Introduction
Originally created by LinkedIn, Apache Kafka is an open-source distributed event streaming framework that was later taken up by the Apache Software Foundation. It is made to manage real-time data streams at scale, effectively, and dependably. Kafka is a highly available and fault-tolerant messaging system that processes, stores, distributes, and processes data in real time. It is based on the distributed commit log concept.
Large-scale, real-time data streams can be effectively handled with Kafka's architecture. Below is a summary of its main elements:
Topics
Topics are essential to Apache Kafka since they are the main means of classifying and arranging data streams. Topics are the fundamental abstraction in Kafka. A named stream of records is a topic in Kafka's writing. It stands for a sensible conduit for the publication and consumption of data. You can divide and arrange data according to several parameters, including application, source, or kind, using topics. Data stream feeds is what they stand for. In a Kafka cluster, data can be shared among several brokers thanks to partitions within each topic. Topics can have several producers and consumers and provide a way to organize and categorize data streams.
Partitions
Partitions are essential to the architecture of Apache Kafka because they offer fault tolerance, parallelism, and scalability. Within a Kafka topic, a partition is the basic unit of data arrangement. To enable efficient data distribution among Kafka brokers and enable horizontal growth, each topic can be further subdivided into one or more partitions. Partition is an immutable, ordered sequence of records that can be hosted on many brokers for scalability and fault tolerance. The essential components of Kafka's parallelism are partitions. Partitions allow Kafka to achieve high performance by distributing data across multiple cluster nodes.
Brokers
A Kafka broker is a core component of the Kafka architecture, serving as a fundamental building block for handling data streams. A Kafka broker is a server instance responsible for storing and managing data in Kafka. Brokers form the nodes of a Kafka cluster, and they work together to handle the storage, replication, and distribution of data streams.In a Kafka cluster, brokers are the servers that handle and store data. They can each host one or more partitions for various topics. Brokers exchange information with one another in order to manage client requests, duplicate data, and preserve cluster metadata.
Producers
Producers in Apache Kafka are components responsible for publishing data records to Kafka topics within the Kafka cluster. Producers are applications or systems that generate and send data records to Kafka topics. They can publish various types of data, such as log messages, sensor readings, user events, or financial transactions, depending on the use case. Applications that publish data to Kafka topics are known as producers. Publishers have the option of selecting the topic to publish in as well as, if desired the partition within the topic. They are in charge of efficiently batching and dispatching messages to Kafka brokers.
Consumers
Within a Kafka cluster, consumers in Apache Kafka are components in charge of reading and processing data records from Kafka topics. Customers subscribe to receive data records published to one or more Kafka topics. Within the Kafka cluster, topics act as the distribution and organization channels for data. Applications that read data from Kafka topics are called consumers. Subscribers to one or more topics can receive communications in the chronological sequence in which they were produced. Kafka allows several users to work together to process data simultaneously by providing consumer groups.
Consumer Groups
An Apache consumer group in Kafka is a group of users who collaborate to use data from one or more Kafka subjects. Consumer groups let several consumer applications cooperate to simultaneously consume data from Kafka topics. Consumer groups are collections of consumers who collaborate to consume data from Kafka topics. Each member of a group reading from a different partition enables parallel message processing. Kafka enables fault tolerance and load balancing by ensuring that messages within a partition are delivered to a single consumer within a group.
Zookeeper
Zookeeper is a tool used in Kafka clusters for coordination and management activities. It keeps track of metadata pertaining to partitions, topics, brokers, and consumer groups. For coordination activities like broker registration and leader election, Kafka depends on Zookeeper; however, Kafka is rapidly transitioning to self-managed metadata.
Replication:
In Kafka, replication plays a crucial role in ensuring fault tolerance and high availability of data. Through partition replication, Kafka ensures fault tolerance and high availability. Data is copied asynchronously to the followers of each partition, which consists of one leader and one or more followers. To maintain data continuity in the event of a broker failure or unavailability, Kafka might choose a new leader from among the available replicas.
Conclusion:
Understanding Kafka's architecture is crucial for designing, deploying, and operating Kafka clusters effectively. It enables organizations to build scalable, fault-tolerant, and real-time data processing pipelines and applications. Kafka architecture offers a robust foundation for building scalable, fault-tolerant, and low-latency data infrastructure solutions. Its versatility, performance, and ecosystem make it a preferred choice for organizations seeking to harness the power of real-time data processing and event-driven architectures
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