringinsideBrackets = 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 StatementparseStatementLine signal = do string signal char ' ' pairs <- many ((,) <$> nonBrackets <*> insideBrackets) finalString <- nonBrackets ... Now we can define our helper function and call it on its own line in do-syntax. Then we’ll return the resulting . This is much easier to read than tracking which functions we map over which sections of the parser: buildStatement Statement parseStatementLine :: Text -> Parser StatementparseStatementLine signal = do string signal char ' ' pairs <- many ((,) <$> 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 ScenarioscenarioParser = do string "Scenario: " title <- consumeLine statements <- many (parseStatement <* char '\n') examples <- (exampleTableParser <|> return (ExampleTable [] [])) return $ Scenario title statements examples Again, we provide an empty 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 as the title string. ExampleTable Background backgroundParser :: Parser ScenariobackgroundParser = 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 FeaturefeatureParser = do string "Feature: " title <- consumeLine maybeBackground <- optional backgroundParser scenarios <- many scenarioParser return $ Feature title maybeBackground scenarios Feature Description 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 FeaturefeatureParser = 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 combinator offered by Attoparsec. choice 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 . 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: noDescriptionLineParser 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! Conclusion That wraps up our exploration of . Come back next week where we’ll finish this series off by learning about . 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. Attoparsec Megaparsec To learn more about cool Haskell libraries, be sure to check out our ! It’ll tell you a little bit about libraries in all kinds of areas like databases and web APIs. Production Checklist If you’ve never written Haskell at all, download our ! It’ll give you all the resources you need to get started on your Haskell journey! Beginner’s Checklist
