فهرست مطالب

• Cover
• Copyright
• Table of Contents
• Foreword
• Preface
• Part I. Kafka
• Chapter 1. A Rapid Introduction to Kafka
• Communication Model
• How Are Streams Stored?
• Topics and Partitions
• Events
• Kafka Cluster and Brokers
• Consumer Groups
• Installing Kafka
• Hello, Kafka
• Summary
• Part II. Kafka Streams
• Chapter 2. Getting Started with Kafka Streams
• The Kafka Ecosystem
• Features at a Glance
• Operational Characteristics
• Comparison to Other Systems
• Use Cases
• Processor Topologies
• High-Level DSL Versus Low-Level Processor API
• Introducing Our Tutorial: Hello, Streams
• Streams and Tables
• Summary
• Chapter 3. Stateless Processing
• Stateless Versus Stateful Processing
• Introducing Our Tutorial: Processing a Twitter Stream
• Project Setup
• Adding a KStream Source Processor
• Serialization/Deserialization
• Filtering Data
• Branching Data
• Translating Tweets
• Merging Streams
• Enriching Tweets
• Serializing Avro Data
• Adding a Sink Processor
• Running the Code
• Empirical Verification
• Summary
• Chapter 4. Stateful Processing
• Benefits of Stateful Processing
• Preview of Stateful Operators
• State Stores
• Introducing Our Tutorial: Video Game Leaderboard
• Project Setup
• Data Models
• Adding the Source Processors
• Registering Streams and Tables
• Joins
• Grouping Records
• Aggregations
• Putting It All Together
• Interactive Queries
• Summary
• Chapter 5. Windows and Time
• Introducing Our Tutorial: Patient Monitoring Application
• Project Setup
• Data Models
• Time Semantics
• Timestamp Extractors
• Windowing Streams
• Emitting Window Results
• Filtering and Rekeying Windowed KTables
• Windowed Joins
• Time-Driven Dataflow
• Summary
• Chapter 6. Advanced State Management
• Persistent Store Disk Layout
• Fault Tolerance
• Rebalancing: Enemy of the State (Store)
• Preventing State Migration
• Reducing the Impact of Rebalances
• Deduplicating Writes with Record Caches
• State Store Monitoring
• Built-in Metrics
• Interactive Queries
• Custom State Stores
• Summary
• Chapter 7. Processor API
• When to Use the Processor API
• Introducing Our Tutorial: IoT Digital Twin Service
• Project Setup
• Data Models
• Adding Source Processors
• Adding Stateless Stream Processors
• Creating Stateless Processors
• Creating Stateful Processors
• Periodic Functions with Punctuate
• Accessing Record Metadata
• Adding Sink Processors
• Interactive Queries
• Putting It All Together
• Combining the Processor API with the DSL
• Processors and Transformers
• Putting It All Together: Refactor
• Summary
• Part III. ksqlDB
• Chapter 8. Getting Started with ksqlDB
• What Is ksqlDB?
• When to Use ksqlDB
• Evolution of a New Kind of Database
• Architecture
• Deployment Modes
• Tutorial
• Chapter 9. Data Integration with ksqlDB
• Kafka Connect Overview
• External Versus Embedded Connect
• Configuring Connect Workers
• Tutorial
• Installing Connectors
• Verifying the Source Connector
• Interacting with the Kafka Connect Cluster Directly
• Introspecting Managed Schemas
• Summary
• Chapter 10. Stream Processing Basics with ksqlDB
• Tutorial: Monitoring Changes at Netflix
• Project Setup
• Source Topics
• Data Types
• Collections
• Working with Streams and Tables
• Basic Queries
• Conditional Expressions
• Writing Results Back to Kafka (Persistent Queries)
• Putting It All Together
• Summary
• Chapter 11. Intermediate and Advanced Stream Processing with ksqlDB
• Project Setup
• Bootstrapping an Environment from a SQL File
• Data Enrichment
• Aggregations
• Materialized Views
• Clients
• Pull Queries
• Push Queries
• Functions and Operators
• Summary
• Part IV. The Road to Production
• Chapter 12. Testing, Monitoring, and Deployment
• Testing
• Monitoring
• Deployment
• Operations
• Upgrading ksqlDB
• Summary
• Appendix A. Kafka Streams Configuration
• Appendix B. ksqlDB Configuration
• Index
• About the Author
• Colophon