What is Kafka topic partitioning and why is it important?
Kafka topic partitioning is a way to split a topic into multiple partitions, allowing for parallel processing and scalability.
How does partitioning improve performance in Kafka?
Partitioning allows multiple consumers to read from different partitions simultaneously, increasing throughput and reducing latency.
Can you explain how data is distributed across partitions?
Data is distributed based on a key. Messages with the same key go to the same partition, ensuring order for those messages.
What happens if a partition fails?
If a partition fails, Kafka can automatically redirect traffic to replicas of that partition, ensuring data availability.
How do I choose the right number of partitions for my topic?
The right number of partitions depends on your workload, the number of consumers, and the desired level of parallelism.
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