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Modern Big Data Processing with Hadoop

You're reading from   Modern Big Data Processing with Hadoop Expert techniques for architecting end-to-end big data solutions to get valuable insights

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781787122765
Length 394 pages
Edition 1st Edition
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Concepts
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Authors (3):
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 R Patil R Patil
Author Profile Icon R Patil
R Patil
 Shindgikar Shindgikar
Author Profile Icon Shindgikar
Shindgikar
 Kumar Kumar
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Kumar
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Toc

Table of Contents (17) Chapters Close

Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
1. Enterprise Data Architecture Principles 2. Hadoop Life Cycle Management FREE CHAPTER 3. Hadoop Design Consideration 4. Data Movement Techniques 5. Data Modeling in Hadoop 6. Designing Real-Time Streaming Data Pipelines 7. Large-Scale Data Processing Frameworks 8. Building Enterprise Search Platform 9. Designing Data Visualization Solutions 10. Developing Applications Using the Cloud 11. Production Hadoop Cluster Deployment Index

Configuring HDFS high availability


Let's take a look at the changes brought about in Hadoop over time.

During Hadoop 1.x

Hadoop 1.x started with the architecture of a single NameNode. All DataNodes used to send their block reports to that single NameNode. There was a secondary NameNode in the architecture, but its sole responsibility was to merge all edits to FSImage. With this architecture, the NameNode became the single point of failure (SPOF). Since it has all the metadata of all the DataNodes of the Hadoop cluster, in the event of NameNode crash, the Hadoop cluster becomes unavailable till the next restart of NameNode repair. If the NameNode cannot be recovered, then all the data in all the DataNodes would be completely lost. In the event of shutting down NameNode for planned maintenance, the HDFS becomes unavailable for normal use. Hence, it was necessary to protect the existing NameNode by taking frequent backups of the NameNode filesystem to minimize data loss.

During Hadoop 2.x and...

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