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This KIP will benefit CEP-44: Kafka integration for Cassandra CDC using Sidecar 

Public Interfaces

1. New Producer/Consumer configs to support Composable Serializer/Deserializer

In some use-cases we might need a way to be able to chain of Kafka serializer/deserializer before applying large message serializer for example need to apply schema or special format like avro or protobuf. We could just create a single org.apache.kafka.common.serialization.largemessage.Serializer that implemented the Kafka Serializer interface, but we would need to create the different versions (AvroSerializer, ProtobufSerializer) with their support for the schema.
Instead the KIP proposing a composable serializer , that still implements the Kafka Serializer interface, but allows concatenating several serializers to perform what we are looking for here. 

This will be enabled by new configs in Producer/Consumer side 

2. org.apache.kafka.common.serialization.largemessage.Serializer

A configurable Kafka serializer that:

• Check if the estimated size of the data (bytes) after applying provided compression (if there is one) it needs to serialize is larger than the configured threshold.
  • If it is large than the provided threshold (`large.message.threshold.bytes`):
    • Use the provided PayloadStore implementation to publish the large message into payload store, generating an id which is the reference to access this later.
    • Encapsulate that ID into a simple Kafka event using a structured format.
    • Pass the new Kafka Event down
    • Pass payload Id as new Kafka Event down to Kafka 
    • Add alarge-message: trueheader
  • If it’s not large then provided threshold (large.message.threshold.bytes):
    • Do nothing, pass the data as it is.

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• Check if the event is large message by looking forlarge-message: trueheader
  
  • If it does have `large-message` header:
    
    • Parse the event and retrieve the ID and the needed useful information on the event.
    • Use the provided PayloadStore implementation to download the original data from the payload-store.
    • Return payload as the final value
  • If it doesn't have the large-message header:
    • Do nothing return the Kafka message as it is

Proposed Changes

1.

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 New ProducerConfig to support Composable Serializer

Configuration

2.

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New ConsumerConfig to support Composable Deserializer

ConfigurationConfiguration

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3. org.apache.kafka.common.serialization.largemessage.Serializer<byte[]>

Configuration

Configuration

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4.org.apache.kafka.common.serialization.largemessage.

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Deserializer<byte[]>

Configuration

5.org.apache.kafka.common.serialization.largemessage.PayloadStore

/**
* An interface for publishing and downloading serialised data to/from payload store.
* The store config will passed down from the original config of the Kafka producer/consumer client. 
*/
public interface PayloadStore implements Configurable, Closeable {
     /**
     * Publish data into the store.
     *
     * 

/**
* An interface for publishing and downloading serialised data to/from payload store.
* The store config will passed down from the original config of the Kafka producer/consumer client. 
*/
public interface PayloadStore implements Configurable, Closeable, Monitorable {
     /**
     * Publish data into the store.
     *
     * @param data data that will be published to the store.
     * @return full path to object in the store.
     * @throw PayloadStoreException in case failed to publish to the store.
     */
     String publish(String topic, byte[] data) throw PayloadStoreException;

    /**
     * Download full data from the store.
     *
     * @param fullPath of the data's reference in the store for example `remote_store/topic_name/<record_random_uuid>`
     * @return content of the object as bytes.
     * @throw PayloadStoreException 
     */
     byte[] download(String fullPath) throw PayloadStoreException;

    /** 
    * Generate an id for the data's reference in the store (Not the full path in the store). 
    * By default the id is a random UUID however some stores might need more smarter way to calculate 
    * its reference id based on the data itself. In such a case please override this method. 
    * @param data data that will be published to the store.
    * @return object id for example `record_random_uuid`
	*/ as string, the default implementation support UTF-8 encoding. If any other encoding is required the 
    *        implementation will need to address this.
    default String id(byte[] data) {
       return java.util.UUID.randomUUID().toString();
     // Java UUID is already composed entirely of ASCII characters which are valid UTF-8
    }
}

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6. org.apache.kafka.common.serialization.largemessage.PayloadStoreException

/**
* Exception class that represent exceptions during interaction with the store.
* this helps the Payload store to decided either to retry or to crash. 
* The final Serializer and Deserializer will propagate this as SerializationException to client.
**/
public class PayloadStoreException extends RuntimeException {
    /**
     * Constructor PayloadStoreException with message and throwable.
     */
    public PayloadStoreException(String message, Throwable t) {
        super(message, t);
    }

    /**
     * Constructor PayloadStoreException with message.
     */
    public PayloadStoreException(String message) {
        super(message);
    }

    /**
     * Constructor PayloadStoreException with throwable.
     */
    public PayloadStoreException(Throwable t) {
        super(t);
    }
}

Example

Producer

Map<String, Object> producerConfig = new HashMap<>();
producerConfig.put("value.serializers", "kafka.serializers.KafkaAvroDeserializer, org.apache.kafka.common.serialization.largemessage.Serializer");
producerConfig.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer"); producerConfig.put("large.message.payload.store.class", "myclient.serializers.payloadpayloadstore.store.CustomS3Store")
producerConfig.put("large.message.threshold.bytes", 1048576);
producerConfig.put("s3.bucket", "my-bucket")
producerConfig.put("s3.retry.attempts", "3");
producerConfig.put("s3.connection.timeout.ms", "5000");
producerConfig.put("bootstrap.servers", "localhost:9092");

KafkaProducer<String, Double>byte[]> producer = new KafkaProducer<>(producerConfig);

Consumer

Considerations

Map<String, Object> consumerConfig = new HashMap<>();
producerConfig.put("value.deserializers", "org.apache.kafka.common.serialization.largemessage.Serializer, kafka.serializers.KafkaAvroDeserializer");
consumerConfig.put("key.deserializer", "org.apache.kafka.common.serialization.StringSerializer"); consumerConfig.put("large.message.payload.store.class", "myclient.payloadstore.CustomS3Store")
consumerConfig.put("s3.bucket", "my-bucket")
consumerConfig.put("s3.retry.attempts", "3");
consumerConfig.put("s3.connection.timeout.ms", "5000");
consumerConfig.put("bootstrap.servers", "localhost:9092");

KafkaConsumer<String, Object> consumer = new KafkaConsumer<>(consumerConfig);

Considerations

• TTL Configuration Risk: If the payload store owner doesn't configure an appropriate TTL that aligns with Kafka topic retention, the payload store may grow indefinitely. This occurs because objects remain in storage even after Kafka no longer references them, leading to unnecessary storage costs.

• TTL Too Short Risk: If the TTL is set too aggressively (shorter than needed), Kafka references may point to objects that no longer exist in the payload store. When this happens:

  • Consumers will encounter NOT_FOUND errors

  • To prevent blocking behavior, consumers should capture SerializationException::getCause and decide what to do if the exception is PayloadException/PayloadNotFoundException.

  • This allows graceful handling of missing payload references

• TTL and usage of Compacted Topic: for applications that do use compacted topics with large payloads, the PayloadStore implementation should handle this by:

  • Consistent ID Generation:PayloadStore implementations should use deterministic IDs based on message content or metadata (rather than random UUIDs) so that identical payloads can reuse the same storage object, reducing storage costs.

  • TTL Strategy:

    • Since compacted topics can retain data indefinitely, users must accept the indefinite storage costs that comes with this case. If cost is an issue they will need to setup a cleanup job that observer Kafka key with null value and delete the associated payload with this key. 

    • Topics with cleanup.policy=compact,delete  will eventually remove old data, so standard TTL approaches work normally.

• TTL Configuration Risk: If the payload store owner doesn't configure an appropriate TTL that aligns with Kafka topic retention, the payload store may grow indefinitely. This occurs because objects remain in storage even after Kafka no longer references them, leading to unnecessary storage costs.

• TTL Too Short Risk: If the TTL is set too aggressively (shorter than needed), Kafka references may point to objects that no longer exist in the payload store. When this happens:

  • Consumers will encounter NOT_FOUND errors

  • To prevent blocking behavior, consumers should capture SerializationException::getCause and decide what to do if the exception is PayloadException/PayloadNotFoundException.

    This allows graceful handling of missing payload references

Recommendation: Set TTL duration to exceed your Kafka topic retention period by a reasonable buffer (e.g., 10-20%) to ensure payload availability throughout the message lifecycle while preventing indefinite storage growth.

• Partial Failure Handling: If payload storage succeeds but Kafka produce fails, the payload will remain in storage until TTL expires. This is acceptable as it only affects storage costs, not correctness. If Kafka message is consumed but payload download fails, the client can capture SerializationException::getCause and determine behavior.

• Critical Timing Constraints: The total timeout for payload store operations (including all retries) cannot exceed `max.block.ms` for Kafka producers or `max.poll.interval.ms` for Kafka consumers. This is a fundamental requirement for any implementation of org.apache.kafka.common.serialization.largemessage.store.PayloadStore.

  • Exceeding these limits will cause producer blocking or consumer rebalancing issues.

• Memory Constrains: Large messages will consume memory during serialization so ensure heap size can accommodate your largest expected message. ConsiderPayloadStoreimplementationsthatsupportcompressiontoreducestoragefootprint.

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