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Coherence Implementation Patterns - Sig Nov 2011

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Coherence Implementation Patterns - Sig Nov 2011

  1. 1. Coherence  Implementa/on   Pa1erns   Ben  Stopford   The  Royal  Bank  of  Scotland  
  2. 2. Some  Ideas     Nothing  More  
  3. 3. Why  do  we  use  Coherence?   Fast?   Scalable?   Applica/on  layer?  
  4. 4. Simplifying  the  Contract  
  5. 5. •  We  don’t  want  ACID  all  of  the  /me   •  We  want  to  pick  the  bits  we  need  when  we   need  them   •  We  want  to  use  the  context  of  our  business   requirement  to  work  our  way  around  the  ones   we  don’t  need.  
  6. 6. Version  your  Objects  
  7. 7. Why  do  we  care?   Without  versioning  it’s  a  free-­‐for-­‐all.     •  What  changed?   •  Was  something  overwri1en?   •  How  can  you  prevent  concurrent  updates?   •  What  did  the  system  look  like  10  seconds  ago.   •  How  can  I  provide  a  consistent  view?   •  How  to  I  ensure  ordering  of  updates  in  an   asynchronous  system?  
  8. 8. Versioning  your  Objects   Versioned   Cache   A.1   A.2   B.1   C.1   C.2   D.1  
  9. 9. Versioning  your  Objects   Cache   A.1   Coherence  Trigger   A.2   New  Version  =  Old  Version  +  1  ??  
  10. 10. Running  a  Coherence  Filter  
  11. 11. Using  Key-­‐Based  Access  
  12. 12. Latest  /  Versioned  Pa1ern   Coherence  Trigger   Write   Versioned   Latest  Cache   Cache   A   A.1   B   A.2   C   B.1   D   C.1   C.2   D.1  
  13. 13. Latest  /  Versioned  Pa1ern   Latest  Cache  Key  =  [Business  Key]   Versioned  Cache  Key  =  [Business  Key][Version]   !" !&'" #" !&(" $" #&'" %" $&'" $&(" %&'"
  14. 14. Suffers  from  data  duplica/on  
  15. 15. Latest  Marker  Pa1ern   Write   Versioned   Coherence  Trigger   Cache   A.1   A.L   B.L   Well  Known   Marker  Version   C.1   C.L   D.L  
  16. 16. However  our  trigger  can’t  use   cache.put()   Why?  
  17. 17. Need  to  consider  the  threading  model  
  18. 18. So  we’ll  need  to  use  the  backing  map   directly   public  void  copyObjectToVersionedCacheAddingVersion(MapTrigger.Entry  entry)   {        MyValue  value  =  (MyValue)entry.getValue();        MyKey  versionedKey  =  (MyKey)value.getKey();          BinaryEntry  binary  =  (BinaryEntry)entry;        Binary  binaryValue  =  binaryEntry.getBinaryValue();          Map  map  =  binary.getContext().getBackingMap("VersionedCacheName");        map.put(toBinary(versionedKey),  binaryValue);   }  
  19. 19. A  third  approach  
  20. 20. The  Collec/ons  Cache   Trigger  Appends  to  Collec/on   collec/onsCache.put(key,  val);   A   [O1,  O2,  O3..]   B   [O1,  O2,  O3..]   collec/onsCache.invoke(key,   C   [O1,  O2,  O3..]   new  LastValueGe1er());   …or  override  backing  store   D   [O1,  O2,  O3..]   Collec/onsCache  
  21. 21. So  we  have  3  pa1erns  for  managing   versioning  whilst  retaining  key   based  access  
  22. 22. Using  versioning  to  manage   concurrent  changes   Mul/  Version  Concurrency  Control     (MVCC)  
  23. 23. Cache   Version  1   Coherence  Trigger   Version  2   New  Version  =  Old  Version  +  1  ??  
  24. 24. Concurrent  Object  Update   (2  Clients  update  the  same  object  at  the  same  /me)   Client1   Client2   A.1   A.2   A.2  
  25. 25. Concurrent  Object  Update   (Client2  fails  to  update  dirty  object)   Client1   Client2   A.1   A.2   Versioned   Cache  
  26. 26. Concurrent  Object  Update   (Client  2  updates  clean  object)   Client1   Client2   A.1   A.2   A.3   Versioned   Cache  
  27. 27. So  a  concurrent  update  results  in   an  error  and  must  be  retried.   &'()*+#$ &'()*+%$ !"#$ !"%$
  28. 28. What’s  going  to  happen  if  we  are   using  putAll?  
  29. 29. Reliable  PutAll   We  want  putAll  to  tell  us  which   objects  failed  the  write  process  
  30. 30. Reliable  PutAll   Node   Node   Client   Extend   Node   Node   Node   Node   Invocable:   •  Split  keys  by  member   Invocable:   •  Send  appropriate  values   •  Write  entries  to   to  each  member   backing  map  (we   •  Collect  any  excep/ons   use  an  EP  for   returned     this)  
  31. 31. This  gives  us  a  reliable  mechanism   for  knowing  what  worked  and  what   failed  
  32. 32. Synthesising  Transac/onality  
  33. 33. The  Fat  Object  Method   Cache   A   B   C   D  
  34. 34. The  Single  Entry  Point  Method   (objects  are  stored  separately)       Collocate  with   key  associa/on  
  35. 35. All  writes  synchronize  on  the   primary  object.   EP  
  36. 36. All  reads  synchronize  on  the   primary  object.   EP  
  37. 37. Wri/ng  Orphaned  Objects   Write  read   entry  point   object  last   Write  orphaned  objects  first   This  mechanism  is  subtly  flawed  
  38. 38. Reading  several  objects  as  an   atomic  unit   aka  Joins  
  39. 39. The  trivial  approach  to  joins   Get   Get   Get   Get   Get   Get   Get   Cost   Ledger Source   Transac MTMs   Legs   Cost   Centers   Books   Books   -­‐/ons   Centers   Network Time  
  40. 40. Server  Side,  Sharded  Joins   Use  KeyAssocia/on  to   keep  related  en//es   together   Orders   Shipping  Log   Common  Key  
  41. 41. Server  Side,  Sharded  Joins   Transactions Aggregator joins Mtms data across cluster Cashflows
  42. 42. So  we  have  a  set  of  mechanisms  for   reading  and  wri/ng  groups  of   related  objects.  
  43. 43. Cluster  Singleton  Service  
  44. 44. A  service  that  automa/cally  restarts   amer  failure  
  45. 45. A  service  that  automa/cally  restarts   amer  failure  
  46. 46. What  is  the  cluster  singleton  good  for   •  Adding  indexes   •  Loading  data   •  Keeping  data  up  to  date   •  Upda/ng  cluster  /me   •  You  can  probably  think  of  a  bunch  of  others   yourselves.  
  47. 47. Code  for  Cluster  Singleton   //run in a new thread on every Cache Server while (true) { boolean gotLock = lockCache.lock("singletonLock", -1); if (gotLock) { //Start singletons wait(); } }
  48. 48. Implemen/ng  Consistent  Views   and  Repeatable  Queries  
  49. 49. Bi-­‐temporal     public  interface  MyBusinessObject{        //data     Business        public  Date  getBusinessDate();   Time        public  Date  validFrom();   System        public  Date  validTo();   Time   }    
  50. 50. Where  does  the  System  Time   come  from?  
  51. 51. You  can’t  use  the   System.currentTimeMillis()  in  a   distributed  environment!  
  52. 52. You  need  a  cluster  synchronised   clock  
  53. 53. Repeatable  Time:  A  guaranteed  Tick   Write  first   Write  Time   Singleton     Service   Read  Time   Write  second   Replicated  Caches   (pessimis/c)  
  54. 54. As  we  add  objects  we  /mestamp  them   with  Write  Time   Write  Time   Singleton     Service  
  55. 55. When  we  read  objects  we  use  Read   Time   Singleton     Service   Read  Time  
  56. 56. Repeatable  Time:  A  guaranteed  Tick   8   7   8   7   8   7   Write  first   Write  Time   Singleton     Service   Read  Time   Write  second   6   7   6   7   6   7   Replicated  Caches   (pessimis/c)  
  57. 57. Event  Based  Processing   Async     Cachestore   cache.put(key,  val);   A   B   C   D  
  58. 58. Messaging  as  a  System  of  Record   Messaging System (use Topics for scalability)
  59. 59. Messaging  as  a  System  of  Record   Trigger   cache.put(key,  val);   A   B   JMS C   TOPIC   D  
  60. 60. Easy  Grid  Implementa/on  in  GUIs  
  61. 61. CQCs  on  a  CQC   GUI  JVM   CQC   CQC   Cache   Define  this  in  config  
  62. 62. How  do  you  release  quickly  to  a   Coherence  cluster?  
  63. 63. Rolling  Restart?  
  64. 64. Disk-­‐Persist  
  65. 65. Final  Thoughts  
  66. 66. Data  is  the  most  important   commodity  that  you  have     Keep  it  safe  
  67. 67. Use  a  Par//on  Listener  
  68. 68. Have  Proac/ve  Monitoring  of   Memory  
  69. 69. Version  your  Objects  
  70. 70. Thanks   Slides  &  related  ar/cles  available  at:     h1p://www.benstopford.com    

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