Spring Boot and Kafka Integration

• Spring Boot is a popular Java framework for building stand-alone, production-ready applications.
• Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications.

Spring Boot Key Concepts

• Auto-configuration automatically configures your application based on added dependencies.
• Starter dependencies are convenient dependency descriptors for applications.
• Actuator provides production-ready features like health checks, metrics, and auditing.
• Spring Boot CLI enables running Groovy scripts.
• Embedded servers such as Tomcat, Jetty, and Undertow are supported.

Common Spring Boot Interview Questions

• Spring Boot simplifies Spring application development with auto-configuration and starter dependencies.
• Advantages include simplified configuration, reduced boilerplate, embedded servers, and Spring project integration.
• Auto-configuration configures applications based on added dependencies.
• Spring Boot Starters are convenient dependency descriptors.
• Projects can be created using Spring Initializr or IDEs like IntelliJ IDEA or Eclipse.
• Applications are run by executing the main class or using Maven/Gradle plugins.
• Actuator offers production-ready features like health checks, metrics, and auditing.
• Customize the banner by creating a banner.txt file in the src/main/resources directory.
• Applications are configured using application.properties or application.yml files.
• Command-line arguments are accessed by injecting String[] args or using the ApplicationArguments interface.
• Spring Profiles configure different application contexts for different environments.
• Use @Profile or set spring.profiles.active to use Spring Profiles.
• Spring Data JPA simplifies JPA-based data access layer development.
• Define interfaces extending JpaRepository to use Spring Data JPA.
• Spring Security provides authentication and authorization support.
• Secure applications by adding the spring-boot-starter-security dependency and configuring security rules.
• Commonly used annotations include @SpringBootApplication, @RestController, @Service, @Repository, @Autowired, @Value, @Component.
• Exceptions are handled using @ControllerAdvice and @ExceptionHandler annotations.
• REST APIs are created using @RestController and @RequestMapping annotations.
• Applications are tested using Spring Boot Test, JUnit, and Mockito.
• Embedded servers include Tomcat, Jetty, and Undertow.
• The default port is changed by setting server.port in application.properties or application.yml.
• Spring Batch is used for robust batch processing applications.
• Integration with technologies like Kafka or RabbitMQ is achieved using Spring Kafka or Spring AMQP starters.
• Logging is implemented using frameworks like Logback or Log4j2.
• Applications are deployed by creating a JAR or WAR file and deploying it to a server or cloud platform.
• Spring Cloud provides tools for building distributed systems and microservices.

Kafka Key Concepts

• Topics are categories or feeds where messages are published.
• Partitions divide topics for parallel processing and scalability.
• Brokers are Kafka servers storing messages.
• Producers are applications publishing messages to topics.
• Consumers are applications subscribing to topics to process messages.
• Zookeeper manages and coordinates Kafka brokers.
• Consumer Groups are groups of consumers consuming messages from topics.
• Offsets are unique incremental IDs for messages within a partition.

Common Kafka Interview Questions

• Kafka is a distributed streaming platform for real-time data pipelines and streaming applications.
• Advantages include high throughput, scalability, fault tolerance, and durability.
• Kafka architecture includes brokers, producers, consumers, and Zookeeper.
• A Kafka topic is a category or feed name for published messages.
• A Kafka partition is a division of a topic, enabling parallel processing and scalability.
• A Kafka broker is a server in a Kafka cluster storing messages.
• Zookeeper manages and coordinates Kafka brokers.
• A Kafka producer publishes messages to Kafka topics.
• A Kafka consumer subscribes to Kafka topics and processes messages.
• A consumer group is consumers working together to consume messages from topics.
• Fault tolerance is ensured by replicating data across multiple brokers.
• Offsets are unique incremental IDs for each message within a partition.
• Kafka uses a pull approach where consumers retrieve data from brokers.
• Kafka handles message delivery semantics with at most once, at least once, and exactly once options.
• In-sync replicas (ISR) are brokers that have completely replicated data, ensuring durability and availability.
• Monitoring is done using tools like Kafka Manager, Burrow, or Prometheus.
• Kafka Streams is a client library for building stream processing applications.
• Kafka Connect is a framework for streaming data between Kafka and other systems.
• High throughput is configured by increasing partitions, brokers, and tuning producer/consumer configurations.
• Low latency is configured by reducing linger.ms and batch.size on the producer side, and increasing fetch.min.bytes on the consumer side.
• Client types include Java, Scala, and other language client libraries.
• Large messages are handled by splitting them into smaller chunks or using compression.
• Common use cases include real-time data pipelines, streaming analytics, event sourcing, and log aggregation.
• Security is implemented using SSL for encryption, SASL for authentication, and ACLs for authorization using Kerberos or similar measures.
• The Kafka Controller manages the state of the Kafka cluster, including broker leadership and partition assignments.
• Kafka doesn't guarantee message order across partitions, only within a partition.
• Kafka is designed for high-throughput, persistent storage, and stream processing, while RabbitMQ is a traditional message broker.
• Producer configuration parameters: bootstrap.servers, key.serializer, value.serializer, acks, linger.ms, batch.size.
• Consumer configuration parameters: bootstrap.servers, key.deserializer, value.deserializer, group.id, auto.offset.reset.
• Configure the number of partitions for a topic by specifying the partitions parameter when creating the topic.

Spring Boot and Kafka Integration

• Spring Kafka simplifies integrating Spring applications with Kafka.
• @KafkaListener creates listener endpoints for handling Kafka messages.
• KafkaTemplate is a Spring helper class for high-level producer operations.
• ProducerFactory is a strategy interface for creating Kafka Producer instances.
• ConsumerFactory is a strategy interface for creating Kafka Consumer instances.

Common Spring Boot Kafka Integration Interview Questions

• Spring Boot and Kafka are integrated using the Spring Kafka project.
• Spring Kafka is configured by adding the spring-kafka dependency and configuring Kafka properties in application.properties or application.yml.
• Messages are sent using KafkaTemplate.
• Messages are received using the @KafkaListener annotation.
• Kafka errors are handled by configuring an ErrorHandler or using a DeadLetterPublishingRecoverer.
• Kafka transactions are configured using @Transactional and a KafkaTransactionManager.
• Multiple Kafka listeners are configured by defining multiple methods with @KafkaListener, each with different topic and groupId.
• Kafka message converters are implemented by configuring a JsonMessageConverter or a custom converter.
• Spring Boot Kafka integration is tested using EmbeddedKafka for integration tests.
• ConsumerFactory creates Kafka Consumer instances, and ProducerFactory creates Kafka Producer instances.
• Kafka consumer concurrency is configured by setting the concurrency attribute in @KafkaListener or configuring a ConcurrentKafkaListenerContainerFactory.
• Manual acknowledgment is set up by setting ackMode to MANUAL in ContainerProperties and using the Acknowledgment object in the listener.
• A DeadLetterQueue is configured using a DeadLetterPublishingRecoverer bean.
• Kafka consumer configuration is customized by setting the properties attribute in @KafkaListener or configuring the ConsumerFactory.
• The GroupId for different consumers is specified using the groupId attribute in @KafkaListener.
• Custom headers are defined using MessageBuilder to add headers to the message before sending it via KafkaTemplate.
• KafkaAdmin automatically configures Kafka topics.

Explore Spring Boot, a Java framework for building applications, and Kafka, a platform for data pipelines. Learn about auto-configuration, starter dependencies, and Actuator in Spring Boot. Understand Kafka's role in real-time data streaming.