What are partitions in Kafka and how to choose the number of consumers?

In Kafka, partitions are logical divisions of a topic that allow scaling message processing and provide parallelism.

Each partition is an ordered, immutable log of messages that can be accessed independently.

Choosing the number of partitions:

• The more partitions, the higher the parallelism and throughput.
• But too many partitions increase the load on brokers and management complexity.
• Usually, the number of partitions is chosen based on expected load and the number of consumers.

Choosing the number of consumers:

• The maximum number of active consumers in a group equals the number of partitions, as each partition can only be processed by one consumer in the group.
• If there are more consumers than partitions, the extra consumers will be idle.
• If fewer, some consumers will process multiple partitions.

Example: If you have a topic with 10 partitions, it is optimal to have up to 10 consumers in the group for maximum parallelism.

Thus, when designing the system, you need to balance between the number of partitions and consumers, considering load, latency requirements, and resources.