فهرست مطالب

• Apache Kafka in Action
• brief contents
• contents
• foreword
• preface
• acknowledgments
• about this book
• about the authors
• about the cover illustration
• Part 1 Getting started
• Chapter 1. Introduction to Apache Kafka
• 1.1 What is Apache Kafka, and how does it solve our problems?
• 1.2 Kafka in enterprise ecosystems
• 1.3 Architectural overview of Kafka
• 1.4 Running and using Kafka
• 1.5 Our learning path
• Chapter 2. First steps with Kafka
• 2.1 Introducing our use case
• 2.2 Producing messages
• 2.3 Consuming messages
• 2.4 Consuming and producing messages in parallel
• 2.5 Graphical user interfaces for Kafka
• Part 2 Concepts
• Chapter 3. Exploring Kafka topics and messages
• 3.1 Topics
• 3.2 Messages
• Chapter 4. Kafka as a distributed log
• 4.1 Logs
• 4.2 Kafka as a distributed system
• 4.3 Components of Kafka
• 4.4 Kafka in corporate use
• Chapter 5. Reliability
• 5.1 Acknowledgments
• 5.2 Transactions
• 5.3 Replication and the leader-follower principle
• Chapter 6. Performance
• 6.1 Configuring topics for performance
• 6.2 Producer performance
• 6.3 Broker configuration and optimization
• 6.4 Consumer performance
• Part 3 Kafka deep dive
• Chapter 7. Cluster management
• 7.1 Apache Kafka Raft cluster management
• 7.2 ZooKeeper Cluster Management
• 7.3 Migrating from ZooKeeper to KRaft
• 7.4 Connection to Kafka
• Chapter 8. Producing and persisting messages
• 8.1 Producer
• 8.2 Broker
• 8.3 Data and file structures
• 8.4 Replication
• Chapter 9. Consuming messages
• 9.1 Fetching messages
• 9.2 Broker handling of consumer fetch requests
• 9.3 Offsets and Consumer
• 9.4 Understanding and managing Kafka consumer groups
• Chapter 10. Cleaning up messages
• 10.1 Why clean up messages?
• 10.2 Kafka’s cleanup methods
• 10.3 Log retention
• 10.4 Log compaction
• Part 4 Kafka in enterprise use
• Chapter 11. Integrating external systems with Kafka Connect
• 11.1 What is Kafka Connect?
• 11.2 Kafka Connect cluster: Distributed Mode
• 11.3 Scalability and fault tolerance of Kafka Connect
• 11.4 Worker configuration
• 11.5 The Kafka Connect REST API
• 11.6 Connector configuration
• 11.7 Single message transformations
• 11.8 Kafka Connect example: JDBC Source Connector
• 11.9 Kafka Connect example: Change data capture connector
• Chapter 12. Stream processing
• 12.1 Stream processing overview
• 12.2 Stream processors
• 12.3 Stream processing using SQL
• 12.4 Stream states
• 12.5 Streaming and time
• 12.6 Scaling Kafka Streams
• Chapter 13. Governance
• 13.1 Schema management
• 13.2 Security
• 13.3 Quotas in Kafka: Protecting the cluster from overload
• Chapter 14. Kafka reference architecture
• 14.1 Useful components and tools
• 14.2 Deployment environments
• 14.3 Hardware requirements
• Chapter 15. Kafka monitoring and alerting
• 15.1 Infrastructure metrics
• 15.2 Broker metrics
• 15.3 Client metrics
• 15.4 Alerting
• 15.5 Kafka deployment environments and their monitoring challenges
• Chapter 16. Disaster management
• 16.1 What could possibly go wrong?
• 16.2 Backing up Kafka
• 16.3 Mirroring Kafka clusters with MirrorMaker
• Chapter 17. Comparison with other technologies
• 17.1 Data on the outside vs. data on the inside
• 17.2 Classic messaging systems vs. Kafka
• 17.3 REST vs. Kafka
• 17.4 Relational databases vs. Kafka
• 17.5 Kafka is the core of a streaming platform
• Chapter 18. Kafka’s role in modern enterprise architectures
• 18.1 Kafka as the core of a data mesh
• 18.2 Liberating data from core systems with Kafka
• 18.3 Kafka for big data
• 18.4 Kafka for the Industrial Internet of Things
• 18.5 What Kafka is not
• Appendix A Setting up a Kafka test environment
• Appendix B Monitoring setup
• index