KafkaTemplate: KafkaTemplate is a central class that simplifies the process of sending messages to Kafka topics. It abstracts the complexities of working with Kafka producers and allows you to send messages using a convenient API.
@KafkaListener Annotation: : Spring for Apache Kafka provides the @KafkaListener annotation, which allows you to create message consumers by annotating methods in your Spring components (e.g., Spring beans). These methods can be configured to listen to specific Kafka topics and process messages.
Consumer Factory: Spring for Apache Kafka allows you to configure and customize Kafka consumers using the ConsumerFactory. This is particularly useful when you need to set consumer-specific properties.
Producer Factory: Similar to the ConsumerFactory, the ProducerFactory allows you to configure and customize Kafka producers, including setting producer-specific properties.
Error Handling: It provides built-in error handling mechanisms for handling exceptions that may occur during message processing. You can configure how the framework handles errors and retries.
Listener Containers:Spring for Apache Kafka manages Kafka message consumers using listener containers. These containers provide options for concurrent message processing and back-off strategies, helping you achieve high throughput and fault tolerance.
Batch Listeners: You can configure batch listeners with the ability to process multiple messages in a single method call, optimizing message processing performance.
Support for Avro and Other Serialization Formats: It supports various message serialization and deserialization formats, including Avro, JSON, and more.
Integration with Spring Boot:Spring for Apache Kafka can be easily integrated into Spring Boot applications, simplifying configuration and allowing for auto-configuration of Kafka-related components.
Transactional Messaging: t supports transactional message processing, allowing you to participate in Spring-managed transactions when producing and consuming messages from Kafka.
Dynamic Topics and Partitions: You can dynamically create topics and partitions based on runtime conditions and requirements.
With Spring for Apache Kafka, you can easily build Kafka-based applications for various use cases, such as event sourcing, log aggregation, stream processing, and more, while leveraging the Spring ecosystem’s benefits and features.