Cloud Firestore

Cloud Firestore is a flexible, scalable database for mobile, web, and server applications from Firebase and Google Cloud. It allows you to save and synchronize data between users in real time. Firestore falls under the NoSQL database category and is oriented towards cloud storage.

Cloud Firestore: differences from Realtime Database

1. Data model: Firestore is a document-oriented database where you have collections and documents. Unlike the hierarchical structure of the Realtime Database, Firestore is document-focused, which may be more flexible for many application types.
2. Scalability: The Realtime Database scales through vertical sharding, while Firestore offers horizontal sharding, making it more scalable for large applications.
3. Queries: Firestore provides richer and more flexible query capabilities compared to the Realtime Database.
4. Security: Security rules for Firestore are more structured and readable, simplifying their creation and maintenance.
5. Pricing: The pricing model also differs. While the Realtime Database charges for the amount of data transferred, Firestore focuses on the number of read, write, and delete operations.

Benefits of using Firestore for Kotlin developers

1. Integration with Kotlin Coroutines: Kotlin and the Firebase SDK provide good integration with Kotlin Coroutines, making asynchronous operations with the database more elegant and concise.
2. Type safety: Thanks to Kotlin's ability to create data classes, Firestore can directly convert documents into Kotlin objects, reducing the chances of data type errors.
3. Reactive programming: Using Kotlin Flow and Firestore's integration with LiveData, developers can easily implement reactive user interfaces.
4. Code reduction: Kotlin is famous for its syntax that reduces code. Combined with the Firestore SDK, this leads to faster and cleaner code when working with data.
5. Good documentation: Firebase provides extensive documentation, examples, and resources for Kotlin developers, simplifying the learning and development process.

Integration of Firebase and Firestore in a Kotlin project

Project Setup:

Before starting, make sure you have Android Studio installed, as well as the Kotlin plugin. If you're creating a new project, choose Kotlin as the primary programming language during creation.

Creating a new project in Firebase Console:

1. Go to the Firebase Console.
2. Click "Add project".
3. Enter the name of your project and follow the on-screen instructions.
4. Once the project is created, choose the "Android" platform to add your application.
5. Enter your app's ID (usually in the format com.example.yourappname).
6. Download the google-services.json file when prompted and save it. This file is required for the Firebase SDK integration.

Add the Firestore dependency to your app-level build.gradle.kts file:

// latest version you can find https://firebase.google.com/docs/android/android-play-services
implementation("com.google.firebase:firebase-firestore-ktx:24.7.0")

Now, you're ready to use Firestore in your Kotlin project. Begin by initializing Firestore:

class MainActivity : ComponentActivity() {
    override fun onCreate(savedInstanceState: Bundle?) {
        super.onCreate(savedInstanceState)

        val db = Firebase.firestore // Access Firestore instance from anywhere in the app

        setContent {
            MyApplicationTheme {
                // A surface container using the 'background' color from the theme
                Surface(
                    modifier = Modifier.fillMaxSize(),
                    color = MaterialTheme.colorScheme.background
                ) {
                    Greeting("Android")
                }
            }
        }
    }
}

Next, you can manage data: add, modify, read, and delete it using the provided Firestore methods.

Data structure: documents and collections

In Firestore, data is structured as documents, which are stored in collections. Documents can contain nested data or references to other documents. In turn, collections are groups of documents.

Example:

• users (collection)
  • user_id (document)
    • name: "John"
    • age: 30
    • addresses (sub-collection)
      • address_id: { street: "Main", city: "CityName" }

Difference between document-oriented and relational models

• Flexibility: In Firestore, there is no fixed data schema. A document can change its structure at any time without needing to modify other documents or collections.
• Scalability: Firestore is optimized for horizontal scaling, automatically distributing data across multiple servers.
• Nested data: Unlike relational DBs where relationships are created using foreign keys, Firestore allows you to store nested data.

Working with Firestore data

CRUD operation examples using Kotlin

• Creating a document:

val db = Firebase.firestore
val user = hashMapOf(
    "name" to "John",
    "age" to 30
)
db.collection("users").add(user)

• Reading a document:

db.collection("users").document("user_id").get().addOnSuccessListener { document ->
    if (document != null) {
        Log.d(TAG, "DocumentSnapshot data: ${document.data}")
    } else {
        Log.d(TAG, "No such document")
    }
}

• Updating a document:

val userRef = db.collection("users").document("user_id")
userRef.update("age", 31)

• Deleting a document:

db.collection("users").document("user_id").delete()

Asynchronous operations with Firestore and Kotlin Coroutines

In Firestore, read and write operations are asynchronous. Using Kotlin Coroutines, we can simplify working with asynchronous queries, making the code more readable.

suspend fun fetchDataFromFirestore(): DocumentSnapshot? {
    return withContext(Dispatchers.IO) {
        Firebase.firestore.collection("your_collection").document("your_document").get().await()
    }
}

Here, await() is an extension provided by the Firebase library for Kotlin Coroutines, which allows you to "wait" for asynchronous operations.

Converting between Firestore documents and Kotlin objects

To simplify the conversion between Firestore documents and Kotlin objects, you can use the @DocumentId and @PropertyName annotations.

data class User(
    @DocumentId val id: String? = null,
    @PropertyName("name") val name: String? = null,
    @PropertyName("age") val age: Int? = null
)

In this way, you can automatically convert Firestore documents into Kotlin objects and vice versa.

Working with real-time change listeners

Firestore offers reactive listeners to detect data changes in real time. For instance:

val docRef = Firebase.firestore.collection("your_collection").document("your_document")
val listenerRegistration = docRef.addSnapshotListener { snapshot, e ->
    if (e != null) {
        // Handle error
        return@addSnapshotListener
    }

    if (snapshot != null && snapshot.exists()) {
        val user = snapshot.toObject(User::class.java)
        // Handle received data
    } else {
        // Document not found
    }
}

To stop listening for changes, you can use:

listenerRegistration.remove()

Queries and indexing with Firestore in Kotlin

Forming complex queries

In Firestore, you can create simple and compound queries to collections and documents. Here are some examples:

• Document selection based on one condition:

  
          val db = Firebase.firestore
          db.collection("users")
            .whereEqualTo("age", 30)
            .get()
            .addOnSuccessListener { /* handle results */ }
          
  

• Combining multiple conditions:

  
          db.collection("users")
            .whereEqualTo("age", 30)
            .whereGreaterThan("score", 100)
            .get()
            .addOnSuccessListener { /* handle results */ }
          
  

Using indexes to optimize queries

Firestore automatically indexes all fields in documents for fast retrieval. However, when using complex queries, Firestore may require the creation of a composite index. In such a case, it will provide a link to create the index in the Firebase console.

To optimize queries, follow the Firebase-provided links for index creation and create them as needed.

Limitations and best practices in query handling

• Limitations: Firestore has certain query constraints. For example, you can't use inequalities (whereGreaterThan, whereLessThan, etc.) on different fields in the same query.
• Pagination: When working with large data sets, it's recommended to paginate your queries using limit and startAfter/startAt.
• Data optimization: Structure your data so that the most frequently made queries are the most efficient. This might involve denormalizing data or storing additional copies of data elsewhere for quick retrieval.
• Monitoring and analysis: Use Firebase Performance Monitoring to track the performance of your queries and identify potential bottlenecks.

Conclusion

Cloud Firestore emerges as a robust, scalable, and flexible NoSQL database solution provided by Firebase and Google Cloud, specifically tailored for mobile, web, and server applications. With its unique document-oriented data model, Firestore offers distinct advantages over the Realtime Database in areas such as scalability, query capabilities, security, and pricing. For Kotlin developers, Firestore stands out due to seamless integration with Kotlin Coroutines, type safety, and a streamlined approach to reactive programming.

Ultimately, when integrating Firebase and Firestore into a Kotlin project, developers benefit not only from the capabilities of Firestore itself but also from the streamlined and efficient code-writing experience that Kotlin offers. Adopting best practices, understanding Firestore's limitations, and leveraging its features will undeniably pave the way for efficient, scalable, and dynamic application development.