Monthly Downloads: 16
Programming language: Haskell
License: BSD 3-clause "New" or "Revised" License
Tags: Database    
Latest version: v0.7.0.1

squeal-postgresql alternatives and similar packages

Based on the "Database" category.
Alternatively, view squeal-postgresql alternatives based on common mentions on social networks and blogs.

Do you think we are missing an alternative of squeal-postgresql or a related project?

Add another 'Database' Package




GitHub CI






Squeal is a deep embedding of SQL into Haskell. By "deep embedding", I am abusing the term somewhat. What I mean is that Squeal embeds both SQL terms and SQL types into Haskell at the term and type levels respectively. This leads to a very high level of type-safety in Squeal.

Squeal embeds not just the structured query language of SQL but also the data manipulation language and the data definition language; that's SELECT, INSERT, UPDATE, DELETE, WITH, CREATE, DROP, and ALTER commands.

Squeal expressions closely match their corresponding SQL expressions so that the SQL they actually generate is completely predictable. They are also highly composable and cover a large portion of SQL.


  • generic encoding of Haskell tuples and records into query parameters and generic decoding of query results into Haskell records using generics-sop
  • access to SQL alias system using the OverloadedLabels extension
  • type-safe NULL and DEFAULT
  • type-safe SQL constraints CHECK, UNIQUE, PRIMARY KEY and FOREIGN KEY
  • type-safe aggregation
  • escape hatches for writing raw SQL
  • mtl compatible monad transformer for executing as well as preparing queries and manipulations and Atkey indexed monad transformer for executing definitions.
  • linear, pure or impure, one-way or rewindable migrations
  • connection pools
  • transactions
  • views
  • array, composite and enumerated types
  • json functions and operations
  • multischema support
  • correlated subqueries
  • window functions
  • text search
  • time functions
  • ranges
  • indexes
  • inlining


stack install squeal-postgresql


Start postgres on localhost port 5432 and create a database named exampledb.

stack test


We welcome contributors. Please make pull requests on the dev branch instead of master. The Issues page is a good place to communicate.


Let's see an example!

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

>>> :set -XDataKinds -XDeriveGeneric -XOverloadedLabels -XFlexibleContexts
>>> :set -XOverloadedStrings -XTypeApplications -XTypeOperators -XGADTs

We'll need some imports.

>>> import Control.Monad.IO.Class (liftIO)
>>> 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.

>>> :{
type UsersColumns =
  '[ "id"   :::   'Def :=> 'NotNull 'PGint4
   , "name" ::: 'NoDef :=> 'NotNull 'PGtext ]
type UsersConstraints = '[ "pk_users" ::: 'PrimaryKey '["id"] ]
type EmailsColumns =
  '[ "id" ::: 'Def :=> 'NotNull 'PGint4
   , "user_id" ::: 'NoDef :=> 'NotNull 'PGint4
   , "email" ::: 'NoDef :=> 'Null 'PGtext ]
type EmailsConstraints =
  '[ "pk_emails"  ::: 'PrimaryKey '["id"]
   , "fk_user_id" ::: 'ForeignKey '["user_id"] "public" "users" '["id"] ]
type Schema =
  '[ "users" ::: 'Table (UsersConstraints :=> UsersColumns)
   , "emails" ::: 'Table (EmailsConstraints :=> EmailsColumns) ]
type DB = Public Schema

Notice the use of type operators.

::: is used to pair an alias Symbol with a SchemasType, a SchemumType, a TableConstraint or a ColumnType. It is intended to connote Haskell's :: operator.

:=> is used to pair TableConstraints with a ColumnsType, yielding a TableType, or to pair an Optionality with a NullType, yielding a ColumnType. It is intended to connote Haskell's => operator

Next, we'll write Definitions to set up and tear down the schema. In Squeal, a Definition like createTable, alterTable or dropTable 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 (Public '[]) DB
  setup =
    createTable #users
      ( serial `as` #id :*
        (text & notNullable) `as` #name )
      ( primaryKey #id `as` #pk_users ) >>>
    createTable #emails
      ( serial `as` #id :*
        (int & notNullable) `as` #user_id :*
        (text & nullable) `as` #email )
      ( primaryKey #id `as` #pk_emails :*
        foreignKey #user_id #users #id
          (OnDelete Cascade) (OnUpdate Cascade) `as` #fk_user_id )

We can easily see the generated SQL is unsurprising looking.

>>> printSQL setup
CREATE TABLE "users" ("id" serial, "name" text NOT NULL, CONSTRAINT "pk_users" PRIMARY KEY ("id"));
CREATE TABLE "emails" ("id" serial, "user_id" int NOT NULL, "email" text NULL, CONSTRAINT "pk_emails" PRIMARY KEY ("id"), CONSTRAINT "fk_user_id" FOREIGN KEY ("user_id") REFERENCES "users" ("id") ON DELETE CASCADE ON UPDATE CASCADE);

Notice that setup starts with an empty public schema (Public '[]) and produces DB. In our createTable commands we included TableConstraints to define primary and foreign keys, making them somewhat complex. Our teardown Definition is simpler.

>>> :{
  teardown :: Definition DB (Public '[])
  teardown = dropTable #emails >>> dropTable #users

>>> printSQL teardown
DROP TABLE "emails";
DROP TABLE "users";

We'll need a Haskell type for Users. We give the type Generics.SOP.Generic and Generics.SOP.HasDatatypeInfo instances so that we can encode and decode Users.

>>> :set -XDerivingStrategies -XDeriveAnyClass
>>> :{
data User = User { userName :: Text, userEmail :: Maybe Text }
  deriving stock (Show, GHC.Generic)
  deriving anyclass (SOP.Generic, SOP.HasDatatypeInfo)

Next, we'll write Statements to insert Users into our two tables. A Statement has three type parameters, the schemas it refers to, input parameters and an output row. 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 serial, 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. We can do this in a single Statement by using a with manipulation.

>>> :{
  insertUser :: Statement DB User ()
  insertUser = manipulation $ with (u `as` #u) e
      u = insertInto #users
        (Values_ (Default `as` #id :* Set (param @1) `as` #name))
        OnConflictDoRaise (Returning_ (#id :* param @2 `as` #email))
      e = insertInto_ #emails $ Select
        (Default `as` #id :* Set (#u ! #id) `as` #user_id :* Set (#u ! #email) `as` #email)
        (from (common #u))
>>> printSQL insertUser
WITH "u" AS (INSERT INTO "users" ("id", "name") VALUES (DEFAULT, ($1 :: text)) RETURNING "id" AS "id", ($2 :: text) AS "email") INSERT INTO "emails" ("user_id", "email") SELECT "u"."id", "u"."email" FROM "u" AS "u"

Next we write a Statement 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 innerJoin to get the right result.

>>> :{
  getUsers :: Statement DB () User
  getUsers = query $ select_
    (#u ! #name `as` #userName :* #e ! #email `as` #userEmail)
    ( from (table (#users `as` #u)
      & innerJoin (table (#emails `as` #e))
        (#u ! #id .== #e ! #user_id)) )
>>> printSQL getUsers
SELECT "u"."name" AS "userName", "e"."email" AS "userEmail" FROM "users" AS "u" INNER JOIN "emails" AS "e" ON ("u"."id" = "e"."user_id")

Let's 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.

>>> :{
  session :: PQ DB DB IO ()
  session = do
    executePrepared_ insertUser users
    usersResult <- execute getUsers
    usersRows <- getRows usersResult
    liftIO $ print usersRows
  withConnection "host=localhost port=5432 dbname=exampledb user=postgres password=postgres" $
    define setup
    & pqThen session
    & pqThen (define teardown)
[User {userName = "Alice", userEmail = Just "[email protected]"},User {userName = "Bob", userEmail = Nothing},User {userName = "Carole", userEmail = Just "[email protected]"}]