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Apache Spark 2.x for Java Developers

You're reading from   Apache Spark 2.x for Java Developers Explore big data at scale using Apache Spark 2.x Java APIs

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787126497
Length 350 pages
Edition 1st Edition
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Authors (2):
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Sourav Gulati Sourav Gulati
Author Profile Icon Sourav Gulati
Sourav Gulati
Sumit Kumar Sumit Kumar
Author Profile Icon Sumit Kumar
Sumit Kumar
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Spark FREE CHAPTER 2. Revisiting Java 3. Let Us Spark 4. Understanding the Spark Programming Model 5. Working with Data and Storage 6. Spark on Cluster 7. Spark Programming Model - Advanced 8. Working with Spark SQL 9. Near Real-Time Processing with Spark Streaming 10. Machine Learning Analytics with Spark MLlib 11. Learning Spark GraphX

Fault tolerance and reliability


Streaming jobs are designed to run continuously and failure in the job can result in loss of data, state, or both. Making streaming jobs fault tolerant becomes one of the essential goals of writing the streaming job in the first place. Any streaming job comes with some guarantees either by design or by implementing certain configuration features, which mandates how many times a message will be processed by the system:

  • At most once guarantee: Records in such systems can either be processed once or not at all. These systems are least reliable as far as streaming solution is concerned.
  • At least once guarantee: The system will process the record at least once and hence by design there will be no loss of messages, but then messages can be processed multiple times giving the problem of duplication. This scenario however is better than the previous case and there are use cases where duplicate data may not cause any problem or can easily be deduced.
  • Exactly once guarantee...
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