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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

fileStream


The fileStream creates an input stream that monitors a Hadoop-compatible filesystem. It reads new files using a given key-value type and input format. Any filenames starting with . are ignored. Invoking an atomic file rename function, a filename starting with . is renamed to a usable filename that can be picked up by the fileStream and have its contents processed:

def fileStream[K: ClassTag, V: ClassTag, F <: NewInputFormat[K, V]:
ClassTag] (directory: String): InputDStream[(K, V)]

textFileStream

The textFileStream command creates an input stream that monitors a Hadoop-compatible filesystem. It reads new files, as text files with the key as Longwritable, the value as text, and the input format as TextInputFormat. Any files that have names starting with . are ignored:

def textFileStream(directory: String): Dstream[String]

binaryRecordsStream

Using binaryRecordsStream, an input stream that monitors a Hadoop-compatible filesystem  is created. Any filenames starting with . are ignored...

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