What Are Kotlin Coroutines? A Simple Overview

Ever written code that waits for a network response while freezing your app? Kotlin coroutines solve this problem elegantly.

Introduced by JetBrains in Kotlin 1.3, coroutines are a game-changing approach to asynchronous programming that makes complex concurrent operations surprisingly simple. They’re lightweight threads that don’t block the main thread, allowing your Android development to remain responsive during time-consuming tasks.

Unlike traditional threading models, coroutines use suspension points to pause and resume execution without wasting resources. This makes them perfect for:

• Network calls
• Database operations
• CPU-intensive processing
• Long-running background tasks

This article explores how coroutines work, why they outshine callback-based solutions, and how to implement them in your projects. You’ll learn about coroutine scope, dispatchers, structured concurrency, and patterns for sequential and parallel execution.

Ready to eliminate callback hell and write cleaner, more efficient code? Let’s dive in.

What Are Kotlin Coroutines?

Kotlin coroutines are a tool for asynchronous programming that simplify writing concurrent code. They allow tasks to run in the background without blocking the main thread, using lightweight threads called coroutines. With structured concurrency and suspend functions, coroutines make code easier to read, manage, and maintain, especially in Android development.

Understanding Asynchronous Programming

The Need for Asynchronous Code

Modern apps demand responsiveness. Users expect smooth experiences with no freezing—even when your app fetches data from a network or processes large files.

Traditional synchronous execution blocks the main thread. When code runs synchronously, each operation must finish before the next begins. This creates serious limitations of synchronous execution when handling time-consuming tasks like network calls or database operations.

The app becomes unresponsive. UI freezes. Users get frustrated.

This is where asynchronous programming comes in.

Asynchronous code lets operations run outside the main execution flow. The program continues running while background tasks complete. This improves app responsiveness dramatically—especially in mobile app development.

Consider these common use cases requiring asynchronous solutions:

• Loading data from remote APIs
• Reading or writing to databases
• Processing images or videos
• Downloading files
• Running complex calculations

Without async capabilities, these operations would lock up your app. Not good.

Traditional Approaches vs. Coroutines

Before Kotlin coroutines, developers relied on other techniques for handling concurrent operations. Let’s look at their shortcomings:

Callback-based solutions were common in older codebases. They work by passing functions to be executed when an operation completes. But as complexity grows, callbacks nest inside each other, creating “callback hell“—code that’s hard to read, debug, and maintain.

// Callback hell example
networkCall { response ->
    processData(response) { result ->
        saveToDatabase(result) { success ->
            updateUI(success) {
                showCompletionMessage()
            }
        }
    }
}

Thread-based solutions offer another approach. Java’s threading model lets you spawn new threads for background work. But threads are expensive—they consume system resources and aren’t designed for large numbers of concurrent operations. Thread management in Kotlin inherited these same issues from Java.

Thread creation is costly. Context switching between threads adds overhead. And managing thread lifecycles manually is error-prone, leading to leaks and crashes.

Kotlin coroutines provide a better alternative. Introduced by JetBrains in Kotlin 1.3, coroutines offer lightweight concurrency without the complexity. They’re essentially lightweight threads that don’t map directly to system threads, allowing thousands to run simultaneously with minimal overhead.

Unlike the callback-based approach, coroutines let you write asynchronous code in a sequential, readable manner. And unlike raw threads, they’re cheap to create and automatically managed.

Core Concepts of Kotlin Coroutines

Coroutine Builders

Coroutine builders are functions that create coroutines. They’re your entry point into the world of structured concurrency. The Kotlin Standard Library provides several key builders:

launch handles “fire-and-forget” operations when you don’t need a result:

// Using the launch coroutine builder
coroutineScope.launch {
    // Long-running task that doesn't return a result
    processDataInBackground()
}
// Code continues immediately without waiting

async/await works for operations that return results:

// Using the async/await pattern
val deferred = coroutineScope.async {
    // Return some result from a long operation
    fetchUserData()
}
// Later, when you need the result:
val userData = deferred.await() // Suspends until result is available

runBlocking bridges synchronous and asynchronous worlds—useful for tests or in main functions:

// Using runBlocking
runBlocking {
    // Code inside here can use suspend functions
    delay(1000)
    println("After one second")
}

Choose the right builder for your use case. It makes a difference.

Suspension Functions

The suspend modifier is the magic behind coroutines. Functions marked with suspend can pause execution without blocking threads.

suspend fun fetchUserData(): UserData {
    // This can pause execution without blocking the thread
    return api.getUserData() // Network call
}

When reaching a suspension point, a coroutine saves its execution state and releases the thread. Later, it resumes from exactly where it left off—possibly on a different thread.

This mechanism enables efficient execution. While one coroutine waits for an I/O operation, others can use the thread. Continuation passing style works behind the scenes to make this possible, though the Kotlin/JVM compiler hides this complexity.

Writing your own suspension functions is straightforward. Just add the suspend keyword and use other suspend functions inside. The compiler handles the rest.

Coroutine Context and Dispatchers

The CoroutineContext defines the environment for coroutine execution. It’s a set of elements that influence coroutine behavior, including:

• The dispatcher that determines which thread(s) the coroutine runs on
• Job objects for cancellation
• Error handlers
• Coroutine names for debugging

Dispatchers in Kotlin control which threads coroutines use. The library provides several built-in dispatchers for different scenarios:

• Dispatchers.Main: Use for UI thread handling on platforms like Android
• Dispatchers.IO: Optimized for I/O-bound operations like network or database access
• Dispatchers.Default: Good for CPU-bound tasks like data processing

You can switch between dispatchers using the withContext function:

suspend fun loadAndProcessData() {
    // In a UI context
    val rawData = withContext(Dispatchers.IO) {
        // Switched to IO dispatcher for network call
        api.fetchData()
    }

    val processed = withContext(Dispatchers.Default) {
        // CPU-intensive processing on Default dispatcher
        processData(rawData)
    }

    // Back on original context
    updateUi(processed)
}

This approach keeps your app responsive by ensuring heavy work happens off the main thread. Main-safe code becomes easier to write.

Understanding these core concepts gives you the foundation to build robust async solutions. Android development with coroutines becomes more manageable, leading to apps that remain responsive even during complex operations.

The Kotlin Coroutines API continues to evolve, with Roman Elizarov and the Kotlin team at JetBrains regularly improving the library. As you grow comfortable with these basics, you’ll discover powerful patterns for handling even the most complex concurrency challenges.

Handling Concurrency with Coroutines

Structured Concurrency

Structured concurrency forms the backbone of Kotlin coroutines. It’s a programming paradigm that ensures no work is lost and no coroutines leak. The concept was championed by Roman Elizarov at JetBrains and has become fundamental to how we manage concurrent operations.

The key idea? Coroutines exist in a hierarchy—parent-child relationships between coroutines. When you launch a coroutine inside another, the new coroutine becomes a child of the parent.

This relationship matters. Parents won’t complete until all children finish. If a parent gets cancelled, all its children get cancelled too. This automatic cancellation and cleanup prevents resource leaks.

coroutineScope {
    // Parent coroutine
    launch {
        // Child 1
        delay(1000)
        println("Child 1 done")
    }

    launch {
        // Child 2
        delay(500)
        println("Child 2 done")
    }
    // coroutineScope won't complete until both children finish
}
println("All work complete")

Before structured concurrency, tracking all running tasks was hard. Forgotten callbacks and abandoned threads caused memory leaks. Coroutine scope solves this by tying coroutine lifetimes to application components.

Avoiding common concurrency leaks becomes simpler with structured concurrency. No more rogue threads draining battery or memory. Everything exists in a well-defined scope with clear boundaries.

Error Handling in Coroutines

Errors happen. How you handle them determines application stability.

Try-catch blocks in coroutines work similarly to regular code, but with an important difference: exceptions propagate up the coroutine hierarchy.

coroutineScope {
    try {
        launch {
            throw Exception("Something went wrong")
        }
    } catch (e: Exception) {
        // This won't catch the exception!
        println("Caught: ${e.message}")
    }
}

This code might surprise you. The exception won’t be caught by the try-catch! Why? Because launch creates a new coroutine that runs independently. By default, exceptions propagate to the parent coroutine but aren’t caught by try-catch in the parent’s body.

For isolated failure handling, use supervisorScope:

supervisorScope {
    launch {
        // This failure won't affect siblings
        throw Exception("Failure in first task")
    }

    launch {
        // This will still run
        println("Second task running")
    }
}

For top-level exception management, you can install global exception handlers:

val handler = CoroutineExceptionHandler { _, exception ->
    println("Caught unhandled exception: ${exception.message}")
}

val scope = CoroutineScope(Dispatchers.Default + handler)
scope.launch {
    throw Exception("Boom!")
}

Exception handling strategies differ based on your needs. For critical operations, use fine-grained control. For background tasks, broader handlers might suffice.

Coroutine Scopes

Coroutine scopes define lifetimes for your coroutines. They provide structure and help prevent leaks.

GlobalScope exists for the entire application lifetime. But beware—it comes with risks. Coroutines launched here aren’t bound to any specific component and can outlive their usefulness.

// Generally not recommended
GlobalScope.launch {
    // This coroutine might outlive its usefulness
    delay(10000)
    updateSomething() // Might run when no longer relevant
}

Instead, create custom scopes for specific components:

class MyService {
    private val serviceScope = CoroutineScope(Dispatchers.Default)

    fun performTask() {
        serviceScope.launch {
            // Work tied to service lifecycle
        }
    }

    fun cleanup() {
        serviceScope.cancel() // Cancels all coroutines when service stops
    }
}

In Android development, lifecycle-aware scopes tie coroutines to UI components:

class MyViewModel : ViewModel() {
    // viewModelScope automatically cancelled when ViewModel cleared
    fun loadData() {
        viewModelScope.launch {
            val result = repository.fetchData()
            _uiState.value = result
        }
    }
}

These scopes, part of Android Studio tooling, prevent memory leaks and zombie processes. The Android development with coroutines ecosystem now provides clear patterns for managing coroutine lifetimes in sync with app components.

Practical Patterns with Coroutines

Sequential Execution

Sometimes operations must happen in order. Running tasks one after another is straightforward with coroutines:

suspend fun performSequentialTasks() {
    val result1 = task1() // Waits for completion
    val result2 = task2(result1) // Uses result from first task
    val result3 = task3(result2) // Uses result from second task
}

The code reads top-to-bottom. No callbacks, no nesting. Just clean, sequential code.

For building chains of dependent operations, coroutines excel:

suspend fun processUserData() {
    val userId = getUserId() // Get user ID first
    val userProfile = fetchProfile(userId) // Then fetch profile
    val friendsList = fetchFriends(userId) // And friends list
    val recommendations = generateRecommendations(userProfile, friendsList) // Process both
    saveResults(recommendations) // Save the final results
}

This pattern allows using results from previous coroutines in a clean, readable way. The Kotlin/JVM compiler transforms this into state machines that handle all the complexity behind the scenes.

Parallel Execution

Need speed? Running multiple tasks at once can dramatically improve performance.

suspend fun loadDashboard() = coroutineScope {
    val news = async { newsRepository.getLatestNews() }
    val notifications = async { notificationService.getPending() }
    val messages = async { messageClient.getUnread() }

    // Wait for all to complete and use their results
    displayDashboard(
        news = news.await(),
        notifications = notifications.await(),
        messages = messages.await()
    )
}

For collections of items, gathering results with awaitAll provides a convenient approach:

suspend fun fetchAllUserData(userIds: List<String>) = coroutineScope {
    val deferred = userIds.map { userId ->
        async { userService.fetchUserData(userId) }
    }

    // Wait for all requests to complete
    val allUserData = deferred.awaitAll()
    processUserData(allUserData)
}

But be careful. Balancing parallelism with system resources matters. Launching thousands of CPU-intensive tasks simultaneously could overwhelm the system. For I/O-bound operations like network calls, high parallelism works well. For CPU-bound tasks, consider limiting concurrency to match available cores.

suspend fun processImagesInParallel(images: List<Image>) = coroutineScope {
    // Limit concurrency for CPU-heavy work
    val availableProcessors = Runtime.getRuntime().availableProcessors()

    images.chunked(availableProcessors).forEach { batch ->
        val results = batch.map { image ->
            async { processImage(image) }
        }.awaitAll()

        saveResults(results)
    }
}

This approach provides controlled parallelism, maximizing throughput without overwhelming resources.

Cancellation and Timeouts

Long-running operations need oversight. Cancelling running coroutines is essential for responsive applications.

val job = scope.launch {
    while(isActive) { // Check cancellation status
        // Do work
        delay(100) // Cooperative cancellation point
    }
}

// Later...
job.cancel() // Cancel the coroutine

For operations that shouldn’t take too long, setting timeouts for long-running tasks provides safety:

withTimeout(5000L) {
    try {
        val result = networkClient.fetchBigData()
        processResult(result)
    } catch (e: TimeoutCancellationException) {
        showTimeoutError()
    }
}

Your code should be cancellation-aware. Add isActive checks in long loops and use suspend functions like delay or yield to create cancellation points.

suspend fun processLargeFile(file: File) = coroutineScope {
    val reader = file.bufferedReader()

    try {
        while (isActive && reader.ready()) {
            val line = reader.readLine()
            processLine(line)

            // Periodically yield to allow cancellation
            if (processed % 1000 == 0) yield()
        }
    } finally {
        // Clean up resources even when cancelled
        reader.close()
    }
}

Handling cancellation correctly ensures your app remains responsive and resources get freed properly.

These patterns form the building blocks for robust asynchronous applications. With Kotlin coroutines, you can create efficient, readable code that handles complex concurrent scenarios with relative ease.

The Stack Overflow community and Kotlin Slack community frequently discuss these patterns, providing a wealth of real-world examples. And as you explore further, you’ll discover how these foundations can solve sophisticated problems in your applications.

Testing Coroutines

Unit Testing Approaches

Testing async code used to be painful. Kotlin coroutines change that. The coroutine testing library provides powerful tools for predictable tests.

First, add the dependencies to your Gradle or Maven project:

testImplementation("org.jetbrains.kotlinx:kotlinx-coroutines-test:1.6.4")

TestCoroutineDispatcher lets you control execution precisely:

class UserRepositoryTest {
    private val testDispatcher = TestCoroutineDispatcher()
    private lateinit var repository: UserRepository

    @Before
    fun setup() {
        Dispatchers.setMain(testDispatcher) // Replace main dispatcher
        repository = UserRepository(testDispatcher)
    }

    @After
    fun tearDown() {
        Dispatchers.resetMain() // Reset main dispatcher
        testDispatcher.cleanupTestCoroutines()
    }

    @Test
    fun `fetch user data returns correct user`() = testDispatcher.runBlockingTest {
        // Test implementation
    }
}

This approach replaces the real dispatchers with test versions. Your tests gain complete control over coroutine execution.

Controlling virtual time in tests is another powerful feature. Need to test a delay without waiting?

@Test
fun `retry logic waits between attempts`() = testDispatcher.runBlockingTest {
    repository.fetchWithRetry()

    // Advance time but don't actually wait
    testDispatcher.advanceTimeBy(1000)

    // Verify second attempt happened
    verify(exactly = 2) { apiService.fetchData() }
}

Testing suspension functions requires special handling. Use runTest for concise syntax:

@Test
fun `process data transforms correctly`() = runTest {
    val result = userProcessor.processUserData(testData)
    assertEquals(expectedOutput, result)
}

The Kotlin Standard Library testing extensions make your tests clean and readable. No more flaky async tests.

Integration Testing with Coroutines

Testing coroutines in larger components requires different strategies. Integration tests often involve multiple systems working together.

For components using coroutines, consider these approaches:

1. Use real dispatchers but shorter timeouts
2. Replace selected dependencies with test doubles
3. Configure test environments for deterministic behavior

When handling real dispatchers in tests, be mindful of concurrency:

@Test
fun `end-to-end flow processes all data`() = runBlocking {
    // Use real dispatchers but with defined timeout
    withTimeout(5000) {
        val result = completeWorkflow.processFile(testFile)

        // Verify results
        assertTrue(result.isSuccess)
        assertEquals(expectedRecordCount, result.recordsProcessed)
    }
}

Avoid these common testing pitfalls:

• Forgetting to clean up test dispatchers
• Using hardcoded delays in tests
• Not handling exceptions in test coroutines
• Testing coroutines without proper synchronization

The Kotlin Coroutines API testing tools have matured significantly since their introduction in Kotlin 1.3. Regular updates from JetBrains continue to improve the developer experience.

Coroutines vs. Other Solutions

Comparing with RxJava

Kotlin Coroutines and RxJava both solve async programming problems, but with different approaches.

The differences in approach and philosophy are substantial:

RxJava uses a reactive, stream-based model. Everything is an Observable or a stream of events. It emphasizes immutability and data transformation pipelines.

Coroutines use a sequential, imperative model with suspension points. They look and feel like normal code with some pause points.

For code complexity comparison, consider a simple task: fetch user data, process it, and show results.

RxJava:

userRepository.getUser(userId)
    .subscribeOn(Schedulers.io())
    .observeOn(AndroidSchedulers.mainThread())
    .map { user -> processUserData(user) }
    .subscribe(
        { result -> showResults(result) },
        { error -> showError(error) }
    )

Coroutines:

viewModelScope.launch {
    try {
        val user = withContext(Dispatchers.IO) {
            userRepository.getUser(userId)
        }
        val result = processUserData(user)
        showResults(result)
    } catch (e: Exception) {
        showError(e)
    }
}

The coroutine version reads like synchronous code. The RxJava version introduces new concepts and operators.

When to choose each option depends on your needs:

Choose RxJava when:

• You need complex stream transformations
• You’re working with multiple events over time
• You need fine-grained control over subscription lifecycle
• Your team already has RxJava expertise

Choose Coroutines when:

• You want simpler, more readable code
• You have mostly simple async operations
• You prefer imperative programming style
• You want better integration with Kotlin features

Many teams transition from RxJava to Kotlin Flow, which combines reactive programming with coroutine simplicity.

Comparing with Java’s CompletableFuture

Java’s CompletableFuture was a step forward for async Java code. How do coroutines compare?

Syntax and readability differences are immediately apparent:

CompletableFuture:

CompletableFuture.supplyAsync(() -> fetchUserData(userId))
    .thenApply(user -> processUser(user))
    .thenAccept(result -> showResult(result))
    .exceptionally(error -> {
        showError(error);
        return null;
    });

Coroutines:

launch {
    try {
        val user = fetchUserData(userId)
        val processed = processUser(user)
        showResult(processed)
    } catch (error: Exception) {
        showError(error)
    }
}

The coroutine version is shorter, more readable, and handles errors naturally with try-catch.

Performance considerations slightly favor coroutines. They’re more lightweight than CompletableFutures, especially when creating thousands of concurrent operations. The structured concurrency model also helps prevent leaks.

For interoperability between the two, Kotlin provides utilities:

// Convert CompletableFuture to Deferred
val deferred = completableFuture.asDeferred()

// Convert suspend function to CompletableFuture
val future = suspendFunction.asCompletableFuture()

This makes gradual migration possible, letting you integrate coroutines into existing Java codebases.

The Java Virtual Machine ecosystem continues to evolve. Newer Java versions introduce features like virtual threads, which share some conceptual similarities with coroutines but implement them differently.

Overall, coroutines represent a more expressive and Kotlin-native approach to async programming. They integrate deeply with the language, while CompletableFuture feels more like a library solution.

For teams working on the JVM, especially with Kotlin Multiplatform projects, coroutines provide a consistent async model across platforms. This consistency is valuable for codebases targeting multiple platforms.

The active Kotlin Slack community and abundant resources on GitHub make learning coroutines easier than ever. As adoption grows, the patterns and best practices continue to mature, cementing coroutines as the preferred async solution in the Kotlin ecosystem.

FAQ on Kotlin Coroutines

How do coroutines differ from regular threads?

Coroutines are much lighter than threads in the Java Virtual Machine. Where threads consume significant memory (about 1MB each), coroutines use only a few dozen bytes. You can launch thousands of coroutines on a single thread. They also feature structured concurrency for better resource management and simplified cancellation.

What problem do coroutines solve?

Coroutines solve the callback hell problem in asynchronous programming. They eliminate deeply nested callbacks when performing sequential operations like network calls or database access. This makes code more readable, maintainable, and less error-prone while keeping your app responsive during I/O-bound operations.

What’s the difference between launch and async?

Launch is a coroutine builder for fire-and-forget operations that don’t return results. Async returns a Deferred value (similar to a promise) that you can await later. Use launch for operations where you don’t need the result, and async/await when you need to get a value back.

What are suspend functions?

Functions marked with the suspend modifier can pause execution without blocking threads. They’re the building blocks of coroutines. Suspend functions can only be called from other suspend functions or within a coroutine. The Kotlin/JVM compiler transforms them into state machines with callback-based code behind the scenes.

What are coroutine dispatchers?

Dispatchers in Kotlin determine which thread pool coroutines run on. Dispatchers.Main runs on the UI thread (for Android development), Dispatchers.IO optimizes I/O operations, and Dispatchers.Default handles CPU-intensive work. Use withContext to switch between them while keeping your code sequential.

How do you handle errors in coroutines?

Coroutines use try-catch blocks for error handling, similar to regular code. For broader handling, use supervisorScope to prevent child failures from canceling siblings, or implement a CoroutineExceptionHandler. Remember that exceptions propagate up the coroutine hierarchy by default.

What is structured concurrency?

Structured concurrency ensures that when a coroutine starts another coroutine, the parent doesn’t complete until all children finish. This creates a clear hierarchy, prevents leaks, and enables automatic cancellation. It’s a paradigm shift from traditional threading models that makes concurrent code more reliable.

How do coroutines compare to RxJava?

Coroutines are simpler for basic asynchronous tasks, while RxJava excels at complex reactive streams. Coroutines use imperative style with suspension points; RxJava uses functional reactive programming. Coroutines often require less boilerplate and have a gentler learning curve than the reactive programming approach.

How do you test code that uses coroutines?

Use the kotlinx-coroutines-test library with TestCoroutineDispatcher to control execution precisely. This lets you advance virtual time without delays and test coroutines deterministically. For integration tests, consider using runBlocking with timeouts. The testing tools have improved significantly since Kotlin 1.3.

Conclusion

Understanding what are Kotlin coroutines unlocks a powerful approach to writing clean, efficient concurrent code. These lightweight threads transform how developers handle asynchronous operations across mobile app development and server-side applications.

Coroutines offer distinct advantages over traditional approaches:

• Non-blocking code that keeps your UI responsive
• Structured concurrency for safer resource management
• Clear syntax without callback hell
• Efficient background processing with minimal overhead
• Simplified error handling with familiar try-catch blocks

As the Kotlin programming language continues to grow in popularity, mastering coroutines becomes increasingly valuable. The ecosystem around them expands with each KotlinConf, bringing improved testing tools, integration with libraries like Room database and Retrofit, and better support in Android Studio.

Whether you’re managing network calls, processing data, or handling complex UI updates and animations, coroutines provide an elegant solution worth adopting. Start implementing them in your projects today—your future self will thank you for cleaner, more maintainable code.

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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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