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Big Data Analytics

You're reading from   Big Data Analytics Real time analytics using Apache Spark and Hadoop

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785884696
Length 326 pages
Edition 1st Edition
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Concepts
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Author (1):
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Venkat Ankam Venkat Ankam
Author Profile Icon Venkat Ankam
Venkat Ankam
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Table of Contents (12) Chapters Close

Preface 1. Big Data Analytics at a 10,000-Foot View 2. Getting Started with Apache Hadoop and Apache Spark FREE CHAPTER 3. Deep Dive into Apache Spark 4. Big Data Analytics with Spark SQL, DataFrames, and Datasets 5. Real-Time Analytics with Spark Streaming and Structured Streaming 6. Notebooks and Dataflows with Spark and Hadoop 7. Machine Learning with Spark and Hadoop 8. Building Recommendation Systems with Spark and Mahout 9. Graph Analytics with GraphX 10. Interactive Analytics with SparkR Index

Lifecycle of Spark program


The following steps explain the lifecycle of a Spark application with standalone resource manager, and Figure 3.8 shows the scheduling process of a spark program:

  1. The user submits a spark application using the spark-submit command.

  2. Spark-submit launches the driver program on the same node in (client mode) or on the cluster (cluster mode) and invokes the main method specified by the user.

  3. The driver program contacts the cluster manager to ask for resources to launch executor JVMs based on the configuration parameters supplied.

  4. The cluster manager launches executor JVMs on worker nodes.

  5. The driver process scans through the user application. Based on the RDD actions and transformations in the program, Spark creates an operator graph.

  6. When an action (such as collect) is called, the graph is submitted to a DAG scheduler. The DAG scheduler divides the operator graph into stages.

  7. A stage comprises tasks based on partitions of the input data. The DAG scheduler pipelines operators...

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