What is Hadoop? Nov 20 2013 - IRMAC

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Another version of the What is Hadoop deck with a couple of more slides on YARN.

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What is Hadoop? Nov 20 2013 - IRMAC

  1. 1. Adam  Muise  –  Hortonworks   WELCOME  TO  HADOOP  
  2. 2. Who  am  I?  
  3. 3. Why  are  we  here?  
  4. 4. Data  
  5. 5. “Big  Data”  is  the  marke=ng  term   of  the  decade  
  6. 6. What  lurks  behind  the  hype  is   the  democra=za=on  of  Data.  
  7. 7. You  need  to  deal  with  Data.  
  8. 8. You’re  probably  not  as  good  at   that  as  you  think.  
  9. 9. Put  it  away,  delete  it,  tweet  it,   compress  it,  shred  it,  wikileak-­‐it,  put   it  in  a  database,  put  it  in  SAN/NAS,   put  it  in  the  cloud,  hide  it  in  tape…  
  10. 10. You  are  obsessive  compulsive   about  collec=ng  and  structuring   your  data.  
  11. 11. Seek  help.  
  12. 12. Let’s  talk  challenges…  
  13. 13. Volume   Volume   Volume   Volume  
  14. 14. Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume  
  15. 15. Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume  Volume   Volume  
  16. 16. Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume   Volume  Volume   Volume   Volume   Volume  
  17. 17. Storage,  Management,  Processing   all  become  challenges  with  Data  at   Volume  
  18. 18. Tradi=onal  technologies  adopt  a   divide,  drop,  and  conquer  approach  
  19. 19. Another  EDW   Analy=cal  DB   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   The  solu=on?   EDW   Data   Data   Data   Data   Data   Data   Data   Data   Data   OLTP   Data   Data   Data   Data   Data   Data   Data   Data   Data   Yet  Another  EDW   Data   Data   Data   Data   Data   Data   Data   Data   Data  
  20. 20. Another  EDW   Analy=cal  DB   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   OLTP   Ummm…you   dropped  something   EDW   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Yet  Another  EDW   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data  Data   Data   Data   Data   Data  Data   Data   Data   Data   Data   Data   Data   Data   Data  Data   Data   Data  Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data  Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data  
  21. 21. Analyzing  the  data  usually  raises   more  interes=ng  ques=ons…  
  22. 22. …which  leads  to  more  data  
  23. 23. Wait,  you’ve  seen  this  before.   Data   Data   Data   …   Sausage  Factory   Data   Data   Data   Data   Data   Data   Data   Data   Data   …   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data  Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data  
  24. 24. Data  begets  Data.  
  25. 25. What  keeps  us  from  Data?  
  26. 26. “Prices,  Stupid  passwords,  and   Boring  Sta=s=cs.”     -­‐  Hans  Rosling   h"p://www.youtube.com/watch?v=hVimVzgtD6w  
  27. 27. Your  data  silos  are  lonely  places.   EDW   Accounts   Customers   Web  Proper=es   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data  
  28. 28. …  Data  likes  to  be  together.   EDW   Accounts   Customers   Data   Data   Web  Proper=es   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data  
  29. 29. CDR   Data   Data   Data   Machine  Data   Facebook   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Weather  Data   TwiYer   Data   Data  likes  to  socialize  too.   Data   Data   EDW   Data   Data   Data   Data   Data   Data   Accounts   Data   Web  Proper=es   Data   Data   Data   Customers   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data  
  30. 30. New  types  of  data  don’t  quite  fit  into   your  pris=ne  view  of  the  world.   Logs   Data   Data   Data   Data   Data  Data   Data   Machine  Data   Data   Data   Data   Data   Data  Data   Data   My  LiYle  Data  Empire   Data   ?   Data   ?   Data   Data   Data   Data   Data   ?  ?   Data   Data  
  31. 31. To  resolve  this,  some  people  take   hints  from  Lord  Of  The  Rings...  
  32. 32. …and  create  One-­‐Schema-­‐To-­‐ Rule-­‐Them-­‐All…   EDW   Data   Data   Data   Data   Data   Schema   Data   Data   Data   Data  
  33. 33. ETL   Data   Data   Data   ETL   ETL   ETL   EDW   Data   Data   Data   Data   Data   Schema   Data   Data   Data   Data   …but  that  has  its  problems  too.   ETL   Data   Data   Data   ETL   ETL   ETL   EDW   Data   Data   Data   Data   Data   Schema   Data   Data   Data   Data  
  34. 34. So  what  is  the  answer?  
  35. 35. Enter  the  Hadoop.   ………   hYp://www.fabulouslybroke.com/2011/05/ninja-­‐elephants-­‐and-­‐other-­‐awesome-­‐stories/  
  36. 36. Hadoop  was  created  because  Big  IT   never  cut  it  for  the  Internet   Proper=es  like  Google,  Yahoo,   Facebook,  TwiYer,  and  LinkedIn  
  37. 37. Tradi=onal  architecture  didn’t   scale  enough…   App   App   App   App   App   App   App   App   DB   DB   DB   SAN   App   App   App   App   DB   DB   DB   SAN   DB   DB   DB   SAN  
  38. 38. Databases  become  bloated  and   useless  
  39. 39. $upercompu=ng   Tradi=onal  architectures  cost  too   much  at  that  volume…   $/TB   $pecial   Hardware  
  40. 40. So  what  is  the  answer?  
  41. 41. If  you  could  design  a  system  that   would  handle  this,  what  would  it   look  like?  
  42. 42. It  would  probably  need  a  highly   resilient,  self-­‐healing,  cost-­‐efficient,   distributed  file  system…   Storage   Storage   Storage   Storage   Storage   Storage   Storage   Storage   Storage  
  43. 43. It  would  probably  need  a  completely   parallel  processing  framework  that   took  tasks  to  the  data…   Processing   Processing  Processing   Storage   Storage   Storage   Processing   Processing  Processing   Storage   Storage   Storage   Processing   Processing  Processing   Storage   Storage   Storage  
  44. 44. It  would  probably  run  on  commodity   hardware,  virtualized  machines,  and   common  OS  pladorms   Processing   Processing  Processing   Storage   Storage   Storage   Processing   Processing  Processing   Storage   Storage   Storage   Processing   Processing  Processing   Storage   Storage   Storage  
  45. 45. It  would  probably  be  open  source  so   innova=on  could  happen  as  quickly   as  possible  
  46. 46. It  would  need  a  cri=cal  mass  of   users  
  47. 47. {Processing  +  Storage}   =   {MapReduce/YARN+  HDFS}  
  48. 48. HDFS  stores  data  in  blocks  and   replicates  those  blocks   block1   Processing   Processing  Processing   Storage   Storage   Storage   block2   block2   Processing   Processing  Processing   block1   Storage   Storage   Storage   block3   block2   Processing   Storage   block3   Processing  Processing   block1   Storage   Storage   block3  
  49. 49. If  a  block  fails  then  HDFS  always  has   the  other  copies  and  heals  itself   block1   Processing   Processing  Processing   block3   Storage   Storage   Storage   block2   block2   Processing   Processing  Processing   block1   Storage   Storage   Storage   block3   block2   Processing   Storage   block3   Processing  Processing   block1   Storage   Storage   X
  50. 50. MapReduce  is  a  programming   paradigm  that  completely  parallel   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Mapper   Mapper   Mapper   Mapper   Mapper   Reducer   Data   Data   Data   Reducer   Data   Data   Data   Reducer   Data   Data   Data  
  51. 51. MapReduce  has  three  phases:   Map,  Sort/Shuffle,  Reduce   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Mapper   Mapper   Key,  Value   Key,  Value   Key,  Value   Reducer   Key,  Value   Key,  Value   Key,  Value   Mapper   Reducer   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Mapper   Reducer   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Key,  Value   Mapper   Key,  Value   Key,  Value   Key,  Value  
  52. 52. MapReduce  applies  to  a  lot  of   data  processing  problems   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Data   Mapper   Mapper   Mapper   Mapper   Mapper   Reducer   Data   Data   Data   Reducer   Data   Data   Data   Reducer   Data   Data   Data  
  53. 53. Introducing  YARN  
  54. 54. YARN  =  Yet  Another  Resource   Nego=ator  
  55. 55. YARN  abstracts  resource   management  so  you  can  run  more   than  just  MapReduce   MapReduce  V2   MapReduce  V?   STORM   Giraph   Tez   YARN   HDFS2   MPI   HBase   …  and   more  
  56. 56. Node  Manager   Resource  Manager   Container   Scheduler   Pig   AppMaster   Container   Resource  Manager   +   Node  Managers   =  YARN   Node  Manager   Container   Container   Storm   Node  Manager   Node  Manager   MapReduce   AppMaster   Container   Container   Container   Container   Container   AppMaster  
  57. 57. YARN  turns  Hadoop  into  a  smart   phone:  An  App  Ecosystem   hortonworks.com/yarn/  
  58. 58. Check  out  the  book  too…   Preview  at:   hortonworks.com/yarn/  
  59. 59. YARN  is  an  essen=al  part  of  a   balanced  breakfast  in  Hadoop  2.2.0  
  60. 60. Hadoop  has  other  open  source   projects…  
  61. 61. Tez  =  {  Generic  Tasks  +  Pipelining  }   Super  Fast  MapReduce  
  62. 62. Hive  =  {SQL  -­‐>  Tez  ||  MapReduce}   SQL-­‐IN-­‐HADOOP  
  63. 63. Pig  =  {PigLa=n  -­‐>  Tez  ||   MapReduce}  
  64. 64. HCatalog  =  {metadata*  for   MapReduce,  Hive,  Pig,  HBase}   *metadata  =  tables,  columns,  par==ons,  types  
  65. 65. Oozie  =  Job::{Task,  Task,  if  Task,   then  Task,  final  Task}  
  66. 66. Falcon   Feed   Feed   Feed   Feed   Hadoop   DR   Feed   Replica=on   Feed   Feed   Hadoop   Feed  
  67. 67. Flume   Files   Flume   JMS   Weblogs   Events   Flume   Flume   Flume   Flume   Flume   Hadoop  
  68. 68. Sqoop   DB   DB   Sqoop   Hadoop   Sqoop  
  69. 69. Ambari  =  {install,  manage,   monitor}  
  70. 70. HBase  =  {real-­‐=me,  distributed-­‐ map,  big-­‐tables}  
  71. 71. Storm  =  {Complex  Event  Processing,   Near-­‐Real-­‐Time,  Provisioned  by   YARN  }  
  72. 72. Storm   HDFS   YARN   Pig   MapReduce   Apache  Hadoop   HCatalog   Hive   HBase   Ambari   Sqoop   Falcon   Flume  
  73. 73. Storm   Pig   HDFS   YARN   MapReduce   Hortonworks  Data  Pladorm   HCatalog   Hive   HBase   Ambari   Sqoop   Falcon   Flume  
  74. 74. What  else  are  we  working  on?   hortonworks.com/labs/  
  75. 75. Hadoop  is  the  new  Data  Opera=ng   System  for  the  Enterprise  
  76. 76. There is NO second place Hortonworks   …the  Bull  Elephant  of  Hadoop  Innova@on   © Hortonworks Inc. 2012: DO NOT SHARE. CONTAINS HORTONWORKS CONFIDENTIAL & PROPRIETARY INFORMATION Page  76  

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