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+OutlineIntroduction

Architecture of Hadoop

HDFS

MapReduce

Comparison

Why Hadoop

Conclusion

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+What is Hadoop ? open-source software framework

process and store big data

Easy to use and implement, economic, flexible

lots of nodes(server)

written in JAVA

free license

created by Doug Cutting and Mike Cafarella in 2005

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+Advantages of Interpreted Language

Cross-platform(ex: Windows, Ubuntu, Mac OS X)

smaller executable program size

easier to modify during both development and execution

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+Architecture of Hadoop

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+Hadoop in Enterprise

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The Dell representation of the Hadoop ecosystem.

+Hadoop in Enterprise

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+Who is using Hadoop ?

more than half of the Fortune 50 uses Hadoop by 2013

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+HDFSHadoop Distributed File System

Client: user

name node: manage and store metadata, namespace of files

Data node: store files

each data node sends its status to name node periodically

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+HDFS: Writing data in HDFS Each file will be divided into blocks(in size 64

or 128MB) , and have three copies in different data nodes.

Client asks name node to get a list of data node sorted by distance, and send the file to the nearest one , then the data node will send the file to the rest node.

When above operation done, data node will send “done” to name node.

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+HDFS: Reading data in HDFSClient send filename to the name

node , then the name node will send a list of the blocks of files sorted by distance.

Client use the list to get the file from data node.

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+HDFS: failurenode failure

communication failure

data corruption

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+HDFS: handle failureHandle writing failure:

name node will skip the data node without an ACK.

Handle reading failure:recall that when reading a file, client will get a list of data node content the file.

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+HDFS: handle failureName node handle node failure :

name node will find out the data the failure node have, and copy those data from others and restore them to other data node.

Note that HDFS can’t guarantee at least one copy of data is alive.

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+MapReducesimilar to divide-and-conquer

First, use “Map” to divide tasks

Second, use “Shuffle” to “transfer the data from the mapper nodes to a reducer’s node and decompress if needed. “

Third, use “Reduce” to “execute the user-defined reduce function to produce the final output data. “

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+MapReduce-Map

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+MapReduce-shuffle

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+MapReduce-Reduce

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+MapReduce

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+Comparison

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+Comparison

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+Why Hadoop?technically

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Comparison of Grep Task Result with Vertica and DBMS-X

+Why Hadoop?

Simple structure vs. Optimization

Transaction time not minimized

Lower performance with same number of nodes

No compelling reason to choose Hadoop

technically

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+Why Hadoop?commercially

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+Why Hadoop

Cheap (Buy more servers to beat DBMS)

Flexible (Both in design and deployment)

Easier to design

Easier to scale up

Combine with other system to achieve better performance

commercially

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+ConclusionHadoop is much easier for users to

implement and more economic

MapReduce advocates should study the techniques used in parallel DBMSs

Hybrid systems are also popular

With improvement of performance, we believe Hadoop will lead the trend of big data computing

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+Reference http://hadoop.apache.org/

http://www.runpc.com.tw/content/cloud_content.aspx?id=105318

http://en.wikipedia.org/wiki/Apache_Hadoo

https://www.facebookbrand.com/

http://assets.fontsinuse.com/static/use-media-items/15/14246/full-2048x768/522903b7/Yahoo_Logo.png

http://wiki.apache.org/hadoop/PoweredBy

http://semiaccurate.com/assets/uploads/2011/09/Amazon-logo.jpg

http://www.conceptcupboard.com/blog/wp-content/uploads/2013/09/google.jpg

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+Reference http://datashieldcorp.com/files/2013/11/adobe-LOGO-2.jpg

http://upload.wikimedia.org/wikipedia/commons/7/77/The_New_York_Times_logo.png

http://i.dell.com/sites/content/business/solutions/whitepapers/en/Documents/hadoop-introduction.pdf

http://hadoop.intel.com/pdfs/IntelDistributionReferenceArchitecture.pdf

http://www.google.com.tw/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CDQQFjAB&url=http%3A%2F%2Fwww.classcloud.org%2Fcloud%2Fraw-attachment%2Fwiki%2FHinet100402%2F02.HadoopOverview.pdf&ei=IE2XUtLfBMfxiAea_oHQCA&usg=AFQjCNFoIXxLJrOnoul4cKJpQ8v3_kuTYg

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+Reference http://www.accenture.com/SiteCollectionDocuments/PDF/

Accenture-Hadoop-Deployment-Comparison-Study.pdf

https://www.google.com.tw/url?sa=t&rct=j&q&esrc=s&source=web&cd=1&ved=0CCkQFjAA&url=http%3A%2F%2Fwww.psgtech.edu%2Fyrgcc%2Fattach%2FMAP%2520REDUCE%2520PROGRAMMING.ppt&ei=7lGXUtvCJsy5iAfWtYH4Bw&usg=AFQjCNGWRKJLal-tvbvORULZV6_Te2y74g&sig2=Ba77ihsV1SEqcNeEFkRzfg

https://www.cs.duke.edu/starfish/files/hadoop-models.pdf

http://dotnetmis91.blogspot.tw/2010/04/hdfs-hadoop-mapreduce.html

http://wiki.apache.org/hadoop/HDFS

http://www.ewdna.com/2013/04/Hadoop-HDFS-Comics.html

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+Reference http://en.wikipedia.org/wiki/Interpreted_language

A Comparison of Approaches to Large-Scale Data Analysis by Sam Madden

http://www.cc.ntu.edu.tw/chinese/epaper/0011/20091220_1106.htm

http://web.cs.wpi.edu/~cs561/s12/Lectures/6/Hadoop.pdf

http://www.mobilemartin.com/mobile/show-me-the-mobile-money.jpg

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