Before starting this tutorial, please create a Topic first. Learn How to create a Topic in Kafka.
The Core Container consists of the Core, Beans, Context, and Expression Language modules.
→ It consists of 4 modules viz Core, beans, Context, spEL. These 4 modules provide the fundamentals of spring. It means on this module only the complete spring is based.
Core and beans provide the fundamental part…
— Yes, it is possible but not a good idea to create a topic in this way.
kafka-console-producer.sh --bootstrap-server 127.0.0.1:9092 --topic new_topic
→ Think KafkaTemplate as a JDBCTemplate for database interactions.
KafkaTemplate wraps a producer and provides convenient methods to send data to Kafka topics. The following listing shows the relevant methods from
— If you look below it has many different overloaded versions of
KafkaTemplate with send method.
ListenableFuture<SendResult<K, V>> sendDefault(V data); ListenableFuture<SendResult<K, V>> sendDefault(K key, V data); ListenableFuture<SendResult<K, V>> sendDefault(Integer partition, K key, V data); ListenableFuture<SendResult<K, V>> sendDefault(Integer partition, Long timestamp, K key, V data); ListenableFuture<SendResult<K, V>> send(String topic, V data); ListenableFuture<SendResult<K, V>> send(String topic, K key, V data); ListenableFuture<SendResult<K, V>> send(String topic, Integer partition, K key, V…
KafkaTemplate.send() is going to send the message to Kafka. But in reality, it goes through different layers before the message is sent to Kafka.
— The very first layer is the serializer. Any records sent to Kafka need to be serialized to bytes.
There are two serialization techniques that need to be applied to new records.
— This configuration is mandatory for any producer. The clients need to provide the
key.serializer value and
The Kafka client java libraries come with some predefined serializers.
— The second layer is partitioner. This layer determines which partition the…
group-idis mandatory, it plays a major role when it comes to scalable message consumption.
group-idplays a major role when it comes to scalable message consumption.
let’s consider we have a topic
test-topic and 4 partitions. Now we have a consumer-ready with group 1. we have a single consumer pulling all the partitions in the topic and processing them.
The pull loop is always single-threaded, so in this case, a single thread is going to pull from all the partitions.
We have a zookeeper and Kafka cluster. In this example, we have a cluster with 3 brokers.
topicand create-topic command is issued to the zookeeper. The zookeeper takes care of redirecting the request to the controller.
Here we have a Kafka cluster and a representation of how the topic is distributed across the cluster and we have some records present in the file system.
As we all know clients are producers and consumers always talk to the leader to retrieve data from the partition.
Let’s say a broker-1 goes for some reason. Right now this is the broker which is the leader of partition-0. All the data which is returned to the partition-0 is residing in the file system of this broker-1. Once it goes down there is no way for the clients to access this…