فهرست مطالب

• Kafka Streams in Action
• Praise for the first edition
• brief contents
• contents
• foreword
• preface
• acknowledgments
• about this book
• about the author
• about the cover illustration
• Part 1
• Chapter 1. Welcome to the Kafka event streaming platform
• 1.1 Event streaming
• 1.2 What is an event?
• 1.3 An event stream example
• 1.4 Introducing the Apache Kafka event streaming platform
• 1.5 A concrete example of applying the Kafka event streaming platform
• Summary
• Chapter 2. Kafka brokers
• 2.1 Introducing Kafka brokers
• 2.2 Produce requests
• 2.3 Fetch requests
• 2.4 Topics and partitions
• 2.5 Sending your first messages
• 2.6 Segments
• 2.7 Tiered storage
• 2.8 Cluster metadata
• 2.9 Leaders and followers
• 2.10 Checking for a healthy broker
• Summary
• Part 2
• Chapter 3. Schema Registry
• 3.1 Objects
• 3.2 What is a schema, and why do you need one?
• 3.3 Subject name strategies
• 3.4 Schema compatibility
• 3.5 Schema references
• 3.6 Schema references and multiple events per topic
• 3.7 Schema Registry (de)serializers
• 3.8 Serialization without Schema Registry
• Summary
• Chapter 4. Kafka clients
• 4.1 Introducing Kafka clients
• 4.2 Producing records with the KafkaProducer
• 4.3 Consuming records with the KafkaConsumer
• 4.4 Exactly-once delivery in Kafka
• 4.5 Using the Admin API for programmatic topic management
• 4.6 Handling multiple event types in a single topic
• Summary
• Chapter 5. Kafka Connect
• 5.1 An introduction to Kafka Connect
• 5.2 Integrating external applications into Kafka
• 5.3 Getting started with Kafka Connect
• 5.4 Applying Single Message Transforms
• 5.5 Adding a sink connector
• 5.6 Building and deploying your own connector
• Summary
• Part 3
• Chapter 6. Developing Kafka Streams
• 6.1 A look at Kafka Streams
• 6.2 Kafka Streams DSL
• 6.3 Hello World for Kafka Streams
• 6.4 Masking credit card numbers and tracking purchase rewards in a retail sales setting
• 6.5 Interactive development
• 6.6 Choosing which events to process
• Summary
• Chapter 7. Streams and state
• 7.1 Stateful vs. stateless
• 7.2 Adding stateful operations to Kafka Streams
• 7.3 Stream-stream joins
• 7.4 State stores in Kafka Streams
• Summary
• Chapter 8. The KTable API
• 8.1 KTable: The update stream
• 8.2 KTables are stateful
• 8.3 The KTable API
• 8.4 KTable aggregations
• 8.5 GlobalKTable
• 8.6 Table joins
• Summary
• Chapter 9. Windowing and timestamps
• 9.1 Understanding the role of windows and the different types
• 9.2 Handling out order data with grace—literally
• 9.3 Final windowed results
• 9.4 Timestamps in Kafka Streams
• 9.5 The TimestampExtractor
• 9.6 Stream time
• Summary
• Chapter 10. The Processor API
• 10.1 Working with sources, processors, and sinks to create a topology
• 10.2 Digging deeper into the Processor API with a stock analysis processor
• 10.3 Data-driven aggregation
• 10.4 Integrating the Processor API and the Kafka Streams API
• Summary
• Chapter 11. ksqlDB
• 11.1 Understanding ksqlDB
• 11.2 More about streaming queries
• 11.3 Persistent vs. push vs. pull queries
• 11.4 Creating Streams and Tables
• 11.5 Schema Registry integration
• 11.6 ksqlDB advanced features
• Summary
• Chapter 12. Spring Kafka
• 12.1 Introducing Spring
• 12.2 Using Spring to build Kafka-enabled applications
• 12.3 Spring Kafka Streams
• Summary
• Chapter 13. Kafka Streams Interactive Queries
• 13.1 Kafka Streams and information sharing
• 13.2 Learning about Interactive Queries
• Summary
• Chapter 14. Testing
• 14.1 Understanding the difference between unit and integration testing
• Summary
• Appendices
• Appendix A—Schema compatibility workshop
• Appendix B—Confluent resources
• Appendix C—Working with Avro, Protobuf, and JSON Schema
• Appendix D—Understanding Kafka Streams architecture
• Index