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Alex Hales
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Alex HalesTeacher
Asked: August 17, 20222022-08-17T01:14:56+00:00 2022-08-17T01:14:56+00:00In: Apache Kafka, apache-kafka, cassandra, Java, kafka-consumer-api, redis

java – How to handle kafka publishing failure in robust way

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I am super late to the party. But I see something missing in above answers 🙂

The strategy of choosing some distributed system like Cassandra is a decent idea. Once the Kafka is up and normal, you can retry all the messages that were written into this.

I would like to answer on the part of “knowing how many messages failed to publish at a given time”

From the tags, I see that you are using apache-kafka and kafka-consumer-api.You can write a custom call back for your producer and this call back can tell you if the message has failed or successfully published. On failure, log the meta data for the message.

Now, you can use log analyzing tools to analyze your failures. One such decent tool is Splunk.

Below is a small code snippet than can explain better about the call back I was talking about:

public class ProduceToKafka {

  private ProducerRecord<String, String> message = null;

 // TracerBulletProducer class has producer properties
  private KafkaProducer<String, String> myProducer = TracerBulletProducer
      .createProducer();

  public void publishMessage(String string) {

    ProducerRecord<String, String> message = new ProducerRecord<>(
        "topicName", string);

    myProducer.send(message, new MyCallback(message.key(), message.value()));
  }

  class MyCallback implements Callback {

    private final String key;
    private final String value;

    public MyCallback(String key, String value) {
      this.key = key;
      this.value = value;
    }


    @Override
    public void onCompletion(RecordMetadata metadata, Exception exception) {
      if (exception == null) {
        log.info("--------> All good !!");
      } else {
        log.info("--------> not so good  !!");
        log.info(metadata.toString());
        log.info("" + metadata.serializedValueSize());
        log.info(exception.getMessage());

      }
    }
  }

}

If you analyze the number of "--------> not so good !!" logs per time unit, you can get the required insights.

God speed !

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