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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

Architecture

Let's understand how GraphFrames works by taking a look at the architecture.

The key thing in order to make use of DataFrames, Catalyst, and Tungsten is that the GraphFrames engine is based on relational queries.

This concept is illustrated in the following image:

Graph-relational translation

So how can a graph query translate into a relational one?

Imagine that we have already found the vertex A, B, and C. Now we are searching for the edge from C to D. This query is illustrated in the following image:

This is pretty straightforward as we can scan the vertex table and search for entries where the Src (source) field is C. Once we have found out that the Dst (destination) field points to D (let's assume that...

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