Attoparsec: The Clarity of Do-Syntax

In last week’s article we completed our look at the Applicative Parsing 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: Attoparsec. 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.

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The Parser Type

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 Parsertype, a full monad. So in general we’ll be writing parsers with the following types:

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 Applicative! 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 valueParser, nullParser, and boolParser expressions can remain the same:

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 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 ValuenumberParser = 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 String. (The Parser monad we’re using parses things as Text rather than 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 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 cellParser uses many letter instead of valueParser. So now we can put these pieces together for our 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 buildExampleTable like in applicative syntax.

Statements

The 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 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 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 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 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 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 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!

Conclusion

That wraps up our exploration of Attoparsec. Come back next week where we’ll finish this series off by learning 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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Attoparsec: The Clarity of Do-Syntax was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.