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Mastering Apache Spark 2.x

You're reading from Mastering Apache Spark 2.x Advanced techniques in complex Big Data processing, streaming analytics and machine learning

Product type Paperback

Published in Jul 2017

Publisher Packt

ISBN-13 9781786462749

Length 354 pages

Edition 2nd Edition

Languages

Scala

Tools

Apache Spark

Concepts

Big Data

Author (1):

Romeo Kienzler

View More author details

Table of Contents (15) Chapters

Preface

1. A First Taste and What’s New in Apache Spark V2 FREE CHAPTER

2. Apache Spark SQL

3. The Catalyst Optimizer

4. Project Tungsten

5. Apache Spark Streaming

6. Structured Streaming

7. Apache Spark MLlib

8. Apache SparkML

9. Apache SystemML

10. Deep Learning on Apache Spark with DeepLearning4j and H2O

11. Apache Spark GraphX

12. Apache Spark GraphFrames

13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience

14. Apache Spark on Kubernetes

How transparent fault tolerance and exactly-once delivery guarantee is achieved

Apache Spark structured streaming supports full crash fault tolerance and exactly-once delivery guarantee without the user taking care of any specific error handling routines. Isn't this amazing? So how is this achieved?

Note

Full crash fault tolerance and exactly-once delivery guarantee are terms of systems theory. Full crash fault tolerance means that you can basically pull the power plug of the whole data center at any point in time, and no data is lost or left in an inconsistent state. Exactly-once delivery guarantee means, even if the same power plug is pulled, it is guaranteed that each tuple- end-to-end from the data source to the data sink - is delivered - only, and exactly, once. Not zero times and also not more than one time. Of course those concepts must also hold in case a single node fails or misbehaves (for example- starts throttling).

First of all, states between individual batches and offset ranges...

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Authors (1)

Romeo Kienzler

Romeo Kienzler

Romeo Kienzler works as the chief data scientist in the IBM Watson IoT worldwide team, helping clients to apply advanced machine learning at scale on their IoT sensor data. He holds a Master's degree in computer science from the Swiss Federal Institute of Technology, Zurich, with a specialization in information systems, bioinformatics, and applied statistics.

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