Streaming Systems. The What, Where, When, and How of Large-Scale Data Processing - Helion

ebook

Autor: Tyler Akidau, Slava Chernyak, Reuven Lax
ISBN: 978-14-919-8382-9
stron: 352, Format: ebook
Data wydania: 2018-07-16
Księgarnia: Helion

Cena książki: 152,15 zł (poprzednio: 176,92 zł)
Oszczędzasz: 14% (-24,77 zł)

Osoby, które kupiły tę książkę, wybierały także »

Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way.

Expanded from Tyler Akidau’s popular blog posts "Streaming 101" and "Streaming 102", this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax.

You’ll explore:

How streaming and batch data processing patterns compare
The core principles and concepts behind robust out-of-order data processing
How watermarks track progress and completeness in infinite datasets
How exactly-once data processing techniques ensure correctness
How the concepts of streams and tables form the foundations of both batch and streaming data processing
The practical motivations behind a powerful persistent state mechanism, driven by a real-world example
How time-varying relations provide a link between stream processing and the world of SQL and relational algebra

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Spis treści

Streaming Systems. The What, Where, When, and How of Large-Scale Data Processing eBook -- spis treści

Preface Or: What Are You Getting Yourself Into Here?
- Navigating This Book
  - Takeaways
- Conventions Used in This Book
- Online Resources
  - Figures
  - Code Snippets
- OReilly Safari
- How to Contact Us
- Acknowledgments
I. The Beam Model
1. Streaming 101
- Terminology: What Is Streaming?
  - On the Greatly Exaggerated Limitations of Streaming
  - Event Time Versus Processing Time
- Data Processing Patterns
  - Bounded Data
  - Unbounded Data: Batch
    - Fixed windows
    - Sessions
  - Unbounded Data: Streaming
    - Time-agnostic
      - Filtering
      - Inner joins
    - Approximation algorithms
    - Windowing
      - Windowing by processing time
      - Windowing by event time
- Summary
2. The What, Where, When, and How of Data Processing
- Roadmap
- Batch Foundations: What and Where
  - What: Transformations
  - Where: Windowing
- Going Streaming: When and How
  - When: The Wonderful Thing About Triggers Is Triggers Are Wonderful Things!
  - When: Watermarks
  - When: Early/On-Time/Late Triggers FTW!
  - When: Allowed Lateness (i.e., Garbage Collection)
  - How: Accumulation
- Summary
3. Watermarks
- Definition
- Source Watermark Creation
  - Perfect Watermark Creation
  - Heuristic Watermark Creation
- Watermark Propagation
  - Understanding Watermark Propagation
  - Watermark Propagation and Output Timestamps
  - The Tricky Case of Overlapping Windows
- Percentile Watermarks
- Processing-Time Watermarks
- Case Studies
  - Case Study: Watermarks in Google Cloud Dataflow
  - Case Study: Watermarks in Apache Flink
  - Case Study: Source Watermarks for Google Cloud Pub/Sub
- Summary
4. Advanced Windowing
- When/Where: Processing-Time Windows
  - Event-Time Windowing
  - Processing-Time Windowing via Triggers
  - Processing-Time Windowing via Ingress Time
- Where: Session Windows
- Where: Custom Windowing
  - Variations on Fixed Windows
    - Unaligned fixed windows
    - Per-element/key fixed windows
  - Variations on Session Windows
    - Bounded sessions
  - One Size Does Not Fit All
- Summary
5. Exactly-Once and Side Effects
- Why Exactly Once Matters
- Accuracy Versus Completeness
  - Side Effects
  - Problem Definition
- Ensuring Exactly Once in Shuffle
- Addressing Determinism
- Performance
  - Graph Optimization
  - Bloom Filters
  - Garbage Collection
- Exactly Once in Sources
- Exactly Once in Sinks
- Use Cases
  - Example Source: Cloud Pub/Sub
  - Example Sink: Files
  - Example Sink: Google BigQuery
- Other Systems
  - Apache Spark Streaming
  - Apache Flink
- Summary
II. Streams and Tables
6. Streams and Tables
- Stream-and-Table Basics Or: a Special Theory of Stream and Table Relativity
  - Toward a General Theory of Stream and Table Relativity
- Batch Processing Versus Streams and Tables
  - A Streams and Tables Analysis of MapReduce
    - Map as streams/tables
    - Reduce as streams/tables
  - Reconciling with Batch Processing
- What, Where, When, and How in a Streams and Tables World
  - What: Transformations
  - Where: Windowing
    - Window merging
  - When: Triggers
  - How: Accumulation
  - A Holistic View of Streams and Tables in the Beam Model
- A General Theory of Stream and Table Relativity
- Summary
7. The Practicalities of Persistent State
- Motivation
  - The Inevitability of Failure
  - Correctness and Efficiency
- Implicit State
  - Raw Grouping
  - Incremental Combining
- Generalized State
  - Case Study: Conversion Attribution
  - Conversion Attribution with Apache Beam
- Summary
8. Streaming SQL
- What Is Streaming SQL?
  - Relational Algebra
  - Time-Varying Relations
  - Streams and Tables
- Looking Backward: Stream and Table Biases
  - The Beam Model: A Stream-Biased Approach
  - The SQL Model: A Table-Biased Approach
    - Materialized views
- Looking Forward: Toward Robust Streaming SQL
  - Stream and Table Selection
  - Temporal Operators
    - Where: windowing
    - When: triggers
      - A SQL-ish default: per-record triggers
      - Watermark triggers
      - Repeated delay triggers
      - Data-driven triggers
    - How: accumulation
      - Retractions in a SQL world
      - Discarding mode, or lack thereof
- Summary
9. Streaming Joins
- All Your Joins Are Belong to Streaming
- Unwindowed Joins
  - FULL OUTER
  - LEFT OUTER
  - RIGHT OUTER
  - INNER
  - ANTI
  - SEMI
- Windowed Joins
  - Fixed Windows
  - Temporal Validity
    - Temporal validity windows
    - Temporal validity joins
      - Watermarks and temporal validity joins
- Summary
10. The Evolution of Large-Scale Data Processing
- MapReduce
- Hadoop
- Flume
- Storm
- Spark
- MillWheel
- Kafka
- Cloud Dataflow
- Flink
- Beam
- Summary
Index