Reactive编程新手求助：Kafka文件事件消息发送及框架疑问

Hey there! Let's work through your questions step by step—reactive programming can feel overwhelming at first, but breaking it down will help you get your file-to-Kafka scenario up and running.

1. What's the difference between smallrye-reactive-messaging and smallrye-reactive-streams-operators? #

Think of them as two layers of the reactive ecosystem:

• smallrye-reactive-streams-operators is the foundational library. It implements the Reactive Streams specification and provides a set of operators (like map, filter, flatMap) to create, transform, and compose reactive streams. It's all about the low-level mechanics of handling streams with backpressure.
• smallrye-reactive-messaging is a higher-level framework built on top of the operators library. It's focused on message-driven integration—think connecting to brokers like Kafka, MQTT, or even HTTP endpoints, handling message serialization/deserialization, managing subscriptions, and abstracting away boilerplate for sending/receiving streams of messages. It uses the reactive streams operators under the hood but adds opinionated tools for real-world messaging scenarios.

In short: Use the operators library when you need to build custom stream logic, use reactive messaging when you need to integrate with messaging systems like Kafka.

2. Why isn't the else branch sending messages, and how to create a continuous stream for file events? #

The core issue with your current code is that your @Outgoing method is only called once when the application starts. When you return ReactiveStreams.of(...), you're creating a finite stream that emits one element and then completes. Even if you update currentMessage later, the method doesn't re-run to create a new stream.

To build a continuous stream that reacts to new file events, you need a way to emit new elements as events happen. The best approach here is to use a Subject (a component that acts as both a publisher and a subscriber—you can send new elements to it whenever a file is detected, and it will forward them to the stream).

Here's how to adjust your code (using Mutiny, the default reactive API for SmallRye):

First, define a Subject as an instance variable:

import io.smallrye.mutiny.Multi;
import io.smallrye.mutiny.subscription.MultiEmitter;
import org.eclipse.microprofile.reactive.messaging.Message;

// This subject will hold our stream of file messages
private MultiEmitter<? super MessageWrapper> fileEmitter;
private Multi<MessageWrapper> fileEventStream;

// Initialize the stream when the app starts
@PostConstruct
public void initStream() {
    this.fileEventStream = Multi.createFrom().emitter(emitter -> {
        this.fileEmitter = emitter;
    });
}

// Call this method whenever a new file is detected
private void onFileDetected(MessageWrapper fileMessage) {
    if (fileEmitter != null && !fileEmitter.isCancelled()) {
        fileEmitter.emit(fileMessage);
    }
}

Then update your @Outgoing method to return the continuous stream:

@Outgoing("my-topic")
public PublisherBuilder<Message<MessageWrapper>> generate() {
    // Convert the Mutiny Multi to a PublisherBuilder and wrap each element in a Message
    return ReactiveStreams.fromPublisher(fileEventStream)
                          .map(Message::of);
}

Now, whenever you call onFileDetected() with a new file's MessageWrapper, it will be emitted into the stream and sent to Kafka automatically.

3. Returning Integer floods Kafka, and confusion around libraries (Vert.x, SmallRye, RxJava2, MicroProfile) #

Let's clarify two things here:

1. Why returning Integer floods Kafka: When you return a non-stream type (like an Integer) from an @Outgoing method, SmallRye Reactive Messaging interprets it as a stream that should emit that value indefinitely (it keeps re-emitting the same value over and over). That's why you see a flood of integers—this behavior is meant for cases where you want a repeating stream (like the interval example you mentioned, which emits a value at fixed intervals).

2. Choosing the right libraries: You don't need to use all of them! Here's a simplified breakdown:

   • SmallRye Reactive Messaging: Your main tool for integrating with Kafka (it's built on MicroProfile Reactive Messaging, so it's standard for Java reactive messaging).
   • Mutiny: The default reactive API for SmallRye (and Quarkus, if you're using that). It's designed to be intuitive and integrates seamlessly with SmallRye and Vert.x. You'll use Multi for streams and Uni for single results.
   • Vert.x: If you need to interact with the filesystem (like watching a folder for new files), Vert.x has great reactive filesystem APIs. SmallRye works well with Vert.x, so you can use Vert.x to detect file events and feed them into your Mutiny stream.
   • RxJava2: A popular reactive library, but not necessary if you're using SmallRye + Mutiny—they cover the same use cases, and Mutiny is more tightly integrated with the SmallRye ecosystem.
   • CompletionStage/CompletableFuture: Use these for single asynchronous operations (like fetching a single file's metadata), not for continuous streams. For streams, stick to Multi (Mutiny) or Publisher (Reactive Streams).

Stick to SmallRye Reactive Messaging + Mutiny first—they'll give you everything you need for your file-to-Kafka scenario without unnecessary complexity.

4. Differences between ReactiveStreams.fromCompletionStage, fromProcessor, fromPublisher, fromSubscriber #

Each of these methods is for bridging different reactive components into a PublisherBuilder:

• fromCompletionStage(CompletionStage<T>): Converts a single asynchronous result (like a CompletableFuture) into a stream that emits exactly one element (the result of the CompletionStage) and then completes. Use this when you have a one-off asynchronous task and want to include its result in a stream.
  Example: Wrapping a call to a REST API that returns a CompletableFuture<FileMetadata>.

• fromProcessor(Processor<T, R>): Takes a Processor (a component that acts as both a Subscriber and a Publisher) and wraps it into a PublisherBuilder. Processors are useful for custom stream logic that needs to handle both receiving elements (as a subscriber) and emitting transformed elements (as a publisher)—for example, a processor that batches incoming file events before emitting them to Kafka.

• fromPublisher(Publisher<T>): Converts any existing Reactive Streams-compliant Publisher (like a Mutiny Multi, RxJava Flowable, or Vert.x ReadStream) into a PublisherBuilder. This is your go-to method when you already have a stream from another library and want to use SmallRye's operators to process it.

• fromSubscriber(Subscriber<T>): Creates a PublisherBuilder that forwards all elements it receives to the provided Subscriber. This is less common, but useful when you need to integrate a custom Subscriber (like a legacy logging subscriber) into your stream pipeline. For example, if you have a Subscriber that writes file events to a database, you can use this to connect it to your main stream.

内容的提问来源于stack exchange，提问作者bdeweer