• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
HDFS presented by VIJAY
 

HDFS presented by VIJAY

on

  • 512 views

 

Statistics

Views

Total Views
512
Views on SlideShare
512
Embed Views
0

Actions

Likes
0
Downloads
41
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as OpenOffice

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    HDFS presented by VIJAY HDFS presented by VIJAY Presentation Transcript

    • Hadoop Distributed File System (HDFS) SEMINAR GUIDE Mr. PRAMOD PAVITHRAN HEAD OF DIVISION COMPUTER SCIENCE & ENGINEERING SCHOOL OF ENGINEERING, CUSAT PRESENTED BY VIJAY PRATAP SINGH REG NO: 12110083 S7, CS-B ROLL NO: 81
    • CONTENTS WHAT IS HADOOP PROJECT COMPONENTS IN HADOOP MAP/REDUCE HDFS ARCHITECTURE WRITE & READ IN HDFS GOALS OF HADOOP COMPARISION WITH OTHER SYSTEMS CONCLUSION REFERENCES
    • WHAT IS HADOOP ?
    • WHAT IS HADOOP ?
    • WHAT IS HADOOP ?
    • WHAT IS HADOOP ? o Hadoop is an open-source software framework . o Hadoop framework consists on two main layers ● Distributed file system (HDFS) ● Execution engine (MapReduce) o Supports data-intensive distributed applications. o Licensed under the Apache v2 license. o It enables applications to work with thousands of computation-independent computers and petabytes of data
    • WHY HADOOP ?
    • PROJECT COMPONENTS IN HADOOP
    • MAP/REDUCE o Hadoop is the popular open source implementation of map/reduce o MapReduce is a programming model for processing large data sets o MapReduce is typically used to do distributed computing on clusters of computers o MapReduce can take advantage of locality of data, processing data on or near the storage assets to decrease transmission of data. oThe model is inspired by the map and reduce functions o"Map" step: The master node takes the input, divides it into smaller sub-problems, and distributes them to slave nodes. The slave node processes the smaller problem, and passes the answer back to its master node. o"Reduce" step: The master node then collects the answers to all the sub-problems and combines them in some way to form the final output
    • MAP REDUCE ENGINE
    • HDFS Highly scalable file system ◦ 6K nodes and 120PB ◦ Add commodity servers and disks to scale storage and IO bandwidth Supports parallel reading & processing of data ◦ Optimized for streaming reads/writes of large files ◦ Bandwidth scales linearly with the number of nodes and disks Fault tolerant & easy management ◦ Built in redundancy ◦ Tolerate disk and node failure ◦ Automatically manages addition/removal of nodes ◦ One operator per 3K nodes Scalable, Reliable & Manageable
    • LIMITATIONS OF EXISTING DATA ANALYTICS ARCHITECTURE
    • BIG DATA
    • INCREASING BIG DATA
    • HADOOP'S APPROACH
    • HADOOP'S APPROACH
    • HADOOP'S APPROACH
    • ARCHITECTURE OF HADOOP
    • HADOOP MASTER/SLAVE ARCHITECTURE
    • ARCHITECTURE OF HDFS
    • ARCHITECTURE OF HDFS
    • CLIENT INTERACTION TO HADOOP
    • HDFS WRITE Client Rack Awareness Rack 1:DN 1 Rack 2:DN7,9 Rack 1 Core Switch Switch SwitchF DataNode 1 DataNode 9 DataNode 7 Rack 5 BA C Name Node I want to write file.txt Block A OK, Write to DataNode [1,7,9] Ready DN 7,9 Ready DN 9 Ready
    • PIPELINED WRITE Client Rack Awareness Rack 1:DN 1 Rack 2:DN7,9 Rack 1 Core Switch Switch SwitchF DataNode 1 DataNode 9 DataNode 7 Rack 5 BA C Name Node A A A
    • PIPELINED WRITE Client Rack Awareness Rack 1:DN 1 Rack 2:DN7,9 Rack 1 Core Switch Switch SwitchF DataNode 1 DataNode 9 DataNode 7 Rack 5 BA C Name Node A A A Block Received Success MetaData File.txt = Block: DN: 1,7,9 A
    • HDFS READ Client Rack 1 Core Switch Switch Switch DataNode 1 DataNode 9 DataNode 7 Rack 5 Name Node I want to Read file.txt Block A Available at DataNode [1,7,9] A A A MetaData File.txt = Block: DN: 1,7,9 A
    • HDFS SHELL COMMANDS ● bin/hadoop fs -ls ● bin/hadoop fs -mkdir ● bin/hadoop fs -copyFromLocal ● bin/hadoop fs -copyToLocal ● bin/hadoop fs -moveToLocal ● bin/hadoop fs -rm ● bin/hadoop fs -tail ● bin/hadoop fs -chmod ● bin/hadoop fs -setrep -w 4 -R /dir1/s-dir/
    • GOALS OF HDFS Very Large Distributed File System ◦10K nodes, 100 million files, 10PB Assumes Commodity Hardware ◦Files are replicated to handle hardware failure ◦Detect failures and recover from them Optimized for Batch Processing ◦Data locations exposed so that computations can move to where data resides ◦Provides very high aggregate bandwidth
    • SCALABILITY OF HADOOP
    • EASE TO PROGRAMMERS
    • HADOOP VS. OTHER SYSTEMS
    • HADOOP USERS
    • TO LEARN MORE Source code ◦http://hadoop.apache.org/version_control.html ◦http://svn.apache.org/viewvc/hadoop/common/trunk/ Hadoop releases ◦http://hadoop.apache.org/releases.html Contribute to it ◦http://wiki.apache.org/hadoop/HowToContribute
    • CONCLUSION Hdfs provides a reliable, scalable and manageable solution for working with huge amounts of data Future secure Hdfs has been deployed in clusters of 10 to 4k datanodes ◦Used in production at companies such as yahoo! , FB , Twitter , ebay ◦Many enterprises including financial companies use hadoop
    • REFERENCES [1] M. Zukowski, S. Heman, N. Nes, And P. Boncz. Cooperative Scans: Dynamic Bandwidth Sharing In A DBMS. In VLDB ’07: Proceedings Of The 33rd International Conference On Very Large Data Bases, Pages 23–34, 2007. [2] Tom White, Hadoop The Definite Guide, O’reilly Media ,Third Edition, May 2012 [3] Jeffrey Shafer, Scott Rixner, And Alan L. Cox, The Hadoop Distributed Filesystem: Balancing Portability And Performance, Rice University, Houston, TX [4] Konstantin Shvachko, Hairong Kuang, Sanjay Radia, Robert Chansler, The Hadoop Distributed File System, Yahoo, Sunnyvale, California, USA [5] Jens Dittrich, Jorge-arnulfo Quian, E-ruiz, Information Systems Group, Efficient Big Data Processing In Hadoop Mapreduce , Saarland University
    • Thankyou.
    • Queries