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October 13, 2022

The Kafka producer is conceptually much simpler than the consumer since it has no need for group coordination. A producer partitioner maps each message to a topic partition, and the producer sends a produce request to the leader of that partition.
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Annapurna Singh




A producer partitioner maps each message to a topic partition, and the producer sends a produce request to the leader of that partition. The partitioners shipped with Kafka guarantee that all messages with the same non-empty key will be sent to the same partition.

The Kafka producer is conceptually much simpler than the consumer since it has no need for group coordination. A producer partitioner maps each message to a topic partition, and the producer sends a produce request to the leader of that partition. The partitioners shipped with Kafka guarantee that all messages with the same non-empty key will be sent to the same partition.

Each partition in the Kafka cluster has a leader and a set of replicas among the brokers. All writes to the partition must go through the partition leader. The replicas are kept in sync by fetching from the leader. When the leader shuts down or fails, the next leader is chosen from among the in-sync replicas. Depending on how the producer is configured, each produce request to the partition leader can be held until the replicas have successfully acknowledged the write. This gives the producer some control over message durability at some cost to overall throughput.

Messages written to the partition leader are not immediately readable by consumers regardless of the producer’s acknowledgement settings. When all in-sync replicas have acknowledged the write, then the message is considered committed, which makes it available for reading. This ensures that messages cannot be lost by a broker failure after they have already been read. Note that this implies that messages which were acknowledged by the leader only (that is, acks=1) can be lost if the partition leader fails before the replicas have copied the message. Nevertheless, this is often a reasonable compromise in practice to ensure durability in most cases while not impacting throughput too significantly.

Most of the subtlety around producers is tied to achieving high throughput with batching/compression and ensuring message delivery guarantees as mentioned above. In the next section, the most common settings to tune producer behavior are discussed.

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    • Kafka Use Cases Unlimited
    • Who uses Kafka? Unlimited
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    • Module 1. Kafka Overview Deck Unlimited
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    • Producer Review Unlimited
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    • Nice Shutdown producer.close() Unlimited
    • Restart Producer then shut it down Unlimited
    • Module 10. Producer Shutdown Deck Unlimited
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    • Kafka Design Motivation Scale Unlimited
    • Kafka Design Motivation Use Cases Unlimited
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    • Replicated Log Partitions Unlimited
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    • Let’s say Unlimited
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