Desk joins in Fluent 4

Database fashions

Fluent is a Swift ORM framework written for Vapor. You should utilize fashions to characterize rows in a desk, migrations to create the construction for the tables and you may outline relations between the fashions utilizing Swift property wrappers. That is fairly a easy method of representing mum or dad, baby or sibling connections. You possibly can “keen load” fashions via these predefined relation properties, which is nice, however generally you do not need to have static sorts for the relationships.

I am engaged on a modular CMS and I can not have hardcoded relationship properties contained in the fashions. Why? Properly, I would like to have the ability to load modules at runtime, so if module A relies upon from module B via a relation property then I can not compile module A independently. That is why I dropped many of the cross-module relations, nonetheless I’ve to write down joined queries. 😅

Buyer mannequin

On this instance we’re going to mannequin a easy Buyer-Order-Product relation. Our buyer mannequin may have a fundamental identifier and a reputation. Take into account the next:

ultimate class CustomerModel: Mannequin, Content material {
    static let schema = "prospects"
    
    @ID(key: .id) var id: UUID?
    @Area(key: "identify") var identify: String

    init() { }

    init(id: UUID? = nil, identify: String) {
        self.id = id
        self.identify = identify
    }
}

Nothing particular, only a fundamental Fluent mannequin.

Order mannequin

Prospects may have a one-to-many relationship to the orders. Which means a buyer can have a number of orders, however an order will all the time have precisely one related buyer.

ultimate class OrderModel: Mannequin, Content material {
    static let schema = "orders"
    
    @ID(key: .id) var id: UUID?
    @Area(key: "date") var date: Date
    @Area(key: "customer_id") var customerId: UUID

    init() { }

    init(id: UUID? = nil, date: Date, customerId: UUID) {
        self.id = id
        self.date = date
        self.customerId = customerId
    }
}

We might reap the benefits of the @Guardian and @Little one property wrappers, however this time we’re going to retailer a customerId reference as a UUID kind. Afterward we’re going to put a overseas key constraint on this relation to make sure that referenced objects are legitimate identifiers.

Product mannequin

The product mannequin, identical to the shopper mannequin, is completely unbiased from the rest. 📦

ultimate class ProductModel: Mannequin, Content material {
    static let schema = "merchandise"
    
    @ID(key: .id) var id: UUID?
    @Area(key: "identify") var identify: String

    init() { }

    init(id: UUID? = nil, identify: String) {
        self.id = id
        self.identify = identify
    }
}

We will create a property with a @Sibling wrapper to specific the connection between the orders and the merchandise, or use joins to question the required knowledge. It actually would not matter which method we go, we nonetheless want a cross desk to retailer the associated product and order identifiers.

OrderProductModel

We will describe a many-to-many relation between two tables utilizing a 3rd desk.

ultimate class OrderProductModel: Mannequin, Content material {
    static let schema = "order_products"
    
    @ID(key: .id) var id: UUID?
    @Area(key: "order_id") var orderId: UUID
    @Area(key: "product_id") var productId: UUID
    @Area(key: "amount") var amount: Int

    init() { }

    init(id: UUID? = nil, orderId: UUID, productId: UUID, amount: Int) {
        self.id = id
        self.orderId = orderId
        self.productId = productId
        self.amount = amount
    }
}

As you’ll be able to see we will retailer additional data on the cross desk, in our case we’re going to affiliate portions to the merchandise on this relation proper subsequent to the product identifier.

Migrations

Happily, Fluent provides us a easy approach to create the schema for the database tables.

struct InitialMigration: Migration {

    func put together(on db: Database) -> EventLoopFuture<Void> {
        db.eventLoop.flatten([
            db.schema(CustomerModel.schema)
                .id()
                .field("name", .string, .required)
                .create(),
            db.schema(OrderModel.schema)
                .id()
                .field("date", .date, .required)
                .field("customer_id", .uuid, .required)
                .foreignKey("customer_id", references: CustomerModel.schema, .id, onDelete: .cascade)
                .create(),
            db.schema(ProductModel.schema)
                .id()
                .field("name", .string, .required)
                .create(),
            db.schema(OrderProductModel.schema)
                .id()
                .field("order_id", .uuid, .required)
                .foreignKey("order_id", references: OrderModel.schema, .id, onDelete: .cascade)
                .field("product_id", .uuid, .required)
                .foreignKey("product_id", references: ProductModel.schema, .id, onDelete: .cascade)
                .field("quantity", .int, .required)
                .unique(on: "order_id", "product_id")
                .create(),
        ])
    }

    func revert(on db: Database) -> EventLoopFuture<Void> {
        db.eventLoop.flatten([
            db.schema(OrderProductModel.schema).delete(),
            db.schema(CustomerModel.schema).delete(),
            db.schema(OrderModel.schema).delete(),
            db.schema(ProductModel.schema).delete(),
        ])
    }
}

If you wish to keep away from invalid knowledge within the tables, you need to all the time use the overseas key and distinctive constraints. A overseas key can be utilized to verify if the referenced identifier exists within the associated desk and the distinctive constraint will guarantee that just one row can exists from a given subject.

Becoming a member of database tables utilizing Fluent 4

We’ve to run the InitialMigration script earlier than we begin utilizing the database. This may be accomplished by passing a command argument to the backend software or we will obtain the identical factor by calling the autoMigrate() technique on the applying occasion.

For the sake of simplicity I will use the wait technique as an alternative of async Futures & Guarantees, that is high quality for demo functions, however in a real-world server software you need to by no means block the present occasion loop with the wait technique.

That is one potential setup of our dummy database utilizing an SQLite storage, however in fact you should utilize PostgreSQL, MySQL and even MariaDB via the accessible Fluent SQL drivers. 🚙

public func configure(_ app: Utility) throws {

    app.databases.use(.sqlite(.file("db.sqlite")), as: .sqlite)

    app.migrations.add(InitialMigration())

    strive app.autoMigrate().wait()

    let prospects = [
        CustomerModel(name: "Bender"),
        CustomerModel(name: "Fry"),
        CustomerModel(name: "Leela"),
        CustomerModel(name: "Hermes"),
        CustomerModel(name: "Zoidberg"),
    ]
    strive prospects.create(on: app.db).wait()
    
    let merchandise = [
        ProductModel(name: "Hamburger"),
        ProductModel(name: "Fish"),
        ProductModel(name: "Pizza"),
        ProductModel(name: "Beer"),
    ]
    strive merchandise.create(on: app.db).wait()

    
    let order = OrderModel(date: Date(), customerId: prospects[0].id!)
    strive order.create(on: app.db).wait()

    let beerProduct = OrderProductModel(orderId: order.id!, productId: merchandise[3].id!, amount: 6)
    strive beerProduct.create(on: app.db).wait()
    let pizzaProduct = OrderProductModel(orderId: order.id!, productId: merchandise[2].id!, amount: 1)
    strive pizzaProduct.create(on: app.db).wait()
}

We’ve created 5 prospects (Bender, Fry, Leela, Hermes, Zoidberg), 4 merchandise (Hamburger, Fish, Pizza, Beer) and one new order for Bender containing 2 merchandise (6 beers and 1 pizza). 🤖

Inside be a part of utilizing one-to-many relations

Now the query is: how can we get the shopper knowledge primarily based on the order?

let orders = strive OrderModel
    .question(on: app.db)
    .be a part of(CustomerModel.self, on: OrderModel.$customerId == CustomerModel.$id, technique: .internal)
    .all()
    .wait()

for order in orders {
    let buyer = strive order.joined(CustomerModel.self)
    print(buyer.identify)
    print(order.date)
}

The reply is fairly easy. We will use an internal be a part of to fetch the shopper mannequin via the order.customerId and buyer.id relation. Once we iterate via the fashions we will ask for the associated mannequin utilizing the joined technique.

Joins and plenty of to many relations

Having a buyer is nice, however how can I fetch the related merchandise for the order? We will begin the question with the OrderProductModel and use a be a part of utilizing the ProductModel plus we will filter by the order id utilizing the present order.

for order in orders {
    

    let orderProducts = strive OrderProductModel
        .question(on: app.db)
        .be a part of(ProductModel.self, on: OrderProductModel.$productId == ProductModel.$id, technique: .internal)
        .filter(.$orderId == order.id!)
        .all()
        .wait()

    for orderProduct in orderProducts {
        let product = strive orderProduct.joined(ProductModel.self)
        print(product.identify)
        print(orderProduct.amount)
    }
}

We will request the joined mannequin the identical method as we did it for the shopper. Once more, the very first parameter is the mannequin illustration of the joined desk, subsequent you outline the relation between the tables utilizing the referenced identifiers. As a final parameter you’ll be able to specify the kind of the be a part of.

Inside be a part of vs left be a part of

There’s a nice SQL tutorial about joins on w3schools.com, I extremely suggest studying it. The principle distinction between an internal be a part of and a left be a part of is that an internal be a part of solely returns these information which have matching identifiers in each tables, however a left be a part of will return all of the information from the bottom (left) desk even when there aren’t any matches within the joined (proper) desk.

There are various various kinds of SQL joins, however internal and left be a part of are the commonest ones. If you wish to know extra in regards to the different sorts you need to learn the linked article. 👍

Abstract

Desk joins are actually useful, however you need to watch out with them. It’s best to all the time use correct overseas key and distinctive constraints. Additionally think about using indexes on some rows if you work with joins, as a result of it might enhance the efficiency of your queries. Pace will be an essential issue, so by no means load extra knowledge from the database than you really need.

There is a matter on GitHub in regards to the Fluent 4 API, and one other one about querying particular fields utilizing the .subject technique. Lengthy story quick, joins will be nice and we want higher docs. 🙉