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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

A cost-based optimizer for machine learning algorithms


Let's start with an example to exemplify how Apache SystemML works internally. Consider a recommender system.

An example - alternating least squares

A recommender system tries to predict the potential items that a user might be interested in, based on a history from other users.

So let's consider a so-called item-user or product-customer matrix, as illustrated here:

This is a so-called sparse matrix because only a couple of cells are populated with non-zero values indicating a match between a customer i and a product j. Either by just putting a one in the cell or any other numerical value, for example, indicating the number of products bought or a rating for that particular product j from customer i. Let's call this matrix rui, where u stands for user and i for item.

Those of you familiar with linear algebra might know that any matrix can be factorized by two smaller matrices. This means that you have to find two matrices pu and qi that,...

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