Apache Kafka, primarily known for handling streaming data, can also be configured to support a request-reply or synchronous messaging pattern, though it is inherently designed for high-throughput, asynchronous message processing. This article outlines how to implement a synchronous communication model using Kafka, which is not a typical use case but can be necessary for certain applications requiring immediate responses.

Overview of Request-Reply in Kafka

In the request-reply pattern, a sender (client) sends a message (request) and waits synchronously for a response from the receiver (server). This pattern is typical in web services but is not directly supported by Kafka, which is built for decoupled, asynchronous message streams.

Key Challenges

1. Message Correlation: Associating requests with their respective responses.
2. Response Consumer Design: Ensuring the consumer of the reply listens only for responses relevant to its request.

Implementing Request-Reply in Kafka

Here's a step-by-step guide to implementing the request-reply pattern:

Step 1: Topic Setup

You need two topics:

• A request-topic for sending requests.
• A response-topic for sending back responses.

Step 2: Producing Requests

Each request message should include a unique identifier (correlation ID) to correlate the response with the request.

ProducerRecord<String, String> record = new ProducerRecord<>(requestTopic, key, message);
record.headers().add("correlationId", correlationId.getBytes(StandardCharsets.UTF_8));
producer.send(record);

Step 3: Consuming Requests and Producing Responses

On the server side, read from the request-topic, process the request, and produce a response to the response-topic. Include the same correlation ID in the response header.

1ConsumerRecord<String, String> record = consumer.poll(Duration.ofMillis(100)).iterator().next();
2// Process the request
3String response = processRequest(record.value());
4
5ProducerRecord<String, String> responseRecord = new ProducerRecord<>(responseTopic, record.key(), response);
6responseRecord.headers().add("correlationId", record.headers().lastHeader("correlationId").value());
7producer.send(responseRecord);

Step 4: Consuming Responses

The original requester consumes from the response-topic. It uses a consumer that filters messages based on the correlation ID.

1String myCorrelationId = "..."; // this should be the same as what was sent
2ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
3for (ConsumerRecord<String, String> record : records) {
4    if (Arrays.equals(record.headers().lastHeader("correlationId").value(), myCorrelationId.getBytes(StandardCharsets.UTF_8))) {
5        // This is the response to our request
6        handleResponse(record.value());
7        break;
8    }
9}

Alternative Approach: Using Kafka Streams

Kafka Streams API can be leveraged to manage state more efficiently, which is crucial for tracking request-reply pairs.

1KStreamBuilder builder = new KStreamBuilder();
2KStream<byte[], String> requests = builder.stream("request-topic");
3KTable<byte[], String> responses = requests
4    .mapValues(value -> processRequest(value))
5    .to("response-topic");
6
7requests.join(responses, /* joiner here */, JoinWindows.of(TimeUnit.MINUTES.toMillis(5)))
8    .foreach((key, value) -> processFinal(value));

Best Practices and Considerations

• Timeouts and Error Handling: Implement timeouts and handle errors for scenarios where responses may never come.
• Scalability: Monitor performance, as synchronous operations can introduce bottlenecks.

Summary

In conclusion, while Kafka does not natively support synchronous messaging, with careful design focusing on message correlation and response handling, it is feasible to implement a request-reply pattern effectively.