This note introduces the parsing recipe pattern. This Haskell parsing pattern addresses the newtype proliferation problem that arises with typeclass-based generic parsers like Cassava or Aeson. At the same time, the recipe pattern keeps the ergonomy of auto-deriving parsers.
It’s a pattern I haven’t seen yet anywhere else as of December 2023 , and I think it could be a good addition to Haskell’s parsing ecosystem.

Motivation

I develop a personal Haskell utility that parses financial statement data from various institutions (findata-transcoder). In that problem domain, statements use all kinds of formats that I need to parse:

# coop.csv — A supermarket sale record
"13/02/2023","Sale","Coop","$1234.56"

# bank.csv — A bank deposit record
"Jan 16, 2023", "Deposit", "900.12 USD"

# broker.json — A forex transaction
{
  date: "2023-12-02",
  name: "Forex",
  from: "USD",
  // ...
}

For each financial statement, I create record type that represents a single record in a statement (most statements are collections of records) and create a parser for it.

data FooRecord = FooRecord
  { frDay :: Day
  , frTitle :: Text
  }

parseRecord :: Text -> Maybe FooRecord
parseStatement :: Text -> Maybe [FooRecord]

Haskell already has a strong ecosystem for writing parsers using monadic parser combinators. This is one of the reasons why I chose to use Haskell for this problem. However, the existing functionality was somewhat lacking for me.

Existing Parsing Approaches

Megaparsec

A Parsec-derived library Megaparsec is a swiss-army knife. It can handle parsing most things imaginable, but it’s low-level. It requires you to write your own record parsers like so:

fooRecordP :: Parsec Void Text FooRecord
fooRecordP = do
  day <- dayP
  MP.char ','
  title <- titleP
  return $ FooRecord day title

That’s tedious and boilerplate’y. If our record type’s structure already follows its CSV row structure, then couldn’t we somehow autogenerate a parser? The answer is yes.

Cassava and Aeson

Cassava and Aeson are high-level parsing libraries for CSV and JSON files respectively. One of their features is that one can get a parser for free based on the record type:

deriving stock instance Generic FooRecord

-- The default implementation uses the generic instance to
-- figure out the structure of the JSON object.
instance FromJson Record
-- This now works
>>> decode "{ \"frDay\": \"2023-11-12\", \"frTitle\": \"Foo\" }"
FooRecord { -- ...
          }

The big drawback to these high-level libraries is that they are based on type classes. We tie a type to how its parsed. For record types that’s fine but it’s not so much for primitives. In the motivating example we saw how different financial institutions have different standards for formatting things like dates, monetary amounts, numbers, etc. The solution to this is using the newtype pattern to have different parsing instances, but it then leads to record types like this:

data CharlesSchwabRecord = CharlesSchwabRecord {
  day :: CharlesSchwabDay,
  amount :: CharlesSchwabAmount,
  -- ...
}

data InteractiveBrokersRecord = InteractiveBrokersRecord {
  day :: InteractiveBrokersDay,
  amount :: InteractiveBrokersAmount,
}

-- Note: there are other approaches, but none of them satisfactory.

We are polluting core business logic with different day types that only differ in how they are parsed. That’s neither ergonomical nor elegant.

The challenge here is to devise a generic parsing engine that will combine the benefits of Megaparsec and Cassava/Aeson:

  1. Allow defining pure record types without tightly coupling them to a specific parsing implementation.
  2. Allow defining parsers as terms, so that we can freely change parsing implementations without changing the record type. We want what Megaparsec does with its Parsec terms.
  3. Keep the ergonomics of automatically deriving parsers from a record’s type structure.

Parsing Recipe Pattern

Let’s start with a sketch of what a solution to those three design goals might look like:

-- | The pure record type.
data FooRecord = FooRecord
  { frDay :: Day
  , frTitle :: Text
  }

-- | The higher-kinded type version of FooRecord.
data FooRecordH f = FooRecordH f
  { frrDay :: f Day
  , frrTitle :: f Text
  }

-- | A recipe of a record is a record of parsers
type FooRecordRecipe = FooRecordH Parser

-- | A recipe that uses ISO 8601 for the day format.
exampleFooRecordRecipe :: FooRecordRecipe
exampleFooRecordRecipe = FooRecordRecipe iso8601DayP textP

-- | Cooks any recipe into a parser of the record
cook :: recordH Parser -> Parser (recordH Identity)
cook = _cook

-- | Collapses FooRecordH into the record type.
--
-- Useful for ergonomics to be combined with cook: fmap toFooRecord . cook
toFooRecord :: FooRecordH Identity -> FooRecord
toFooRecord (FooRecordH (Identity d) (Identity t)) = FooRecord d h

The sketch above shows the essence of the recipe pattern:

  • We have recipes: a higher-kinded type variant that represents a record of field parsers.
  • We have the process of cooking. Cooking translates a recipe into the desired parser of a record. What makes the cooking process special is that it takes into account row parsing details, e.g., that fields need to be separated by a comma (e.g., for CSV cooking) or that records need to be surrounded by braces (for JSON parser cooking).

Within this scheme, the only thing a programmer needs to do is define the higher-kinded type of their record and provide a parsing recipe. cook will be a generic function. It’s implemented it once per file format.
There’s some higher-kinded type related boilerplate that can be further eliminated by Barbies. I didn’t do it in this post to keep the number of concepts manageable.

Kitchen

What’s left to do to is show how to implement cook. We’ll do it using generics. For simplicity, I’ll focus on a CSV-specific implementation, but the pattern can work with any kind of parser.

Let’s first implement cooking for generics:

type CsvParser = MegaParsec.Parsec Void Text

-- If we are given a generic recipe, we can cook it.
--
-- Ignore (k -> Type) and `a`'s. Those are Generic's curiosities.
class GCsvCookable (repRecipe :: k -> Type) (repFood :: k -> Type) where
  gCook :: repRecipe a -> CsvParser (repFood a)

-- Throw away any metadata you may encounter.
instance (GCsvCookable recipe food)
   => GCsvCookable (M1 i c recipe) (M1 i c food) where
  gCook (M1 x) = M1 <$> gCook x

-- The moment you encounter a product type (i.e., fields of a record),
-- separate them with comma.
instance
  ( GCsvCookable recipe0 food0
  , GCsvCookable recipe1 food1
  ) =>
  GCsvCookable (recipe0 :*: recipe1) (food0 :*: food1)
  where
  gCook (a :*: b) = do
    ra <- gCook a
    void $ MP.char ','
    rb <- gCook b
    return $ ra :*: rb

-- The moment you encounter a recipe field, parse it with the field's recipe.
--
-- Note that we only implement GCsvCookable for fields of type `CsvParser food`.
-- If we try to cook a record type that is not a recipe, compilation will fail.
instance GCsvCookable
    (K1 Generic.R (CsvParser food))
    (K1 Generic.R (Identity food)) where
  -- Optionally, we may also consider optional unwrapping from quotes.
  -- We don't do it in this example.
  gCook (K1 a) = K1 . Identity <$> a

Now we implement the cooking default for any generic type that has a recipe-like structure:

-- The generic implementation for cooking.
gCookDefault ::
  ( Generic recipe
  , Generic food
  , GCsvCookable (Generic.Rep recipe) (Generic.Rep food)
  ) =>
  recipe ->
  CsvParser food
gCookDefault recipe = Generic.to <$> gCook (Generic.from recipe)

-- We can make the structure of recipes and food clearer in `cook`.
cook ::
  ( Generic (recordH CsvParser)
  , Generic (recordH Identity)
  , GCsvCookable (Generic.Rep (recordH CsvParser))
                 (Generic.Rep (recordH Identity))
  ) =>
  (recordH CsvParser) ->
  CsvParser (recordH Identity)
cook = gCookDefault

Voila, bon appétit.

You can check the full implementation at my GitHub.

Further Extensions

In this example, I’ve showcased the recipe pattern for a constrained problem of parsing unnamed CSV records. The machinery though (which I like to call the kitchen) can be made significantly more generic to accommodate named CSV records, JSON objects or arrays. The CsvParser type can be made into any kind of monad or possibly even a monad transformer. The way to do this is to make GCsvCookable into a more generic class and give gCook more parameters that could control things like separators, wrappers, and other record-level options.

I haven’t seen this particular pattern documented before, but it’s built on the shoulders of giants.

I’ve already mentioned Cassava and Aeson. There’s also Tomland. Libraries like those rely heavily on generics to provide default parser implementations. They don’t seem to facilitate the use of recipes or HKTs though.

bsequence

In Barbies, there’s already bsequence, which has a similar type signature as our cook:

bsequence' :: (Applicative e, TraversableB b) => b e -> e (b Identity)

The difference lies in GCsvCookable which a parsing-specific alternative for TraversableB. which is adjusted specially for parsing.

Barbies also has an ingenious naming approach, which helps working with the library. It inspired me to do the same for the recipe pattern.

Blog Posts

„Higher Kinded Option Parsing” by Chris Penner shows the use of HKTs to extract data from an environment, to combine with defaults, and then have a pure option type to work with. All of these operations are relevant to recipes and higher-kinded record types as well. Chris doesn’t however go into how HKTs can help with parsing records.