In last week’s article, we introduced the Applicative parsing library. We learned about the
RE type and the basic combinators like
string. We saw how we could combine those together with applicative functions like
<*> to parse strings into data structures. This week, we’ll put these pieces together in an actual parser for our Gherkin syntax. To follow along with the code examples, check out Parser.hs on the Github repository.
Starting next week, we’ll explore some other parsing libraries, starting with Attoparsec. For a little more information about those and many other libraries, download our Production Checklist! It summarizes many libraries on topics from databases to Web APIs.
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In keeping with our approach from the last article, we’re going to start with smaller elements of our syntax. Then we can use these to build larger ones with ease. To that end, let’s build a parser for our
Value type, the most basic data structure in our syntax. Let’s recall what that looks like:
data Value = ValueNull | ValueBool Bool | ValueString String | ValueNumber Scientific
Since we have different constructors, we’ll make a parser for each one. Then we can combine them with alternative syntax:
valueParser :: RE Char Value valueParser = nullParser <|> boolParser <|> numberParser <|> stringParser
Now our parsers for the null values and boolean values are easy. For each of them, we’ll give a few different options about what strings we can use to represent those elements. Then, as with the larger parser, we’ll combine them with
nullParser :: RE Char Value nullParser = (string “null” <|> string “NULL” <|> string “Null”) *> pure ValueNull boolParser :: RE Char 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”
Notice in both these cases we discard the actual string with
*> and then return our constructor. We have to wrap the desired result with
Number and String Values
Numbers and strings are a little more complicated since we can’t rely on hard-coded formats. In the case of numbers, we’ll account for integers, decimals, and negative numbers. We'll ignore scientific notation for now. An integer is simple to parse, since we’ll have many characters that are all numbers. We use
some instead of
many to enforce that there is at least one:
numberParser :: RE Char Value numberPaser = … where integerParser = some (psym isNumber)
A decimal parser will read some numbers, then a decimal point, and then more numbers. We'll insist there is at least one number after the decimal point.
numberParser :: RE Char Value numberPaser = … where integerParser = some (psym isNumber) decimalParser = many (psym isNumber) <*> sym ‘.’ <*> some (psym isNumber)
Finally, for negative numbers, we’ll read a negative symbol and then one of the other parsers:
numberParser :: RE Char Value numberPaser = … where integerParser = some (psym isNumber) decimalParser = many (psym isNumber) <*> sym ‘.’ <*> some (psym isNumber) negativeParser = sym ‘-’ <*> (decimalParser <|> integerParser)
However, we can’t combine these parsers as is! Right now, they all return different results! The integer parser returns a single string. The decimal parser returns two strings and the decimal character, and so on. In general, we’ll want to combine each parser's results into a single string and then pass them to the
read function. This requires mapping a couple functions over our last two parsers:
numberParser :: RE Char Value numberPaser = … where integerParser = some (psym isNumber) decimalParser = combineDecimal <$> many (psym isNumber) <*> sym ‘.’ <*> some (psym isNumber) negativeParser = (:) <$> sym ‘-’ <*> (decimalParser <|> integerParser) combineDecimal :: String -> Char -> String -> String combineDecimal base point decimal = base ++ (point : decimal)
Now all our number parsers return strings, so we can safely combine them. We'll map the
ValueNumber constructor over the value we read from the string.
numberParser :: RE Char Value numberPaser = (ValueNumber . read) <$> (negativeParser <|> decimalParser <|> integerParser) where ...
Note that order matters! If we put the integer parser first, we’ll be in trouble! If we encounter a decimal, the integer parser will greedily succeed and parse everything before the decimal point. We'll either lose all the information after the decimal, or worse, have a parse failure.
The last thing we need to do is read a string. We need to read everything in the example cell until we hit a vertical bar, but then ignore any whitespace. Luckily, we have the right combinator for this, and we’ve even written a
trim function already!
stringParser :: RE Char Value stringParser = (ValueString . trim) <$> readUntilBar
And now our
valueParser will work as expected!
Building an Example Table
Now that we can parse individual values, let’s figure out how to parse the full example table. We can use our individual value parser to parse a whole line of values! The first step is to read the vertical bar at the start of the line.
exampleLineParser :: RE Char [Value] exampleLineParser = sym ‘|’ *> ...
Next, we’ll build a parser for each cell. It will read the whitespace, then the value, and then read up through the next bar.
exampleLineParser :: RE Char [Value] exampleLineParser = sym ‘|’ *> ... where cellParser = many isNonNewlineSpace *> valueParser <* readThroughBar isNonNewlineSpace :: RE Char Char isNonNewlineSpace = psym (\c -> isSpace c && c /= ‘\n’)
Now we read
many of these and finish by reading the newline:
exampleLineParser :: RE Char [Value] exampleLineParser = sym ‘|’ *> many cellParser <* readThroughEndOfLine where cellParser = many isNonNewlineSpace *> valueParser <* readThroughBar
Now, we need a similar parser that reads the title column of our examples. This will have the same structure as the value cells, only it will read normal alphabetic strings instead of values.
exampleColumnTitleLineParser :: RE Char [String] exampleColumnTitleLineParser = sym ‘|’ *> many cellParser <* readThroughEndOfLine where cellParser = many isNonNewlineSpace *> many (psym isAlpha) <* readThroughBar
Now we can start building the full example parser. We’ll want to read the string, the column titles, and then the value lines.
exampleTableParser :: RE Char ExampleTable exampleTableParser = (string “Examples:” *> readThroughEndOfLine) *> exampleColumnTitleLineParser <*> many exampleLineParser
We’re not quite done yet. We’ll need to apply a function over these results that will produce the final
ExampleTable. And the trick is that we want to map up the example keys with their values. We can accomplish this with a simple function. It will return zip the keys over each value list using
exampleTableParser :: RE Char ExampleTable exampleTableParser = buildExampleTable <$> (string “Examples:” *> readThroughEndOfLine) *> exampleColumnTitleLineParser <*> many exampleLineParser where buildExampleTable :: [String] -> [[Value]] -> ExampleTable buildExampleTable keys valueLists = ExampleTable keys (map (zip keys) valueLists)
Now we that we can parse the examples for a given scenario, we need to parse the Gherkin statements. To start with, let’s make a generic parser that takes the keyword as an argument. Then our full parser will try each of the different statement keywords:
parseStatementLine :: String -> RE Char Statement parseStatementLine signal = … parseStatement :: RE Char Statement parseStatement = parseStatementLine “Given” <|> parseStatementLine “When” <|> parseStatementLine “Then” <|> parseStatementLine “And”
Now we’ll get the signal word out of the way and parse the statement line itself.
parseStatementLine :: String -> RE Char Statement parseStatementLine signal = string signal *> sym ' ' *> ...
Parsing the statement is tricky. We want to parse the keys inside brackets and separate them as keys. But we also want them as part of the statement’s string. To that end, we’ll make two helper parsers. First,
nonBrackets will parse everything in a string up through a bracket (or a newline).
nonBrackets :: RE Char String nonBrackets = many (psym (\c -> c /= ‘\n’ && c /= ‘<’))
We’ll also want a parser that parses the brackets and returns the keyword inside:
insideBrackets :: RE Char String insideBrackets = sym ‘<’ *> many (psym (/= ‘>’)) <* sym ‘>’
Now to read a statement, we start with non-brackets, and alternate with keys in brackets. Let's observe that we start and end with non-brackets, since they can be empty. Thus we can represent a line a list of non-bracket/bracket pairs, followed by a last non-bracket part. To make a pair, we combine the parser results in a tuple using the
(,) constructor enabled by
parseStatementLine :: String -> RE Char Statement parseStatementLine signal = string signal *> sym ‘ ‘ *> many ((,) <$> nonBrackets <*> insideBrackets) <*> nonBrackets
From here, we need a recursive function that will build up our final statement string and the list of keys. We do this with
parseStatementLine :: String -> RE Char Statement parseStatementLine signal = string signal *> sym ‘ ‘ *> (buildStatement <$> many ((,) <$> nonBrackets <*> insideBrackets) <*> nonBrackets) 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)
The last thing we need is a final helper that will take the result of
buildStatement and turn it into a
Statement. We’ll call this
finalizeStatement, and then we’re done!
parseStatementLine :: String -> RE Char Statement parseStatementLine signal = string signal *> sym ‘ ‘ *> (finalizeStatement . buildStatement <$> many ((,) <$> nonBrackets <*> insideBrackets) <*> nonBrackets) 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) finalizeStatement :: (String, [String]) -> Statement finalizeStatement (regex, variables) = Statement regex variables
Now that we have all our pieces in place, it’s quite easy to write the parser for scenario! First we get the title by reading the keyword and then the rest of the line:
scenarioParser :: RE Char Scenario scenarioParser = string “Scenario: “ *> readThroughEndOfLine ...
After that, we read many statements, and then the example table. Since the example table might not exist, we’ll provide an alternative that is a pure, empty table. We can wrap everything together by mapping the
Scenario constructor over it.
scenarioParser :: RE Char Scenario scenarioParser = Scenario <$> (string “Scenario: “ *> readThroughEndOfLine) <*> many (statementParser <* sym ‘\n’) <*> (exampleTableParser <|> pure (ExampleTable  ))
We can also make a “Background” parser that is very similar. All that changes is that we read the string “Background” instead of a title. Since we’ll hard-code the title as “Background”, we can include it with the constructor and map it over the parser.
backgroundParser :: RE Char Scenario backgroundParser = Scenario “Background” <$> (string “Background:” *> readThroughEndOfLine) *> many (statementParser <* sym ‘\n’) <*> (exampleTableParser <|> pure (ExampleTable  ))
Finally the Feature
We’re almost done! All we have left is to write the
featureParser itself! As with scenarios, we’ll start with the keyword and a title line:
featureParser :: RE Char Feature featureParser = Feature <$> (string “Feature: “ *> readThroughEndOfLine) <*> ...
Now we’ll use the
optional combinator to parse the
Background if it exists, but return
Nothing if it doesn’t. Then we’ll wrap up with parsing many scenarios!
featureParser :: RE Char Feature featureParser = Feature <$> (string “Feature: “ *> readThroughEndOfLine) <*> (optional backgroundParser) <*> (many scenarioParser)
Note that here we’re ignoring the “description” of a feature we proposed as part of our original syntax. Since there are no keywords for that, it turns out to be painful to deal with it using applicative parsing. When we look at monadic approaches starting next week, we’ll see it isn’t as hard there.
This wraps up our exploration of applicative parsing. We can see how well suited Haskell is for parsing. The functional nature of the language means it's easy to start with small building blocks like our first parsers. Then we can gradually combine them to make something larger. It can be a little tricky to wrap our heads around all the different operators and combinators. But once you understand the ways in which these let us combine our parsers, they make a lot of sense and are easy to use.
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