Grokking streaming systems : real-time event processing /

Grokking Streaming Systems introduces real-time event streaming applications in clear, reader-friendly language. This engaging book illuminates core concepts like data parallelization, event windows, and backpressure without getting bogged down in framework-specific details. As you go, you'll b...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Fischer, Josh (Autor), Wang, Ning, 1974- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Shelter Island : Manning Publications, 2022.
Temas:
Streaming technology (Telecommunications)
Webcasts as Topic
En continu (Télécommunications)
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • inside front cover
  • Grokking Streaming Systems
  • Copyright
  • brief contents
  • contents
  • front matter
  • preface
  • acknowledgments
  • about this book
  • about the authors
  • Part 1. Getting started with streaming
  • 1 Welcome to Grokking Streaming Systems
  • What is stream processing?
  • Streaming system examples
  • Streaming systems and real time
  • How a streaming system works
  • Applications
  • Backend services
  • Inside a backend service
  • Batch processing systems
  • Inside a batch processing system
  • Stream processing systems
  • Inside a stream processing system
  • The advantages of multi-stage architecture
  • The multi-stage architecture in batch and stream processing systems
  • Compare the systems
  • A model stream processing system
  • Summary
  • Exercise
  • 2 Hello, streaming systems!
  • The chief needs a fancy tollbooth
  • It started as HTTP requests, and it failed
  • AJ and Miranda take time to reflect
  • AJ ponders about streaming systems
  • Comparing backend service and streaming
  • How a streaming system could fit
  • Queues: A foundational concept
  • Data transfer via queues
  • Our streaming framework (the start of it)
  • The Streamwork framework overview
  • Zooming in on the Streamwork engine
  • Core streaming concepts
  • More details of the concepts
  • The streaming job execution flow
  • Your first streaming job
  • Executing the job
  • Inspecting the job execution
  • Look inside the engine
  • Keep events moving
  • The life of a data element
  • Reviewing streaming concepts
  • Summary
  • Exercises
  • 3 Parallelization and data grouping
  • The sensor is emitting more events
  • Even in streaming, real time is hard
  • New concepts: Parallelism is important
  • New concepts: Data parallelism
  • New concepts: Data execution independence
  • New concepts: Task parallelism
  • Data parallelism vs. task parallelism
  • Parallelism and concurrency
  • Parallelizing the job
  • Parallelizing components
  • Parallelizing sources
  • Viewing job output
  • Parallelizing operators
  • Viewing job output
  • Events and instances
  • Event ordering
  • Event grouping
  • Shuffle grouping
  • Shuffle grouping: Under the hood
  • Fields grouping
  • Fields grouping: Under the hood
  • Event grouping execution
  • Look inside the engine: Event dispatcher
  • Applying fields grouping in your job
  • Event ordering
  • Comparing grouping behaviors
  • Summary
  • Exercises
  • 4 Stream graph
  • A credit card fraud detection system
  • More about the credit card fraud detection system
  • The fraud detection business
  • Streaming isn't always a straight line
  • Zoom into the system
  • The fraud detection job in detail
  • New concepts
  • Upstream and downstream components
  • Stream fan-out and fan-in
  • Graph, directed graph, and DAG
  • DAG in stream processing systems
  • All new concepts in one page
  • Stream fan-out to the analyzers
  • Look inside the engine
  • There is a problem: Efficiency

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