BSD-3-Clause licensed by Eitan Chatav
Maintained by [email protected]
This version can be pinned in stack with:squeal-postgresql-,2631








stack install squeal-postgresql


Squeal is a deep embedding of PostgreSQL in Haskell. Let’s see an example!

First, we need some language extensions because Squeal uses modern GHC features.

  , DeriveGeneric
  , OverloadedLabels
  , OverloadedStrings
  , TypeApplications
  , TypeOperators

Here comes the Main module and imports.

module Main (main) where

import Control.Monad.Base (liftBase)
import Data.Int (Int32)
import Data.Text (Text)

import Squeal.PostgreSQL

We’ll use generics to easily convert between Haskell and PostgreSQL values.

import qualified Generics.SOP as SOP
import qualified GHC.Generics as GHC

The first step is to define the schema of our database. This is where we use DataKinds and TypeOperators. The schema consists of a type-level list of tables, a ::: pairing of a type level string or Symbol and a list a columns, itself a ::: pairing of a Symbol and a ColumnType. The ColumnType describes the PostgreSQL type of the column as well as whether or not it may contain NULL and whether or not inserts and updates can use a DEFAULT. For our schema, we’ll define two tables, a users table and an emails table.

type Schema =
  '[ "users"  :::
       '[ "id"   ::: 'Optional ('NotNull 'PGint4)
        , "name" ::: 'Required ('NotNull 'PGtext)
   , "emails" :::
       '[ "id"      ::: 'Optional ('NotNull 'PGint4)
        , "user_id" ::: 'Required ('NotNull 'PGint4)
        , "email"   ::: 'Required ('Null 'PGtext)

Next, we’ll write Definitions to set up and tear down the schema. In Squeal, a Definition is a createTable, alterTable or dropTable command and has two type parameters, corresponding to the schema before being run and the schema after. We can compose definitions using >>>. Here and in the rest of our commands we make use of overloaded labels to refer to named tables and columns in our schema.

setup :: Definition '[] Schema
setup = 
  createTable #users
    ( serial `As` #id :*
      (text & notNull) `As` #name :* Nil )
    [ primaryKey (Column #id :* Nil) ]
  createTable #emails
    ( serial `As` #id :*
      (int & notNull) `As` #user_id :*
      text `As` #email :* Nil )
    [ primaryKey (Column #id :* Nil)
    , foreignKey (Column #user_id :* Nil) #users (Column #id :* Nil)
      OnDeleteCascade OnUpdateCascade ]

Notice that setup starts with an empty schema '[] and produces Schema. In our createTable commands we included TableConstraints to define primary and foreign keys, making them somewhat complex. Our tear down Definition is simpler.

teardown :: Definition Schema '[]
teardown = dropTable #emails >>> dropTable #users

Next, we’ll write Manipulations to insert data into our two tables. A Manipulation is an insertInto, update or deleteFrom command and has three type parameters, the schema it refers to, a list of parameters it can take as input, and a list of columns it produces as output. When we insert into the users table, we will need a parameter for the name field but not for the id field. Since it’s optional, we can use a default value. However, since the emails table refers to the users table, we will need to retrieve the user id that the insert generates and insert it into the emails table. Take a careful look at the type and definition of both of our inserts.

insertUser :: Manipulation Schema
  '[ 'Required ('NotNull 'PGtext)]
  '[ "fromOnly" ::: 'Required ('NotNull 'PGint4) ]
insertUser = insertInto #users
  ( Values (def `As` #id :* param @1 `As` #name :* Nil) [] )
  OnConflictDoNothing (Returning (#id `As` #fromOnly :* Nil))

insertEmail :: Manipulation Schema
  '[ 'Required ('NotNull 'PGint4), 'Required ('Null 'PGtext)] '[]
insertEmail = insertInto #emails ( Values
  ( def `As` #id :*
    param @1 `As` #user_id :*
    param @2 `As` #email :* Nil) [] )
  OnConflictDoNothing (Returning Nil)

Next we write a Query to retrieve users from the database. We’re not interested in the ids here, just the usernames and email addresses. We need to use an inner join to get the right result. A Query is like a Manipulation with the same kind of type parameters.

getUsers :: Query Schema '[]
  '[ "userName" ::: 'Required ('NotNull 'PGtext)
   , "userEmail" ::: 'Required ('Null 'PGtext) ]
getUsers = select
  (#u ! #name `As` #userName :* #e ! #email `As` #userEmail :* Nil)
  ( from (Table (#users `As` #u)
    & InnerJoin (Table (#emails `As` #e))
      (#u ! #id .== #e ! #user_id)) )

Now that we’ve defined the SQL side of things, we’ll need a Haskell type for users. We give the type Generic and HasDatatypeInfo instances so that we can decode the rows we receive when we run getUsers. Notice that the record fields of the User type match the column names of getUsers.

data User = User { userName :: Text, userEmail :: Maybe Text }
  deriving (Show, GHC.Generic)
instance SOP.Generic User
instance SOP.HasDatatypeInfo User

Let’s also create some users to add to the database.

users :: [User]
users = 
  [ User "Alice" (Just "[email protected]")
  , User "Bob" Nothing
  , User "Carole" (Just "[email protected]")

Now we can put together all the pieces into a program. The program connects to the database, sets up the schema, inserts the user data (using prepared statements as an optimization), queries the user data and prints it out and finally closes the connection. We can thread the changing schema information through by using the indexed PQ monad transformer and when the schema doesn’t change we can use Monad and MonadPQ functionality.

main :: IO ()
main = void $
  withConnection "host=localhost port=5432 dbname=exampledb" . runPQ $
    define setup
    & pqThen session
    & thenDefine teardown
    session = do
      idResults <- traversePrepared insertUser (Only . userName <$> users)
      ids <- traverse (fmap fromOnly . getRow (RowNumber 0)) idResults
      traversePrepared_ insertEmail (zip (ids :: [Int32]) (userEmail <$> users))
      usersResult <- runQuery getUsers
      usersRows <- getRows usersResult
      liftBase $ print (usersRows :: [User])