By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Hey guys I want to work with Kafka Streams real time processing in my spring boot project. Let me start by saying that if you are new to Kafka streams, adding spring-boot on top of it is adding another level of complexity, and Kafka streams has a big learning curve as is.

Here are the basics to get you going: pom:. Now the configuration object. The code below assumes you are creating two stream apps, and keep in mind that each app represents its own processing topology:. This approach uses KafkaStreams bean calling kafkaStreams.

Define KStream bean in you app. As an example, this is a very basic consumer application. It simply consumes data and logs records from the KStream to the standard output. In this application, we defined a single input binding. Spring will bfa mastery weapon enchant this binding with a name process-in-0i.

You use this binding name to set other properties such as topic name. For example, spring. Learn more. Asked 1 year, 8 months ago. Active 3 months ago. Viewed 18k times. I did producer and consumer now I want to stream real time. Active Oldest Votes.Choosing the right messaging system during your architectural planning is always a challenge, yet one of the most important considerations to nail. As a developer, I write applications daily that need to serve lots of users and process huge amounts of data in real time.

Spring Boot is a framework that allows me to go through my development process much faster and easier than before. It has come to play a crucial role in my organization. As the number of our users quickly grew, we realized our apparent need for something that could process as many as 1, events per second.

And since that moment, Kafka has been a vital tool in my pocket. Why did I choose it, you ask? Based on my experience, I provide here a step-by-step guide on how to include Apache Kafka in your Spring Boot application so that you can start leveraging its benefits too. I recommend using the Confluent CLI for your development to have Apache Kafka and other components of a streaming platform up and running.

After reading this guide, you will have a Spring Boot application with a Kafka producer to publish messages to your Kafka topic, as well as with a Kafka consumer to read those messages. Now, you can see what it looks like. Next, we need to create the configuration file. We need to somehow configure our Kafka producer and consumer to be able to publish and read messages to and from the topic.

Instead of creating a Java class, marking it with Configuration annotation, we can use either application. Spring Boot allows us to avoid all the boilerplate code we used to write in the past, and provide us with much more intelligent way of configuring our application, like this:. If you want to get more about Spring Boot auto-configuration, you can read this short and useful article. For a full list of available configuration properties, you can refer to the official documentation.

To set it up, enter the following:. In your real application, you can handle messages the way your business requires you to. If we already have a consumer, then we already have all we need to be able to consume Kafka messages.

To fully show how everything that we created works, we need to create a controller with single endpoint. The message will be published to this endpoint, and then handled by our producer.

In fewer than 10 steps, you learned how easy it is to add Apache Kafka to your Spring Boot project. If you followed this guide, you now know how to integrate Kafka into your Spring Boot project, and you are ready to go with this super tool! You can also find all the code in this article on GitHub. This is a guest post by Igor Kosandyak, a Java software engineer at Oril, with extensive experience in various development areas.

While ksqlDB and Kafka Streams […]. Less than six months ago, we announced support for Microsoft Azure in Confluent Cloud, which allows developers using Azure as a public cloud to build event streaming applications with Apache […]. This website uses cookies to enhance user experience and to analyze performance and traffic on our website.

Spring Boot + Apache Kafka Hello World Example

We also share information about your use of our site with our social media, advertising, and analytics partners. November 1, Last Updated: November 7, StringDeserializer value-deserializer: org. StringDeserializer producer: bootstrap-servers: localhost key-serializer: org. StringSerializer value-serializer: org. StringSerializer If you want to get more about Spring Boot auto-configuration, you can read this short and useful article.

Step 4: Create a producer Creating a producer will write our messages to the topic.Skip to content. Instantly share code, notes, and snippets. Code Revisions 1 Stars 2 Forks 4. Embed What would you like to do? Embed Embed this gist in your website. Share Copy sharable link for this gist. Learn more about clone URLs. Download ZIP. With SSL configured spring : application : name : my-stream-app kafka : bootstrap-servers : - server - server ssl : truststore-location : file:ca-truststore-client.

JsonNode ; import com. ObjectNode ; import org. Serdes ; import org. KafkaStreams ; import org. StreamsBuilder ; import org. StreamsConfig ; import org. Topology ; import org. GlobalKTable ; import org. KStream ; import org. Materialized ; import org. Autowired ; import org. Value ; import org. KafkaProperties ; import org. Bean ; import org. Configuration ; import org. JsonDeserializer ; import org. JsonSerde ; import java.

kafka consumer spring boot example github

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment.Why do I need to import static org. Hi fjoallandYou don't need to. I just used it for the assertions at the end of the test. Thanks vzickner. Anyway, I like your example, it's working for me. But do you think it's possible to test my Service with this example? I have a service who is communicating with a kafka server, and the problem is, when I import this service in my test and run the specific method who communicate with Kafka, it will send a message in my real kafka server.

I don't want that, I thought kafka-test will make a kafka server mock everywhere and not only in my test. Hi fjoallandthe test should work without the assertions as well.

The embedded kafka server is only enabled when you have the EmbeddedKafka annotation. So you would need to skip this annotation for the other tests. Hi ChDenister, do you have the EmbeddedKafka annotation? That one is registering the bean for the EmbeddedKafkaBroker. See also my blog post and the documentation. Hi shoreviewanalyticsI assume you have a timing issue and the message is sent before you are listening.

In the related blog posts are different ways explained see Configure Kafka Consumer. Have you replaced the. I wonder if anyone is using Linux? I am using Ubuntu I built a new bare bones project and added this code and still it just hangs. I am able to other code just not the embedded Kafka. Never mind, if I use version and a recent example all is well.

And why it is a difference if we use the deprecated version of poll long instead of poll Duration. Could you please explain to a completely newbee the reason of this different behaviour? It is a bug? What will be the solution when the deprecated poll long method would be available anymore in the future?

Thanks for that! I'm also having trouble with the embeddedkafka annotation. I always seem to run into the following error in intelliJ when trying to Autowire the embeddedKafkaBroker and I can't figure out why:.

Skip to content.I made a small project, heart-rate-calculatorwhose goal is to take a file containing heartbeat information in input, compute and write the heart rates in another file. That was quite a nice project, but I was wondering if I want to go further, what the project would look like? Mmh, a bit more tricky. This is a good opportunity to play with Kafka and Kafka Streams!

In this blog post, I will show how to build a resilient system to compute and generate heart rates from heartbeat data. I will use docker-compose to mount the whole environment. I want to design a scalable, real-time and distributed pipeline to display the heart rate from a continuous stream of heartbeats provided by smart-watches. One way is to use Apache Kafkawhich is a distributed streaming platform, to help me achieve that and that was the whole of this project: learn and play with Kafka.

Before diving in developing the micro-services, I need to define the models that will circulate around the micro-services: HeartBeat and HeartRate. With Avro, you define the model schemas that contain the fields of the data along with their types. Once the Avro object is formed, it can easily be serialized as an array of bytes, which is the type Kafka likes. Avro also support schema evolutivity: each schema that is registered in the registry has their own version. It stresses out the backward and forward compatibility by having strict verification in the schema registry.

What is the schema registry? The Schema Registry is a serving layer for the Avro metadata.

kafka consumer spring boot example github

It provides RESTful interfaces for storing and retrieving Apache Avro schemas, stores a versioned history of all schemas based on a specified subject name strategy, provides multiple compatibility settings and allows evolution of schemas according to the configured compatibility settings and expanded Avro support.

It provides serializers that plug into Apache Kafka clients that handle schema storage and retrieval for Kafka messages that are sent in the Avro format. Using the Avro specificationsthe Avro model looks like this:. I needed to have the declaration of those two models in a single schema file because the Schema Registry does not support having a schema that is dependent of another schema file. The smart-watches must send their data somewhere.

The pom. Just a simple dependency to spring-boot-starter-web and we can create the controller. As you notice, I used Spring Kafka to help me easily configure the Kafka producer client KafkaTemplate by providing the right properties in my application. Kafka likes bytes array, so we need to specify the serializers to use when serializing the key and the value of the message.

However, the timestamp is not registered correctly.Based on this configuration, you could also switch your Kafka producer from sending JSON to other serialization methods. This sample application also demonstrates the usage of three Kafka consumers within the same consumer group, so the messages are load-balanced between the three. Each consumer implements a different deserialization approach.

To better understand the configuration, have a look at the diagram below. As you can see, we create a Kafka topic with three partitions. On the consumer side, there is only one application, but it implements three Kafka consumers with the same group.

This is the configuration needed for having them in the same Kafka Consumer Group. When we start the application, Kafka assigns each consumer a different partition. This consumer group will receive the messages in a load-balanced manner. The logic we are going to build is simple. Each time we call a given REST endpoint, hellothe app will produce a configurable number of messages and send them to the same topic, using a sequence number as the Kafka key.

It will wait using a CountDownLatch for all messages to be consumed before returning a message, Hello Kafka! There will be three consumers, each using a different deserialization mechanism, that will decrement the latch count when they receive a new message. First, you need to have a running Kafka cluster to connect to. For this application, I will use docker-compose and Kafka running in a single node.

This is clearly far from being a production configuration, but it is good enough for the goal of this post. Note that I configured Kafka to not create topics automatically.

We will create our topic from the Spring Boot application since we want to pass some custom configuration anyway. If you want to play around with these Docker images e.

The easiest way to get a skeleton for our app is to navigate to start. Then, download the zip file and use your favorite IDE to load the sources. You may need to rename the application. These are the configuration values we are going to use for this sample application:. The second block is application-specific. This is the Java class that we will use as Kafka message. To keep the application simple, we will add the configuration in the main Spring Boot class. Eventually, we want to include here both producer and consumer configuration, and use three different variations for deserialization.

Remember that you can find the complete source code in the GitHub repository. There are a few basic Serializers available in the core Kafka library javadoc for Strings, all kind of number classes and byte arrays, plus the JSON ones provided by Spring Kafka javadoc. That gives you a lot of flexibility to optimize the amount of data traveling through Kafka, in case you need to do so.

But you have to consider two main advantages of doing this:. This is the first implementation of the controller, containing only the logic producing the messages. In the constructor, we pass some configuration parameters and the KafkaTemplate that we customized to send String keys and JSON values. As you can see, there is no implementation yet for the Kafka consumers to decrease the latch count.

After the latch gets unlocked, we return the message Hello Kafka! That way, you can check the number of messages received.

As mentioned previously on this post, we want to demonstrate different ways of deserialization with Spring Boot and Spring Kafka and, at the same time, see how multiple consumers can work in a load-balanced manner when they are part of the same consumer-group. This configuration may look extense but take into account that, to demonstrate these three types of deserialization, we have repeated three times the creation of the ConsumerFactory and the KafkaListenerContainerFactory instances so we can switch between them in our consumers.Apache Kafka is a distributed messaging system which is initially developed by Jay Kreps when he was working in Linkedin.

Apache Kafka designed as a distributed system which helps to scale horizontally. In this blogpost, project simulates a basic product data pipeline of e-commerce site.

Apache Kafka will be using as data hub between data producer and data consumer. This will help systems can be developed with high coherence and low coupling. One part of the system is just create a message and publish it to Kafka under a specific topic name, if any other system require that message then just need to subscribe that topic and read the messages from Kafka. It means Kafka helps to build an event driven asynchronous architecture. In this blogpost one part of the system publish message under Product.

One of the other part of the system subscribe that topics and consume these messages. If it is exist just update the price change. One of the difference from other messaging platforms that Kafka is not directly send messages to specific receivers. So Kafka provides messages and subscribers consume it independently from publishers.

How to Work with Apache Kafka in Your Spring Boot Application

In above configuration class, KafkaConfiguration. So factory beans are defined at KafkaConfiguration.

kafka consumer spring boot example github

It is the address of the broker. In a kafka cluster this field has more than one value which are separated via comma. In our example we are running one Kafka broker, which is not a good example in real world kafka application, where address is coming from kafka. Every record message sent to Kafka has a key and value, and before send the record to Kafka we need to serialize that key information, Kafka is hold the data as byte arrays. So this configuration define the serializer class for key.

When producer send message to broker, it will also get an acknowledge message from broker when message arrived. In cluster environment Kafka may have few different brokers. So this parameter define when an acknowledge should be send to producer. If it is set to 1 then producer will get an acknowledge when message arrive to leader broker. This parameter controls the amount of time wait for additional messages before sending the current batch.

Producer sends a batch of messages either when the current batch is full or when the linger. So if this parameter set it as higher value it will increase the latency but at the same time it will increase the throughput because we are sending more messages in a batch.

Kafka hold the data as byte array so when reading the message key from Kafka, it is needed to deserialize to an object. Kafka hold the data as byte array so when reading the message value from Kafka we need to deserialize it to an object.

This property determine consumer commit strategy. If it is true, consumer will commit periodically after fetch the records from brokers. By default it is 5 seconds. If it is false, commit should be done by developer after processing the records.

If we are adding a new consumer after rebalancing is done, new consumer starts to consume messages from partitions. When consumer fetch records from broker it reads the records in a batch. This property determine how many records can be read maximum in one fetch request.

Microservices Design Pattern - When to use Kafka and REST? - Tech Primers

For postgres configuration read configure postgres section in spring boot docker post. In below configuration defined 4 different services as zookeeper, kafka, postgres and spring-boot-kafka. One of the important configuration is spring-boot-kafka service kafka.