How does stream materialization work in Akka Streams? What are its key components, and how do they affect performance and resource utilization?
Stream materialization in Akka Streams involves converting a blueprint (a graph) into a running stream. It occurs when the run() method is called on a RunnableGraph, which instantiates and connects all stages of the graph.
Key components include :
1. Materializer : Responsible for allocating resources and executing the stream. The default ActorMaterializer uses actors to run each stage.
2. Attributes : Configure aspects like dispatcher or buffer sizes, affecting performance and resource utilization.
3. Fusion : Combines multiple stages into one, reducing overhead and improving throughput.
4. Async boundaries : Introduce parallelism by allowing different parts of the graph to execute concurrently, potentially increasing resource usage but also improving performance.
Performance and resource utilization are affected by factors such as fusion, async boundaries, and attributes. Proper configuration can lead to optimal trade-offs between throughput, latency, and memory consumption.
