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Apache Spark 2: Data Processing and Real-Time Analytics

You're reading from   Apache Spark 2: Data Processing and Real-Time Analytics Master complex big data processing, stream analytics, and machine learning with Apache Spark

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Product type Course
Published in Dec 2018
Publisher Packt
ISBN-13 9781789959208
Length 616 pages
Edition 1st Edition
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Authors (7):
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Sridhar Alla Sridhar Alla
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Sridhar Alla
Romeo Kienzler Romeo Kienzler
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Romeo Kienzler
Siamak Amirghodsi Siamak Amirghodsi
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Siamak Amirghodsi
Broderick Hall Broderick Hall
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Broderick Hall
Md. Rezaul Karim Md. Rezaul Karim
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Md. Rezaul Karim
Meenakshi Rajendran Meenakshi Rajendran
Author Profile Icon Meenakshi Rajendran
Meenakshi Rajendran
Shuen Mei Shuen Mei
Author Profile Icon Shuen Mei
Shuen Mei
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Table of Contents (23) Chapters Close

Title Page
Copyright
About Packt
Contributors
Preface
1. A First Taste and What's New in Apache Spark V2 2. Apache Spark Streaming FREE CHAPTER 3. Structured Streaming 4. Apache Spark MLlib 5. Apache SparkML 6. Apache SystemML 7. Apache Spark GraphX 8. Spark Tuning 9. Testing and Debugging Spark 10. Practical Machine Learning with Spark Using Scala 11. Spark's Three Data Musketeers for Machine Learning - Perfect Together 12. Common Recipes for Implementing a Robust Machine Learning System 13. Recommendation Engine that Scales with Spark 14. Unsupervised Clustering with Apache Spark 2.0 15. Implementing Text Analytics with Spark 2.0 ML Library 16. Spark Streaming and Machine Learning Library 1. Other Books You May Enjoy Index

Summary


We could have provided streaming examples for other systems as well, but there was no room in this chapter. Twitter streaming has been examined by example in the Checkpointing section. This chapter has provided practical examples of data recovery via checkpointing in Spark Streaming. It has also touched on the performance limitations of checkpointing and shown that the checkpointing interval should be set at five to ten times the Spark stream batch interval.

Checkpointing provides a stream-based recovery mechanism in the case of Spark application failure. This chapter has provided some stream-based worked examples for TCP, File, Flume, and Kafka-based Spark stream coding. All the examples here are based on Scala and compiled with sbt. In case you are more familiar with Maven the following tutorial explains how to set up a Maven based Scala project: http://www.scala-lang.org/old/node/345.

You have been reading a chapter from
Apache Spark 2: Data Processing and Real-Time Analytics
Published in: Dec 2018
Publisher: Packt
ISBN-13: 9781789959208
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