Category : Apache Kafka | Sub Category : Apache Kafka | By Prasad Bonam Last updated: 2023-08-03 01:13:07 Viewed : 306
Explore the main components of Kafka architecture:
Apache Kafka is a distributed streaming platform designed to handle high-throughput, real-time data feeds with fault tolerance and scalability. It is widely used for building data pipelines and streaming applications. The architecture of Kafka consists of several key components that work together to provide a robust and reliable streaming infrastructure. Lets explore the main components of Kafka architecture:
Topics: A topic is a logical channel or category to which records (data) are published by producers and from which records are consumed by consumers. Topics are divided into multiple partitions to enable parallelism and scalability. Each partition is an ordered, immutable sequence of records.
Partitions: A topic can be divided into multiple partitions, and each partition is a linearly ordered and immutable sequence of records. Partitions allow data to be distributed across multiple brokers, enabling horizontal scaling and parallel processing.
Brokers: Brokers are the Kafka servers responsible for handling data storage and movement. Each broker manages one or more partitions. Kafka brokers work together to form a Kafka cluster.
Producers: Producers are applications or systems that publish data records to Kafka topics. They send messages to specific topics and partitions, and the broker handles the data distribution across the partitions.
Consumers: Consumers are applications or systems that read data records from Kafka topics. They consume messages from partitions and can be organized into consumer groups for load balancing and parallel processing.
Consumer Groups: A consumer group is a logical group of consumers that share the load of consuming records from a topic. Each record published to a topic is delivered to only one consumer within each consumer group, allowing for parallel processing of data.
Offsets: Offsets are used to track the progress of consumers within each partition. Each consumer maintains its offset, indicating the position in the partition from which it has consumed data. This allows consumers to resume reading from where they left off in case of failures.
ZooKeeper: Kafka relies on Apache ZooKeeper for maintaining cluster metadata, leadership election, and coordination. ZooKeeper helps keep track of broker status, topics, partitions, and consumer groups.
Kafka Connect: Kafka Connect is a framework and runtime for easily integrating external systems with Kafka. Connectors allow data to be ingested from various sources (source connectors) and sent to different sinks (sink connectors).
Schema Registry: The Schema Registry is a service that manages schemas for data records produced and consumed by Kafka. It enforces schema compatibility to ensure data consistency and evolution.
Data Flow: When a producer publishes a message to a topic, the message is written to the appropriate partition in the broker. Consumers in consumer groups read from these partitions and process the data independently. Each consumer maintains its offset, allowing it to resume reading from where it left off.
Kafkas distributed architecture and fault-tolerant design make it suitable for handling high-volume, real-time data streams, making it a popular choice for building scalable and robust data streaming pipelines and applications.