squeal

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introduction

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.

features

• 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, invertible migrations
• connection pools
• transactions
• views
• array, composite and enumerated types
• json operations

installation

stack install squeal-postgresql

testing

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

stack test

contributing

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

usage

Let's see an example!

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

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

We'll need some imports.

>>> import Control.Monad (void)
>>> import Control.Monad.Base (liftBase)
>>> import Data.Int (Int32)
>>> import Data.Text (Text)
>>> import Squeal.PostgreSQL
>>> import Squeal.PostgreSQL.Render

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 Schema =
  '[ "users" ::: 'Table (
      '[ "pk_users" ::: 'PrimaryKey '["id"] ] :=>
      '[ "id"   :::   'Def :=> 'NotNull 'PGint4
       , "name" ::: 'NoDef :=> 'NotNull 'PGtext
       ])
  , "emails" ::: 'Table (
      '[ "pk_emails"  ::: 'PrimaryKey '["id"]
       , "fk_user_id" ::: 'ForeignKey '["user_id"] "users" '["id"]
       ] :=>
      '[ "id"      :::   'Def :=> 'NotNull 'PGint4
       , "user_id" ::: 'NoDef :=> 'NotNull 'PGint4
       , "email"   ::: 'NoDef :=>    'Null 'PGtext
       ])
  ]
:}

Notice the use of type operators.

::: is used to pair an alias GHC.TypeLits.Symbol with 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 a ColumnConstraint with a NullityType, 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.

>>> :{
let
  setup :: Definition '[] Schema
  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
          OnDeleteCascade OnUpdateCascade `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 schema '[] and produces Schema. In our createTable commands we included TableConstraints to define primary and foreign keys, making them somewhat complex. Our teardown Definition is simpler.

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

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

Next, we'll write Manipulations to insert data into our two tables. A Manipulation like insertRow, update or deleteFrom 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 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. Take a careful look at the type and definition of both of our inserts.

>>> :{
let
  insertUser :: Manipulation Schema '[ 'NotNull 'PGtext ] '[ "fromOnly" ::: 'NotNull 'PGint4 ]
  insertUser = insertRow #users
    (Default `as` #id :* Set (param @1) `as` #name)
    OnConflictDoNothing (Returning (#id `as` #fromOnly))
:}

>>> :{
let
  insertEmail :: Manipulation Schema '[ 'NotNull 'PGint4, 'Null 'PGtext] '[]
  insertEmail = insertRow #emails
    ( Default `as` #id :*
      Set (param @1) `as` #user_id :*
      Set (param @2) `as` #email )
    OnConflictDoNothing (Returning Nil)
:}

>>> printSQL insertUser
INSERT INTO "users" ("id", "name") VALUES (DEFAULT, ($1 :: text)) ON CONFLICT DO NOTHING RETURNING "id" AS "fromOnly"
>>> printSQL insertEmail
INSERT INTO "emails" ("id", "user_id", "email") VALUES (DEFAULT, ($1 :: int4), ($2 :: text)) ON CONFLICT DO NOTHING

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.

>>> :{
let
  getUsers :: Query Schema '[]
    '[ "userName"  ::: 'NotNull 'PGtext
     , "userEmail" :::    'Null 'PGtext ]
  getUsers = 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")

Now that we've defined the SQL side of things, we'll need a Haskell type for users. We give the type Generics.SOP.Generic and Generics.SOP.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.

>>> :{
let
  users :: [User]
  users = 
    [ User "Alice" (Just "alice@gmail.com")
    , User "Bob" Nothing
    , User "Carole" (Just "carole@hotmail.com")
    ]
:}

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.

>>> :{
let
  session :: PQ Schema Schema IO ()
  session = do
    idResults <- traversePrepared insertUser (Only . userName <$> users)
    ids <- traverse (fmap fromOnly . getRow 0) idResults
    traversePrepared_ insertEmail (zip (ids :: [Int32]) (userEmail <$> users))
    usersResult <- runQuery getUsers
    usersRows <- getRows usersResult
    liftBase $ print (usersRows :: [User])
in
  void . withConnection "host=localhost port=5432 dbname=exampledb" $
    define setup
    & pqThen session
    & pqThen (define teardown)
:}
[User {userName = "Alice", userEmail = Just "alice@gmail.com"},User {userName = "Bob", userEmail = Nothing},User {userName = "Carole", userEmail = Just "carole@hotmail.com"}]