Data Flow within the Kafka Cluster

Understanding the workflow of both producers and consumers is essential for grasping the dynamics of data transmission within the Kafka cluster.

– Producers – Initiating the Data Flow:

Producers in Kafka:

• Data Initiation: The primary job of Kafka consumers revolves around consuming data which is facilitated by pushing records to assigned topics by producers.
• Asynchronous Messaging: Producers can send asynchronous messages, and since off-cluster decisions do not require Kafka cluster acknowledgments, their operations may continue without any interruption.

Publishing Messages to Topics:

• Topic Specification: Producers are responsible for setting the topic whenever a brand publishes messages, where the data will be stored and processed.
• Record Format: Messages are structured as keys, value, and their respective metadata. In other words, the key is the identifier, while the value is the message contents, and the metadata is the information attached to the record.

// Sample Kafka Producer in Java
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

KafkaProducer<String, String> producer = new KafkaProducer<>(props);

// Sending a message to the "example-topic" topic
ProducerRecord<String, String> record = new ProducerRecord<>("example-topic", "key", "Hello, Kafka!");
producer.send(record);

// Closing the producer
producer.close();

– Consumers – Processing the Influx of Data

The Role of Consumers in Kafka:

• Data Consumption: Through subscription, consumers process data exchanged via Producer channeling in Kafka to become the key actor of the Kafka ecosystem.
• Parallel Processing: Consumers can systematize simultaneously through the utilization of consumer networks in that way enabling fast and detailed processes on the same basis as directories or databases are used.

Subscribing to Topics:

• Topic Subscription: In contrast to the broadcasting model, consumers subscribe to specific topics of interest and will receive only the data streams into their end systems that they need for their actual purpose.
• Consumer Group Dynamics: Several subscribers can create a joint consumer group to conduct jointly received topics without interference from others.
• Consumer Groups for Parallel Processing: Consumer Groups for Parallel Processing:
• Group Coordination: The Consumer Group takes care of the concurrency aspect and ensures that the messages are being processed by just one consumer at a time and not all.
• Parallel Scaling: The ability of consumer groups to parallel scale makes an impact on quality, enabling additional consumers to join and increasing processing capacity.

Maintaining Consumer Offsets:

• Offset Tracking: Message offsets are the consumer records themselves, and existing offsets suggest the position of the last message on each partition.
• Fault Tolerance: Tracking off-sets will allow consumers to keep abreast of the last consumed message, so they can proceed from where they left if the processing fails. This option is fault-tolerant.

// Sample Kafka Consumer in Java
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("group.id", "example-group");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);

// Subscribing to the "example-topic" topic
consumer.subscribe(Collections.singletonList("example-topic"));

// Polling for messages
while (true) {
    ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
    for (ConsumerRecord<String, String> record : records) {
        // Process the received message
        System.out.printf("Received message: key=%s, value=%s%n", record.key(), record.value());
    }
}

The Role of Zookeeper: Orchestrating Kafka’s Symphony

While Kafka no longer depends on Zookeeper after version 2.8.0, understanding its historical significance is valuable.

Historical Significance of Zookeeper in Kafka:

• Coordination Service: Main task of Zookeeper was always to manage the cluster, controlling the essence of the roles that the investigators were to be involve in.
• Metadata Management: The zookeeper service in the Kafka data processing pipeline maintained metadata about brokers, partitions and consumer groups and produced consistency in the cluster.

Managing Broker Metadata:

• Dynamic Broker Discovery: Brokers were made discover-able since the broker discovery was dynamic and clients can maintain connectivity to available brokers.
• Metadata Updates: The responsibility for altering broker metadata was entrusted to the zoosepper, aimed at keeping the clients in the loop about the latest changes occurring in the Kafka cluster.

Leader Election and Configuration Tracking: Leader Election and Configuration Tracking:

• Leader Election: The choice of a leader within a single partition was done solely by zookeeper dictating which of the brokers was at the time the leader.
• Configuration Tracking: Zookeeper provide ways to trace configuration changes within Kafka cluster with the purpose of providing nodes that operate with the newest settings.

Apache Kafka has by now made a perfect fit for developing reliable internet-scale streaming applications which are also fault-tolerant and capable of handling real-time and scalable needs. In this article, we will look into Kafka Cluster architecture in Java by putting that in the spotlight.

In this article, we will learn about, Apache Kafka – Cluster Architecture.