Скачать или смотреть Understanding the onBackpressureError and Its Impact in Spring WebFlux

Explore the intricacies of `onBackpressureError` in Spring WebFlux, how it triggers errors, and learn best practices for handling backpressure.
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Understanding the onBackpressureError in Spring WebFlux

When working with Spring WebFlux, developers often encounter nuanced behaviors related to backpressure handling. A common problem is receiving errors after a certain number of emitted elements when utilizing onBackpressureError. For instance, you might find that after processing 255 elements in your stream, an Exceptions$OverflowException is thrown. This raises questions: What triggers this error, and how can we manage it effectively?

This guide will explore the underlying issues leading to this error and present possible solutions for better handling backpressure in your WebFlux applications.

Why Does the Error Occur?

The error you experience after the 255th emitted element stems from a specific combination of operators in your reactive chain. Specifically, the interaction between .onBackpressureError() and .publishOn(Schedulers.parallel()) is critical.

Understanding the PublishOn Operator

The publishOn operator is designed to manage the flow of data between upstream and downstream components by shifting the execution context. By default, it uses an internal queue to optimize this transition, which can become a bottleneck if the producer (data source) emits items faster than the consumer (data receiver) can process them.

Here's how the default implementation works:

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In this snippet, the Queues.SMALL_BUFFER_SIZE is set to 256, indicating that the queue can only hold up to 256 elements at a time. If the consumer is slower than the producer—resulting in a full queue—the backpressure strategy defined by onBackpressureError() comes into play, which in this case results in overflow errors.

The Role of onBackpressureError()

When you set .onBackpressureError(), you are essentially instructing the system to throw an error when this internal queue overflows. To mitigate this, you have several options:

Increase Queue Size: If you change onBackpressureError() to .onBackpressureBuffer(512), for example, you extend the internal queue's capacity, allowing your application to process more items before encountering an overflow.

Understanding the Requested Parameter

When dealing with backpressure in Project Reactor, it's essential to grasp how the requested parameter behaves. By default, when you subscribe to a Flux, the consumer requests an unbounded number of elements. However, operations like publishOn modify this behavior.

Analyzing the Request Flow

You can monitor the requested amount by adding logging to your reactive chain:

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This logging will produce output similar to:

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Here, you can see that the requested amount is altered once you introduce the publishOn operator, thus limiting how many elements the downstream can handle concurrently.

Best Practices for Managing Backpressure

Use Appropriate Backpressure Strategies: Assess your application needs and select suitable backpressure strategies such as onBackpressureBuffer, onBackpressureDrop, or onBackpressureError.

Monitor Consumer Processing Speed: Adjust processing or consuming speeds based on how quickly your downstream consumers can handle emitted elements.

Adjust Queue Sizes: When necessary, tweak the buffer size settings of your queues in operators like publishOn to better suit your specific use case.

Implement Detailed Logging: Enhance your subscribers with logging for better traceability when managing data flows, especially concerning the requested size and the backpressure state.

Conclusion

The onBackpressureError in Spring WebFlux is a powerful feature, but