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Big Data Analytics with Hadoop 3

You're reading from   Big Data Analytics with Hadoop 3 Build highly effective analytics solutions to gain valuable insight into your big data

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788628846
Length 482 pages
Edition 1st Edition
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Author (1):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
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Table of Contents (13) Chapters Close

Preface 1. Introduction to Hadoop FREE CHAPTER 2. Overview of Big Data Analytics 3. Big Data Processing with MapReduce 4. Scientific Computing and Big Data Analysis with Python and Hadoop 5. Statistical Big Data Computing with R and Hadoop 6. Batch Analytics with Apache Spark 7. Real-Time Analytics with Apache Spark 8. Batch Analytics with Apache Flink 9. Stream Processing with Apache Flink 10. Visualizing Big Data 11. Introduction to Cloud Computing 12. Using Amazon Web Services

Driver failure recovery


We can achieve driver failure recovery with the help of StreamingContext.getOrCreate(). As previously mentioned, this will either initialize a StreamingContext from a checkpoint that already exists, or create a new one. 

We will not implement a function called createStreamContext0, which creates a StreamingContext and sets up DStreams to interpret tweets and generate the top five most-used hashtags, using a window every 15 seconds. Instead of invoking createStreamContext() and then calling ssc.start(), we will invoke getOrCreate(), so that if a checkpoint exists, then the StreamingContext will be recreated from the data in the checkpoint Directory. If there is no such directory, or if the application is on its first run, then createStreamContext() will be invoked:

val ssc = StreamingContext.getOrCreate(checkpointDirectory,
createStreamContext _)

The following code shows how the function is defined, and how getOrCreate() can be invoked:

val checkpointDirectory = "checkpoints...
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