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Strategies for Aggregation, Data Cardinality, and Batching

In: Kafka Troubleshooting in Production

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  • Elad Eldor

Abstract

This chapter digs into various adjustments you can make to the Kafka producer that can notably increase your Kafka system's speed, response time, and efficiency. The chapter starts by exploring the partitioning strategy, which aims for an equilibrium between distributing messages evenly and clustering related messages together. It will then dive into adjusting parameters like linger.ms and batch.size to improve speed and decrease response time. From there, you learn how the uniqueness and spread of data values, known as data cardinality, impact Kafka's performance. And finally, you explore why, in some cases, duplicating data for different consumers can be a smart move.

Suggested Citation

  • Elad Eldor, 2023. "Strategies for Aggregation, Data Cardinality, and Batching," Springer Books, in: Kafka Troubleshooting in Production, chapter 0, pages 17-24, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4842-9490-1_2
    DOI: 10.1007/978-1-4842-9490-1_2
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