Firestore Data Model - tuarua/Firebase-ANE GitHub Wiki

The contents of this page are based on the original Firebase Documentation

Cloud Firestore is a NoSQL, document-oriented database. Unlike a SQL database, there are no tables or rows. Instead, you store data in documents, which are organized into collections.

Each document contains a set of key-value pairs. Cloud Firestore is optimized for storing large collections of small documents.

All documents must be stored in collections. Documents can contain subcollections and nested objects, both of which can include primitive fields like strings or complex objects like lists.

Collections and documents are created implicitly in Cloud Firestore. Simply assign data to a document within a collection. If either the collection or document does not exist, Cloud Firestore creates it.

Documents

In Cloud Firestore, the unit of storage is the document. A document is a lightweight record that contains fields, which map to values. Each document is identified by a name. A document representing a user alovelace might look like this:

alovelace: {
    first: "Ada"
    last: "Lovelace"
    born: 1815 
}

Note: Cloud Firestore supports a variety of data types for values: boolean, number, string, geo point, binary blob, and timestamp. You can also use arrays or nested objects, called maps, to structure data within a document.

Complex, nested objects in a document are called maps. For example, you could structure the user's name from the example above with a map, like this:

alovelace: {
    name: {
        first: "Ada"
        last: "Lovelace"
    }
    born : 1815
}

You may notice that documents look a lot like JSON. In fact, they basically are. There are some differences (for example, documents support extra data types and are limited in size to 1 MB), but in general, you can treat documents as lightweight JSON records.

Collections

Documents live in collections, which are simply containers for documents. For example, you could have a users collection to contain your various users, each represented by a document:

users: [
    alovelace: {
        first: "Ada",
        last: "Lovelace",
        born: 1815 
    },
    aturing: {
        first: "Alan"
        last: "Turing"
        born: 1912
    }
]

Cloud Firestore is schemaless, so you have complete freedom over what fields you put in each document and what data types you store in those fields. Documents within the same collection can all contain different fields or store different types of data in those fields. However, it's a good idea to use the same fields and data types across multiple documents, so that you can query the documents more easily.

A collection contains documents and nothing else. It can't directly contain raw fields with values, and it can't contain other collections. (See Hierarchical Data for an explanation of how to structure more complex data in Cloud Firestore.)

The names of documents within a collection are unique. You can provide your own keys, such as user IDs, or you can let Cloud Firestore create random IDs for you automatically (for example, by calling add()).

You do not need to "create" or "delete" collections. After you create the first document in a collection, the collection exists. If you delete all of the documents in a collection, it no longer exists.

References

Every document in Cloud Firestore is uniquely identified by its location within the database. The previous example showed a document alovelace within the collection users. To refer to this location in your code, you can create a reference to it.

var alovelaceDocumentRef:DocumentReference = db.collection("users").document("alovelace")

A reference is a lightweight object that just points to a location in your database. You can create a reference whether or not data exists there, and creating a reference does not perform any network operations.

You can also create references to collections:

var usersCollectionRef:CollectionReference = db.collection("users");

Note: Collection references and document references are two distinct types of references and let you perform different operations. For example, you could use a collection reference for querying the documents in the collection, and you could use a document reference to read or write an individual document.

For convenience, you can also create references by specifying the path to a document or collection as a string, with path components separated by a forward slash (/). For example, to create a reference to the alovelace document:

var aLovelaceDocumentReference:DocumentReference = db.document("users/alovelace");

Subcollections

The best way to store messages in this scenario is by using subcollections. A subcollection is a collection associated with a specific document.

Note: Querying across subcollections is not currently supported in Cloud Firestore. If you need to query data across collections, use root-level collections.

You can create a subcollection called messages for every room document in your rooms collection:

rooms: [
    roomA: {
    name: "my chat room"
        messages: [
        message1: {
            from: "alex"
            msg: "Hello World!"
        },
        ...
     ],
     ... 
]

In this example, you would create a reference to a message in the subcollection with the following code:

var messageRef:DocumentReference = db.collection("rooms")
                    .document("roomA")
                    .collection("messages").document("message1");

Notice the alternating pattern of collections and documents. Your collections and documents must always follow this pattern. You cannot reference a collection in a collection or a document in a document.

Subcollections allow you to structure data hierarchically, making data easier to access. To get all messages in roomA, you can simply access the db.collection('rooms').doc('roomA').collection('messages') collection.

Documents in subcollections can contain subcollections as well, allowing you to further nest data. You can nest data up to 100 levels deep.

Warning: Deleting a document does not delete its subcollections!

When you delete a document that has associated subcollections, the subcollections are not deleted.

They are still accessible by reference. For example, there may be a document referenced by db.collection('coll').doc('doc').collection('subcoll').doc('subdoc') even though the document referenced by db.collection('coll').doc('doc') no longer exists. If you want to delete documents in subcollections when deleting a document, you must do so manually, as shown in Delete Collections.