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Learning PySpark

You're reading from  Learning PySpark

Product type Book
Published in Feb 2017
Publisher Packt
ISBN-13 9781786463708
Pages 274 pages
Edition 1st Edition
Languages
Authors (2):
Tomasz Drabas Tomasz Drabas
Profile icon Tomasz Drabas
Denny Lee Denny Lee
Profile icon Denny Lee
View More author details

Table of Contents (20) Chapters

Learning PySpark
Credits
Foreword
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Understanding Spark 2. Resilient Distributed Datasets 3. DataFrames 4. Prepare Data for Modeling 5. Introducing MLlib 6. Introducing the ML Package 7. GraphFrames 8. TensorFrames 9. Polyglot Persistence with Blaze 10. Structured Streaming 11. Packaging Spark Applications Index

Actions


Actions, in contrast to transformations, execute the scheduled task on the dataset; once you have finished transforming your data you can execute your transformations. This might contain no transformations (for example, .take(n) will just return n records from an RDD even if you did not do any transformations to it) or execute the whole chain of transformations.

The .take(...) method

This is most arguably the most useful (and used, such as the .map(...) method). The method is preferred to .collect(...) as it only returns the n top rows from a single data partition in contrast to .collect(...), which returns the whole RDD. This is especially important when you deal with large datasets:

data_first = data_from_file_conv.take(1)

If you want somewhat randomized records you can use .takeSample(...) instead, which takes three arguments: First whether the sampling should be with replacement, the second specifies the number of records to return, and the third is a seed to the pseudo-random numbers...

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