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Learning Spark SQL

You're reading from   Learning Spark SQL Architect streaming analytics and machine learning solutions

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781785888359
Length 452 pages
Edition 1st Edition
Languages
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Author (1):
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Aurobindo Sarkar Aurobindo Sarkar
Author Profile Icon Aurobindo Sarkar
Aurobindo Sarkar
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Spark SQL FREE CHAPTER 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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