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  • Book Overview & Buying Mastering Hadoop
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Mastering Hadoop

Mastering Hadoop

By : Karanth
4 (3)
Buy this Book
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Mastering Hadoop

Mastering Hadoop

4 (3)
By: Karanth
Buy this Book

Overview of this book

Do you want to broaden your Hadoop skill set and take your knowledge to the next level? Do you wish to enhance your knowledge of Hadoop to solve challenging data processing problems? Are your Hadoop jobs, Pig scripts, or Hive queries not working as fast as you intend? Are you looking to understand the benefits of upgrading Hadoop? If the answer is yes to any of these, this book is for you. It assumes novice-level familiarity with Hadoop.
Table of Contents (15 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book?
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Hadoop 2.X
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1. Hadoop 2.X
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The inception of Hadoop
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The evolution of Hadoop
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Hadoop 2.X
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Hadoop distributions
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Summary
2
2. Advanced MapReduce
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2. Advanced MapReduce
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MapReduce input
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The RecordReader class
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Hadoop's "small files" problem
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Filtering inputs
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The Map task
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The Reduce task
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MapReduce output
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MapReduce job counters
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Handling data joins
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Summary
3
3. Advanced Pig
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3. Advanced Pig
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Pig versus SQL
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Different modes of execution
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Complex data types in Pig
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Compiling Pig scripts
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Development and debugging aids
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The advanced Pig operators
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User-defined functions
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Pig performance optimizations
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Best practices
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Summary
4
4. Advanced Hive
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4. Advanced Hive
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The Hive architecture
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Data types
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File formats
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The data model
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Hive query optimizers
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Advanced DML
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UDF, UDAF, and UDTF
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Summary
5
5. Serialization and Hadoop I/O
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5. Serialization and Hadoop I/O
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Data serialization in Hadoop
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Avro serialization
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File formats
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Compression
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Summary
6
6. YARN – Bringing Other Paradigms to Hadoop
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6. YARN – Bringing Other Paradigms to Hadoop
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The YARN architecture
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Developing YARN applications
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Monitoring YARN
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Job scheduling in YARN
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YARN commands
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Summary
7
7. Storm on YARN – Low Latency Processing in Hadoop
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7. Storm on YARN – Low Latency Processing in Hadoop
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Batch processing versus streaming
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Apache Storm
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Storm on YARN
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Summary
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8. Hadoop on the Cloud
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8. Hadoop on the Cloud
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Cloud computing characteristics
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Hadoop on the cloud
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Amazon Elastic MapReduce (EMR)
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Summary
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9. HDFS Replacements
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9. HDFS Replacements
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HDFS – advantages and drawbacks
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Amazon AWS S3
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Implementing a filesystem in Hadoop
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Implementing an S3 native filesystem in Hadoop
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Summary
10
10. HDFS Federation
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10. HDFS Federation
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Limitations of the older HDFS architecture
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Architecture of HDFS Federation
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HDFS high availability
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HDFS block placement
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Summary
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11. Hadoop Security
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11. Hadoop Security
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The security pillars
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Authentication in Hadoop
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Authorization in Hadoop
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Data confidentiality in Hadoop
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Audit logging in Hadoop
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Summary
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12. Analytics Using Hadoop
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12. Analytics Using Hadoop
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Data analytics workflow
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Machine learning
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Apache Mahout
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Document analysis using Hadoop and Mahout
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RHadoop
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Summary
13
A. Hadoop for Microsoft Windows
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A. Hadoop for Microsoft Windows
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Deploying Hadoop on Microsoft Windows
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Summary
14
Index
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Index

Job scheduling in YARN

Most cluster resources are multitenant in nature, that is, a number of teams or people share the cluster resources. Allocation of resources to satisfy the needs of all these tenants becomes important and is the responsibility of the scheduler. Individual clusters per team or person is not viable as they render poor utilization.

YARN provides a pluggable model to schedule policies. The initial versions of Hadoop had a simple First in First Out (FIFO) scheduler. However, FIFO was found to be inadequate in dealing with the complexities of multitenancy. We will discuss two other scheduling strategies that are used in Hadoop today, CapacityScheduler and FairScheduler.

CapacityScheduler

The concept behind CapacityScheduler is to guarantee a tenant-promised capacity on a shared cluster. If other tenants utilize less than the requested capacity, the scheduler allows the tenant to tap into these unused resources. The number one goal of CapacityScheduler is not to allow a single...

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Mastering Hadoop
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