preface
Kafka is widely used because of its high throughput, persistence, distribution and support for streaming data processing.
In recent years, big data technology has developed vigorously, and various platform technologies surrounding big data processing, including components, tools and frameworks, have become richer and richer. There are a growing number of open source tools and practices, of which message queues are an important part. For a large system, we often need to build the logic of the entire system around messages, and Kafka is one of the most popular messaging systems.
Introduction to Kafka and practice
In chapter 1, the basic concepts of Kafka are briefly introduced to facilitate readers to have a general understanding of Kafka.
Chapter 2 introduces the configuration of Kafka installation environment and the compilation of Kafka source code in detail, this chapter for the following chapters Kafka principle and basic operations to prepare.
In chapter 3, the implementation principle and details of Kafka basic components are analyzed. If you just want to learn about Kafka and not how Kafka works, you can skip this chapter. But I think this chapter is worth your time if you really want to grasp the details of Kafka and its implementation.
Chapter 4 analyzes the core process of Kafka, mainly from the Kafka start process to the creation of a topic, producers send messages, consumer consumption message process is briefly introduced. This chapter is an overview of how Kafka works. If you skipped chapter 3 on component implementation principles, you should definitely read this chapter because you can get a better understanding of the main roles and responsibilities of the Kafka runtime, and lay a solid foundation for the actual Kafka section.
Chapter 5 begins with Kafka in action. This chapter through the Kafka script demo, a detailed introduction to Kafka basic application of the operation steps, basic coverage of Kafka related operations, so please follow the book when reading the actual combat.
The API application of Kafka is introduced in detail in Chapter 6. If you don’t use the API for calling Kafka in your practical work, you may want to skip this chapter.
Chapter 7 introduces KafkaStreams. KafkaStreams is a Java library for Kafka that supports streaming data processing. If you don’t want to use this feature, you can skip this chapter.
Chapter 8 introduces the application of Kafka in data acquisition, including integration with Log4j, Flume and HDFS.
In chapter 9, Kafka integrates ELK (Elasticsearch, Logstash, and Kibana) to implement log collection platform.
Chapter 10 introduces Spark and the application of the integration of Kafka and Spark in offline computing and real-time computing through two simple examples.
Limited to the space of the platform, but also for better reading, the chapter is no longer summarized, interested can help forward the article, pay attention to the private reply [learning] to obtain
Kfaka of actual combat
Chapter 1 introduction to Kafka
Chapter 2 Producers
Chapter 3 Consumers
Chapter 4 theme and Zoning
Chapter 5 Log Storage
Chapter 6 delves into the server
Chapter 7 Delves into the client
Chapter 8 Reliability research
Chapter 9 Kafka application
Chapter 10 Kafka Monitoring
Chapter 11 advanced applications
Chapter 12 integration of Kafka and Spark
Appendix A Kafka source code environment setup
Kafka source code analysis and combat
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