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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

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Length 354 pages
Edition 2nd Edition
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Author (1):
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Romeo Kienzler Romeo Kienzler
Author Profile Icon Romeo Kienzler
Romeo Kienzler
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Table of Contents (15) Chapters Close

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

Using SQL

After using the previous Scala example to create a data frame from a JSON input file on HDFS, we can now define a temporary table based on the data frame and run SQL against it.

The following example shows you the temporary table called washing_flat being defined and a row count being created using count(*):

The schema for this data was created on the fly (inferred). This is a very nice function of the Apache Spark DataSource API that has been used when reading the JSON file from HDFS using the SparkSession object. However, if you want to specify the schema on your own, you can do so.

Defining schemas manually

So first, we have to import some classes. Follow the code to do this:

import org.apache.spark.sql.types._

So...

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