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Kafka’s architecture is designed to handle large-scale real-time data streams efficiently. Here’s a comprehensive overview:

  1. Topics:
    • Data is organized into topics, which are essentially feeds of messages or events.
    • Topics can be thought of as logs where messages are appended.
    • Each topic can have multiple partitions, allowing for parallel processing and scalability.
  2. Producers:
    • Producers are applications or processes that publish data to Kafka topics.
    • Producers write messages to Kafka topics, which are then stored in the topic’s partitions.
    • Producers can choose to specify a key for each message, which determines the partition to which the message will be sent.
  3. Brokers:
    • Kafka brokers are servers responsible for handling and managing Kafka topics and partitions.
    • A Kafka cluster typically consists of multiple brokers, each running Kafka software.
    • Brokers store and serve messages, handle producer requests, and serve consumer requests.
  4. Partitions:
    • Each topic is divided into partitions, which are individual ordered logs of messages.
    • Partitions allow Kafka to scale by distributing data across multiple servers (brokers) and enabling parallel processing of messages.
    • Each partition is replicated across multiple brokers for fault tolerance.
    • Messages within a partition are assigned a sequential offset number, allowing consumers to keep track of their position in the stream.
  5. Replication:
    • Kafka maintains multiple replicas of each partition to ensure fault tolerance and high availability.
    • Replicas are copies of the partition’s log stored on different brokers.
    • One replica serves as the leader, handling read and write requests, while the other replicas serve as followers and replicate data from the leader.
  6. Consumers:
    • Consumers are applications or processes that subscribe to Kafka topics to consume data.
    • Consumers can read messages from one or more partitions within a topic.
    • Consumer groups enable parallel consumption of messages, with each consumer group having its own set of consumers and offset tracking.
  7. ZooKeeper:
    • ZooKeeper is used for managing and coordinating Kafka brokers in a cluster.
    • It helps in leader election, maintaining broker and topic metadata, and detecting broker failures.
    • While ZooKeeper was a critical component in earlier versions of Kafka, newer versions are moving towards removing this dependency.

Kafka’s distributed architecture enables it to handle high-throughput, fault-tolerant, and scalable data streaming applications. It is widely used for real-time data processing, event sourcing, log aggregation, and messaging in various industries.

By Aijaz Ali

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