This version is still in development and is not considered stable yet. For the latest stable version, please use Spring for Apache Kafka 3.3.10!

Kafka Queues (Share Consumer)

Starting with version 4.0, Spring for Apache Kafka provides support for Kafka Queues through share consumers, which are part of Apache Kafka 4.0.0 and implement KIP-932 (Queues for Kafka). This feature is currently in early access.

Kafka Queues enable a different consumption model compared to traditional consumer groups. Instead of the partition-based assignment model where each partition is exclusively assigned to one consumer, share consumers can cooperatively consume from the same partitions, with records being distributed among the consumers in the share group.

Share Consumer Factory

The ShareConsumerFactory is responsible for creating share consumer instances. Spring Kafka provides the DefaultShareConsumerFactory implementation.

Configuration

You can configure a DefaultShareConsumerFactory similar to how you configure a regular ConsumerFactory:

@Bean
public ShareConsumerFactory<String, String> shareConsumerFactory() {
    Map<String, Object> props = new HashMap<>();
    props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
    props.put(ConsumerConfig.GROUP_ID_CONFIG, "my-share-group");
    props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    return new DefaultShareConsumerFactory<>(props);
}

Constructor Options

The DefaultShareConsumerFactory provides several constructor options:

// Basic configuration
new DefaultShareConsumerFactory<>(configs);

// With deserializer suppliers
new DefaultShareConsumerFactory<>(configs, keyDeserializerSupplier, valueDeserializerSupplier);

// With deserializer instances
new DefaultShareConsumerFactory<>(configs, keyDeserializer, valueDeserializer, configureDeserializers);

Deserializer Configuration

You can configure deserializers in several ways:

  1. Via Configuration Properties (recommended for simple cases):

    props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);

  2. Via Setters:

    factory.setKeyDeserializer(new StringDeserializer());
factory.setValueDeserializer(new StringDeserializer());

  3. Via Suppliers (for cases where deserializers need to be created per consumer):

    factory.setKeyDeserializerSupplier(() -> new StringDeserializer());
factory.setValueDeserializerSupplier(() -> new StringDeserializer());

Set configureDeserializers to false if your deserializers are already fully configured and should not be reconfigured by the factory.

Lifecycle Listeners

You can add listeners to monitor the lifecycle of share consumers:

factory.addListener(new ShareConsumerFactory.Listener<String, String>() {
    @Override
    public void consumerAdded(String id, ShareConsumer<String, String> consumer) {
        // Called when a new consumer is created
        System.out.println("Consumer added: " + id);
    }

    @Override
    public void consumerRemoved(String id, ShareConsumer<String, String> consumer) {
        // Called when a consumer is closed
        System.out.println("Consumer removed: " + id);
    }
});

Share Message Listener Containers

ShareKafkaMessageListenerContainer

The ShareKafkaMessageListenerContainer provides a container for share consumers with support for concurrent processing:

@Bean
public ShareKafkaMessageListenerContainer<String, String> container(
        ShareConsumerFactory<String, String> shareConsumerFactory) {

    ContainerProperties containerProps = new ContainerProperties("my-topic");
    containerProps.setGroupId("my-share-group");

    ShareKafkaMessageListenerContainer<String, String> container =
        new ShareKafkaMessageListenerContainer<>(shareConsumerFactory, containerProps);

    container.setupMessageListener(new MessageListener<String, String>() {
        @Override
        public void onMessage(ConsumerRecord<String, String> record) {
            System.out.println("Received: " + record.value());
        }
    });

    return container;
}

Container Properties

Share containers support a subset of the container properties available for regular consumers:

  • topics: Array of topic names to subscribe to

  • groupId: The share group ID

  • clientId: The client ID for the consumer

  • kafkaConsumerProperties: Additional consumer properties

Share consumers do not support:

  • Explicit partition assignment (TopicPartitionOffset)

  • Topic patterns

  • Manual offset management

Concurrency

The ShareKafkaMessageListenerContainer supports concurrent processing by creating multiple consumer threads within a single container. Each thread runs its own ShareConsumer instance that participates in the same share group.

Unlike traditional consumer groups where concurrency involves partition distribution, share consumers leverage Kafka’s record-level distribution at the broker. This means multiple consumer threads in the same container work together as part of the share group, with the Kafka broker distributing records across all consumer instances.

Concurrency is Additive Across Application Instances

From the share group’s perspective, each ShareConsumer instance is an independent member, regardless of where it runs. Setting concurrency=3 in a single container creates 3 share group members. If you run multiple application instances with the same share group ID, all their consumer threads combine into one pool.

For example: * Application Instance 1: concurrency=3 → 3 share group members * Application Instance 2: concurrency=3 → 3 share group members * Total: 6 share group members available for the broker to distribute records to

This means setting concurrency=5 in a single container is operationally equivalent to running 5 separate application instances with concurrency=1 each (all using the same group.id). The Kafka broker treats all consumer instances equally and distributes records across the entire pool.

Configuring Concurrency Programmatically

@Bean
public ShareKafkaMessageListenerContainer<String, String> concurrentContainer(
        ShareConsumerFactory<String, String> shareConsumerFactory) {

    ContainerProperties containerProps = new ContainerProperties("my-topic");
    containerProps.setGroupId("my-share-group");

    ShareKafkaMessageListenerContainer<String, String> container =
        new ShareKafkaMessageListenerContainer<>(shareConsumerFactory, containerProps);

    // Set concurrency to create 5 consumer threads
    container.setConcurrency(5);

    container.setupMessageListener(new MessageListener<String, String>() {
        @Override
        public void onMessage(ConsumerRecord<String, String> record) {
            System.out.println("Received on " + Thread.currentThread().getName() + ": " + record.value());
        }
    });

    return container;
}

Configuring Concurrency via Factory

You can set default concurrency at the factory level, which applies to all containers created by that factory:

@Bean
public ShareKafkaListenerContainerFactory<String, String> shareKafkaListenerContainerFactory(
        ShareConsumerFactory<String, String> shareConsumerFactory) {

    ShareKafkaListenerContainerFactory<String, String> factory =
        new ShareKafkaListenerContainerFactory<>(shareConsumerFactory);

    // Set default concurrency for all containers created by this factory
    factory.setConcurrency(3);

    return factory;
}

Per-Listener Concurrency

The concurrency setting can be overridden per listener using the concurrency attribute:

@Component
public class ConcurrentShareListener {

    @KafkaListener(
        topics = "high-throughput-topic",
        containerFactory = "shareKafkaListenerContainerFactory",
        groupId = "my-share-group",
        concurrency = "10"  // Override factory default
    )
    public void listen(ConsumerRecord<String, String> record) {
        // This listener will use 10 consumer threads
        System.out.println("Processing: " + record.value());
    }
}

Concurrency Considerations

  • Thread Safety: Each consumer thread has its own ShareConsumer instance and manages its own acknowledgments independently

  • Client IDs: Each consumer thread receives a unique client ID with a numeric suffix (e.g., myContainer-0, myContainer-1, etc.)

  • Metrics: Metrics from all consumer threads are aggregated and accessible via container.metrics()

  • Lifecycle: All consumer threads start and stop together as a unit

  • Work Distribution: The Kafka broker handles record distribution across all consumer instances in the share group

  • Explicit Acknowledgment: Each thread independently manages acknowledgments for its records; unacknowledged records in one thread don’t block other threads

Concurrency with Explicit Acknowledgment

Concurrency works seamlessly with explicit acknowledgment mode. Each consumer thread independently tracks and acknowledges its own records:

@KafkaListener(
    topics = "order-queue",
    containerFactory = "explicitShareKafkaListenerContainerFactory",
    groupId = "order-processors",
    concurrency = "5"
)
public void processOrder(ConsumerRecord<String, String> record, ShareAcknowledgment acknowledgment) {
    try {
        // Process the order
        processOrderLogic(record.value());
        acknowledgment.acknowledge(); // ACCEPT
    }
    catch (RetryableException e) {
        acknowledgment.release(); // Will be redelivered
    }
    catch (Exception e) {
        acknowledgment.reject(); // Permanent failure
    }
}

Record Acquisition and Distribution Behavior:

Share consumers use a pull-based model where each consumer thread calls poll() to fetch records from the broker. When a consumer polls, the broker’s share-partition leader:

  • Selects records in "Available" state

  • Moves them to "Acquired" state with a time-limited acquisition lock (default 30 seconds, configurable via group.share.record.lock.duration.ms)

  • Prefers to return complete record batches for efficiency

  • Applies max.poll.records as a soft limit, meaning complete record batches will be acquired even if it exceeds this value

While records are acquired by one consumer, they are not available to other consumers. When the acquisition lock expires, unacknowledged records automatically return to "Available" state and can be delivered to another consumer.

The broker limits the number of records that can be acquired per partition using group.share.partition.max.record.locks. Once this limit is reached, subsequent polls temporarily return no records until locks expire.

Implications for Concurrency:

  • Each consumer thread independently polls and may acquire different numbers of records per poll

  • Record distribution across threads depends on polling timing and batch availability

  • Multiple threads increase the pool of consumers available to acquire records

  • With low message volume or single partitions, records may concentrate on fewer threads

  • For long-running workloads, distribution tends to be more even

Configuration:

  • Each thread polls and processes records independently

  • Acknowledgment constraints apply per-thread (one thread’s unacknowledged records don’t block other threads)

  • Concurrency setting must be greater than 0 and cannot be changed while the container is running

Annotation-Driven Listeners

@KafkaListener with Share Consumers

You can use @KafkaListener with share consumers by configuring a ShareKafkaListenerContainerFactory:

@Configuration
@EnableKafka
public class ShareConsumerConfig {

    @Bean
    public ShareConsumerFactory<String, String> shareConsumerFactory() {
        Map<String, Object> props = new HashMap<>();
        props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
        return new DefaultShareConsumerFactory<>(props);
    }

    @Bean
    public ShareKafkaListenerContainerFactory<String, String> shareKafkaListenerContainerFactory(
            ShareConsumerFactory<String, String> shareConsumerFactory) {
        return new ShareKafkaListenerContainerFactory<>(shareConsumerFactory);
    }
}

Then use it in your listener:

@Component
public class ShareMessageListener {

    @KafkaListener(
        topics = "my-queue-topic",
        containerFactory = "shareKafkaListenerContainerFactory",
        groupId = "my-share-group"
    )
    public void listen(ConsumerRecord<String, String> record) {
        System.out.println("Received from queue: " + record.value());
        // Record is automatically acknowledged with ACCEPT
    }
}

Share Group Offset Reset

Unlike regular consumer groups, share groups use a different configuration for offset reset behavior. You can configure this programmatically:

private void configureShareGroup(String bootstrapServers, String groupId) throws Exception {
    Map<String, Object> adminProps = new HashMap<>();
    adminProps.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);

    try (Admin admin = Admin.create(adminProps)) {
        ConfigResource configResource = new ConfigResource(ConfigResource.Type.GROUP, groupId);
        ConfigEntry configEntry = new ConfigEntry("share.auto.offset.reset", "earliest");

        Map<ConfigResource, Collection<AlterConfigOp>> configs = Map.of(
            configResource, List.of(new AlterConfigOp(configEntry, AlterConfigOp.OpType.SET))
        );

        admin.incrementalAlterConfigs(configs).all().get();
    }
}

Record Acknowledgment

Share consumers support two acknowledgment modes that control how records are acknowledged after processing.

Implicit Acknowledgment (Default)

In implicit mode, records are automatically acknowledged based on processing outcome:

Successful processing: Records are acknowledged as ACCEPT Processing errors: Records are acknowledged as REJECT

@Bean
public ShareKafkaListenerContainerFactory<String, String> shareKafkaListenerContainerFactory(
    ShareConsumerFactory<String, String> shareConsumerFactory) {
    // Implicit mode is the default - no additional configuration needed
    return new ShareKafkaListenerContainerFactory<>(shareConsumerFactory);
}

Explicit Acknowledgment

In explicit mode, the application must manually acknowledge each record using the provided ShareAcknowledgment.

There are two ways to configure explicit acknowledgment mode:

Option 1: Using Kafka Client Configuration

@Bean
public ShareConsumerFactory<String, String> explicitShareConsumerFactory() {
    Map<String, Object> props = new HashMap<>();
    props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
    props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    props.put(ConsumerConfig.SHARE_ACKNOWLEDGEMENT_MODE_CONFIG, "explicit"); // Official Kafka client config
    return new DefaultShareConsumerFactory<>(props);
}

Option 2: Using Spring Container Configuration

@Bean
public ShareConsumerFactory<String, String> explicitShareConsumerFactory() {
    Map<String, Object> props = new HashMap<>();
    props.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
    props.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    props.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    props.put(ConsumerConfig.SHARE_ACKNOWLEDGEMENT_MODE_CONFIG, "explicit");
    return new DefaultShareConsumerFactory<>(props);
}

@Bean
public ShareKafkaListenerContainerFactory<String, String> explicitShareKafkaListenerContainerFactory(
    ShareConsumerFactory<String, String> explicitShareConsumerFactory) {
    // The factory will detect the explicit acknowledgment mode from the consumer factory configuration
    return new ShareKafkaListenerContainerFactory<>(explicitShareConsumerFactory);
}

Configuration Precedence

When both configuration methods are used, Spring Kafka follows this precedence order (highest to lowest):

  1. Container Properties: containerProperties.setExplicitShareAcknowledgment(true/false)

  2. Consumer Config: ConsumerConfig.SHARE_ACKNOWLEDGEMENT_MODE_CONFIG ("implicit" or "explicit")

  3. Default: false (implicit acknowledgment)

Acknowledgment Types

Share consumers support three acknowledgment types:

ACCEPT: Record processed successfully, mark as completed
RELEASE: Temporary failure, make record available for redelivery
REJECT: Permanent failure, do not retry

ShareAcknowledgment API

The ShareAcknowledgment interface provides methods for explicit acknowledgment:

public interface ShareAcknowledgment {
    void acknowledge();
    void release();
    void reject();
}

Listener Interfaces

Share consumers support specialized listener interfaces for different use cases:

Basic Message Listener

Use the standard MessageListener for simple cases:

@KafkaListener(topics = "my-topic", containerFactory = "shareKafkaListenerContainerFactory")
public void listen(ConsumerRecord<String, String> record) {
    System.out.println("Received: " + record.value());
    // Automatically acknowledged in implicit mode
}

AcknowledgingShareConsumerAwareMessageListener

This interface provides access to the ShareConsumer instance with optional acknowledgment support. The acknowledgment parameter is nullable and depends on the container’s acknowledgment mode:

Implicit Mode Example (acknowledgment is null)
@KafkaListener(
    topics = "my-topic",
    containerFactory = "shareKafkaListenerContainerFactory"  // Implicit mode by default
)
public void listen(ConsumerRecord<String, String> record,
                  @Nullable ShareAcknowledgment acknowledgment,
                  ShareConsumer<?, ?> consumer) {

    // In implicit mode, acknowledgment is null
    System.out.println("Received: " + record.value());

    // Access consumer metrics if needed
    Map<MetricName, ? extends Metric> metrics = consumer.metrics();

    // Record is auto-acknowledged as ACCEPT on success, REJECT on error
}
Explicit Mode Example (acknowledgment is non-null)
@Component
public class ExplicitAckListener {
    @KafkaListener(
        topics = "my-topic",
        containerFactory = "explicitShareKafkaListenerContainerFactory"
    )
    public void listen(ConsumerRecord<String, String> record,
                      @Nullable ShareAcknowledgment acknowledgment,
                      ShareConsumer<?, ?> consumer) {

        // In explicit mode, acknowledgment is non-null
        try {
            processRecord(record);
            acknowledgment.acknowledge(); // ACCEPT
        }
		catch (RetryableException e) {
            acknowledgment.release(); // Will be redelivered
        }
		catch (Exception e) {
            acknowledgment.reject(); // Permanent failure
        }
    }

    private void processRecord(ConsumerRecord<String, String> record) {
        // Business logic here
    }
}

Acknowledgment Constraints

In explicit acknowledgment mode, the container enforces important constraints:

Poll Blocking: Subsequent polls are blocked until all records from the previous poll are acknowledged.
One-time Acknowledgment: Each record can only be acknowledged once.
Error Handling: If processing throws an exception, the record is automatically acknowledged as `REJECT`.
In explicit mode, failing to acknowledge records will block further message processing. Always ensure records are acknowledged in all code paths.

Acknowledgment Timeout Detection

To help identify missing acknowledgments, Spring Kafka provides configurable timeout detection. When a record is not acknowledged within the specified timeout, a warning is logged with details about the unacknowledged record.

@Bean
public ShareKafkaListenerContainerFactory<String, String> shareKafkaListenerContainerFactory(
    ShareConsumerFactory<String, String> shareConsumerFactory) {
    ShareKafkaListenerContainerFactory<String, String> factory =
        new ShareKafkaListenerContainerFactory<>(shareConsumerFactory);

    // Set acknowledgment timeout (default is 30 seconds)
    factory.getContainerProperties().setShareAcknowledgmentTimeout(Duration.ofSeconds(30));

    return factory;
}

When a record exceeds the timeout, you’ll see a warning like:

WARN: Record not acknowledged within timeout (30 seconds).
In explicit acknowledgment mode, you must call ack.acknowledge(), ack.release(),
or ack.reject() for every record.

This feature helps developers quickly identify when acknowledgment calls are missing from their code, preventing the common issue of "Spring Kafka does not consume new records any more" due to forgotten acknowledgments.

Acknowledgment Examples

Mixed Acknowledgment Patterns

@KafkaListener(topics = "order-processing", containerFactory = "explicitShareKafkaListenerContainerFactory")
    public void processOrder(ConsumerRecord<String, String> record, ShareAcknowledgment acknowledgment) {
        String orderId = record.key();
        String orderData = record.value();
        try {
            if (isValidOrder(orderData)) {
                if (processOrder(orderData)) {
                    acknowledgment.acknowledge(); // Success - ACCEPT
                }
                else {
                    acknowledgment.release(); // Temporary failure - retry later
                }
            }
            else {
                acknowledgment.reject(); // Invalid order - don't retry
            }
        }
        catch (Exception e) {
            // Exception automatically triggers REJECT
            throw e;
        }
}

Conditional Acknowledgment

@KafkaListener(topics = "data-validation", containerFactory = "explicitShareKafkaListenerContainerFactory")
public void validateData(ConsumerRecord<String, String> record, ShareAcknowledgment acknowledgment) {
    ValidationResult result = validator.validate(record.value());
    switch (result.getStatus()) {
        case VALID:
            acknowledgment.acknowledge(AcknowledgeType.ACCEPT);
            break;
        case INVALID_RETRYABLE:
            acknowledgment.acknowledge(AcknowledgeType.RELEASE);
            break;
        case INVALID_PERMANENT:
            acknowledgment.acknowledge(AcknowledgeType.REJECT);
            break;
    }
}

Poison Message Protection and Delivery Count

KIP-932 includes broker-side poison message protection to prevent unprocessable records from being endlessly redelivered.

How It Works

Every time a record is acquired by a consumer in a share group, the broker increments an internal delivery count. The first acquisition sets the delivery count to 1, and each subsequent acquisition increments it. When the delivery count reaches the configured limit (default: 5), the record moves to Archived state and is not eligible for additional delivery attempts.

Configuration

The maximum delivery attempts can be configured per share group using the Admin API:

private void configureMaxDeliveryAttempts(String bootstrapServers, String groupId) throws Exception {
    Map<String, Object> adminProps = new HashMap<>();
    adminProps.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);

    try (Admin admin = Admin.create(adminProps)) {
        ConfigResource configResource = new ConfigResource(ConfigResource.Type.GROUP, groupId);

        // Default is 5, adjust based on your retry tolerance
        ConfigEntry maxAttempts = new ConfigEntry("group.share.delivery.attempt.limit", "10");

        Map<ConfigResource, Collection<AlterConfigOp>> configs = Map.of(
            configResource, List.of(new AlterConfigOp(maxAttempts, AlterConfigOp.OpType.SET))
        );

        admin.incrementalAlterConfigs(configs).all().get();
    }
}

Delivery Count is Not Exposed to Applications

The delivery count is maintained internally by the broker and is not exposed to consumer applications. This is an intentional design decision in KIP-932. The delivery count is approximate and serves as a poison message protection mechanism, not a precise redelivery counter. Applications cannot query or access this value through any API.

For application-level retry logic, use the acknowledgment types:

  • RELEASE - Make record available for redelivery (contributes to delivery count)

  • REJECT - Mark as permanently failed (does not cause redelivery)

  • ACCEPT - Successfully processed (does not cause redelivery)

The broker automatically prevents endless redelivery once group.share.delivery.attempt.limit is reached, moving the record to Archived state.

Retry Strategy Recommendations

@KafkaListener(topics = "orders", containerFactory = "explicitShareKafkaListenerContainerFactory")
public void processOrder(ConsumerRecord<String, String> record, ShareAcknowledgment ack) {
    try {
        // Attempt to process the order
        orderService.process(record.value());
        ack.acknowledge(); // ACCEPT - successfully processed
    }
    catch (TransientException e) {
        // Temporary failure (network issue, service unavailable, etc.)
        // Release the record for redelivery
        // Broker will retry up to group.share.delivery.attempt.limit times
        logger.warn("Transient error processing order, will retry: {}", e.getMessage());
        ack.release(); // RELEASE - make available for retry
    }
    catch (ValidationException e) {
        // Permanent semantic error (invalid data format, business rule violation, etc.)
        // Do not retry - this record will never succeed
        logger.error("Invalid order data, rejecting: {}", e.getMessage());
        ack.reject(); // REJECT - permanent failure, do not retry
    }
    catch (Exception e) {
        // Unknown error - typically safer to reject to avoid infinite loops
        // But could also release if you suspect it might be transient
        logger.error("Unexpected error processing order, rejecting: {}", e.getMessage());
        ack.reject(); // REJECT - avoid poison message loops
    }
}

The broker’s poison message protection ensures that even if you always use RELEASE for errors, records won’t be retried endlessly. They will automatically be archived after reaching the delivery attempt limit.

Differences from Regular Consumers

Share consumers differ from regular consumers in several key ways:

  1. No Partition Assignment: Share consumers cannot be assigned specific partitions

  2. No Topic Patterns: Share consumers do not support subscribing to topic patterns

  3. Cooperative Consumption: Multiple consumers in the same share group can consume from the same partitions simultaneously

  4. Record-Level Acknowledgment: Supports explicit acknowledgment with ACCEPT, RELEASE, and REJECT types

  5. Different Group Management: Share groups use different coordinator protocols

  6. No Batch Processing: Share consumers process records individually, not in batches

  7. Broker-Side Retry Management: Delivery count tracking and poison message protection are managed by the broker, not exposed to applications

Limitations and Considerations

Current Limitations

  • In preview: This feature is in preview mode and may change in future versions

  • No Message Converters: Message converters are not yet supported for share consumers

  • No Batch Listeners: Batch processing is not supported with share consumers

  • Poll Constraints: In explicit acknowledgment mode, unacknowledged records block subsequent polls within each consumer thread