How do you handle backpressure in a stateful Akka Stream application? What are the trade-offs involved with different strategies?
In a stateful Akka Stream application, backpressure is handled using built-in mechanisms like asynchronous boundaries and buffer configurations. Asynchronous boundaries decouple stages, allowing them to run concurrently, while buffers store elements temporarily between stages.
There are several strategies for handling backpressure :
1. Increase buffer size : Larger buffers can handle more data but consume more memory.
2. Adjust overflow strategy : Strategies include dropping elements, failing the stream, or applying backpressure upstream. Each has trade-offs in terms of data loss, error handling, and performance.
3. Throttle downstream processing : Slowing down consumers allows producers to catch up, but may impact overall throughput.
4. Use partitioning and merging : Splitting streams into smaller parallel flows can improve performance, but requires careful management of resources and potential synchronization issues.
5. Implement custom stages : Custom stages allow fine-grained control over backpressure behavior, but require deeper understanding of Akka Streams internals.
Trade-offs involve balancing resource usage (memory, CPU), latency, throughput, and complexity. The optimal solution depends on specific use cases and requirements.