Hadoop is an open-source software framework . Hadoop framework consists on two main layers Distributed file system (HDFS) Execution engine (MapReduce) Supports data-intensive distributed applications. Licensed under the Apache v2 license.  It enables applications to work with thousands of computation-independent computers and petabytes of data Hadoop is the popular open source implementation of map/reduce MapReduce is a programming model for processing large data sets MapReduce is typically used to do distributed computing on clusters of computers MapReduce can take advantage of locality of data, processing data on or near the storage assets to decrease transmission of data. The model is inspired by the map and reduce functions  "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. "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 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 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 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.

1. Hadoop
Distributed File System
(HDFS)
SEMINAR GUIDE
Mr. PRAMOD PAVITHRAN
HEAD OF DIVISION
COMPUTER SCIENCE & ENGINEERING
SCHOOL OF ENGINEERING, CUSAT
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ROLL NO: 81

2. CONTENTS
WHAT IS HADOOP
PROJECT COMPONENTS IN HADOOP
MAP/REDUCE
HDFS
ARCHITECTURE
GOALS OF HADOOP
COMPARISION WITH OTHER SYSTEMS
CONCLUSION
REFERENCES

3. WHAT IS HADOOP…???

4. WHAT IS HADOOP…???

5. WHAT IS HADOOP…???

6. WHAT IS HADOOP…???
o Hadoop is an open-source software framework .
o Hadoop framework consists on two main layers
o Distributed file system (HDFS)
o 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

7. WHY HADOOP…???

8. PROJECT COMPONENTS IN
HADOOP

9. 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

10. 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

11. ISSUES IN CURRENT SYSTEM

12. BIG DATA

13. INCREASING BIG DATA

14. HADOOP’S APPROACH
Big Data
Computation
Computation
Computation
Computation
Combined Result

15. ARCHITECTURE OF HADOOP

16. HADOOP MASTER/SLAVE
ARCHITECTURE

17. MAP REDUCE ENGINE

18. MAP REDUCE ENGINE

19. ARCHITECTURE OF HDFS

20. ARCHITECTURE OF HDFS

21. CLIENT INTERACTION TO
HADOOP

22. • A
Rack 1
DataNode 1
DataNode 9
DataNode 7
Client
F
CBA
Rack 5
NameNode
Rack Awareness
Rack 1:DN 1
Rack 2:DN7,9
Core Switch
Switch Switch
I want to
write file.txt
block A
Ok, Write to
Data Nodes
[1,7,9]
Ready
DN
7+9 Ready
9
Ready!A A
A
HDFS
WRITE

23. • A
Rack 1
DataNode 1
DataNode 9
DataNode 7
Client
F
CBA
Rack 5
NameNode
Rack Awareness
Rack 1:DN 1
Rack 2:DN7,9
Core Switch
Switch Switch
A A
A
Block Received
Success
Metadata
File.txt =
Blk
DN : 1,7,9
A
HDFS WRITE
(PIPELINED)

24. • A
Rack 1
DataNode 1
DataNode 9
DataNode 7
Client
F
CBA
Rack 5
NameNode
Rack Awareness
Rack 1:DN 1
Rack 2:DN7,9
Core Switch
Switch Switch
I want to read
file.txt block
A
Available at
nodes
[1,7,9]
A A
A
HDFS READ

25. 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

26. SCALABILITY OF HADOOP

27. EASE TO PROGRAMMERS

28. HADOOP VS. OTHER SYSTEMS

29. HADOOP USERS

30. 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

31. 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

32. 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

33. Thankyou… 

34. Queries