How does Apache Kafka handle data replication?

Vaibhav Kothia May 26, 2023 0 Comments

Apache Kafka handles data replication by replicating messages from a leader to one or more followers. The leader is responsible for managing the message replication process, while the followers passively replicate the leader.

For example, let’s say there is a Kafka cluster with three nodes, A, B, and C. Node A is the leader and nodes B and C are the followers. When a message is published to the cluster, it is first written to the leader (node A). The leader then replicates the message to the followers (nodes B and C). If the leader fails, one of the followers (node B or C) will be elected as the new leader and will continue to replicate messages to the other followers.

What are the main components of Apache Kafka?

Vaibhav Kothia May 26, 2023 0 Comments

1. Brokers: A Kafka cluster consists of one or more servers (Kafka brokers) running Kafka. Each broker is identified by its id, and it contains certain topic partitions. For example, a broker with id 1 may contain topic partitions 0 and 1.

2. Topics: A topic is a category or feed name to which messages are published. For example, a topic can be a user activity log or a financial transaction log.

3. Producers: Producers are processes that publish data to topics. For example, a producer may publish a user purchase event to a topic called “user_purchases”.

4. Consumers: Consumers are processes that subscribe to topics and process the published messages. For example, a consumer may subscribe to the “user_purchases” topic and process each message to update the user’s profile in the database.

5. Zookeeper: Apache Zookeeper is a distributed coordination service that helps maintain configuration information and provide synchronization across the cluster. It is used by Kafka to manage the cluster.

What is Apache Kafka?

Vaibhav Kothia May 26, 2023 0 Comments

Apache Kafka is an open-source distributed streaming platform that enables you to build real-time streaming data pipelines and applications. It is a high-throughput, low-latency platform that can handle hundreds of megabytes of reads and writes per second from thousands of clients.

For example, a company may use Apache Kafka to build a real-time data pipeline to collect and analyze customer data from multiple sources. The data can then be used to create personalized recommendations, trigger automated actions, or power a dashboard.