Taming Asynchronous Beasts: Debugging and Performance Tuning in a Coroutine World | Marcin Moskała - AI Summary & Article | ClarifyTube

This article aims to provide a comprehensive guide for debugging and optimizing the performance of applications utilizing Kotlin coroutines. It covers essential techniques for observability, effective debugging strategies, and performance measurement and optimization, addressing both Android and backend development. The content begins with the foundational importance of logging, emphasizing its role in understanding system behavior and tracing exceptions, especially when traditional stack traces in coroutines fall short. It then delves into practical debugging tools like breakpoints and the improved variable observation in newer Kotlin versions. The article also provides insights into performance measurement using tools like Micrometer for backend systems and various testing strategies for Android. Finally, it addresses common coroutine-related performance pitfalls, such as improper dispatcher usage, unchecked external service calls, and the critical role of structured concurrency and cancellation, offering practical solutions and best practices to ensure robust and efficient applications.

Observability for Coroutines

Debugging is a multi-step process, with the actual use of a debugger being the final and often least crucial part. The most important tool for effective debugging is logging. Well-implemented logs provide a detailed narrative of system operations, allowing developers to understand the sequence of events leading up to an exception or problem. For backend systems, logs are typically sent to aggregators like Kibana, which enable efficient filtering, searching, and visualization of log data to understand the context of issues.

For Android applications, while similar aggregation services exist (e.g., Datadog, Sentry), constant log transmission is often avoided due to its impact on battery life and network usage. Instead, services like Firebase and Sentry's breadcrumbs feature are more commonly utilized. Breadcrumbs capture a sequence of user interactions and application events leading up to a crash, providing crucial context that standard stack traces in coroutines often lack. This log-based context is particularly vital given that coroutines' stack traces can be nearly useless, often only showing the last function call due to their internal construction.

To improve logging in systems with intensive Kotlin coroutine usage, several hints are provided:

Logging the start and finish of coroutines can be beneficial, especially when facing issues with coroutine behavior. However, this should be done judiciously to avoid excessive logging overhead.
Naming coroutines, especially with parameterized and meaningful names, can provide better context in logs, aiding in understanding their execution and interaction within complex systems.
Adding a Coroutine ID to logs is highly recommended. This ID, available by default in debug mode via a system property, is lightweight and can be enabled in production. It uniquely identifies each coroutine, making it easier to distinguish logs from concurrently running coroutines and trace the execution path of a specific coroutine within log aggregators like Kibana.
Developers can pack any relevant data into the coroutine context, such as request details or user information, which then propagates to all child coroutines. This custom context can be easily read and appended to log messages through a wrapper function over the logging library, ensuring consistent and enriched log data.
For backend systems, while MDC (Mapped Diagnostic Context) values are traditionally used for thread-local context, they are unsuitable for coroutines due to their ability to switch threads. The MDC context for coroutines can be used, but it requires careful understanding and proper re-setting upon modification.

The CoroutineExceptionHandler can be used to customize how exceptions are handled. On the backend, it allows for attaching extra information to exceptions, such as contextual data from the coroutine scope. On Android, its usage is more controversial because, without it, a default exception handler breaks the application, forcing developers to address every exception. Many applications prefer to use a custom handler to silently send crash reports and prevent a full application crash, improving user experience. A particularly useful practice is to include ViewModel information in the exception report if a coroutine was started from a ViewModel, providing more relevant context than a limited stack trace.

Debugging Coroutines

Debugging a problem involves three structured steps: observation and analysis, reproduction, and investigation. Logs and analytical systems facilitate the initial observation and analysis phase. Once the problem is understood, reproduction is key, ideally through automated tests (unit or end-to-end) that replicate the exact conditions leading to the error. Investigation, the final step, involves using debugging tools like breakpoints.

When using an IDE debugger, breakpoints are fundamental. New IDE versions have significantly improved debugging with multiple coroutines. Previously, all coroutines would hit the same breakpoint, making it difficult to trace a specific execution. Now, "step over" and "run into cursor" actions guarantee execution on the same coroutine, streamlining the debugging process.

"However, right now ‘step over’ and ‘run into cursor’ both guaranteed to run on the same coroutine, which is which is great. They are just skipping other coroutines' execution and guarantee that the same coroutine will be there."

A significant enhancement in Kotlin 2.2 is that variables are no longer optimized out in debug mode, providing a comprehensive view of variable states during debugging. For older Kotlin versions, the "-Xdebug" flag can be used but is dangerous for production due to potential memory leaks.

The "Coroutines" tab in the debugger is invaluable for understanding the state of concurrently running coroutines. It displays all active jobs and their statuses. A common problem, such as coroutines being "created" but never "started/resumed," can be identified here, indicating issues like a lack of synchronization. Solutions often involve using `CoroutineScope.awaitChildren` or explicitly calling `Job.join` to ensure coroutines complete before proceeding. The debugger can also show parallel stacks for coroutines, providing a more coherent virtual stack trace than the raw stack trace.

For highly complex systems with numerous coroutines, the `kotlinx-coroutines-debug` agent can be used (only in debug mode, not production or Android runtime). This agent allows programmatic inspection and iteration over the coroutine tree, offering deep insights into their relationships and execution flow. However, it can incur waiting periods and is primarily useful for local testing or unit tests.

Measuring and Optimizing Performance

Performance measurement is crucial for identifying bottlenecks and ensuring efficient application behavior. For backend systems, tools like Micrometer, integrated with monitoring platforms like Grafana, are standard. Key metrics to track include request latency, error rates, CPU usage, thread pool usage, disk and network I/O, cache hit/miss rates, and execution times of specific functions. Custom business metrics are also vital, sometimes even triggering automatic rollbacks if performance declines post-deployment.

For Kotlin Flows, measuring buffer queue lengths using a Gauge (incrementing when an element enters, decrementing when it leaves) helps detect potential back pressure issues where the producer waits for the consumer due to a full buffer. Additionally, measuring the number of Flow observers, especially when using `shareIn`, can reveal if the sharing mechanism is actually beneficial or if there's only a single observer.

"If our buffer is often full or and especially if our buffer is nearly always non-empty or not having a small number of elements, then you probably have a problem, you should investigate why is it so."

On Android, while detailed real-time monitoring like backend systems is less common due to resource constraints, basic tools like Google Android Vitals, Firebase Performance Monitoring, and JetStats (for missed frames) offer sufficient insights into user experience. For more in-depth analysis, many companies implement performance tests on real devices, often using "phone farms" or external services like SmartDust, which provide access to a global network of diverse devices.

Stress testing is another critical aspect. Simple stress tests, similar to unit tests but with large datasets, can be used for local profiling and analysis. For backend services, comprehensive end-to-end performance tests involving local service and database instances provide the broadest picture of application performance. The IntelliJ Profiler offers valuable tools like flame graphs to visualize CPU time and memory allocations, helping identify performance hotspots. Analyzing these graphs can pinpoint functions consuming the most resources, guiding optimization efforts. It's crucial to always measure performance both before and after any optimization, as perceived improvements might not translate into actual gains or could even degrade performance.

Optimizing Coroutines

Understanding and managing Kotlin coroutines and threads is key to optimization. Coroutines are lightweight abstractions that run on top of actual threads managed by dispatchers. When a coroutine needs to execute, a dispatcher assigns it an available thread. If no threads are available, the coroutine might wait in a queue, which can indicate a performance bottleneck. While internal queue sizes aren't exposed via public API, some dispatchers created from executors in backend systems expose this data in their `toString()` output.

If dispatchers like `Dispatchers.Default` or `Dispatchers.Main` exhibit long queues, it often indicates blocking operations being performed on them. Such operations (e.g., disk I/O) should be wrapped with `Dispatchers.IO`. However, this practice can be misused, particularly in Android, where suspending functions (which are non-blocking) are sometimes unnecessarily wrapped with `Dispatchers.IO`, adding unnecessary overhead. The BlockHound library with `kotlinx-coroutines-debug` integration can help by throwing exceptions when a dispatcher that shouldn't be blocked is indeed blocked, though it should only be used in debug mode due to potential false positives from external libraries.

For persistent issues with large queues on `Dispatchers.IO`, creating independent dispatchers with limited parallelism (`Dispatchers.IO.limitedParallelism`) can prevent over-utilization while protecting different usages. These custom dispatchers should be injected for testability.

Overloading external services by sending too many requests is a common problem, leading to poor responses or even DoS (Denial of Service) attack prevention mechanisms from the service providers. To prevent this, rate limiters are essential. A simple rate limiter can be implemented with a Semaphore, limiting concurrent requests. For more sophisticated control, libraries like Resilience4j offer configurable rate limiting, such as requests per minute.

Cancellation and Structured Concurrency

Cancellation is vital for resource management, particularly on Android and in backend services. When a request or connection is terminated, all associated coroutine processes should be canceled to release resources like network ports and threads. This relies on structured concurrency, where cancellation propagates through suspending functions and to all child coroutines started on the same scope.

"When the request gets canceled all the processes started that should be canceled. We should leave our leave our, you know, stop our requests to release ports to release threads that might be used for other other purposes and calculations."

Coroutines started on an external scope, however, will only be canceled when that specific external scope is canceled, allowing for intentionally independent background processes. Conversely, starting a coroutine with a separate job or supervisor job that is not tied to the current coroutine scope is considered an anti-pattern if the intention is for it to be cancelled with its parent. For truly independent processes, they should be launched on a distinct, clearly defined scope.

Additional general optimization hints include:

Extracting variables to avoid recalculation, potentially using `lazy` initialization for single-time, on-demand calculations.
Using appropriate data collections (e.g., `Set` for existence checks, `Map` for key-value associations) for significant performance gains.
Employing the JVM inline value classes instead of regular data classes for wrapper-like objects to reduce memory overhead.
Utilizing inline functions, especially for those with functional parameters, to minimize the overhead of lambda creations.

Takeaways

Logging as a Primary Debugging Tool: Logging is the most crucial tool for debugging, offering a narrative of system behavior and context for exceptions, especially when traditional stack traces for coroutines are insufficient.
Enhanced Debugging with Kotlin 2.2: Kotlin 2.2 significantly improves debugging by preventing variable optimization in debug mode, providing a comprehensive view of variable states. IDEs also offer better single-coroutine tracing with features like "step over" and "run into cursor."
Coroutine ID and Context for Observability: Naming coroutines and using Coroutine IDs (available in debug mode and lightweight enough for production) helps differentiate logs from concurrent coroutines. Embedding custom data into the coroutine context through wrapper functions provides rich, propagated contextual information for logging.
Strategic Performance Measurement: For backend, Micrometer and Grafana track critical metrics like latency, CPU usage, and queue lengths. For Android, while full logging is costly, tools like Android Vitals and performance tests on real device farms are effective. Always measure performance before and after optimizations.
Optimizing Dispatcher Usage: Blocking operations should use `Dispatchers.IO`. Misusing `Dispatchers.IO` for non-blocking suspending functions adds unnecessary overhead. BlockHound can help detect improper blocking in debug mode, and `Dispatchers.IO.limitedParallelism` can manage thread limits for specific operations.
Rate Limiting and Structured Concurrency: Implement rate limiters (e.g., using `Semaphore` or Resilience4j) to prevent overloading external services. Ensure structured concurrency for proper cancellation, which propagates through child coroutines and prevents resource leaks. Avoid anti-patterns like launching with supervisor jobs if you expect cancellation with the parent.

Taming Asynchronous Beasts: Debugging and Performance Tuning in a Coroutine World | Marcin Moskała