• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Hadoop by sunitha
 

Hadoop by sunitha

on

  • 796 views

 

Statistics

Views

Total Views
796
Views on SlideShare
796
Embed Views
0

Actions

Likes
2
Downloads
27
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

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

    Hadoop by sunitha Hadoop by sunitha Presentation Transcript

    • ExplainedSunitha Raghurajan
    • Data…Data….Data….• We live in a data world ????• Total FaceBook Users:835,525,280 (March 31 st 2012)• The New York Stock Exchange generates about one terabyte of new trade data per• day.• • Facebook hosts approximately 10 billion photos, taking up one petabyte of storagehttp://www.internetworldstats.com/facebook.htm
    • Data…is growing ????From Gantz et al., “The Diverse and Exploding Digital Universe,” March 2008 (http://www.emc.com/collateral/analyst-reports/diverse-exploding-digital-universe.pdf).
    • Problem??• How do we store and analyze the date???• one terabyte drives the transfer speed is around 100 MB/s- more than two and a half hours to read all the data off the disk. Writing more slower• We had 100 drives holding one hundredth of the data.• Reliability issues ( failure in hard drive)• Combine data from 100 drives?.• Existing Tools inadequate to process large data sets
    • Why can’t we use RDBMS?• An RDBMS is good for point queries or updates, where the dataset has been indexed to deliver low-latency retrieval and update times of a relatively small amount of data. Longer time to read data CPU Memory Disk
    • Hadoop is the answer!!!!!• Hadoop is an open source project licensed under the Apache v2 license http://hadoop.apache.org/• Used for processing large datasets in parallel with the use of low level commodity machines.• Hadoop is build on two main parts. An special file system called Hadoop Distributed File System (HDFS) and the Map Reduce Framework.
    • Hadoop History• Hadoop was created by Doug Cutting, who named it after his sons toy elephant .• 2002-2004 Nutch Open Source web-scale, crawler- based search• 2004-2006 Google File System & MapReduce papers published.Added DFS & MapReduce impl to Nutch• 2006-2008 Yahoo hired Doug Cutting• On February 19, 2008, Yahoo! Inc. launched what it claimed was the worlds largest Hadoop production application• The Yahoo! Search Webmap is a Hadoop application that runs on more than 10,000 core Linux cluster and produces data that is now used in every Yahoo! Web search query.[22]
    • Who uses Hadoop ?Amazon American AirlinesAOL AppleeBay Federal Reserve Board of Governorsfoursquare Fox Interactive MediaFaceBook StumbleUponGemvara Hewlett-PackardIBM MicroSoftTwitter NYTimesNetFlix Linkedin
    • Why Hadoop?• Reliable: The software is fault tolerant, it expects and handles hardware and software failures• Scalable: Designed for massive scale of processors, memory, and local attached storage• Distributed: Handles replication. Offers massively parallel programming model, MapReduce
    • What is MapReduce??? – Programming model used by Google – A combination of the Map and Reduce models with an associated implementation – Used for processing and generating large data sets
    • MapReduce Explained• The basic idea is that you divide the job into two parts: a Map, and a Reduce.• Map basically takes the problem, splits it into sub-parts, and sends the sub-parts to different machines – so all the pieces run at the same time.• Reduce takes the results from the sub-parts and combines them back together to get a single answer.
    • Distributed Grep Split data grep matches Split data grep matchesVery All big Split data grep matches cat matchesdata Split data grep matches
    • MAP REDUCE ARCHITURE
    • How Map and Reduce WorkTogether
    • Map Reduce R M EVery Partitioning A D Result big Function P Udata C E• Map: – Accepts input key/value pair Reduce : – Emits intermediate key/value pair Accepts intermediate key/value* pair Emits output key/value pair
    • http://ayende.com/blog/4435/map-reduce-a-visual-explanation
    • RDBMS compared toMapReduceData Gigabytes PetabytesSizeAccess Interactive and Batch batchUpdates Read and write Write once, read many many times timesintegrity High LowScaling Nonlinear LinearStructur Static schema Dynamic schemae
    • Hadoop Family Pig A platform for manipulating large data sets Scripting Machine Mahout Machine Learning Algorithms Learning Bigtable-like structured storage HBASE for Hadoop HDFS Non-Rel RDBMS HIVE data warehouse system Non-Rel RDBMS Distribute and replicated data HDFS among machines Hadoop common MapReduce Distribute and monitor tasks Zoo Keeper Distributed Contributed Service
    • When to use Hadoop?• Complex information processing is needed• Unstructured data needs to be turned into structured data• Queries can’t be reasonably expressed using SQL• Heavily recursive algorithms• Complex but parallelizable algorithms needed, such as geo-spatial analysis or genome sequencing• Machine learning• Data sets are too large to fit into database RAM, discs, or require too many cores (10’s of TB up to PB)• Data value does not justify expense of constant real-time availability, such as archives or special interest info, which can be moved to Hadoop and remain available at lower cost• Results are not needed in real time• Fault tolerance is critical• Significant custom coding would be required to handle job scheduling• Reference:http://timoelliott.com/blog/2011/09/hadoop-big-data-and- enterprise-business-intelligence.html
    • Building Blocks of Hadoop• Running a set of daemons on different servers on the network •NameNode •DataNode •Secondary NameNode •JobTracker •TaskTracker
    • • Questions????
    • References• Hadoop in Action By Chuck Lam• Hadoop The Definitive Guide By Tom White• http://hadoop.apache.org/