Integrating OpenClaw Rating API with Apache Kafka for Real‑Time, High‑Throughput Feedback

6.1. Set up Kafka Cluster

For quick prototyping, spin up a single‑node cluster using Docker:

docker run -d \
  --name kafka \
  -p 9092:9092 \
  -e KAFKA_BROKER_ID=1 \
  -e KAFKA_ZOOKEEPER_CONNECT=zookeeper:2181 \
  -e KAFKA_ADVERTISED_LISTENERS=PLAINTEXT://localhost:9092 \
  confluentinc/cp-kafka:latest

In production, consider a multi‑broker setup with Enterprise AI platform by UBOS for automated provisioning.

6.2. Create Topics

Two topics are sufficient for most rating pipelines:

• ratings.raw – Raw events from the API.
• ratings.enriched – Events after schema validation and enrichment.

docker exec kafka kafka-topics \
  --create --topic ratings.raw --partitions 6 --replication-factor 1 \
  --bootstrap-server localhost:9092

docker exec kafka kafka-topics \
  --create --topic ratings.enriched --partitions 6 --replication-factor 1 \
  --bootstrap-server localhost:9092

6.3. Configure OpenClaw Producer

Create a Maven project and add the following dependencies:

<dependencies>
    <dependency>
        <groupId>org.apache.kafka</groupId>
        <artifactId>kafka-clients</artifactId>
        <version>3.5.0</version>
    </dependency>
    <dependency>
        <groupId>com.fasterxml.jackson.core</groupId>
        <artifactId>jackson-databind</artifactId>
        <version>2.15.2</version>
    </dependency>
    <dependency>
        <groupId>org.apache.httpcomponents</groupId>
        <artifactId>httpclient</artifactId>
        <version>4.5.14</version>
    </dependency>
</dependencies>

Implement the producer that pulls ratings from the OpenClaw endpoint and pushes them to ratings.raw:

import org.apache.kafka.clients.producer.*;
import org.apache.kafka.common.serialization.StringSerializer;
import com.fasterxml.jackson.databind.ObjectMapper;
import org.apache.http.client.fluent.Request;
import java.util.Properties;

public class OpenClawKafkaProducer {
    private static final String KAFKA_BOOTSTRAP = "localhost:9092";
    private static final String TOPIC = "ratings.raw";
    private static final String OPENCLAW_ENDPOINT = "https://api.openclaw.io/v1/ratings";
    private static final String API_TOKEN = System.getenv("OPENCLAW_TOKEN");

    private final Producer<String, String> producer;
    private final ObjectMapper mapper = new ObjectMapper();

    public OpenClawKafkaProducer() {
        Properties props = new Properties();
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, KAFKA_BOOTSTRAP);
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
        props.put(ProducerConfig.ACKS_CONFIG, "all");
        props.put(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG, true);
        this.producer = new KafkaProducer<>(props);
    }

    public void pollAndPublish() throws Exception {
        String response = Request.Get(OPENCLAW_ENDPOINT)
                .addHeader("Authorization", "Bearer " + API_TOKEN)
                .execute()
                .returnContent()
                .asString();

        // Assume the API returns a JSON array of rating objects
        Rating[] ratings = mapper.readValue(response, Rating[].class);
        for (Rating r : ratings) {
            String key = r.getItemId(); // partition by item
            String value = mapper.writeValueAsString(r);
            ProducerRecord<String, String> record = new ProducerRecord<>(TOPIC, key, value);
            producer.send(record, (metadata, exception) -> {
                if (exception != null) {
                    System.err.println("Failed to send rating: " + exception.getMessage());
                }
            });
        }
        producer.flush();
    }

    public static void main(String[] args) throws Exception {
        OpenClawKafkaProducer p = new OpenClawKafkaProducer();
        while (true) {
            p.pollAndPublish();
            Thread.sleep(5_000); // poll every 5 seconds
        }
    }
}

// Simple POJO for deserialization
class Rating {
    private String userId;
    private String itemId;
    private int score;
    private String metadata;
    // getters & setters omitted for brevity
}

This producer runs continuously, pulling new ratings every few seconds and guaranteeing exactly‑once delivery thanks to idempotent producer settings.

6.4. Consume Ratings in Real‑Time

Below is a minimal consumer that reads from ratings.raw, validates the schema, enriches the payload, and forwards it to ratings.enriched:

import org.apache.kafka.clients.consumer.*;
import org.apache.kafka.clients.producer.*;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.apache.kafka.common.serialization.StringSerializer;
import com.fasterxml.jackson.databind.ObjectMapper;
import java.time.Duration;
import java.util.Collections;
import java.util.Properties;

public class RatingEnricher {
    private static final String BOOTSTRAP = "localhost:9092";
    private static final String INPUT_TOPIC = "ratings.raw";
    private static final String OUTPUT_TOPIC = "ratings.enriched";

    private final Consumer<String, String> consumer;
    private final Producer<String, String> producer;
    private final ObjectMapper mapper = new ObjectMapper();

    public RatingEnricher() {
        Properties consumerProps = new Properties();
        consumerProps.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, BOOTSTRAP);
        consumerProps.put(ConsumerConfig.GROUP_ID_CONFIG, "rating-enricher");
        consumerProps.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
        consumerProps.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
        consumerProps.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest");
        this.consumer = new KafkaConsumer<>(consumerProps);
        this.consumer.subscribe(Collections.singletonList(INPUT_TOPIC));

        Properties producerProps = new Properties();
        producerProps.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, BOOTSTRAP);
        producerProps.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
        producerProps.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
        this.producer = new KafkaProducer<>(producerProps);
    }

    public void process() {
        while (true) {
            ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
            for (ConsumerRecord<String, String> record : records) {
                try {
                    Rating rating = mapper.readValue(record.value(), Rating.class);
                    // Simple enrichment: add processing timestamp
                    rating.setMetadata(rating.getMetadata() + ";processedAt=" + System.currentTimeMillis());
                    String enriched = mapper.writeValueAsString(rating);
                    ProducerRecord<String, String> out = new ProducerRecord<>(OUTPUT_TOPIC, record.key(), enriched);
                    producer.send(out);
                } catch (Exception e) {
                    System.err.println("Failed to process rating: " + e.getMessage());
                }
            }
        }
    }

    public static void main(String[] args) {
        new RatingEnricher().process();
    }
}

Deploy this consumer as a Docker container or a Kubernetes pod for resilience. The enriched topic can feed downstream services such as recommendation engines, dashboards, or alerting pipelines.

6.5. Code Samples (Java/Scala)

If you prefer Scala, the same logic can be expressed with the kafka-streams DSL. The following snippet demonstrates a stream that reads, validates, and writes back:

import org.apache.kafka.streams.scala._
import org.apache.kafka.streams.scala.kstream._
import org.apache.kafka.streams.{KafkaStreams, StreamsConfig}
import java.util.Properties
import spray.json._

object RatingStreamApp extends App {
  implicit val ratingFormat = jsonFormat4(Rating)

  val props = new Properties()
  props.put(StreamsConfig.APPLICATION_ID_CONFIG, "rating-enricher")
  props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092")

  val builder = new StreamsBuilder()
  val ratings: KStream[String, String] = builder.stream[String, String]("ratings.raw")

  val enriched = ratings.mapValues { jsonStr =>
    val rating = jsonStr.parseJson.convertTo[Rating]
    rating.copy(metadata = rating.metadata + s";processedAt=${System.currentTimeMillis}")
  }.mapValues(_.toJson.compactPrint)

  enriched.to("ratings.enriched")
  new KafkaStreams(builder.build(), props).start()
}

case class Rating(userId: String, itemId: String, score: Int, metadata: String)

Both Java and Scala implementations share the same Kafka configuration principles, making it easy to adopt the language that best fits your team.

7.1. Throughput Tuning

• Increase num.partitions for the ratings.raw topic to match the expected QPS (e.g., 12 partitions for 120k msgs/sec).
• Enable batch.size (32 KB) and linger.ms (5 ms) on the producer to improve network efficiency.
• Use compression.type=snappy to reduce payload size without adding noticeable latency.

7.2. Latency Optimizations

For sub‑second latency, configure the consumer with fetch.min.bytes=1 and max.poll.records=500. Also, place the Kafka brokers in the same VPC as your application servers to minimize network hops.

7.3. Fault Tolerance

Leverage Kafka’s built‑in replication (set replication.factor=3) and enable unclean.leader.election.enable=false to avoid data loss during broker failures. Pair this with a Workflow automation studio that restarts failed consumer pods automatically.