Kafka Workflow: Producers to Consumers Explained

The main process of Kafka involves producers sending data to the Kafka cluster and consumers reading data from the Kafka cluster. Specifically, the workflow of Kafka is as follows:

1. The producer sends data to a specific topic in the Kafka cluster.
2. A Kafka cluster stores received data in partitions and backs up the data based on the configured replication strategy.
3. Consumers subscribe to specific topics from a Kafka cluster and consume data based on the configured consumer group.
4. Consumers in each consumer group will read data from different partitions based on the partition allocation strategy to achieve load balancing and high availability.
5. After consumers have read the data, they will proceed with the necessary processing such as storage, analysis, and other operations.
6. Once the data has been successfully consumed by the consumer, Kafka will update the consumer’s offset and keep track of the progress of the consumer’s consumption.
7. The Kafka cluster will periodically clean up data that has already been consumed by consumers, deleting data based on the configured data retention policy.

In general, Kafka’s workflow involves producers sending data to the Kafka cluster, consumers reading data from the Kafka cluster, and utilizing mechanisms like partitioning, replication, and consumer groups to achieve high availability, high throughput, and low latency data processing.