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Hadoop Real-World Solutions Cookbook- Second Edition

You're reading from   Hadoop Real-World Solutions Cookbook- Second Edition Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784395506
Length 290 pages
Edition 2nd Edition
Tools
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Author (1):
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Tanmay Deshpande Tanmay Deshpande
Author Profile Icon Tanmay Deshpande
Tanmay Deshpande
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with Hadoop 2.X FREE CHAPTER 2. Exploring HDFS 3. Mastering Map Reduce Programs 4. Data Analysis Using Hive, Pig, and Hbase 5. Advanced Data Analysis Using Hive 6. Data Import/Export Using Sqoop and Flume 7. Automation of Hadoop Tasks Using Oozie 8. Machine Learning and Predictive Analytics Using Mahout and R 9. Integration with Apache Spark 10. Hadoop Use Cases Index

Running Spark on YARN


In the previous recipe, we took a look at how to use Spark's built-in cluster manager; in this recipe, we are going to explore how to use YARN as a cluster manager to execute the Spark application.

Getting ready

To perform this recipe, you should have a running Hadoop cluster. You should also have performed the previous recipe.

How to do it...

As mentioned in the previous recipe, we can either use Spark's built-in cluster manager, or we can use an external cluster manager such as YARN. In order to execute the Spark application on YARN, we need to edit SPARK_HOME/conf/spark-env.sh, and add the following properties to it:

export HADOOP_CONF_DIR=/usr/local/hadoop/etc/hadoop
export YARN_CONF_DIR=/usr/local/hadoop/etc/Hadoop

Here, /usr/local/hadoop/etc/hadoop is the directory where we have our Hadoop and YARN configuration files.

Now, let's execute the same Spark application on YARN using the following command:

./bin/spark-submit --class org.apache.spark.examples.SparkPi \
    ...
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