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