Apache Kafka Practice

Increasing the throughput of a Kafka topic can be effectively achieved by increasing the number of partitions for that topic. Kafka uses partitions to parallelize the consumption of messages. Each partition can be consumed by a separate consumer, which allows multiple consumers to read from the same topic simultaneously. By distributing the data across a greater number of partitions, you enable more consumers to process messages concurrently, thereby increasing the overall throughput of the topic.

Each partition is an ordered log, and as messages are produced to the topic, they are distributed among the partitions based on the partitioning strategy being used (e.g., round-robin, hash). Thus, adding more partitions allows Kafka to more efficiently utilize the available resources, such as CPU and I/O, leading to improved performance in message production and consumption.

While other options may seem relevant, they do not directly contribute to increasing throughput as effectively as adding partitions does. For example, reducing the retention limit primarily impacts how long messages are stored, not the rate at which they can be processed. Consolidating multiple topics into one could also complicate the architecture and may not provide any performance gain, as it could lead to more competition for the same set of consumers. Disabling message acknowledgments might seem like it would speed things up by