How to reload or retry Android Kotlin Flow after data available in database?

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04-10-2024

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Reloading or Retrying Kotlin Flow after Data is Available in the Database

One of the common challenges faced when working with Android Kotlin Flows is managing data updates from the database. Imagine you're building an application that displays a list of items fetched from a remote API and stored locally in a database. Now, suppose a user updates an item in the database directly through the app, bypassing the API call. How do you ensure the UI reflects these changes in the Flow?

This article dives into the intricacies of reloading or retrying Kotlin Flows when new data becomes available in the database. We'll explore different approaches, analyze their advantages and drawbacks, and equip you with the knowledge to implement this behavior effectively in your Android applications.

Scenario:

Let's say we have a simple app that fetches a list of products from a remote API and stores them in a Room database.

// Product Entity
@Entity(tableName = "products")
data class Product(
    @PrimaryKey(autoGenerate = true) val id: Int = 0,
    val name: String,
    val price: Double,
    // ... other fields
)

// Product DAO
@Dao
interface ProductDao {
    @Query("SELECT * FROM products")
    fun getAllProducts(): Flow<List<Product>>

    @Insert(onConflict = OnConflictStrategy.REPLACE)
    suspend fun insert(product: Product)
}

// ViewModel
class ProductViewModel(private val productDao: ProductDao) : ViewModel() {
    val products: Flow<List<Product>> = productDao.getAllProducts()

    // Function to update a product in the database
    fun updateProduct(product: Product) {
        viewModelScope.launch {
            productDao.insert(product)
        }
    }
}

In this example, the products Flow in the ViewModel automatically emits updates whenever there are changes in the products table in the database. However, if the user updates a product directly in the database, this change might not be reflected in the UI immediately.

Solutions:

Here are three possible approaches to address this issue:

1. Using SharedFlow:

• Create a SharedFlow to broadcast database updates.
• Subscribe to this SharedFlow in the ViewModel and trigger a re-emission of the products Flow.

// In the ViewModel
private val databaseUpdateEvent = MutableSharedFlow<Unit>()

init {
    // Start listening for updates in database
    viewModelScope.launch {
        databaseUpdateEvent.collect {
            // Re-emit the products Flow
            products = productDao.getAllProducts().distinctUntilChanged()
        }
    }
}

fun updateProduct(product: Product) {
    viewModelScope.launch {
        productDao.insert(product)
        databaseUpdateEvent.emit(Unit) // Trigger re-emission
    }
}

Advantages:

• Simple and efficient for handling database updates.
• Provides a dedicated channel for communicating changes.

Disadvantages:

• Might not be optimal for complex updates or when a specific change needs to be tracked.

2. Manually Refreshing the Flow:

• Trigger a re-emission of the products Flow within the updateProduct function.
• Use distinctUntilChanged operator to avoid unnecessary re-emissions.

fun updateProduct(product: Product) {
    viewModelScope.launch {
        productDao.insert(product)
        // Re-emit the products Flow
        products = productDao.getAllProducts().distinctUntilChanged()
    }
}

Advantages:

• Straightforward approach for simple use cases.

Disadvantages:

• Can lead to unnecessary re-emissions if changes aren't reflected in the database.
• Might become complex for handling multiple update events.

3. Using a MediatorLiveData:

• Utilize MediatorLiveData to combine the products Flow and the databaseUpdateEvent (SharedFlow).
• Update the MediatorLiveData whenever there's a new database update.

// In the ViewModel
private val mediatorLiveData = MediatorLiveData<List<Product>>()
private val databaseUpdateEvent = MutableSharedFlow<Unit>()

init {
    mediatorLiveData.addSource(products) { 
        mediatorLiveData.value = it 
    }
    mediatorLiveData.addSource(databaseUpdateEvent) {
        mediatorLiveData.value = productDao.getAllProducts().value // Re-emit the products Flow
    }
}

fun updateProduct(product: Product) {
    viewModelScope.launch {
        productDao.insert(product)
        databaseUpdateEvent.emit(Unit) // Trigger re-emission
    }
}

Advantages:

• More robust and flexible for managing complex data updates.

Disadvantages:

• Can be more complex to set up and manage than other approaches.

Choosing the Right Approach:

• For simple database updates, the SharedFlow approach is often the most suitable.
• If you need more fine-grained control over data changes, the MediatorLiveData approach offers greater flexibility.
• If you are working with a small number of updates, the manual refresh approach might suffice.

Additional Considerations:

• Always use distinctUntilChanged to avoid unnecessary re-emissions.
• Ensure the database updates are handled within a coroutine scope to avoid blocking the main thread.
• Implement proper error handling to gracefully manage potential issues during the update process.

Conclusion:

Effectively handling database updates in Android Kotlin Flows requires careful consideration of the specific use case. The approaches discussed in this article provide a comprehensive understanding of the different options available. By choosing the most appropriate method and implementing robust error handling, you can build an application that seamlessly reflects real-time data updates, enhancing the user experience.