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Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
Hadoop and MapReduce
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Hadoop and MapReduce

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  • 1. HADOOP Framework and ApplicationsPrepared by: TEAM HADOOP slide1/22
  • 2. CONTENTS  WHY HADOOP?  INTRODUCTION TO MapReducePrepared by: TEAM HADOOP slide 2/22
  • 3. WHAT? “... to create building blocks for programmers who just happen to have lots of data to store, lots of data to analyze, or lots of machines to coordinate, and who don‟t have the time, the skill, or the inclination to become distributed systems experts to build the infrastructure to handle it.” -Tom White Source: Hadoop: The Definitive GuidePrepared by: TEAM HADOOP slide 3/22
  • 4. WHAT?  Hadoop contains many subprojects:  Hadoop Common  Chukwa  HBase  ZooKeeper  Pig  Zombie  Hive  MapReduce We will focus on MapReducePrepared by: TEAM HADOOP slide 4/22
  • 5. WHO & WHEN?  Pre-2004 : Cutting and Cafarella develop open source projects for web-scale indexing, crawling and search.Prepared by: TEAM HADOOP slide 5/22
  • 6. WHO & WHEN?  2004: Jeffrey Dean and Sanjay Ghemawat introduce map reduce model used internally at Google.Prepared by: TEAM HADOOP slide 6/22
  • 7. WHO & WHEN?  2006:Hadoop becomes official Apache project, Cutting joins Yahoo!Yahoo adopts Hadoop.Prepared by: TEAM HADOOP slide 7/22
  • 8. TRENDSPrepared by: TEAM HADOOP slide 8/22
  • 9. WHO USES IT?Prepared by: TEAM HADOOP slide 9/22
  • 10. Roughly how long to read 1TB from a commodity hard disk?Prepared by: TEAM HADOOP slide 10/22
  • 11. Roughly how long to read 1TB from a commodity hard disk? Around 4 hoursWITH HADOOP.. 62 seconds…Prepared by: TEAM HADOOP slide 11/22
  • 12. INTRODUCTION TO MapReduce "Break large problem into smaller parts, solve in parallel, combine results." Prepared by: TEAM HADOOP slide 12/22
  • 13. Typical scenario  How many times is the word „IT‟ present? You‟ll probably count but in a 30k paged document, can you??Prepared by: TEAM HADOOP slide 13/22
  • 14. Map Reduce Typical Illustration Prepared by: TEAM HADOOP slide 14/22
  • 15. Map Reduce paradigm Input Output Map Reduce Shuffle/SortPrepared by: TEAM HADOOP slide 15/22
  • 16. Map Reduce paradigm  Map: transforms input record to intermediate (key, value) pairPrepared by: TEAM HADOOP slide 16/22
  • 17. Map Reduce paradigm  Reduce: transforms all records for given key to final output.Prepared by: TEAM HADOOP slide 17/22
  • 18. Map reduce principles Move code to data (local computation) Abstract away fault Allow programs to scale tolerance, synchronization, etc. transparently w.r.t size of inputPrepared by: TEAM HADOOP slide 18/22
  • 19. Implementation: HardwarePrepared by: TEAM HADOOP sroy choudhury7@gmail.com slide 19/22
  • 20. Map Reduce: strengths  Batch, offline jobs  Write-once, read-many across full data set  Usually, though not always, simple computations  I/O bound by disk/network bandwidthPrepared by: TEAM HADOOP slide 20/22
  • 21. What it‟s not! What it‟s not:  High-performance parallel computing, e.g. MPI  Low-latency random access relational database  Always the right solutionPrepared by: TEAM HADOOP slide 21/22
  • 22. THANK YOU! QUESTIONS?Prepared by: TEAM HADOOP slide 22/22

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