BOOTSTRAP_SERVERS_CONFIG, Spring Kafka Bootstrap Servers Properties, spring: kafka: client-id: square-finder bootstrap-servers: - localhost:9091 - localhost:9092 - localhost:9093 template: default-topic: input-topic Overview: In this article, Lets do stream processing using Kafka. The first group, Connection, is properties dedicated to setting up the connection to the event stream instance.While, in this example, only one server is defined, spring.kafka.bootstrap-servers can take a comma-separated list of server URLs. Sending messages to Kafka through Reactive Streams. In the the tutorial, we use jsa.kafka.topic to define a Kafka topic name to produce and receive messages. Overall: Spring Boot’s default configuration is quite reasonable for any moderate uses of Kafka. Export some RestAPIs 4. Also, learn to produce and consumer messages from a Kafka topic. – jsa.kafka.topic is an additional configuration. In applicatiopn.properties, the configuration properties have been separated into three groups:. The above snippet creates a Kafka producer with some properties. Kafka streams topology. BOOTSTRAP_SERVERS_CONFIG: The Kafka broker's address. Consumer (which recieve messages from the Kafka Server). The goal of the Gateway application is to set up a Reactive stream from a webcontroller to the Kafka cluster. First a few concepts: Kafka is run as a cluster on one or more servers that can span multiple datacenters. A crucial configuration parameter is the BOOTSTRAP_SERVER_CONFIG. In Spring Boot, spring.kafka.bootstrap-servers is the parameter responsible for connecting to Kafka. Both of these alternatives work on similar principles: You need to create an object associated with the Kafka broker (embedded or containerized), get the connection address from it, and pass the address to the application parameters. So I use Spring Kafka again to create them when the application starts up: Example of configuring Kafka Streams within a Spring Boot application with an example of SSL configuration - KafkaStreamsConfig.java If you need more in-depth information, check the official reference documentation . Stream processing is a real time continuous data processing. Our applications are built on top of Spring 5 and Spring Boot 2, enabling us to quickly set up and use Project Reactor. To use Kafka streams, we need to define a Kafka Streams topology, which is basically a sequence of actions. If this tutorial was helpful and you’re on the hunt for more on stream processing using Kafka Streams, ksqlDB, and Kafka, don’t forget to check out Kafka Tutorials. spring.kafka.consumer.group-id=kafka-intro spring.kafka.bootstrap-servers=kafka:9092 You can customize how to interact with Kafka much further, but this is a topic for another blog post. 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