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Saikrishna Cheruvu
Saikrishna Cheruvu
BigData🔸PySpark🔸Hadoop🔸SQL🔸AWS🔸GCP🔸AZURE🔸Snowflake🔸DWH🔸Power BI🔸DBT
Published Oct 14, 2022
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Kafka brokers
How brokers and topics are related?
Consider a scenario where Topic-A has three partitions and Topic B has two partitions. Brokers 101, 102, and 103 are the final three Kafka brokers. Broker 101 will therefore have Topic-A, Partition 0, while Broker 102 will have Topic-A, Partition 2. This is not an error. Broker 103 is then discussing Topic-A, Partition 1. Therefore, as we can see, the subject divisions will be distributed among all brokers in any sequence. We also have Topic-B, Partition 1 on Broker 101, and Topic-B, Partition 0 on Broker 102 for this topic. Thus, in this instance, We can see that the data is spread, and since the two partitions have already been added to our Kafka broker, it is expected that Broker 103 does not have any Topic-B data partitions. And this is Kafka's power. As you can see from the example, the data and your partitions will be distributed throughout all brokers. This is how Kafka scales, and it is what is referred to as horizontal scaling. The more partitions and brokers we add, the more evenly the data will be dispersed throughout our whole cluster. We also take note of the fact that the brokers only have the data that they ought to have—not all of it.
Kafka broker discovery
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3 Comments
Sivabalakrishnan Srirangaraman
Software engineer
1mo
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Clear explanation
1Reaction
Anurag Upadhyay
Software Engineer @TBO || Full Stack Developer Intern @perception.ai || Specialist @Codeforces ||1800+ @CodeChef || 1800+ Leetcode || IT 23
4mo
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well explained
1Reaction
PrakashBabu Polisetty
VP- Lead Software Engineer [√ JAVA √ J2EE √ SpringBoot √ Microservices √ PCF √ Kafka √ MongoDB √ Cloud(AWS|AZURE) √ Python || System Design & Cloud Enthusiast ]
1y
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Good post..
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