This chapter covers
- Serialization and partitioning in Kafka
- Acknowledgment handling and broker interactions
- Message reception and persistence
- Optimization within Kafka brokers
- Kafka’s data and file structures
- Replication mechanisms and system performance
This chapter delves into the intricacies of producing and persisting messages in Apache Kafka, which are crucial components of its distributed data architecture. We’ll explore how Kafka manages data serialization, partitioning, acknowledgment handling, and broker interactions, which are essential for ensuring reliability and scalability in real-time data processing. Understanding these aspects is key to optimizing message reception, persistence, and overall system performance within Kafka’s ecosystem. By examining Kafka’s data and file structures, replication mechanisms, and their effect on system efficiency, we gain insights into how these foundational elements contribute to Kafka’s robustness and operational excellence in modern data pipelines.
8.1 Producer
Typically, our producers use either the official Kafka Java library or, if our producer isn’t running in the Java Virtual Machine (JVM), a library that is based on the C library librdkafka (https://github.com/confluentinc/librdkafka).
TIP We generally advise against using other libraries because, although they may sometimes be easier to use, they often lack many features and optimizations.