How to resolve the issue of Kafka message duplication?

The Kafka duplicate consumption issue refers to the situation where consumers may consume the same message multiple times while consuming messages. This usually happens in the following scenarios:

1. Consumers made a mistake in handling the message process, failing to submit the offset in a timely manner, which led to the need for re-consumption next time.
2. When a consumer malfunctions or restarts, rejoining the consumer group may result in messages that have already been consumed being consumed again.
3. There was an issue with the Kafka cluster or replicas that caused messages to be sent repeatedly.

To solve this problem, the following methods can be adopted:

1. Automatically commit offset: by setting enable.auto.commit=true in the consumer configuration, Kafka can automatically commit the consumer’s offset. This helps ensure that messages are accurately marked as consumed to avoid duplicates.
2. Manually submit offsets: Manually submitting offsets in the consumer code can be done by calling commitSync() or commitAsync() methods after processing the message. This ensures that the offsets are only committed after successful message processing to avoid duplicate consumption.
3. Enable idempotent consumer: By utilizing Kafka’s idempotent consumer feature, idempotent processing can be implemented on the consumer side to ensure that each message is processed only once.
4. Establish the appropriate consumer group ID: The consumer group ID is a unique identifier used by Kafka to identify a consumer group. Ensure that each consumer group ID has only one consumer instance to avoid multiple consumer instances duplicating the consumption of the same message.
5. Adjust the consumer parameters appropriately: You can adjust parameters such as max.poll.records, fetch.min.bytes, fetch.max.wait.ms, etc., to optimize the consumer’s consumption performance and reduce the possibility of duplicate consumption.
6. Set up the appropriate Kafka parameters: Adjusting Kafka parameters such as message.max.bytes (maximum message byte size) and replica.fetch.max.bytes (maximum replica fetch byte size) can optimize message transmission and storage, reducing the possibility of duplicate sending.

In conclusion, by properly configuring the parameters of consumers and Kafka, as well as using appropriate methods to commit consumer offsets, the issue of duplicate consumption in Kafka can be effectively resolved.