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Home > Cloud & Networking > Cloud Computing > Cloudera Administration Handbook
Cloudera Administration Handbook
Cloudera Administration Handbook

Cloudera Administration Handbook: A complete, hands-on guide to building and maintaining large Apache Hadoop clusters using Cloudera Manager and CDH5

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Cloudera Administration Handbook

Chapter 2. HDFS and MapReduce

We now have a basic understanding of the Apache Hadoop architecture and its inner workings. In this chapter, we will dive deeper into the two major components of Apache Hadoop—HDFS and MapReduce, and will cover the following topics:

  • Essentials of Hadoop Distributed File System
  • The read/write operational flow in HDFS
  • Exploring HDFS commands
  • Getting acquainted with MapReduce

Essentials of HDFS

HDFS is a distributed filesystem that has been designed to run on top of a cluster of industry standard hardware. The architecture of HDFS is such that there is no specific need for high-end hardware. HDFS is a highly fault-tolerant system and can handle failures of nodes in a cluster without loss of data. The primary goal behind the design of HDFS is to serve large data files efficiently. HDFS achieves this efficiency and high throughput in data transfer by enabling streaming access to the data in the filesystem.

The following are the important features of HDFS:

  • Fault tolerance: Many computers working together as a cluster are bound to have hardware failures. Hardware failures such as disk failures, network connectivity issues, and RAM failures could disrupt processing and cause major downtime. This could lead to data loss as well slippage of critical SLAs. HDFS is designed to withstand such hardware failures by detecting faults and taking recovery actions as required...

The read/write operational flow in HDFS

To get a better understanding of HDFS, we need to understand the flow of operations for the following two scenarios:

  • A file is written to HDFS
  • A file is read from HDFS

HDFS uses a single-write, multiple-read model, where the files are written once and read several times. The data cannot be altered once written. However, data can be appended to the file by reopening it. All files in the HDFS are saved as data blocks.

Writing files in HDFS

The following sequence of steps occur when a client tries to write a file to HDFS:

  1. The client informs the namenode daemon that it wants to write a file. The namenode daemon checks to see whether the file already exists.
  2. If it exists, an appropriate message is sent back to the client. If it does not exist, the namenode daemon makes a metadata entry for the new file.
  3. The file to be written is split into data packets at the client end and a data queue is built. The packets in the queue are then streamed to the datanodes in the...

Understanding the namenode UI

Hadoop provides web interfaces for each of its services. The namenode UI or the namenode web interface is used to monitor the status of the namenode and can be accessed using the following URL:

http://<namenode-server>:50070/

The namenode UI has the following sections:

  • Overview: The general information section provides basic information of the namenode with options to browse the filesystem and the namenode logs.

    The following is the screenshot of the Overview section of the namenode UI:

    Understanding the namenode UI

    The Cluster ID parameter displays the identification number of the cluster. This number is same across all the nodes within the cluster.

    A block pool is a set of blocks that belong to a single namespace. The Block Pool Id parameter is used to segregate the block pools in case there are multiple namespaces configured when using HDFS federation. In HDFS federation, multiple namenodes are configured to scale the name service horizontally. These namenodes are configured to share...

Understanding the secondary namenode UI

The secondary namenode is a checkpoint service for the namenode daemon that performs periodic merging of the edits log and the fsimage file. The secondary namenode UI can be accessed using the following URL:

http://<secondary-namenode-server>:50090/

The following screenshot shows the secondary namenode UI:

Understanding the secondary namenode UI

Just like the namenode UI, the secondary namenode UI also displays the Hadoop version. All checkpoint related information is available in this UI, which are given as follows:

  • Name Node Address: This is the RPC address of the primary namenode daemon. Secondary namenode uses this address to connect to primary namenode.
  • Start Time: This is the start timestamp of the secondary namenode service.
  • Last Checkpoint Time: This the timestamp of the last checkpoint action performed by the secondary namenode daemon.
  • Checkpoint Period: This property defines the schedule to perform the checkpoint. In the preceding screenshot, the value is 3,600 seconds. This...

Exploring HDFS commands

To perform filesystem related tasks, the commands begin with hdfs dfs. The filesystem commands have been designed to behave similarly to the corresponding Unix/Linux filesystem commands.

What is a URI? URI stands for Uniform Resource Identifier. In the commands that are listed as follows, you will observe the use of URI for file locations. The URI syntax to access a file in HDFS is hdfs://namenodehost/parent/child/<file>.

Commonly used HDFS commands

The following are some of the most commonly used HDFS commands:

  • ls: This command lists files in HDFS.

    The syntax of the ls command is hdfs dfs -ls <args>. The following is the screenshot showing an example of the ls command:

    Commonly used HDFS commands
  • cat: This command displays the contents of file/files in the terminal.

    The syntax of the cat command is hdfs dfs -cat URI [URI …]. The following is a sample output of the cat command:

    Commonly used HDFS commands
  • copyFromLocal: This command copies a file/files from the local filesystem to HDFS.

    The syntax of the copyFromLocal...

Essentials of HDFS

HDFS is a distributed filesystem that has been designed to run on top of a cluster of industry standard hardware. The architecture of HDFS is such that there is no specific need for high-end hardware. HDFS is a highly fault-tolerant system and can handle failures of nodes in a cluster without loss of data. The primary goal behind the design of HDFS is to serve large data files efficiently. HDFS achieves this efficiency and high throughput in data transfer by enabling streaming access to the data in the filesystem.

The following are the important features of HDFS:

  • Fault tolerance: Many computers working together as a cluster are bound to have hardware failures. Hardware failures such as disk failures, network connectivity issues, and RAM failures could disrupt processing and cause major downtime. This could lead to data loss as well slippage of critical SLAs. HDFS is designed to withstand such hardware failures by detecting faults and taking recovery actions as required...

The read/write operational flow in HDFS

To get a better understanding of HDFS, we need to understand the flow of operations for the following two scenarios:

  • A file is written to HDFS

  • A file is read from HDFS

HDFS uses a single-write, multiple-read model, where the files are written once and read several times. The data cannot be altered once written. However, data can be appended to the file by reopening it. All files in the HDFS are saved as data blocks.

Writing files in HDFS

The following sequence of steps occur when a client tries to write a file to HDFS:

  1. The client informs the namenode daemon that it wants to write a file. The namenode daemon checks to see whether the file already exists.

  2. If it exists, an appropriate message is sent back to the client. If it does not exist, the namenode daemon makes a metadata entry for the new file.

  3. The file to be written is split into data packets at the client end and a data queue is built. The packets in the queue are then streamed to the datanodes in the...

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Description

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.

What you will learn

  • Understand the Apache Hadoop architecture and the future of distributed processing frameworks
  • Use HDFS and MapReduce for all filerelated operations
  • Install and configure CDH to bring up an Apache Hadoop cluster
  • Configure HDFS High Availability and HDFS Federation to prevent single points of failure
  • Install and configure Cloudera Manager to perform administrator operations
  • Implement security by installing and configuring Kerberos for all services in the cluster
  • Add, remove, and rebalance nodes in a cluster using cluster management tools
  • Understand and configure the different backup options to back up your HDFS

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 18, 2014
Length: 254 pages
Edition : 1st
Language : English
ISBN-13 : 9781783558971
Category :
Cloud & Networking
Concepts :
Cloud Computing
Tools :
CentOS

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Length: 254 pages
Edition : 1st
Language : English
ISBN-13 : 9781783558971
Category :
Cloud & Networking
Concepts :
Cloud Computing
Tools :
CentOS

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Table of Contents

10 Chapters
1. Getting Started with Apache Hadoop Chevron down icon Chevron up icon
1. Getting Started with Apache Hadoop
History of Apache Hadoop and its trends
Components of Apache Hadoop
Understanding the Apache Hadoop daemons
Introducing Cloudera
Introducing CDH
Responsibilities of a Hadoop administrator
Summary
2. HDFS and MapReduce Chevron down icon Chevron up icon
2. HDFS and MapReduce
Essentials of HDFS
The read/write operational flow in HDFS
Understanding the namenode UI
Understanding the secondary namenode UI
Exploring HDFS commands
Getting acquainted with MapReduce
Summary
3. Cloudera's Distribution Including Apache Hadoop Chevron down icon Chevron up icon
3. Cloudera's Distribution Including Apache Hadoop
Getting started with CDH
Understanding the CDH components
Installing CDH
Installing the CDH components
Summary
4. Exploring HDFS Federation and Its High Availability Chevron down icon Chevron up icon
4. Exploring HDFS Federation and Its High Availability
Implementing HDFS Federation
Implementing HDFS High Availability
Jobtracker high availability
Summary
5. Using Cloudera Manager Chevron down icon Chevron up icon
5. Using Cloudera Manager
Introducing Cloudera Manager
Understanding the Cloudera Manager architecture
Installing Cloudera Manager
Navigating the Cloudera Manager Web console
Configuring High Availability using Cloudera Manager
Summary
6. Implementing Security Using Kerberos Chevron down icon Chevron up icon
6. Implementing Security Using Kerberos
Understanding authentication and authorization
Introducing Kerberos
Understanding the Kerberos Architecture
Installing Kerberos
Configuring Kerberos for Apache Hadoop
Authorization in Apache Hadoop
Summary
7. Managing an Apache Hadoop Cluster Chevron down icon Chevron up icon
7. Managing an Apache Hadoop Cluster
Configuring Hadoop services using Cloudera Manager
Role management in Cloudera Manager
Managing hosts using Cloudera Manager
Managing multiple clusters with Cloudera Manager
Rebalancing a Hadoop cluster from Cloudera Manager
Summary
8. Cluster Monitoring Using Events and Alerts Chevron down icon Chevron up icon
8. Cluster Monitoring Using Events and Alerts
Monitoring Hadoop services from Cloudera Manager
Understanding events and alerts
Summary
9. Configuring Backups Chevron down icon Chevron up icon
9. Configuring Backups
Understanding backups
Understanding HDFS backups
Using the distributed copy (DistCp)
Configuring backups using Cloudera Manager
Summary
Index Chevron down icon Chevron up icon
Index

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SC Dec 18, 2019
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Basic information - no insight and non advanced topics.
Amazon Verified review Amazon
Amazon Customer Dec 01, 2015
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The book describes most of the procedures needed for a cluster manager/DBA to install and maintain a CDH5 cluster.However, it gives the detailed steps for how to do things with Cloudera manager, without getting any deeper and trying to explain what happens behind the scenes.If you're already familiar with Hadoop internals and want a quick reference about maintaining CDH5 cluster then this book is for you. If you want to really learn about Hadoop and how it works then you should search somewhere else.
Amazon Verified review Amazon
Sketch Nov 07, 2015
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
While it is pretty good at the bulk of the Cloudera management suite, it does miss the mark on some key factors - specifically Kerberos. Granted, official Cloudera documentation is woefully inadequate, this book only brings up other issues with nothing to bridge the two. It gives simple instructions which are accurate (i.e. Hive, etc...), however it gives no detail as to how the given service works - which may not be the point of the book, but it would help.
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SA Jan 15, 2015
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Not really a deep in to subject.. you better of reading cloudera manual. that is what this is basically.
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Ganapathy Kokkeshwara Oct 09, 2014
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This is a must read book for anyone who is in the process of learning the administration of Cloudera distribution of hadoop. Other than learning, the book can also be used for reference. In the first 2 chapters , the author talks about Apache Hadoop, its various components, HDFS and Mapreduce. These chapters provides a very informative introduction to Apache Hadoop and the ecosystem associated with it. These chapters are useful for anyone interested in learning hadoop technology let alone for Cloudera administrators.In the 3rd chapter author talks about Cloudera distribution of Apache Hadoop and the other components that are distributed with Cloudera Hadoop Distribution ( CDH) such as Flume ,Sqoop, Pig, Hive, Zookeeper etc. The components are explained in simple terms that can be understood by most of the technical persons. I liked the 'screenshots' of the UI for each of the components that made it little bit easier to understand and comprehend. This chapter also covers the installation of CDH and the various components. I liked the fact that author covers the installation from Cloudera Manager as well as from Operating System’s package manager , thus providing more options for the administrator.Rest of the chapters cover administrating the high availability , implementing security using Kerberos , managing cluster and monitoring. Chapter 9 talks about backing up the Hadoop cluster. I liked the fact that author took time to explain the various types of backup and storage media for backups before actually getting into technical nitty-gritty of back up of big data.Throughout the book , author also writes briefly about the impact on administering the CDH when deployed on cloud and provides the relevant web links for further reference.
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About the author

Profile icon Menon
Menon
Rohit Menon is a senior system analyst living in Denver, Colorado. He has over 7 years of experience in the field of Information Technology, which started with the role of a real-time applications developer back in 2006. He now works for a product-based company specializing in software for large telecom operators. He graduated with a master's degree in Computer Applications from University of Pune, where he built an autonomous maze-solving robot as his final year project. He later joined a software consulting company in India where he worked on C#, SQL Server, C++, and RTOS to provide software solutions to reputable organizations in USA and Japan. After this, he started working for a product-based company where most of his time was dedicated to programming the finer details of products using C++, Oracle, Linux, and Java. He is a person who always likes to learn new technologies and this got him interested in web application development. He picked up Ruby, Ruby on Rails, HTML, JavaScript, CSS, and built www.flicksery.com, a Netflix search engine that makes searching for titles on Netflix much easier. On the Hadoop front, he is a Cloudera Certified Apache Hadoop Developer. He blogs at www.rohitmenon.com, mainly on topics related to Apache Hadoop and its components. To share his learning, he has also started www.hadoopscreencasts.com, a website that teaches Apache Hadoop using simple, short, and easy-to-follow screencasts. He is well versed with wide variety of tools and techniques such as MapReduce, Hive, Pig, Sqoop, Oozie, and Talend Open Studio.
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Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks? Chevron down icon Chevron up icon
  • You can get the information you need immediately
  • You can easily take them with you on a laptop
  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.