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Cloudera Administration Handbook

Cloudera Administration Handbook

By : Menon
3.5 (10)
Buy this Book
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Cloudera Administration Handbook

Cloudera Administration Handbook

3.5 (10)
By: Menon
Buy this Book

Overview of this book

An easy-to-follow Apache Hadoop administrator’s guide filled with practical screenshots and explanations for each step and configuration. This book is great for administrators interested in setting up and managing a large Hadoop cluster. If you are an administrator, or want to be an administrator, and you are ready to build and maintain a production-level cluster running CDH5, then this book is for you.
Table of Contents (11 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. Getting Started with Apache Hadoop
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1. Getting Started with Apache Hadoop
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History of Apache Hadoop and its trends
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Components of Apache Hadoop
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Understanding the Apache Hadoop daemons
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Introducing Cloudera
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Introducing CDH
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Responsibilities of a Hadoop administrator
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Summary
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2. HDFS and MapReduce
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2. HDFS and MapReduce
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Essentials of HDFS
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The read/write operational flow in HDFS
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Understanding the namenode UI
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Understanding the secondary namenode UI
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Exploring HDFS commands
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Getting acquainted with MapReduce
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Summary
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3. Cloudera's Distribution Including Apache Hadoop
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3. Cloudera's Distribution Including Apache Hadoop
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Getting started with CDH
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Understanding the CDH components
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Installing CDH
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Installing the CDH components
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Summary
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4. Exploring HDFS Federation and Its High Availability
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4. Exploring HDFS Federation and Its High Availability
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Implementing HDFS Federation
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Implementing HDFS High Availability
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Jobtracker high availability
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Summary
5
5. Using Cloudera Manager
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5. Using Cloudera Manager
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Introducing Cloudera Manager
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Understanding the Cloudera Manager architecture
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Installing Cloudera Manager
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Navigating the Cloudera Manager Web console
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Configuring High Availability using Cloudera Manager
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Summary
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6. Implementing Security Using Kerberos
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6. Implementing Security Using Kerberos
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Understanding authentication and authorization
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Introducing Kerberos
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Understanding the Kerberos Architecture
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Installing Kerberos
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Configuring Kerberos for Apache Hadoop
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Authorization in Apache Hadoop
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Summary
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7. Managing an Apache Hadoop Cluster
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7. Managing an Apache Hadoop Cluster
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Configuring Hadoop services using Cloudera Manager
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Role management in Cloudera Manager
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Managing hosts using Cloudera Manager
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Managing multiple clusters with Cloudera Manager
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Rebalancing a Hadoop cluster from Cloudera Manager
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Summary
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8. Cluster Monitoring Using Events and Alerts
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8. Cluster Monitoring Using Events and Alerts
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Monitoring Hadoop services from Cloudera Manager
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Understanding events and alerts
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Summary
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9. Configuring Backups
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9. Configuring Backups
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Understanding backups
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Understanding HDFS backups
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Using the distributed copy (DistCp)
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Configuring backups using Cloudera Manager
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Summary
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Index
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Index

Components of Apache Hadoop

Apache Hadoop is composed of two core components. They are:

  • HDFS: The HDFS is responsible for the storage of files. It is the storage component of Apache Hadoop, which was designed and developed to handle large files efficiently. It is a distributed filesystem designed to work on a cluster and makes it easy to store large files by splitting the files into blocks and distributing them across multiple nodes redundantly. The users of HDFS need not worry about the underlying networking aspects, as HDFS takes care of it. HDFS is written in Java and is a filesystem that runs within the user space.
  • MapReduce: MapReduce is a programming model that was built from models found in the field of functional programming and distributed computing. In MapReduce, the task is broken down to two parts: map and reduce. All data in MapReduce flows in the form of key and value pairs, <key, value>. Mappers emit key and value pairs and the reducers receive them, work on them, and produce the final result. This model was specifically built to query/process the large volumes of data stored in HDFS.

We will be going through HDFS and MapReduce in depth in the next chapter.

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