What Are Kotlin Flows? Explained with Examples

Handling asynchronous data streams in modern Android development often feels like trying to drink from a firehose. Kotlin flows offer a solution.

Built on top of the Kotlin coroutines library, flows provide a reactive programming approach for managing sequential data processing with elegant error handling. They represent cold streams of values that can be collected within a coroutine’s structured concurrency context.

Unlike traditional callbacks or LiveData, flows excel at:

• Creating complex stream transformations using operators like map, filter, and combine
• Managing backpressure when producers emit faster than consumers can process
• Providing concurrency control with flow context management
• Supporting cold flow behavior where data is produced only when collected

Developed by JetBrains as part of the kotlinx.coroutines package, flows bridge the gap between imperative and reactive programming paradigms in the Android ecosystem.

This guide explores flow basics, implementation patterns, and real-world examples to help you master this essential tool for reactive streams processing.

What Are Kotlin Flows?

Kotlin Flows are a part of Kotlin’s coroutine library, designed for handling asynchronous data streams. They emit multiple values sequentially, making them ideal for observing data changes over time. Unlike traditional callbacks, Flows are structured, cancellable, and support operators for transforming data, improving readability and error handling in reactive programming.

Core Features of Data Classes

Data classes in Kotlin programming language provide developers with a powerful tool for handling sequential data processing and state management. Unlike regular classes, data classes come with built-in functions that eliminate boilerplate code.

toString() implementation

The toString() method generates a readable string representation automatically. No more manual debugging output!

data class User(val name: String, val age: Int)

val user = User("Alex", 30)
println(user) // Output: User(name=Alex, age=30)

This feature makes flow debugging and logging substantially easier during Android app development.

equals() and hashCode()

Data classes implement structural equality checking. Two instances with identical property values are considered equal.

val user1 = User("Alex", 30)
val user2 = User("Alex", 30)
println(user1 == user2) // true

This behavior mirrors value objects in domain modeling and supports flow collection operations that rely on equality comparisons.

copy() function

Copy creates modified instances while preserving immutability – a core principle of reactive programming.

val olderUser = user.copy(age = 31)

The copy function significantly enhances flow transformations and enables functional programming patterns within Kotlin coroutines library.

Component functions for destructuring

Component functions enable destructuring declarations. They make multiple flow emissions much cleaner.

val (name, age) = user
println("$name is $age years old")

These component functions become especially valuable when working with Kotlin flow API operations like zip or combine.

Practical Applications

Data Transfer Objects (DTOs)

API response mapping

Data classes excel at mapping responses from asynchronous data streams. They’re perfect for converting JSON to type-safe objects.

data class ApiResponse(val success: Boolean, val data: List<User>)

When paired with StateFlow or SharedFlow, DTOs create clean boundaries between network layers and business logic in the MVVM pattern.

Database entity representation

Data classes naturally represent database entities. They integrate seamlessly with Room and other Android architecture components.

@Entity
data class UserEntity(
    @PrimaryKey val id: String,
    val name: String,
    val age: Int
)

This approach brings type safety advantages to Kotlin/Native projects and simplifies flow collection scope management.

Cross-layer data passing

Data classes facilitate information transfer between application layers. Their immutability prevents unexpected modifications during flow upstream operations.

Value Objects

Immutable data representation

Value objects encapsulate domain concepts. Their immutability makes them thread-safe for coroutines flow API usage.

data class Money(val amount: BigDecimal, val currency: Currency)

These objects work beautifully within flow operators and help maintain consistent state during flow lifecycle events.

Domain modeling with data classes

Domain models built with data classes promote clean architecture. They’re perfect for representing business rules in structured concurrency patterns.

data class Order(
    val id: String,
    val items: List<Product>,
    val customer: Customer,
    val status: OrderStatus
)

When combined with flow buffer operations, domain models create expressive and maintainable business logic.

Type safety advantages

Type safety prevents bugs at compile time. Data classes make validation explicit through their structure.

fun processOrder(order: Order) {
    // Type safety ensures all required fields exist
}

This safety supports error propagation in flow exception handling scenarios.

Configuration Objects

Application settings

Configuration data classes centralize settings. They’re ideal for managing flow dispatchers and concurrency patterns.

data class AppConfig(
    val apiUrl: String,
    val cacheSize: Int,
    val refreshInterval: Long
)

These configuration objects integrate well with Kotlin Flow testing strategies.

Default parameters and named arguments

Default parameters create flexible configurations. Named arguments improve readability.

data class NetworkConfig(
    val timeout: Duration = 30.seconds,
    val retryCount: Int = 3,
    val cacheEnabled: Boolean = true
)

This pattern shines when implementing flow retry mechanism or timeout handling in android asynchronous programming.

Serialization benefits

Data classes serialize effortlessly with libraries like kotlinx.serialization or Moshi. This simplifies flow caching strategies.

@Serializable
data class UserPreferences(val theme: String, val notifications: Boolean)

When paired with flow resource management techniques, serialization enables efficient state persistence across application lifecycle events.

The combination of these features makes Kotlin data classes a foundational tool for developers working with reactive streams and flow-based architectures. JetBrains created a truly elegant solution for representing data in modern Android development.

Advanced Usage Patterns

Working with Collections of Data Classes

Filtering and mapping

Collections of data classes shine with Kotlin Flow API operations. Filter streams effortlessly.

val adultUsers = userFlow
    .filter { it.age >= 18 }
    .collect { /* process adult users */ }

This approach enables efficient asynchronous data streams processing, mirroring reactive programming paradigms found in RxJava.

Grouping and aggregation

Data classes work seamlessly with flow combine operations for complex aggregations. Build sophisticated dashboards from multiple sources.

userFlow
    .groupBy { it.country }
    .flatMapLatest { (country, usersInCountry) ->
        usersInCountry.map { country to it }
    }
    .collect { (country, user) -> 
        // Process users grouped by country
    }

These patterns help when implementing multiple flow collection or pagination in Android development projects.

Sorting with natural ordering

Data classes support natural ordering through Comparable interfaces. Sort collections intuitively.

data class Task(val priority: Int, val title: String): Comparable<Task> {
    override fun compareTo(other: Task) = priority.compareTo(other.priority)
}

val sortedTasks = taskFlow
    .toList()
    .sorted()

This functionality integrates perfectly with flow conflation mechanisms for displaying prioritized items in MVVM pattern implementations.

Data Class Composition

Nesting data classes

Nested data classes create hierarchical structures. They model complex domains clearly.

data class Address(val street: String, val city: String, val zip: String)
data class Customer(val name: String, val address: Address, val email: String)

When used with flow emissions in Kotlin multiplatform projects, nested structures maintain consistency across platforms.

Composition vs inheritance

Favor composition over inheritance with data classes. Build complex behaviors through containment.

// Instead of inheritance:
data class EnhancedUser(
    val user: User,
    val preferences: UserPreferences
)

This pattern aligns with structured concurrency principles and simplifies flow downstream operations in reactive streams.

Building complex structures

Data classes excel at representing complex domain models. Use them to create expressive business logic.

data class OrderItem(val product: Product, val quantity: Int)
data class Order(
    val id: String,
    val customer: Customer,
    val items: List<OrderItem>,
    val status: OrderStatus
)

These structures work elegantly with flow transformations and error handling in Android application lifecycle contexts.

Pattern Matching and Destructuring

Using component functions

Component functions enable powerful destructuring expressions. Extract values elegantly.

val orders = listOf(Order("1", customer1, listOf(item1, item2), OrderStatus.CONFIRMED))

orders.forEach { (id, customer, items, status) ->
    // Process order components
}

This syntax integrates beautifully with flow operators like flatMapLatest and debounce in Android asynchronous programming.

When expressions with data classes

When expressions perform sophisticated pattern matching. They make code more readable.

val message = when(orderEvent) {
    is OrderCreated -> "New order: ${orderEvent.orderId}"
    is OrderShipped -> "Order shipped to ${orderEvent.address}"
    is OrderCancelled -> "Order cancelled: ${orderEvent.reason}"
}

This construct enhances flow error propagation mechanisms provided by the Kotlin coroutines library.

Smart casts in action

The Kotlin compiler intelligently casts types after checks. It eliminates redundant casts.

if (response is SuccessResponse) {
    // Smart cast - no need to cast response to SuccessResponse
    processData(response.data)
}

Smart casts streamline flow exception handling and make error conditions more explicit in Android Jetpack applications.

Interoperability Considerations

Java Interoperability

How Java sees Kotlin data classes

Java treats Kotlin data classes as regular classes with getters, setters, and explicit methods. Interoperability is seamless.

// Java code
User user = new User("Alex", 30);
String name = user.getName();
User olderUser = user.copy(31); // Using Kotlin-generated methods

This compatibility simplifies migration strategies for teams transitioning from Java to Kotlin while adopting reactive programming.

Using data classes in mixed codebases

Data classes work effortlessly in mixed Java/Kotlin projects. They reduce friction between language boundaries.

// Kotlin data class
data class Product(val id: String, val name: String, val price: Double)

// Used in Java
public class JavaInventory {
    public void addProduct(Product product) {
        // Java code using Kotlin data class
    }
}

This interoperability is crucial when implementing flow implementation patterns in apps with legacy Java components.

Common pitfalls and solutions

Be cautious with nullable types in Java interop. Platform types can cause runtime exceptions.

// Avoid this with Java interop
data class User(val name: String?) // Nullable String

// Better for Java interop
data class User(val name: String) // Non-nullable with @NotNull annotation

Proper nullable type handling ensures stability in flow resource management scenarios where Java and Kotlin code interact.

Serialization and Deserialization

JSON mapping with libraries

Kotlin data classes integrate seamlessly with JSON libraries. They simplify API response mapping.

@Serializable
data class WeatherData(val temperature: Double, val conditions: String)

// Using kotlinx.serialization
val json = Json.encodeToString(WeatherData(22.5, "Sunny"))

This approach streamlines handling asynchronous data streams from network sources in Android development contexts.

Custom serializers

Create custom serializers for special formatting needs. They handle complex transformation requirements.

object DateSerializer : KSerializer<LocalDate> {
    override val descriptor = PrimitiveSerialDescriptor("LocalDate", PrimitiveKind.STRING)

    override fun serialize(encoder: Encoder, value: LocalDate) {
        encoder.encodeString(value.toString())
    }

    override fun deserialize(decoder: Decoder): LocalDate {
        return LocalDate.parse(decoder.decodeString())
    }
}

@Serializable
data class Event(
    val title: String,
    @Serializable(with = DateSerializer::class)
    val date: LocalDate
)

Custom serializers enhance flow caching strategies and improve performance in data-intensive applications.

Versioning and backward compatibility

Maintain backward compatibility with optional fields and default values. Support evolving APIs gracefully.

@Serializable
data class UserProfile(
    val id: String,
    val name: String,
    val email: String,
    val phone: String? = null,  // Added later, nullable with default
    val preferences: UserPreferences = UserPreferences()  // Added later with default
)

This approach facilitates flow migration strategies and prevents breaking changes when APIs evolve over time.

The rich interoperability features of Kotlin data classes make them invaluable tools for developers working with complex systems. Whether you’re building new applications with Android Jetpack or integrating with legacy Java codebases, data classes provide elegant solutions for handling structured data in reactive programming contexts.

Performance Aspects

Memory Footprint

Object allocation patterns

Data classes create lightweight objects. They’re optimized by the Kotlin compiler.

data class Point(val x: Int, val y: Int)

When used with cold flows in reactive programming, these small objects generate minimal garbage collection pressure.

Comparison with alternatives

Data classes outperform manual implementations. The Kotlin standard library optimizes them internally.

// Compared to manually implementing all methods:
class ManualPoint(val x: Int, val y: Int) {
    override fun equals(other: Any?): Boolean {
        if (this === other) return true
        if (other !is ManualPoint) return false
        return x == other.x && y == other.y
    }

    override fun hashCode(): Int = 31 * x + y

    override fun toString(): String = "ManualPoint(x=$x, y=$y)"
}

This efficiency becomes critical when processing asynchronous data streams in Android application lifecycle contexts.

Impact on garbage collection

Large collections of small data classes can increase garbage collection. Use object pools for high-frequency operations.

val pool = ObjectPool<NetworkRequest>(maxSize = 100) {
    NetworkRequest()
}

suspend fun makeRequest() {
    pool.borrow { request ->
        // Use the pooled object
        api.execute(request)
    }
    // Object automatically returned to pool
}

This approach minimizes memory pressure during flow emissions in performance-critical sections.

Optimization Techniques

Using inline classes with data classes

Inline classes reduce boxing overhead. They’re perfect for simple value wrappers.

@JvmInline
value class UserId(val value: String)

data class User(val id: UserId, val name: String)

This technique enhances flow performance considerations in Android development by reducing memory allocations.

Appropriate use cases for performance

Data classes excel with medium-sized objects. For tiny frequent objects, consider value classes.

// For very small, frequently created objects:
@JvmInline
value class Temperature(val celsius: Double)

// For medium-sized objects with multiple properties:
data class WeatherData(
    val temperature: Temperature,
    val humidity: Double,
    val windSpeed: Double
)

This strategy optimizes flow buffer operations and improves reactive streams processing.

Benchmarking data class operations

Measure performance in your specific context. JMH benchmarks reveal true costs.

@Benchmark
fun createDataClass(): UserDto = UserDto("name", "email")

@Benchmark
fun copyDataClass(state: BenchmarkState): UserDto = 
    state.user.copy(email = "new@example.com")

Benchmarking identifies bottlenecks in flow collection scope and helps optimize flow concurrency patterns.

Best Practices and Design Patterns

When to Use Data Classes

Appropriate use cases

Data classes shine for immutable data containers. Use them for DTOs, value objects, and domain models.

// Perfect for API responses
data class ApiResponse<T>(
    val data: T?, 
    val error: ErrorInfo?
)

// Great for domain entities
data class Customer(
    val id: String,
    val name: String,
    val email: String
)

These use cases align perfectly with flow implementations in structured concurrency environments.

When not to use data classes

Avoid data classes for types requiring validation logic or custom equality. Use regular classes instead.

// Not ideal as data class:
class Email private constructor(val value: String) {
    companion object {
        fun create(value: String): Result<Email> {
            return if (isValid(value)) {
                Result.success(Email(value))
            } else {
                Result.failure(IllegalArgumentException("Invalid email"))
            }
        }

        private fun isValid(value: String): Boolean {
            // Validation logic
            return value.contains("@")
        }
    }
}

This distinction is crucial when implementing flow error handling in Kotlin multiplatform projects.

Alternatives for different scenarios

Consider regular classes for behavioral types and inline classes for performance-critical wrappers.

// Behavior-rich class:
class ShoppingCart(items: List<Item> = emptyList()) {
    private val _items = items.toMutableList()
    val items: List<Item> get() = _items.toList()

    fun addItem(item: Item) {
        _items.add(item)
    }

    fun removeItem(id: String) {
        _items.removeAll { it.id == id }
    }
}

Understanding these alternatives helps when building complex reactive programming patterns with Kotlin coroutines library.

Coding Conventions

Naming conventions

Follow Kotlin’s style guide. Use descriptive noun phrases for data classes.

// Good names:
data class UserProfile(...)
data class OrderDetails(...)
data class PaymentMethod(...)

// Less clear names:
data class UserStuff(...) // Too vague
data class ProcessPayment(...) // Sounds like an action, not data

Clear naming enhances code readability in flow transformation pipelines.

Parameter ordering

Place required parameters first, followed by optional ones with defaults.

data class SearchFilters(
    // Required parameters first
    val query: String,
    val category: String,
    // Optional parameters with defaults
    val sortBy: String = "relevance",
    val page: Int = 1,
    val pageSize: Int = 20
)

This convention simplifies flow pagination and upstream/downstream operations.

Documentation guidelines

Document public data classes thoroughly. Include property descriptions and usage examples.

/**
 * Represents user notification preferences
 *
 * @property email Whether the user wants to receive email notifications
 * @property push Whether the user wants to receive push notifications
 * @property marketing Whether the user has opted in to marketing communications
 *
 * @sample com.example.NotificationSamples.createDefaultPreferences
 */
data class NotificationPreferences(
    val email: Boolean = true,
    val push: Boolean = true,
    val marketing: Boolean = false
)

Good documentation improves adoption in team environments, especially when working with flow implementation patterns.

Testing Strategies

Unit testing data classes

Test equals(), hashCode(), and copy() behaviors explicitly. Verify serialization if used.

@Test
fun `equals compares all properties`() {
    val user1 = User("id", "name", "email")
    val user2 = User("id", "name", "email")
    val user3 = User("different", "name", "email")

    assertEquals(user1, user2)
    assertNotEquals(user1, user3)
}

Thorough testing ensures reliability in flow error propagation scenarios.

Mock objects and test fixtures

Create factory functions for test instances. They simplify testing setup.

// Test factory
object TestData {
    fun createUser(
        id: String = "test-id",
        name: String = "Test User",
        email: String = "test@example.com"
    ) = User(id, name, email)
}

// In tests
val user = TestData.createUser(name = "Custom Name")

These factories accelerate the development of Kotlin Flow testing suites.

Property-based testing approaches

Use property-based testing for validating invariants. Generate random instances to find edge cases.

@Test
fun `copy preserves unmodified properties`() {
    forAll(userGenerator) { original ->
        // Only modify one property
        val copied = original.copy(name = "New Name")

        // All other properties should remain the same
        assertEquals(original.id, copied.id)
        assertEquals(original.email, copied.email)
        // But the modified property should change
        assertEquals("New Name", copied.name)

        true
    }
}

This testing style uncovers subtle issues in flow collection and state management.

By following these practices, you’ll maximize the benefits of Kotlin data classes in your projects. They provide an elegant foundation for building robust, maintainable applications with flow-based architectures in the JetBrains ecosystem.

FAQ on Kotlin Flows

How do Flows differ from RxJava?

Flows are Kotlin-native and coroutine-based, while RxJava is Java-based with its own threading model. Flows use suspending functions instead of callbacks. They’re typically lighter-weight, have simpler error handling through structured concurrency, and offer natural backpressure handling. Flow operators feel more intuitive to Kotlin developers.

What’s the difference between cold and hot flows?

Cold flows (basic Flow) produce data only when collected, similar to sequences. Each collector receives all values from the beginning. Hot flows (StateFlow/SharedFlow) emit values regardless of collectors and can be observed by multiple collectors simultaneously, making them ideal for Android UI state management.

When should I use StateFlow vs SharedFlow?

Use StateFlow for representing UI state that always has a value and needs to be retained. It’s perfect for MVVM patterns in Android development. Use SharedFlow for events or actions that don’t have a “current value” concept and when you need fine-grained replay control for flow downstream operations.

How do I handle errors in Kotlin Flows?

Use try-catch blocks in the flow builder or dedicated operators like catch and onCompletion. Flow error handling integrates with Kotlin’s exception system:

flow { emit(1) }
    .map { value -> performOperation(value) }
    .catch { e -> emit(fallbackValue) }
    .collect { /* process values */ }

Can Flows replace LiveData in Android?

Yes, particularly StateFlow and SharedFlow. They offer more functional operators for flow transformations, better testing support, and work outside Android context. Many Android architecture components now support Flow through Lifecycle.flowWithLifecycle() extension, making migration from LiveData straightforward.

How do Flow contexts work?

Flows use three contexts: collection context (where collect() runs), emission context (where flow builder runs), and in-between context (for operators). Context switching is controlled with flowOn():

flow { /* runs in IO context */ }
    .flowOn(Dispatchers.IO)
    .collect { /* runs in Main context */ }

What’s the best way to test Flows?

Use TestCoroutineScope and the turbine library for testing asynchronous flows. Create test doubles for dependencies, control execution with runBlockingTest, and verify emissions:

@Test
fun `test flow emissions`() = runBlockingTest {
    viewModel.stateFlow.test {
        assertEquals(initialState, awaitItem())
        viewModel.performAction()
        assertEquals(newState, awaitItem())
        cancelAndIgnoreRemainingEvents()
    }
}

How do I handle multiple Flows together?

Combine related flows using operators like zip, combine, and merge:

val combined = combine(
    userFlow,
    settingsFlow
) { user, settings -> 
    UserWithSettings(user, settings)
}

These operators are essential for complex state management in Android application lifecycle scenarios.

What are the performance considerations with Flows?

Flows create intermediate objects during flow transformations, which can impact memory usage. For high-performance scenarios, minimize the number of operators, use buffer() for flow backpressure control, and consider SharedFlow with replay for frequently accessed values. Benchmark critical flow operations in your specific use case.

Conclusion

Understanding what are Kotlin flows provides a gateway to reactive programming excellence in modern Android development. These powerful components of the kotlinx.coroutines package transform how we handle asynchronous operations with elegant stream processing capabilities. Flow API adoption continues to grow across the Kotlin programmer community.

Flows deliver significant advantages:

• Thread safety through structured concurrency principles
• Resource efficiency with cold flows that activate only when collected
• Composability using intermediate operators like flatMapLatest and debounce
• Lifecycle awareness when integrated with Android Jetpack components

The development team at JetBrains has created a compelling alternative to RxJava that feels natural in Kotlin’s ecosystem. Whether you’re building a simple data pipeline or implementing complex reactive streams with flow error propagation, Kotlin’s Flow interface provides the tools you need.

As you implement flow-based architectures in your next project, remember that choosing the right flow type—basic Flow, StateFlow, or SharedFlow—can dramatically impact your application’s performance and maintainability. Make the switch today and experience the power of Kotlin Flow.

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Bogdan Sandu

Bogdan is a seasoned web designer and tech strategist, with a keen eye on emerging industry trends.

With over a decade in the tech field, Bogdan blends technical expertise with insights on business innovation in technology.

A regular contributor to TMS Outsource's blog, where you'll find sharp analyses on software development, tech business strategies, and global tech dynamics.

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