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Hadoop 2.x Administration Cookbook

You're reading from   Hadoop 2.x Administration Cookbook Administer and maintain large Apache Hadoop clusters

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Product type Paperback
Published in May 2017
Publisher Packt
ISBN-13 9781787126732
Length 348 pages
Edition 1st Edition
Tools
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Author (1):
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Aman Singh Aman Singh
Author Profile Icon Aman Singh
Aman Singh
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Table of Contents (14) Chapters Close

Preface 1. Hadoop Architecture and Deployment FREE CHAPTER 2. Maintaining Hadoop Cluster HDFS 3. Maintaining Hadoop Cluster – YARN and MapReduce 4. High Availability 5. Schedulers 6. Backup and Recovery 7. Data Ingestion and Workflow 8. Performance Tuning 9. HBase Administration 10. Cluster Planning 11. Troubleshooting, Diagnostics, and Best Practices 12. Security Index

Hadoop streaming


In this recipe, we will look at how we can execute jobs on an Hadoop cluster using scripts written in Bash or Python. It is not mandatory to use only Java for programming MapReduce code; any language can be used by evoking the Hadoop streaming utility. Do not confuse this with real-time streaming, which is different from what we will be discussing here.

Getting ready

To step through the recipes in this chapter, make sure you have a running cluster with HDFS and YARN setup correctly as discussed in the previous chapters. This can be a single node cluster or a multinode cluster, as long as the cluster is configured correctly.

It is not necessary to know Java to run MapReduce programs on Hadoop. Users can carry forward their existing scripting knowledge and use Bash or Python to run the job on Hadoop.

How to do it...

  1. Connect to an edge node in the cluster and switch to user hadoop.

  2. The streaming JAR is also under the location as Hadoop /opt/cluster/hadoop/share/hadoop/tools/lib/hadoop...

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