Fix: CancelAndJoin Doesn't Wait In Kotlin RPC

Have you ever encountered a situation where you're using Kotlin's cancelAndJoin with a suspend RPC function, and it seems like the cancellation on the server-side isn't completing as expected? You're not alone! This article dives deep into a peculiar bug in kotlinx-rpc where canceling a running suspend RPC call from a client happens immediately, without ensuring the server has finished its cancellation process. Let's explore the issue, understand the root cause, and discuss how to work around it.

The cancelAndJoin Conundrum in Kotlin RPC

So, you're cruising along, building a distributed system with Kotlin, leveraging the power of kotlinx-rpc. You have a suspend function call, and you decide to cancel it using cancelAndJoin. Seems straightforward, right? Well, not quite. The problem arises because the cancellation on the client-side doesn't wait for the server to complete its cancellation process. This can lead to unexpected behavior and potentially even resource leaks.

Actual Behavior: Speedy Cancellation, Lingering Server

When you cancel an RPC request, the job on the client-side completes almost instantly. This gives the illusion that everything is cleaned up. However, the server might still be chugging along, trying to finish its work. The client is off to the races, while the server is still tying its shoelaces. This discrepancy can be problematic, especially if the server-side operation involves critical cleanup or resource management.

Expected Behavior: Patient Cancellation, Clean Exit

Ideally, when you cancel an RPC request, the job should patiently wait until the server has fully completed its cancellation process. This is how standard Kotlin coroutines behave. If you were to simulate this scenario without RPC, by directly providing a SampleServiceImpl implementation to the client, the cancellation would wait as expected. It's like waiting for your friend to get their coat before leaving the party, rather than dashing out the door. This consistent behavior is crucial for maintaining the integrity and predictability of your application.

Demonstrating the Issue: A Code Example

To illustrate this bug, let's look at a simplified example. We have a SampleService with a doUncancellableWork function. This function simulates a long-running operation within a NonCancellable context on the server.

Server-Side Code

@Rpc
interface SampleService {
 suspend fun doUncancellableWork()
}

class SampleServiceImpl : SampleService {
 override suspend fun doUncancellableWork() {
 val startTime = System.currentTimeMillis()
 try {
 withContext(NonCancellable) {
 delay(5_000)
 }
 } finally {
 val finishTime = System.currentTimeMillis()
 println("Work ran for ${(finishTime - startTime).milliseconds} before finishing")
 }
 }
}

In this code snippet, the doUncancellableWork function deliberately delays execution for 5 seconds within a NonCancellable context. The finally block ensures that we log the actual execution time, even if the coroutine is cancelled.

Client-Side Code

Now, let's examine the client-side code that triggers the cancellation:

suspend fun main(): Unit = coroutineScope {
 val job = async {
 sampleRpc.doUncancellableWork()
 }
 delay(1000) // let coroutine to connect to server
 val duration = measureTime {
 job.cancelAndJoin()
 }
 println("Cancelling job took $duration")
}

Here, we launch the doUncancellableWork function in an async coroutine. After a 1-second delay, we cancel the job using cancelAndJoin and measure the time it takes. The crucial part is the discrepancy in timing between the client and the server.

The Discrepancy in Execution Times

When you run this example, you'll notice a significant difference in the execution times reported by the client and the server. The client might report that canceling the job took only a few milliseconds, while the server indicates that the work ran for the full 5 seconds before being cancelled.

Sample Outputs:

Server:

Work ran for 5.003s before being cancelled

Client:

Cancelling job took 2.398542ms

This clearly demonstrates that the cancelAndJoin call on the client doesn't wait for the server to complete its cancellation process. It's like hanging up the phone before the other person has finished speaking.

Reproducing the Bug: Step-by-Step

To reproduce this behavior, follow these steps:

1. Kotlin Version: Use Kotlin version 2.2.0.
2. Gradle Version: Use Gradle version 8.14.3.
3. Operating System: This issue is reproducible on Mac OS and JVM.
4. Launch the Server: Run the server using ./gradlew :server:run.
5. Launch the Client: Run the client using ./gradlew :client:run.
6. Observe the Output: Examine the execution times in the console. You'll see that the client reports a very short cancellation time, while the server indicates a much longer execution time.

You can find a complete sample project that demonstrates this issue on GitHub at https://github.com/maxdroz/KrpcBugSample/tree/cancellationIsNotBeingAwaited.

Diving Deeper: Why Does This Happen?

To truly understand this issue, we need to delve into the internals of kotlinx-rpc and how it handles cancellation. The key takeaway is that the client-side cancellation mechanism isn't fully synchronized with the server-side execution. When the client calls cancelAndJoin, it sends a cancellation signal to the server. However, it doesn't wait for the server to acknowledge and complete the cancellation.

The Role of NonCancellable

The NonCancellable context plays a crucial role in this scenario. It prevents the coroutine from being cancelled immediately. Think of it as a shield against cancellation. This is often used for critical cleanup operations that must complete, even if the coroutine is cancelled. In our example, the delay within the NonCancellable context ensures that the server continues to run for the full 5 seconds, despite the cancellation signal from the client.

The Missing Link: Server Acknowledgment

The core of the problem is the lack of acknowledgment from the server. The client assumes that the server will eventually cancel the coroutine, but it doesn't verify this. It's like sending a letter and assuming it's been delivered without confirmation. This assumption can lead to the client completing the cancelAndJoin call prematurely, leaving the server in a state of limbo.

Potential Solutions and Workarounds

So, what can you do to address this issue? Unfortunately, there's no simple, built-in fix in the current version of kotlinx-rpc. However, there are several potential workarounds that you can implement.

1. Implement a Custom Cancellation Mechanism

One approach is to create your own cancellation mechanism that includes server acknowledgment. This involves adding a feedback loop to the cancellation process. For example, the server could send a message back to the client once the cancellation is complete. The client could then wait for this message before completing the cancelAndJoin call.

2. Use a Timeout

Another option is to introduce a timeout on the client-side. This is a pragmatic approach, but it has its limitations. You can set a maximum time to wait for the server to cancel the coroutine. If the timeout expires, you can assume that the cancellation has failed and take appropriate action. However, this approach doesn't guarantee that the server has actually completed the cancellation, and it might lead to false positives if the server is simply taking a long time to cancel.

3. Redesign the API

In some cases, the best solution might be to redesign the API to avoid long-running operations that require cancellation. This is a more fundamental approach, but it can lead to a more robust and predictable system. For example, you could break down the operation into smaller, more manageable chunks that can be completed quickly.

Conclusion: Navigating the cancelAndJoin Caveat

The cancelAndJoin behavior in kotlinx-rpc can be a tricky issue to navigate. It's crucial to understand the discrepancy between client-side and server-side cancellation to avoid unexpected behavior. While there's no built-in fix, the workarounds discussed in this article can help you mitigate the problem. Remember to choose the solution that best fits your specific needs and constraints.

By understanding the nuances of cancelAndJoin and employing appropriate strategies, you can ensure that your Kotlin RPC applications are robust and reliable. Keep experimenting, keep learning, and keep building amazing things!

For more information about Kotlin Coroutines and cancellation, you can visit the official Kotlin documentation: https://kotlinlang.org/docs/cancellation-and-timeouts.html