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Learning Spark SQL

You're reading from Learning Spark SQL

Product type Book

Published in Sep 2017

Publisher Packt

ISBN-13 9781785888359

Pages 452 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Aurobindo Sarkar

Table of Contents (19) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with Spark SQL

2. Using Spark SQL for Processing Structured and Semistructured Data

3. Using Spark SQL for Data Exploration

4. Using Spark SQL for Data Munging

5. Using Spark SQL in Streaming Applications

6. Using Spark SQL in Machine Learning Applications

7. Using Spark SQL in Graph Applications

8. Using Spark SQL with SparkR

9. Developing Applications with Spark SQL

10. Using Spark SQL in Deep Learning Applications

11. Tuning Spark SQL Components for Performance

12. Spark SQL in Large-Scale Application Architectures

Using cluster managers

In section, we will briefly discuss and at a conceptual level. The framework can be deployed through Apache Mesos, YARN, Spark Standalone, or the Kubernetes cluster manager, as depicted:

Mesos can enable easy scalability and replication of data, and is a good unified cluster management solution for heterogeneous workloads.

To use Mesos from Spark, the Spark binaries should be accessible by Mesos and the Spark driver configured to connect to Mesos. Alternatively, you can also install Spark binaries on all the Mesos slaves. The driver creates a job and then issues the tasks for scheduling, while Mesos determines the machines to handle them.

Spark can run over Mesos in two modes: coarse-grained (the default) and fine-grained (deprecated in Spark 2.0.0). In the coarse-grained mode, each Spark executor runs as a single Mesos task. This mode has significantly lower start up overheads, but reserves Mesos resources for the duration of the application. Mesos also supports...

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Authors (1)

Aurobindo Sarkar

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