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Aws Quick Dirty Hadoop Mapreduce Ec2 S3
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Aws Quick Dirty Hadoop Mapreduce Ec2 S3

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Aws Quick Dirty Hadoop Mapreduce Ec2 S3

Aws Quick Dirty Hadoop Mapreduce Ec2 S3

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  • So without further ado lets get this show on the road and run a job concurrently on a few virtual machines.

Transcript

  • 1.  
  • 2. QUICK AND DIRTY PARALLEL PROCESSING ON THE CLOUD Daniel Sikar
  • 3. EC2 S3
  • 4.  
  • 5. Tools
    • AWS Command line tools
    • 6. Elastic MapReduce Ruby library
    • 7. Hadoop
    • 8. s3cmd
  • 9. Hadoop MapReduce Job Tracker HDFS – Distributed file system
  • 10. Hadoop MapReduce usage Data crunching in general Clicks Statistics etc
  • 11. Hadoop Project Mgmt Committee
  • 12. MapReduce ?
  • 13. MapReduce Key Pairs <key,value>
  • 14. MapReduce
  • 15. HTTP Logs Log file A: (...) FreeTouchScreenNokia5230 (...) (...) GetRidofAllSpeedCameras(...) (...) USManWinsLottery (...) (...) BNPToLaunchElectionManifesto (...) Log file B: (...) FreeTouchScreenNokia5230 (...) (...) BodyLanguageTellsAll (...)
  • 16. MapReduce <FreeTouchScreenNokia5230, 1> + <FreeTouchScreenNokia5230, 1> = <FreeTouchScreenNokia5230, 2>
  • 17. Hadoop Streaming Running MapReduce jobs with .exe fiels and scripts $ <list> | mapper | reducer
  • 18. Hadoop Streaming Running MapReduce jobs with .exe fiels and scripts $ <list> | mapper | reducer
  • 19. Real life example of Hadoop Streaming usage
  • 20. Wikipedia Page Access Logs
  • 21. Wine Grape Varieties
  • 22. Wikipedia WGV Page Access Stats
  • 23. Business Decisions
  • 24. Launching a virtual Hadoop Cluster $ elastic-mapreduce --create --name &quot;Wiki log crunch&quot; --alive --num-instances –instance-type c1.medium 20 Created job flow <job flow id> $ ec2din (...)
  • 25.  
  • 26. Hadoop
    • Standalone Operation
    • 27. Pseudo-Distributed Operation
    • 28. Fully-Distributed Operation
    • 29. NameNode
    • 30. JobTracker
    • 31. DataNode + TaskTracker
  • 32. Hadoop
    • Standalone Operation
    • 33. Pseudo-Distributed Operation
    • 34. Fully-Distributed Operation
    • 35. NameNode
    • 36. JobTracker
    • 37. DataNode + TaskTracker
  • 38. Add a step $ elastic-mapreduce --jobflow <jfid> --stream --step-name &quot;Wiki log crunch&quot; --input s3n://dsikar-wikilogs-2009/dec/ --output s3n://dsikar-wikilogs-output/21 --mapper s3n://dsikar-wiki-scripts/wikidictionarymap.pl --reducer s3n://dsikar-wiki-scripts/wikireduce.pl http://<instance public dns>:9100
  • 39. s3cmd # make bucket $ s3cmd mb s3://dsikar-wikilogs # put log files $ s3cmd put pagecounts-200912*.gz s3://dsikar-wikilogs/dec $ s3cmd put pagecounts-201004*.gz s3://dsikar-wikilogs/apr # list log files $ s3cmd ls s3://dsikar-wikilogs/ # put scripts $ s3cmd put *.pl s3://dsikar-wiki-scripts/ # delete log files $ s3cmd del --recursive --force s3://dsikar-wikilogs/ # remove bucket $ s3cmd rb s3://dsikar-wikilogs/
  • 40. Elastic MapReduce --create --list --jobflow --describe --stream --terminate
  • 41. Output files part-00000 part-00001 part-00002 (...)
  • 42. Further aggregation
  • 43. Conclusion Hadoop MapReduce provides out-of-the-box ready-to-go distributed computing.
  • 44. That's all folks and thanks for attending: QUICK AND DIRTY PARALLEL PROCESSING ON THE CLOUD Daniel Sikar
  • 45.