The Power of RecordWildCards

July 29, 2019


With great power comes great responsibility!

Record data types are vital for developing libraries and applications. However, there is a popular opinion that records in Haskell are not well-designed. The Haskell ecosystem has multiple approaches to deal with records pitfalls: a bunch of language extensions, multiple lens libraries, best-practices and naming conventions. But there is still no consensus on the best way to use records.

RecordWildCards is one of the language extensions that improve the situation with records. However, it’s one of the most controversial extensions at the same time. Some people suggest avoiding this extension no matter what. Some prefer to use it everywhere. In this blog post, I’m going to review this extension under any possible angle and tell you when to use and when not to use it.

What is RecordWildCards?

Let’s start with talking about how records are implemented in Haskell. When you define the following data type:

data User = User
    { name :: Text
    , age  :: Int

In Haskell it’s actually syntax sugar for the following code:

data User = User Text Int

name :: User -> Text
name (User n _) = n

age :: User -> Int
age (User _ a) = a

NOTE: in addition to generated functions each record also allows you to use record update syntax.

As you can see, getter functions are generated with the same names and types as the corresponding fields. And you can operate with them as ordinary functions when you write code:

canBuyVodka :: User -> Bool
canBuyVodka user = age user >= 18


The first feature that RecordWildCards allows you to do is to pattern-match on the constructor in a special way by bringing all its fields into scope not as functions but as values instead. So, using this extension we can rewrite code above in the following way:

canBuyVodka :: User -> Bool
canBuyVodka User{..} = age >= 18

In the snippet above age would be the value taken from User and it has type Int. It’s hard to see benefits in this small example. However, when you have a lot of fields and use them multiple times inside a single function, this extension becomes really handy.


The second feature of RecordWildCards is the ability to construct values of the record type from identifiers in scope. Like this:

readUser :: IO User
readUser = do
    name <- getLine
    age  <- readLn
    pure User{..}

Values name and age are used as corresponding fields of the User constructor. This helps to avoid code duplication and eliminates the need to come up with different variable names.

In the following sections, I’m going to highlight common concerns about this extension and recommend best-practices.

Implicit scope

One of the reasons why some people don’t like RecordWildCards is because it’s not clear where the identifiers come from. Consider the following code:

nameOnCard :: User -> Job -> Text
nameOnCard User{..} Job{..} = name <> " | " <> title

The problem with this code is that it’s not obvious from what data types these fields come from: is name a field of User or Job? Hard to tell without looking at the definitions of the corresponding types. This makes code hard to read and maintain.

One of the possible solutions some people recommend is to use the NamedFieldPuns extension. When this extension enabled, you can write the following code instead:

nameOnCard :: User -> Job -> Text
nameOnCard User{name} Job{title} = name <> " | " <> title

NamedFieldPuns is similar to RecordWildCards but it forces you to specify explicitly what fields you are using. In this particular case, the extension solves the problem of figuring out where the variables come from, however, it has its own drawbacks:

  1. When your records have a lot of fields and you use most of them, usage of this extension increases the size of your code significantly.
  2. It introduces code duplication. You write field names twice: on the pattern-matching side and on the call side.

Let’s see how all these problems can be solved with RecordWildCards. Because record fields are top-level functions and because there is no function overloading in Haskell, you can’t have two data types with the same field names in scope (though see the section about DuplicateRecordFields). One of the popular solutions to this difficulty is to prefix field names with the data type name or its abbreviation if the data type name is too long. Turns out that this approach also solves the above problem with RecordWildCards. This naming convention is so common that JSON and lens libraries provide options to strip prefixes automatically. If we define our data type like this:

data User = User
    { userName :: Text
    , userAge  :: Int

Then the function from our example becomes more readable!

nameOnCard :: User -> Job -> Text
nameOnCard User{..} Job{..} = userName <> " | " <> jobTitle

Conclusion: prefix field names with the type name to solve two problems at the same time.

Strict construction

If you construct values using RecordWildCards, you might forget to specify all fields like in the code below:

defaultUser :: User
defaultUser =
    let userName = "Ivan"
    in User{..}

When GHC sees similar code, it outputs a warning that not all fields are initialised. But it’s very easy to miss this warning and get a runtime error later. The answer to this problem is to mark every field of your data type with the strict annotation:

data User = User
    { userName :: !Text
    , userAge  :: !Int

NOTE: you can make all your types strict by default by enabling the StrictData language extension.

If you add ! in front of each type, then all fields will become strict and you will see a compiler error instead of a warning when you forget to initialise some fields. Adding bangs is also considered one of the best-practices to avoid space leaks. It’s very rare wanting to have lazy fields of records.

NOTE: you can add {-# OPTIONS_GHC -Werror=missing-fields #-} to get a compile time error on unitialised lazy fields.

Conclusion: mark fields as strict to have more compile time checks and to avoid potential performance problems.


Another popular concern about RecordWildCards is that you lose compile time checks during pattern-matching when you add more fields. For example, we want to implement a ToJSON instance from the aeson library for our User data type:

instance ToJSON User where
    toJSON User{..} = ["name" .= userName, "age" .= userAge]

Now, if we add one more field to the User type, GHC wouldn’t warn us that we need to update this instance. If we want to see a compile time error we need to write this instance in a different way:

instance ToJSON User where
    toJSON (User name age) = ["name" .= name, "age" .= age]

But let’s look at this problem closer. This is the case where we want to use each field of the constructor. However, not all functions are like that. In our nameOnCard function from the previous paragraph, we don’t want to use all fields, we’re interested only in a subset of them. And we don’t want to update that function when we change definitions of the User or Job types. However, in the ToJSON instance, we want to use all fields. So, the problem is not actually in RecordWildCards. We need to know where to apply this extension, though even here you can use RecordWildCards to make your life easier and here is why:

  1. If you also define a FromJSON instance, you should implement roundtrip property-based tests to make sure that your FromJSON and ToJSON satisfy this property. It’s not possible to skip a FromJSON instance update because you will see a compile time error if you don’t initialise all fields of the type. Thus, if you forget to update ToJSON instance, you will observe a test failure.
  2. If your FromJSON/ToJSON instances are trivial, you can use generics or TemplateHaskell to derive these instances automatically.
  3. If your ToJSON instance is a part of your exposed API then you probably should care about not changing it accidentally. And for this, you need to provide golden tests.

Forgetting to add a field is not the scariest problem actually. A scarier problem is that you can change the type of some field, your roundtrip tests are still passing, but consumers of your JSON API will observe errors. So RecordWildCards is not the most dangerous thing you should worry about here.

You must avoid RecordWildCards only when you really need compile time guarantees to use all fields of the type and when tests are not good. For example, when implementing binary serialisation. If you convert your data type to a sequence of 0s and 1s then failed test output won’t help you much to find where is the problem.

Conclusion: not using RecordWildCards doesn’t help you to avoid all your problems, so implement tests to prevent your code from spontaneous breakages.


We talked about concerns with RecordWildCards but let’s talk about its advantages. Turns out that RecorldWildCards plays nicely with another language extension — ApplicativeDo.

Let’s say we want to build CLI for a tool that allows to query some data and filter it by from and to entries. Terminal command for this tool may look like this:

my-tool query --from 3 --to 42

We can use optparse-applicative library to implement a parser for these options easily. Let’s start with creating our data type for the options:

data Options = Options
    { optionsFrom :: !Int
    , optionsTo   :: !Int

optparse-applicative is built around Applicative functors. So in order to implement a parser for the Options data type you need to write code like this:

fromP, toP :: Parser Int

optionsP :: Parser Options
optionsP = Options
    <$> fromP
    <*> toP

One problem with writing code in this style is that it’s very easy to use the wrong order of fromP and toP parsers when defining a parser for Options and this can lead to bugs. In a CLI you can write either --from 3 --to 42 or --to 42 --from 3 and both work correctly. But in code Options <$> fromP <*> toP is not the same as Options <$> toP <*> fromP. This semantic difference between real-world and expectations from code can lead to unexpected bugs.

This is true in general for such applicative-style code but it’s more important with regards to a CLI. Because it’s not that easy to test a CLI and to my knowledge, not many people really write automatic tests for their CLIs. So in this area of our code, we want to be more careful not to introduce extra bugs.

One of the solutions to the described problem is to introduce newtypes. But it might be too tedious to deal with lots of newtypes. Fortunately, we can use RecordWildCards and the ApplicativeDo extension to solve this problem easier!

optionsP :: Parser Options
optionsP = do
    optionsFrom <- fromP
    optionsTo   <- toP
    pure Options{..}

Now, even if you change the order of optionsFrom and optionsTo variables, the code still works.

Conclusion: RecordWildCards combined with ApplicativeDo allows you to write type-safe and maintainable code.


Due to the records implementation details, it’s not possible to have data types with the same field names in scope in standard Haskell code (as per Haskell2010). However, if you enable the DuplicateRecordFields extension, it becomes possible. You can leverage this extension to convert between data types easily:

data Man = Man { name :: !Text }
data Cat = Cat { name :: !Text }

evilMagic :: Man -> Cat
evilMagic Man{..} = Cat{..}

However, such automatic conversion works only if fields of different types have the exact same names. So, if data types have different prefixes, you need to write a mapping between fields explicitly. But if you decide not to add prefixes for the field names, some pieces of your code that do something else besides mere conversion between data types, can become less readable if you use RecordWildCards in them.

Conclusion: if you convert between data types more often than you use them, you can leverage the combination of RecordWildCards and DuplicateRecordFields extensions.


RecordWildCards is a very useful and convenient extension. It can be used in the wrong way. However, if you follow best-practices, this extension can become your best friend in writing elegant and maintainable code.