Coherence Implementation Patterns - Sig Nov 2011
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The document discusses various patterns and techniques for implementing data coherence and transactionality in a distributed system using Oracle Coherence. It describes approaches for versioning objects to allow for concurrent changes, handling transactions that span multiple objects, and implementing reliable operations like putAll that can handle failures. A key technique is using Coherence's triggers to copy and version objects as they are written. The document also covers cluster singleton services that can automatically restart after failures.
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Coherence Implementation Patterns - Sig Nov 2011
- 1. Coherence Implementa/on Pa1erns Ben Stopford The Royal Bank of Scotland
- 2. Some Ideas Nothing More
- 3. Why do we use Coherence? Fast? Scalable? Applica/on layer?
- 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.
- 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. Versioning your Objects Versioned Cache A.1 A.2 B.1 C.1 C.2 D.1
- 9. Versioning your Objects Cache A.1 Coherence Trigger A.2 New Version = Old Version + 1 ??
- 10. Running a Coherence Filter
- 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. Latest / Versioned Pa1ern Latest Cache Key = [Business Key] Versioned Cache Key = [Business Key][Version] !" !&'" #" !&(" $" #&'" %" $&'" $&(" %&'"
- 14. Suffers from data duplica/on
- 15. Latest Marker Pa1ern Write Versioned Coherence Trigger Cache A.1 A.L B.L Well Known Marker Version C.1 C.L D.L
- 16. However our trigger can’t use cache.put() Why?
- 17. Need to consider the threading model
- 18. So we’ll need to use the backing map directly public void copyObjectToVersionedCacheAddingVersion(MapTrigger.Entry entry) { MyValue value = (MyValue)entry.getValue(); MyKey versionedKey = (MyKey)value.getKey(); BinaryEntry binary = (BinaryEntry)entry; Binary binaryValue = binaryEntry.getBinaryValue(); Map map = binary.getContext().getBackingMap("VersionedCacheName"); map.put(toBinary(versionedKey), binaryValue); }
- 19. A third approach
- 20. The Collec/ons Cache Trigger Appends to Collec/on collec/onsCache.put(key, val); A [O1, O2, O3..] B [O1, O2, O3..] collec/onsCache.invoke(key, C [O1, O2, O3..] new LastValueGe1er()); …or override backing store D [O1, O2, O3..] Collec/onsCache
- 21. So we have 3 pa1erns for managing versioning whilst retaining key based access
- 22. Using versioning to manage concurrent changes Mul/ Version Concurrency Control (MVCC)
- 23. Cache Version 1 Coherence Trigger Version 2 New Version = Old Version + 1 ??
- 24. Concurrent Object Update (2 Clients update the same object at the same /me) Client1 Client2 A.1 A.2 A.2
- 25. Concurrent Object Update (Client2 fails to update dirty object) Client1 Client2 A.1 A.2 Versioned Cache
- 26. Concurrent Object Update (Client 2 updates clean object) Client1 Client2 A.1 A.2 A.3 Versioned Cache
- 27. So a concurrent update results in an error and must be retried. &'()*+#$ &'()*+%$ !"#$ !"%$
- 28. What’s going to happen if we are using putAll?
- 29. Reliable PutAll We want putAll to tell us which objects failed the write process
- 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. This gives us a reliable mechanism for knowing what worked and what failed
- 33. The Fat Object Method Cache A B C D
- 34. The Single Entry Point Method (objects are stored separately) Collocate with key associa/on
- 35. All writes synchronize on the primary object. EP
- 36. All reads synchronize on the primary object. EP
- 37. Wri/ng Orphaned Objects Write read entry point object last Write orphaned objects first This mechanism is subtly flawed
- 38. Reading several objects as an atomic unit aka Joins
- 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. Server Side, Sharded Joins Use KeyAssocia/on to keep related en//es together Orders Shipping Log Common Key
- 41. Server Side, Sharded Joins Transactions Aggregator joins Mtms data across cluster Cashflows
- 42. So we have a set of mechanisms for reading and wri/ng groups of related objects.
- 44. A service that automa/cally restarts amer failure
- 45. A service that automa/cally restarts amer failure
- 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. 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. Implemen/ng Consistent Views and Repeatable Queries
- 49. Bi-‐temporal public interface MyBusinessObject{ //data Business public Date getBusinessDate(); Time public Date validFrom(); System public Date validTo(); Time }
- 50. Where does the System Time come from?
- 51. You can’t use the System.currentTimeMillis() in a distributed environment!
- 52. You need a cluster synchronised clock
- 53. Repeatable Time: A guaranteed Tick Write first Write Time Singleton Service Read Time Write second Replicated Caches (pessimis/c)
- 54. As we add objects we /mestamp them with Write Time Write Time Singleton Service
- 55. When we read objects we use Read Time Singleton Service Read Time
- 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. Event Based Processing Async Cachestore cache.put(key, val); A B C D
- 58. Messaging as a System of Record Messaging System (use Topics for scalability)
- 59. Messaging as a System of Record Trigger cache.put(key, val); A B JMS C TOPIC D
- 60. Easy Grid Implementa/on in GUIs
- 61. CQCs on a CQC GUI JVM CQC CQC Cache Define this in config
- 62. How do you release quickly to a Coherence cluster?
- 63. Rolling Restart?
- 64. Disk-‐Persist
- 65. Final Thoughts
- 66. Data is the most important commodity that you have Keep it safe
- 67. Use a Par//on Listener
- 68. Have Proac/ve Monitoring of Memory
- 70. Thanks Slides & related ar/cles available at: h1p://www.benstopford.com