IHP provides a few basic functions to access the database. On top of Postgres SQL, we try to provide a thin layer to make it easy to do all the common tasks your web application usually does.

The only supported database platform is Postgres. Focusing on Postgres allows us to better integrate advanced Postgres-specific solutions into your application.

In development, you do not need to set up anything to use Postgres. The built-in development server automatically starts a Postgres instance to work with your application. The built-in development Postgres server is only listening on a Unix socket and is not available via TCP.

When the development server is running, you can connect to it via postgresql:///app?host=YOUR_PROJECT_DIRECTORY/build/db with your favorite database tool. When inside the project directory you can also use make psql to open a Postgres REPL connected to the development database (named app), or start psql by pointing at the local sockets file psql --host=/PATH/TO/PROJECT/DIRECTORY/build/db app. The web interface of the development server also has a GUI-based database editor (like phpmyadmin) at http://localhost:8001/ShowDatabase.

Haskell data structures and types are generated automatically based on your database schema.


Once you have created your project, the first step is to define a database schema. The database schema is a SQL file with a lot of CREATE TABLE ... statements. You can find it at Application/Schema.sql.

In a new project, this file will be empty. The uuid-ossp extension is automatically enabled for the database by IHP.

To define your database schema add your CREATE TABLE ... statements to the Schema.sql. For a users table this can look like this:

    id UUID DEFAULT uuid_generate_v4() PRIMARY KEY NOT NULL,
    firstname TEXT NOT NULL,
    lastname TEXT NOT NULL

Haskell data structures and types are automatically generated from the Schema.sql file. They are re-generated on every file change of the Schema.sql. We use the well-known postgresql-simple Haskell library to connect to the database.

Schema Designer

Because the SQL syntax is sometimes hard to remember, the framework provides a GUI-based database editor called IHP Schema Designer. You can find the Schema Designer at http://localhost:8001/Tables.

The Schema Designer in Visual Mode

Keep in mind that the Schema Editor also only modifies the Schema.sql. This works by parsing the SQL DDL-statements and applying transformations on the AST, compiling and writing it back to Schema.sql. When there is a syntax error in the Schema.sql file the visual mode will be unavailable and you have to work with the code editor to fix the problem.

You can add tables, columns, foreign key constraints, and enums. You can also edit these objects by right-clicking them. New tables have an id column by default. Lots of opinionated short-cuts for rapid application development like automatically offering to add foreign key constraints are built-in.

An example of using the context menu for editing a table

When the Visual Editor is not powerful enough, just switch back to the code editor. For convenience, the Schema Designer also allows you to toggle to the Code Editor inside the web browser:

The Schema Designer in Code Mode

Push to DB

After we have added a few data structures to our Schema.sql, our running Postgres database is still empty. This is because we still need to import our database schema into the database.

In the Schema Designer: Click on Push to DB:

Push to DB Button

In the command line: Run make db while the server is running.

This will delete and re-create the current database and import the Schema.sql. After importing the Schema, it will also import the Application/Fixtures.sql which is used for pre-populating the empty database with some data. It’s equivalent to running psql < Schema.sql; psql < Fixtures.sql inside an empty database.

When the development server is started the first time, the Schema.sql and Fixtures.sql are automatically imported.


The Fixtures.sql includes a lot of INSERT INTO statements to pre-fill your database once the schema has been created.

You can manually add INSERT INTO statements to this file. You can also migrate your fixtures by just making the required changes to this SQL file.

You can dump your current database state into the Fixtures.sql by running make dumpdb. This way you can regularly commit the database state to git, so other developers have the same data inside their local development database as you have.

Update DB

You can also update the database while keeping its contents.

In the Schema Designer: Click on Update DB:

Push to DB Button

In the command line: Run make dumpdb and after that make db.

When dumping the database into the Fixtures.sql first and then rebuilding the database with the dump, the contents will be kept when changing the schema.

Haskell Bindings

Model Context

In a pure functional programming language like Haskell, we need to pass the database connection to all functions which need to access the database. We use an implicit parameter ?modelContext :: ModelContext to pass around the database connection without always specifying it. The ModelContext data structure is basically just a wrapper around the actual database connection.

An implicit parameter is a parameter which is automatically passed to certain functions, it just needs to be available in the current scope.

This means that all functions which are running database queries will need to be called from a function which has this implicit parameter in scope. A function doing something with the database, will always have a type signature specifying that it requires the ?modelContext to be available, like this:

myFunc :: (?modelContext :: ModelContext) => IO SomeResult

All controller actions already have ?modelContext in scope and thus can run database queries. Other application entry-points, like e.g. Scripts, also have this in scope.

This also means, that when a function does not specify that it depends on the database connection in its type signature (like ?modelContext :: ModelContext => ..), you can be sure that it’s not doing any database operations.

Haskell Data Structures

For every table in the Schema.sql a corresponding data structure will be generated on the Haskell side. For example, given a table:

    id UUID DEFAULT uuid_generate_v4() PRIMARY KEY NOT NULL,
    firstname TEXT NOT NULL,
    lastname TEXT NOT NULL

The generated Haskell data structure for this table will look like this:

data User = User
    { id :: Id User
    , firstname :: Text
    , lastname :: Text

The id field type Id User is basically just a wrapper around UUID for type-safety reasons. All database field names are mapped from under_score to camelCase on the Haskell side.

When a SQL field can be NULL, the Haskell field type will be contained in Maybe.

In the Schema Designer, you can take a look at the generated Haskell code by right-clicking the table and clicking Show Generated Haskell Code.

Retrieving Records

Querying Records

You can retrieve all records of a table using query

    users <- query @User |> fetch
    forEach users \user -> do
        putStrLn (get #name user)

This will run a SELECT * FROM users query and put a list of User structures.

Fetching a single record

When you have the id of a record, you can also use fetch to get it from the database:

    let userId :: Id User = ...
    user <- fetch userId
    putStrLn (get #name user)

This will run the SQL query SELECT * FROM users WHERE id = ... LIMIT 1.

fetch knows a single entity will be returned for the id, so instead of a list of users, a single user will be returned. In case the entity is not found, an exception is thrown. Use fetchOrNothing to get Nothing instead of an exception when no result is found

Fetching a list of ids

When have you a list of ids of a single record type, you can also just fetch them:

    let userIds :: [Id User] = ...
    users <- fetch userIds

This will run the SQL query SELECT * FROM users WHERE id IN (...). The results in users have type [User].

Fetching a Maybe (Id record)

Sometimes you have an optional id field, like e.g. when having a database schema like this:

    id UUID DEFAULT uuid_generate_v4() PRIMARY KEY NOT NULL,
    description TEXT,
    assigned_user_id UUID

In this case the field assigned_user_id can be null. In our action we want to fetch user when it’s not null, and return Nothing otherwise:

action ShowTask { taskId } = do
    task <- fetch taskId
    assignedUser <- case get #assignedUserId task of
            Just userId -> do
                user <- fetch userId
                pure (Just user)
            Nothing -> pure Nothing

This contains a lot of boilerplate for wrapping and unwrapping the Maybe value. Therefore you can just call fetchOneOrNothing directly on the Maybe (Id User) value:

action ShowTask { taskId } = do
    task <- fetch taskId
    assignedUser <- fetchOneOrNothing (get #assignedUserId task)

Fetching n records (LIMIT)

Use limit to query only up to n records from a table:

    users <- query @User
        |> orderBy #firstname
        |> limit 10
        |> fetch

This will run a SELECT * FROM users ORDER BY firstname LIMIT 10 query and will return the first 10 users ordered by their firstname.

When you are only interested in the first result you can also use fetchOne as a shortcut for |> limit 1:

    firstUser <- query @User
        |> orderBy #firstname
        |> fetchOne

Skipping n records (OFFSET)

Use offset to skip n records from a table:

    users <- query @User
        |> orderBy #firstname
        |> offset 10
        |> fetch

This is most often used together with limit to implement paging.

Counting records (COUNT queries)

You can use fetchCount instead of fetch to get the count of records matching the query:

    activeUsersCount :: Int <- query @User
        |> filterWhere (#isActive, True)
        |> fetchCount

    -- SELECT COUNT(*) FROM users WHERE is_active = 1

Fetching distinct records

Use distinct to fetch distinct records.

    posts <- query @Post
        |> distinct
        |> fetch

Or distinctOn #tableField to fetch distinct records based on the #tableField value.

    users <- query @User
        |> distinctBy #fullName
        |> fetch

Raw SQL Queries

Use the function sqlQuery to run a raw SQL query.

    result <- sqlQuery "SELECT * FROM projects WHERE id = ?" (Only id)

You might need to specify the expected result type, as type inference might not be able to guess it.

    result :: [Project] <- sqlQuery "SELECT * FROM projects WHERE id = ?" (Only id)

Scalar Results

The sqlQuery function always returns a list of rows as the result. When the result of your query is a single value (such as an integer or string) use sqlQueryScalar:

    count :: Int <- sqlQueryScalar "SELECT COUNT(*) FROM projects" ()

    randomString :: Text <- sqlQueryScalar "SELECT md5(random()::text)" ()


Creating a single record

To insert a record into the database, call newRecord to get an empty record value:

    let user = newRecord @User
    -- user = User { id = 0000-0000-0000-0000, firstname = "", lastname = "" }

The newRecord function does not insert the record, it just returns a new empty data structure we can fill with values and then insert into the database.

We can use set to fill in attributes:

    let user = newRecord @User
            |> set #firstname "Max"
            |> set #lastname "Mustermann"

    -- user = User { id = 0000-0000-0000-0000, firstname = "Max", lastname = "Mustermann" }

We use createRecord to insert the above record into the users table:

    let user = newRecord @User
            |> set #firstname "Max"
            |> set #lastname "Mustermann"

    -- user = User { id = 0000-0000-0000-0000, firstname = "Max", lastname = "Mustermann" }

    insertedUser <- user |> createRecord
    -- This will run INSERT INTO users (id, firstname, lastname) VALUES (DEFAULT, "Max", "Mustermann");
    -- insertedUser = User { id = cf633b17-c409-4df5-a2fc-8c3b3d6c2ea7, firstname = "Max", lastname = "Mustermann" }

This can also be rewritten in a more compact form:

    user <- newRecord @User
            |> set #firstname "Max"
            |> set #lastname "Mustermann"
            |> createRecord
    -- user = User { id = "cf633b17-c409-4df5-a2fc-8c3b3d6c2ea7", firstname = "Max", lastname = "Mustermann" }

Creating many records

You can use createMany to insert multiple records with a single INSERT statement:

    let usersToBeInserted [ newRecord @User, newRecord @User, ... ]
    users <- createMany usersToBeInserted

This will run:

INSERT INTO users (id, firstname, lastname)
    VALUES (DEFAULT, "", ""), (DEFAULT, "", "") , (DEFAULT, "", "") ... ;


The function updateRecord runs an UPDATE query for a specific record:

    user <- fetch ("cf633b17-c409-4df5-a2fc-8c3b3d6c2ea7" :: Id User)
        |> set #lastname "Tester"
        |> updateRecord

This will set the lastname of user cf633b17-c409-4df5-a2fc-8c3b3d6c2ea7 to Tester and run an UPDATE query to persist that:

UPDATE users SET firstname = firstname, lastname = "Tester" WHERE id = "cf633b17-c409-4df5-a2fc-8c3b3d6c2ea7"

The UPDATE query will only update columns that have been changed using |> set #someField someValue on the record.


Deleting a single record

Use deleteRecord to run a simple DELETE query:

    user <- fetch ("cf633b17-c409-4df5-a2fc-8c3b3d6c2ea7" :: Id User)
    deleteRecord user

This will execute:

DELETE FROM users WHERE id = "cf633b17-c409-4df5-a2fc-8c3b3d6c2ea7"

Deleting many records

Use deleteRecords to run a DELETE query for multiple records:

    users :: [User] <- ...
    deleteRecords users

This will execute:

DELETE FROM users WHERE id IN (...)

Deleting all records

Use deleteAll to run a DELETE query for all rows in a table:

    deleteAll @User

This will execute:



It’s possible to define and use custom enum types with IHP. An enum can be created using the Schema Designer. The process is pretty much the same as when creating a table.

Adding enums via the Schema Designer

Open the Schema Designer, right-click into the Objects Pane, and then select Add Enum:

You have to give a name to your enum type. The name should be in plural form, like with table names. E.g. we could name our enum colors.

Next, add the enum values by right-clicking into the Values pane and click on Add Value. Here we could add values such as red, blue and yellow.

Adding enums via SQL

Instead of using the Schema Designer, you can also just add the required SQL statement manually into Application/Schema.sql:

CREATE TYPE colors AS ENUM ('blue', 'red', 'yellow');

Using enums in the Haskell Code

The above colors example will allow us to access the enum like this:

let blue :: Colors = Blue
let red :: Colors = Red
let yellow :: Colors = Yellow

You can use the enum as a field type for another record. E.g. we can have posts table and there give each post a color:

    id UUID DEFAULT uuid_generate_v4() PRIMARY KEY NOT NULL,
    body TEXT NOT NULL,
    color colors

You can use fill even with custom enums:

    action CreatePostAction = do
        let post = newRecord @Post
            |> fill @["body", "color"]
            |> ifValid \case
                Left post -> render NewView { .. }
                Right post -> do
                    post <- post |> createRecord
                    setSuccessMessage "Post created"
                    redirectTo PostsAction

In your views, use inputValue to get a textual representation for your enum which works with fill:

<input type="text" value="{inputValue" Blue} />

Database Updates

The Update DB operation is three steps:

  1. Data is read from the database and stored in Fixtures.sql
  2. The database is deleted and the schema in Schema.sql created
  3. The data in Fixtures.sql is (re-)inserted.

The small arrow on the Update DB button shows a menu where it is possible to just run Save DB to Fixtures (step 1) or Push to DB (steps 2 + 3).

Making Changes to the Database

The main purpose of the below steps are for keeping the data rows from Application/Fixtures.sql between updates. If you are not concerned with keeping these rows when refactoring, feel free to skip this section.

The main reason for the steps outlined below is that changes to the database schema are written to Application/Schema.sql only. The actual database is not altered. This means the actual schema and Schema.sql will be out of sync.

When the schemas are out of sync, the INSERT statements in Fixtures.sql will fail. If this happens, and you attempt to update, the target table will be empty after the update. Try again, and the empty table is read from the database and the data in Fixtures.sql is gone.

If you feel this is all a bit less streamlined compared to the rest of the development experience, you are correct. We will work on improving handling changes to the database.

Adding a Column

Nullable or with Default

This is always OK. The existing rows can be re-inserted from your Fixtures.sql without errors. After another update cycle, Fixtures.sql will also contain the new column.

Non-Nullable or Without Default

It’s best to do this in two steps. First, follow the above. After updating the DB, fill the column with data and remove the nullable or default properties.

Data can be updated by manually editing the table in the data view or by running UPDATE statements in the custom query field below the data view.

Renaming a Column

When you are renaming a column, the development process of using Update DB will not work. This is because Update DB will save the old database state into the Fixtures.sql. There it still references the old column names. It will then fail on the next update.

  1. Rename your column col_a to col_b in the Schema Designer
  2. Rename the column in the database by executing ALTER TABLE tablename RENAME COLUMN col_a TO col_b in the custom query field below the data view.

Deleting a Column

Similarly as for renaming, deleting a column currently won’t work automatically either.

  1. Delete your column in the Schema Designer
  2. Delete the column from the database by executing ALTER TABLE tablename DROP COLUMN colname

Alternate Method

There’s always more than one way. This is another.

  1. Make changes in the Schema Designer
  2. Click Save DB to Fixtures in the Schema Designer (Use the arrow next to the Update DB button to see this option)
  3. Edit Fixtures.sql to your heart’s content.
  4. Click Push to DB in the Schema Designer (Use the arrow next to the Update DB button to see this option)

Migrations In Production

IHP currently has no built-in migration system yet. We’re still experimenting with a great way to solve this. Until then, the recommended approach used by digitally induced is to manually migrate your database using DDL statements as shown above.

Supported Database Types

IHP currently has support for the following postgres column types:


You can use the withTransaction function to wrap your database operations in a postgres database transaction:

withTransaction do
   company <- newRecord @Company |> createRecord

   user <- newRecord @User
       |> set #companyId (get #id company)
       |> createRecord

   company <- company
       |> setJust #ownerId (get #id user)
       |> updateRecord

In this example, when the creation of the User fails, the creation of the company will be rolled back. So that no incomplete data is left in the database when there’s an error.

The withTransaction function will automatically commit after it succesfully executed the passed do-block. When any exception is thrown, it will automatically rollback.

Common Pitfalls

Keep in mind that some IHP functions like redirectTo or render throw a ResponseException. So code like below will not work as expected:

action CreateUserAction = do
    withTransaction do
        user <- newRecord @User |> createRecord
        redirectTo NewSessionAction

The redirectTo throws a ResponseException and will cause a rollback. This code should be structured like this:

action CreateUserAction = do
    user <- withTransaction do
        newRecord @User |> createRecord
    redirectTo NewSessionAction