The document outlines a Hadoop administration course offered by Edureka, detailing the objectives, core components, recommended configurations, and operational modes of a Hadoop cluster. It emphasizes the importance of proper hardware setup and cluster management responsibilities, along with integration on cloud platforms like AWS for dynamic resource allocation. Additionally, it highlights various modules of the course covering advanced topics and practical project work.

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Power the Hadoop Cluster with AWS Cloud

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Objectives
At the end of this module, you will be able to
Hadoop Cluster introduction
Recommended Configuration for cluster
Hadoop cluster running modes
Hadoop configuration files
Hadoop Admin Responsibilities
Hadoop cluster set up on AWS Demo

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Hadoop Core Components
Hadoop 2.x Core Components
HDFS YARN
Storage Processing
DataNode
NameNode Resource Manager
Node Manager
Master
Slave
Secondary
NameNode
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RAM: 16GB
Hard disk: 6 x 2TB
Processor: Xenon with 2 cores
Ethernet: 3 x 10 GB/s
OS: 64-bit CentOS
Hadoop Cluster: A Typical Use Case
RAM: 16GB
Hard disk: 6 x 2TB
Processor: Xenon with 2 cores.
Ethernet: 3 x 10 GB/s
OS: 64-bit CentOS
RAM: 64 GB,
Hard disk: 1 TB
Processor: Xenon with 8 Cores
Ethernet: 3 x 10 GB/s
OS: 64-bit CentOS
Power: Redundant Power Supply
RAM: 32 GB,
Hard disk: 1 TB
Processor: Xenon with 4 Cores
Ethernet: 3 x 10 GB/s
OS: 64-bit CentOS
Power: Redundant Power Supply
Active NameNodeSecondary NameNode
DataNode DataNode
RAM: 64 GB,
Hard disk: 1 TB
Processor: Xenon with 8 Cores
Ethernet: 3 x 10 GB/s
OS: 64-bit CentOS
Power: Redundant Power Supply
StandBy NameNode
Optional
RAM: 16GB
Hard disk: 6 x 2TB
Processor: Xenon with 2 cores
Ethernet: 3 x 10 GB/s
OS: 64-bit CentOS
DataNode
DataNode DataNode DataNode
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Seeking cluster growth on storage capacity is often a good method to use!
Cluster Growth Based On Storage Capacity
Data grows by approximately
5TB per week
HDFS set up to replicate each
block three times
Thus, 15TB of extra storage
space required per week
Assuming machines with 5x3TB
hard drives, equating to a new
machine required each week
Assume Overheads to be 30%

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Slave Nodes: Recommended Configuration
Higher-performance vs lower performance components
Save the Money, Buy more Nodes!
 General ( Depends on requirement
‘base’ configuration for a slave Node
» 4 x 1 TB or 2 TB hard drives, in a
JBOD* configuration
» Do not use RAID!
» 2 x Quad-core CPUs
» 24 -32GB RAM
» Gigabit Ethernet
General Configuration
 Multiples of ( 1 hard drive + 2 cores
+ 6-8GB RAM) generally work well
for many types of applications
Special Configuration
Slave Nodes
“A cluster with more nodes performs better than one with fewer, slightly faster nodes”

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Slave Nodes: More Details (RAM)
Slave Nodes (RAM)
Generally each Map or Reduce task
will take 1GB to 2GB of RAM
Slave nodes should not be using
virtual memory
RULE OF THUMB!
Total number of tasks = 1.5 x number
of processor core
Ensure enough RAM is present to
run all tasks, plus the DataNode,
TaskTracker daemons, plus the
operating system

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Master Node Hardware Recommendations
Carrier-class hardware
(Not commodity hardware)
Dual power supplies
Dual Ethernet cards
(Bonded to provide failover)
Raided hard drives
At least 32GB of RAM
Master
Node
Requires

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Hadoop Cluster Modes
Hadoop can run in any of the following three modes:
Fully-Distributed Mode
Pseudo-Distributed Mode
 No daemons, everything runs in a single JVM
 Suitable for running MapReduce programs during development
 Has no DFS
 Hadoop daemons run on the local machine
 Hadoop daemons run on a cluster of machines
Standalone (or Local) Mode

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Configuration Files
Configuration
Filenames
Description of Log Files
hadoop-env.sh
yarn-env.sh
Settings for Hadoop Daemon’s process environment.
core-site.xml
Configuration settings for Hadoop Core such as I/O settings that common to both HDFS and
YARN.
hdfs-site.xml Configuration settings for HDFS Daemons, the Name Node and the Data Nodes.
yarn-site.xml Configuration setting for Resource Manager and Node Manager.
mapred-site.xml Configuration settings for MapReduce Applications.
slaves A list of machines (one per line) that each run DataNode and Node Manager.

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Configuration Files (Contd.)
Deprecated Property Name New Property Name
dfs.data.dir dfs.datanode.data.dir
dfs.http.address dfs.namenode.http-address
fs.default.name fs.defaultFS
The core functionality and usage of these core configuration files are same in Hadoop 2.0 and 1.0 but many new properties
have been added and many have been deprecated
For example:
 ’fs.default.name’ has been deprecated and replaced with ‘fs.defaultFS’ for YARN in core-site.xml
 ‘dfs.nameservices’ has been added to enable NameNode High Availability in hdfs-site.xml
http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-common/DeprecatedProperties.html
 In Hadoop 2.2.0 release, you can use either the old or the new properties
 The old property names are now deprecated, but still work!

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Core
HDFS
core-site.xml
hdfs-site.xml
yarn-site.xmlYARN
mapred-site.xml
Map
Reduce
Hadoop 2.x Configuration Files – Apache Hadoop
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Hadoop Daemons
NameNode daemon
» Runs on master node of the Hadoop Distributed File System (HDFS)
» Directs Data Nodes to perform their low-level I/O tasks
DataNode daemon
» Runs on each slave machine in the HDFS
» Does the low-level I/O work
Resource Manager
» Runs on master node of the Data processing System(MapReduce)
» Global resource Scheduler
Node Manager
» Runs on each slave node of Data processing System
» Platform for the Data processing tasks
Job HistoryServer
» JobHistoryServer is responsible for servicing all job history related requests from client
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Why Cloud?
Challenges in current trend:
Arranging a large common storage area
Providing secure access to the shared data

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Amazon EC2
A cloud web host that allows you to dynamically add and remove computer server resources as you need them,
allowing you to pay for only the capacity that you used.
Good For Hadoop Cluster set : we can bring up enormous cluster with in minutes and then spin it down when we
have finished to reduce costs.

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Hadoop on AWS
ANALYZING…

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DEMO

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Hadoop Admin Responsibilities
Responsible for implementation and administration of Hadoop infrastructure.
Testing HDFS, Hive, Pig and MapReduce access for Applications.
Cluster maintenance tasks like Backup, Recovery, Upgrade, Patching.
Performance tuning and Capacity planning for Clusters.
Monitor Hadoop cluster and deploy security.

19. LIVE Online Class
Class Recording in LMS
24/7 Post Class Support
Module Wise Quiz
Project Work
Verifiable Certificate
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How it Works?

20. Questions
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Course Topics
 Module 1
» Hadoop Cluster Administration
 Module 2
» Hadoop Architecture and Cluster setup
 Module 3
» Hadoop Cluster: Planning and Managing
 Module 4
» Backup, Recovery and Maintenance
 Module 5
» Hadoop 2.0 and High Availability
 Module 6
» Advanced Topics: QJM, HDFS Federation and
Security
 Module 7
» Oozie, Hcatalog/Hive and HBase Administration
 Module 8
» Project: Hadoop Implementation