1. Introduction
In Kotlin, Flow is essential for managing asynchronous data streams, especially within reactive programming contexts like Android development.
Moreover, properly testing flows is essential to ensure that data is emitted as expected, transformations are applied correctly, exceptions are handled gracefully, and behaviors like cancellation and state retention function properly.
We need to be able to test flows in order to guarantee the consistency and correctness of our asynchronous data handling. In this tutorial, we’ll investigate various strategies for testing flows with simple examples and unit tests.
2. Using runTest() for Efficient Coroutine Testing
We’ll need to ensure we’ve added the kotlinx-coroutines-test dependency to our project to be able to use these advanced testing utilities:
<dependency>
<groupId>org.jetbrains.kotlinx</groupId>
<artifactId>kotlinx-coroutines-test</artifactId>
<version>1.7.3</version>
<scope>test</scope>
</dependency>
Kotlin’s runTest() function optimizes coroutine tests by automatically skipping delays. This allows tests to run quickly without compromising accuracy. Additionally, runTest() enhances error handling, ensuring coroutine behaviors like exceptions and cancellations are realistically represented in tests. Throughout this tutorial, we’ll rely on runTest() to streamline flow testing.
3. Verifying Flow Emissions
First, the most straightforward testing strategy is verifying that a flow emits the correct sequence of values. This step ensures the flow behaves as intended.
For example, let’s consider a flow that emits three sequential values:
fun simpleFlow(): Flow<Int> = flow {
emit(1)
emit(2)
emit(3)
}
To test this flow, let’s collect the emitted values into a list and assert that the result matches the expected sequence:
@Test
fun `simpleFlow should emit 1 2 3`() = runTest {
val flow = simpleFlow().toList()
assertEquals(listOf(1, 2, 3), flow)
}
By collecting all values and asserting equality, this test verifies that the flow emits the correct sequence of values. This strategy effectively tests flows that handle predictable, finite data streams.
3.1. Testing Transformations in Flow
Additionally, flows can be used with transformation operators like map() or filter() to manipulate the emitted data. Testing transformations ensures that our operators behave correctly, transforming values as expected before reaching the collector.
Let’s consider a flow that transforms its emitted values by doubling them:
fun transformedFlow(): Flow<Int> = flow {
emit(1)
emit(2)
emit(3)
}.map { it * 2 }
This flow multiplies each emitted integer by two. Now, let’s ensure that the transformation is applied correctly:
@Test
fun `transformedFlow should multiply values by 2`() = runTest {
val result = transformedFlow().toList()
assertEquals(listOf(2, 4, 6), result)
}
Here, the test collects the transformed values and verifies that our transformation was correctly applied. This strategy allows us to verify any business logic that transforms our flows.
4. Handling Exceptions in Flow
It’s important to note that flows can encounter errors, and handling these exceptions is crucial for building robust applications. Consequently, testing how a flow behaves when encountering exceptions allows us to ensure proper error handling.
Let’s consider a flow that throws an exception after emitting a value:
fun errorFlow(): Flow<Int> = flow {
emit(1)
emit(2)
throw Exception("Test Exception")
}.catch{e ->
emit(-1)
}
To test this scenario, we want to verify that the flow emits the correct values and recovers from the expected exception:
@Test
fun `errorFlow should emit values and recover from exception`() = runTest {
val emittedValues = errorFlow().toList()
assertEquals(listOf(1, 2, -1), emittedValues)
}
In this test, we ensure that the flow recovers from the exception and emits the fallback value -1. This approach verifies the flow’s behavior when encountering exceptions and illustrates how recovery mechanisms like the catch() operator can prevent errors from interrupting downstream collectors.
5. Testing Flow Cancellation
Next, let’s investigate different cancelation strategies.
5.1. Implicit Cancellation Checking
In Kotlin, coroutine cancellation is cooperative, meaning that coroutines must regularly check for cancellation to stop execution gracefully.
Many built-in suspending functions like delay() or withTimeout() implicitly check for cancellation by throwing a CancellationException if the coroutine has been canceled. This ensures that operations like delaying or timeout-based tasks do not continue unnecessarily when a coroutine is no longer active.
For example, let’s consider a flow containing delays:
fun implicitCancellationFlow(): Flow<Int> = flow {
emit(1)
delay(500)
emit(2)
delay(500)
emit(3)
}
In this flow, the delay() method will throw a CancellationException if the coroutine running this flow gets canceled.
Finally, let’s test this behavior:
@Test
fun `implicitCancellationFlow stops on cancellation`() = runTest {
val emittedValues = mutableListOf<Int>()
val job = launch {
implicitCancellationFlow().collect { emittedValues.add(it) }
}
advanceTimeBy(600)
job.cancelAndJoin()
assertEquals(listOf(1,2), emittedValues)
}
In this unit test, we launch a coroutine that collects values from implicitCancellationFlow(). We advance the virtual time by 600 milliseconds, allowing enough time for the first delays to complete before canceling the coroutine. The assertion checks that the list only contains two elements because we cancel the coroutine before the last emission.
5.2. Making Code Uncancellable
Sometimes, we may want certain parts of our code to be uncancellable, especially during critical resource cleanup operations. For instance, if we are closing a file or releasing a network connection, we must ensure that these actions are not interrupted.
We can achieve this by wrapping the cleanup code in a withContext(NonCancellable) block:
fun uncancellableFlow(): Flow<Int> = flow {
try {
emit(1)
delay(500)
emit(2)
} finally {
withContext(NonCancellable) {
println("Releasing resources")
}
}
}
In this scenario, even if we cancel the flow, the cleanup code will still execute:
@Test
fun `uncancellableFlow ensures cleanup occurs`() = runTest {
val emittedValues = mutableListOf<Int>()
val job = launch {
uncancellableFlow().collect { emittedValues.add(it) }
}
advanceTimeBy(400)
job.cancelAndJoin()
assertEquals(listOf(1), emittedValues)
}
This test confirms proper resource cleanup even during cancellation. In this case, even though we cancel the coroutine, the cleanup action prints the “Releasing Resource” message to signal the release of necessary resources.
6. Conclusion
In this article, we’ve explored key strategies for effectively testing Kotlin Flows, covering everything from verifying emissions to handling transformations, exceptions, and cancellations. With these techniques, we can confidently ensure that our flows behave as expected in a variety of scenarios.
Furthermore, we also leveraged the runTest() helper, which simplifies coroutine testing by skipping delays and enhancing error management, allowing tests to execute quickly without compromising accuracy.