With the advent of Hadoop, there comes the need for professionals skilled in Hadoop Administration making it imperative to be skilled as a Hadoop Admin for better career, salary and job opportunities.
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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
4. Slide 4
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!
13. Slide 13
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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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 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?