How is Kafka structured? Do you understand how brokers and queues work?

Apache Kafka is a distributed data streaming platform that functions as a high-performance message queue with the ability to store and process large volumes of data in real-time.

Main components of Kafka:

• Brokers — servers that receive, store, and transmit messages. Multiple brokers form a cluster for scalability and fault tolerance.
• Topics — logical channels where messages are published. Each topic is divided into several Partitions for parallel processing.
• Producers — clients that send messages to topics.
• Consumers — clients that read messages from topics.

Topic partitions are stored on brokers and ordered by offset. This allows consumers to read messages sequentially and control which messages have been processed.

Kafka is not a traditional queue with message deletion after reading. Instead, messages are stored for a specified duration or until the log size is reached, allowing multiple consumers to read the same data independently.

Thus, brokers provide message storage and transmission, while topics and partitions enable scaling and ordering of the data stream.

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Example: if you have a "orders" topic, a producer sends new orders there, brokers store them in partitions, and consumers (e.g., order processing services) read these messages for further processing.