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Scala for Data Science

You're reading from   Scala for Data Science Leverage the power of Scala with different tools to build scalable, robust data science applications

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Product type Paperback
Published in Jan 2016
Publisher
ISBN-13 9781785281372
Length 416 pages
Edition 1st Edition
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Author (1):
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Pascal Bugnion Pascal Bugnion
Author Profile Icon Pascal Bugnion
Pascal Bugnion
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Table of Contents (17) Chapters Close

Preface 1. Scala and Data Science FREE CHAPTER 2. Manipulating Data with Breeze 3. Plotting with breeze-viz 4. Parallel Collections and Futures 5. Scala and SQL through JDBC 6. Slick – A Functional Interface for SQL 7. Web APIs 8. Scala and MongoDB 9. Concurrency with Akka 10. Distributed Batch Processing with Spark 11. Spark SQL and DataFrames 12. Distributed Machine Learning with MLlib 13. Web APIs with Play 14. Visualization with D3 and the Play Framework A. Pattern Matching and Extractors Index

Data shuffling and partitions


To understand data shuffling in Spark, we first need to understand how data is partitioned in RDDs. When we create an RDD by, for instance, loading a file from HDFS, or reading a file in local storage, Spark has no control over what bits of data are distributed in which partitions. This becomes a problem for key-value RDDs: these often require knowing where occurrences of a particular key are, for instance to perform a join. If the key can occur anywhere in the RDD, we have to look through every partition to find the key.

To prevent this, Spark allows the definition of a partitioner on key-value RDDs. A partitioner is an attribute of the RDD that determines which partition a particular key lands in. When an RDD has a partitioner set, the location of a key is entirely determined by the partitioner, and not by the RDD's history, or the number of keys. Two different RDDs with the same partitioner will map the same key to the same partition.

Partitions impact performance...

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