
Introduction to Apache Flink
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Content
- Cover
- Copyright
- Table of Contents
- Preface
- How to Use This Book
- Conventions Used in This Book
- Chapter 1. Why Apache Flink?
- Consequences of Not Doing Streaming Well
- Retail and Marketing
- The Internet of Things
- Telecom
- Banking and Financial Sector
- Goals for Processing Continuous Event Data
- Evolution of Stream Processing Technologies
- First Look at Apache Flink
- Batch and Stream Processing
- Flink in Production
- Bouygues Telecom
- Other Examples of Apache Flink in Production
- Where Flink Fits
- Chapter 2. Stream-First Architecture
- Traditional Architecture versus Streaming Architecture
- Message Transport and Message Processing
- The Transport Layer: Ideal Capabilities
- Performance with Persistence
- Decoupling of Multiple Producers from Multiple Consumers
- Streaming Data for a Microservices Architecture
- Data Stream as the Centralized Source of Data
- Fraud Detection Use Case: Better Design with Stream-First Architecture
- Flexibility for Developers
- Beyond Real-Time Applications
- Geo-Distributed Replication of Streams
- Chapter 3. What Flink Does
- Different Types of Correctness
- Natural Fit for Sessions
- Event Time
- Accuracy Under Failures: Keeping Track of State
- Answers When They Matter
- Ease of Development and Operations
- Hierarchical Use Cases: Adopting Flink in Stages
- Chapter 4. Handling Time
- Counting with Batch and Lambda Architectures
- Counting with Streaming Architecture
- Batching in Stream Processing Systems
- Notions of Time
- Windows
- Time Windows
- Count Windows
- Session Windows
- Triggers
- Implementation of Windows
- Time Travel
- Watermarks
- How Watermarks Are Generated
- A Real-World Example: Kappa Architecture at Ericsson
- Chapter 5. Stateful Computation
- Notions of Consistency
- Flink Checkpoints: Guaranteeing Exactly Once
- Savepoints: Versioning State
- End-to-End Consistency and the Stream Processor as a Database
- Flink Performance: the Yahoo! Streaming Benchmark
- Original Application with the Yahoo! Streaming Benchmark
- First Modification: Using Flink State
- Second Modification: Increase Volume Through Improved Data Generator
- Third Modification: Dealing with Network Bottleneck
- Fifth Modification: Increased Cardinality and Direct Query
- Conclusion
- Chapter 6. Batch Is a Special Case of Streaming
- Batch Processing Technology
- Case Study: Flink as a Batch Processor
- Appendix A. Additional Resources
- Going Further with Apache Flink
- More on Time and Windows
- More on Flink's State and Checkpointing
- Handling Batch Processing with Flink
- Flink Use Cases and User Stories
- Stream-First Architecture
- Message Transport: Apache Kafka
- Message Transport: MapR Streams
- Selected O'Reilly Publications by Ted Dunning and Ellen Friedman
- About the Authors
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