Real-Time Data Pipelines with Apache Kafka and Flink
Chapter 1: Stream Processing Architectures and Semantics
Evolution of Distributed Logs
Lambda versus Kappa Architecture
Processing Guarantees and Semantics
Event Time versus Processing Time
Hands-on Practical: Designing a Kappa Pipeline
Chapter 2: Advanced Kafka Producer and Consumer Internals
Replication Protocols and ISRs
Idempotent Producers and Transactions
Custom Partitioning Strategies
Consumer Group Rebalancing protocols
Hands-on Practical: Implementing Transactional Writes
Chapter 3: Flink State Management and Checkpointing
State Backends: HashMap versus RocksDB
Asynchronous Barrier Snapshots
Incremental Checkpointing
State Schema Evolution
Hands-on Practical: State Migration with Savepoints
Chapter 4: Advanced Windowing and Time Attributes
Watermark Generation Strategies
Handling Late Data and Side Outputs
Custom Window Triggers and Evictors
Session Windows and Gap Analysis
Hands-on Practical: Building a Custom Trigger
Chapter 5: Low-Level Operations with ProcessFunctions
The ProcessFunction Hierarchy
Timer Services and Event Scheduling
Broadcast State Pattern
Async I/O for External Lookups
Hands-on Practical: Dynamic Rule Evaluation
Chapter 6: Production Deployment and Reliability
Serialization with Avro and Protobuf
Schema Registry Integration
Kafka Connect for Sink and Source
Failure Recovery Strategies
Hands-on Practical: Schema Evolution in Flight
Chapter 7: Performance Tuning and Monitoring
Identifying Backpressure
Memory Management and Slot Allocation
Tuning RocksDB Performance
Kafka Consumer Lag Analysis
Hands-on Practical: Resolving Skewed Data
Chapter 8: Real-Time AI and Feature Engineering
Online Feature Generation
Model Serving Patterns in Streams
Request-Response over Async Streams
Feature Store Integration
Hands-on Practical: Real-Time Inference Pipeline
Session Windows and Gap Analysis
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- Session Windows, Apache Flink Community, 2024 - Explains the Flink implementation of session windows, including merging, dynamic gaps, and state.
- State & Fault Tolerance, Apache Flink Community, 2024 (Apache Flink Community) - Details Flink's state management, including how state is handled during window merges and implications for different functions.
- Stream Processing with Apache Flink, Fabian Hueske, Vasiliki Kalavri, 2019 (O'Reilly Media) - Provides a thorough overview of Flink's windowing concepts, including session windows, their internals, and state management.
- The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Unified Stream and Batch Processing, Tyler Akidau, Robert Bradshaw, Craig Chambers, Slava Chernyak, Rafael J. Fernández-Moctezuma, Rui Li, Jeremy McIlwain, Daniel Mills, Frances Perry, Eric Rosenthal, Stephen Rutherford, Sam Shah, Kevin Smith, Dale Spoonhower, Alex Unger, Viktor Valkanas, Jean-François Warren, Robert Welsh, Ken Wilkes, and Edward Xue, 2015 Proceedings of the VLDB Endowment, Vol. 8 (VLDB Endowment) DOI: 10.14778/2824032.2824076 - Presents the foundational concepts for advanced windowing in stream processing systems, including event-time processing and watermarks, which are crucial for session windows.
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