Scale Your Data Tier using Windows Server AppFabric Caching (formerly Project “ Velocity ”)

1. PDC09-FT26 Scale Your Data Tier using Windows Server AppFabric Caching(formerly Project “Velocity”) Muralidhar Krishnaprasad Principal Software Architect Microsoft Corporation

2. Grocery Web Site

3. Typical Architecture Users Load Balancer Sticky Routing Application / Web Tier Application Application Application ASP.Net App Session Cart Hosted in memory Catalog sits in Database Data Tier Database

4. And then… Flu Hits!

5. You become a popular Grocer! Web Site’s too slow!! Users Where did my shopping cart go? Web Tier (ASP.Net) … Application Application Application Servers are crashing Database is hot!! Data Tier Database

6. Windows Server AppFabric Caching can help

7. Outline • The Problem! • Caching with the Windows Server AppFabric • What is it? • Why AppFabric caching? • What stage is it in? • Overview and Programming • Caching Futures • Conclusions

8. What is AppFabric Caching? • An explicit, distributed, in-memory application cache for all kinds of data • (CLR objects, rows, XML, Binary data etc.) • Fuse "memory" across machines into a unified cache Caching clients can be across machines or processes Clients Access the Cache as if it was a large single cache Unified Cache View Cache Layer distributes data across the various cache nodes

9. Where does it fit? Users … Web Tier (ASP.Net) Application Application Application Caching Access Layer Caching Access Layer Caching Access Layer Caching Service Caching Service Caching Service Cache Tier Data Tier Database Cloud

10. Outline • The Problem! • Caching with the Windows Server AppFabric • What is it? • Why AppFabric caching? • What stage is it in? • Overview and Programming • Caching Futures • Conclusions

11. Why use AppFabric Caching? 1. Share Data Across Applications No more sticky routing 2. Performance 3. Scale out by adding more boxes 4. High Availability Protect from Web & Cache Server Failure

12. Why use AppFabric Caching? 5. Proven Technology Associated Press Custom News live on CTP3 • AppFabric caching capabilities caches metadata and news for widgets • Serves 16 million hits per day • Increased the amount of cached data 6 times.

13. Why AppFabric Caching? 5. Proven Technology Contd.. • Dell.com uses AppFabric caching! • Channel Partner Portal uses AppFabric caching • Provides targeted content and search results • Tracks customer browse history and downloads. • Next Generation Sales Platform Solutions to use AppFabric caching (currently under construction) • Just two of the many TAP customers in line for production

14. Why AppFabricCaching? – Best yet 6. Do all this at low cost! . • Use inexpensive commodity hardware • Ships as part of Windows Server AppFabric 2010 Wave 1 * Different Features of the cache might require higher level SKUs of Windows Server. AppFabric MONITORING WORKFLOW HOSTING CACHING ACCESS CONTROL SERVICE HOSTING SERVICE BUS MANAGEMENT MULTI-TENANT SCALE OUT • HIGH AVAILABILITY

15. Outline • The Problem! • Caching with the Windows Server AppFabric • What is it? • Why caching? • What stage is it in? • Overview and Programming • Caching Futures • Conclusions

16. Azure CTP • Post RTM • RTM • 2010 Wave 1 • * CY10 When Can I Get Windows Server AppFabric ? • CTP2 • PDC ‘08 • CTP1 • June ‘08 • CTP3 • Mar ‘09 • Beta • PDC • Nov ‘09 (Formerly Called “Velocity”)

17. Outline • The Problem • Caching with the Windows Server AppFabric • Overview and Programming AppFabric Caching • Deployment & Access API • How does AppFabric Caching Scale? • Features • AppFabric Caching Futures • Conclusions

18. Deployment Users Copy Client DLLs and add app.config … <hosts> <host name="BL1CDB8083714“ cachePort="22233" cacheHostName="DistributedCacheService"/> ….. </hosts> <localCacheisEnabled=“true" ../> <security … /> Caching Access Layer Caching Access Layer Web Tier Caching Service Caching Service Caching Service Enable Application Server Cache Role Cache Tier Configuration Store (Can be database, File share, etc.) Stores Global Cache Policies Stores Current Partitioning Information Application Application Application Caching Access Layer

19. Security (new in Beta) • Domain Based Security Option • Domain Account / Local Account based Authentication • Only authorized servers can join the cluster • Only authorized clients can connect to the cluster • Transport Level Security • Turn on/off Signing or Encryption • Can turn off Cache Security • Use Firewalls, IPSec, VLANs to protect cache grant-cacheallowedclientaccount  RedDomain\Machine1$ grant-cacheallowedclientaccount  RedDomain\John

20. Administration & Monitoring • Centralized administration through powershell • Perfmon to monitor the cache (new in Beta) • Logging • Default ETW, support for file logs

21. AppFabric Caching API // Create instance of cachefactory (reads appconfig) DataCacheFactory fac = new DataCacheFactory(); // Get a named cache from the factory DataCache catalog = fac.GetCache("catalogcache"); // Simple Get/Put catalog.Put("toy-101", new Toy("Puzzle", .,.)); // From the same or a different client Toy toyObj = (Toy)catalog.Get("toy-101"); // Region based Get/Put catalog.CreateRegion("toyRegion"); // Both toy and toyparts are put in the same region catalog.Put("toy-101", new Toy( .,.), “toyRegion”); Catalog.Put("toypart-100", new ToyParts(…), “toyRegion”); Toy toyObj = (Toy)catalog.Get("toy-101“,"toyRegion");

22. AppFabric Caching Logical Hierarchy AppFabric Caching Service AppFabric Caching Service AppFabric Caching Service AppFabric Caching Service • Host • Physical processes hosting AppFabric Caching instance. • Named Caches • Can span across machines • Defined in the configuration file • Cache Item • Key, Payload (Object ), Tags, TTL, Timestamps, Version • Regions • Physically co-located Container of Cache Items • May be implicit or explicitly created Named Cache : Product Catalog Named Cache : Electronics Inventory Regions Key Payload Tags Region A 121 xxxx “Toy” “Child” 123 yyyy “Toy” “Chair”.. • Machine -> Cache Host -> Named Caches -> Regions -> Cache Items -> Objects

23. Access APIs – Tagging Items • Add Tags to Items • Tag Search on Default Regions (New in Beta) Tag hotItem = new Tag("hotItem"); catalog.Put("toy-101", new Toy("Puzzle"), new Tag[]{hotItem}, “toyRegion”); catalog.Put("toy-102", new Toy("Bridge"), “toyRegion”); // From the same or a different client List<KeyValuePair<string, object>> toys = catalog.GetAnyMatchingTag("toyRegion", hotItem);

24. Usage Pattern – Cache Aside (Explicit Caching) // Read from Cache Toy toyObj = (Toy) catalog.Get("toy-101"); Application Caching Access Layer // If Not present in the cache if (toyObj == null) { // Read from backend.. toyObj = ReadFromDatabase(); // Populate Cache catalog.Put("toy-101", toyObj); return toyObj; } Caching Service Database

25. Outline • The Problem • Caching with the Windows AppFabric • Overview and Programming AppFabric Caching • Deployment & Access API • How does AppFabric Caching Scale? • Features • Demo • AppFabric Caching Futures • Conclusions

26. Data Distribution - Partitioned Cache Users … Web Tier ASP.Net App ASP.Net App ASP.Net App Caching Client Caching Client Caching Client G H I D E F A B C Cache Service Cache Service Cache Tier Cache Service E G B D H A I C F Scale on Data Size - More machines => More memory to cache Scale on Cache Throughput - More machines => keys distributed across more machines => better throughput

27. Scale Test Output Server 3 Added Throughput Increases Latency Decreases Load Server 2 Added Throughput Increases Latency Decreases Until Server Saturation Single Server Throughput Increases with Increasing Load Until Server Saturation Throughput Latency

28. GridDynamics Blog Engine Tests More info at http://blogs.msdn.com/velocity/ or http://www.griddynamics.com/velocity

29. Outline • The Problem • AppFabric Caching – The Solution • Understanding Cache Data • Overview and Programming AppFabric Caching • Deployment & Access API • How does AppFabric Caching Scale? • Features • Demo • AppFabric Caching Futures • Conclusions

30. Use the right features! • Classify your data • Reference or Activity or Resource Data • Examine Requirements • Performance - Throughput & Latency • Consistency – Tolerable staleness • Eviction - Is the data evictable? • Security - Is there any secure data? • Availability – Survive node or cluster failures?

31. Types of Data Grocery Shop Web Tier Shopping Cart Grocery Inventory Distributed Cache Grocery Catalog

32. Reference Data – Performance • Catalog data doesn’t change often • Unnecessary network cost to access from different machines • Solution – Local Cache Application Application Get(K2) Get(K2) Put(K2, v3) AppFabric Caching Client AppFabric Caching Client Local Cache RoutingTable Routing Table Cache2 Cache3 Cache1 K2, V2 K2, V2 Primary for K1,V1 Primary for K3,V3 Primary for K2,V2 K1, V1 K2, V3 K3, V3

33. Reference Data – Bulk Get • Enumerate all items in Region • Bulk Fetch from region (New in Beta) • 200-300k ops per second Catalog.BulkGet( new List<string>(){“toy-101”, “toy-102”} , “toyRegion”);

34. Activity Data – Session Cart Integration Load Balance Requests No more sticky routing <sessionState mode="Custom“ customProvider="SessionStoreProvider"> <providers> <add name="SessionStoreProvider" type=“Microsoft.Data.Caching.DataCacheSessionStoreProvider, ClientLibrary“ cacheName="<YourNamedCache>"/> </providers> </sessionState> Drop in AppFabric Caching SessionStoreProvider … Caching Access Layer Caching Access Layer Session State stored in AppFabric Caching New in Beta – Allow session state to be shared amongst multiple applications Scale your Session Store Dynamically Cache Service Caching Service Caching Service Highly Available Application Application Application Caching Access Layer

35. Activity Data - Availability Application Application PUT Get(K2) AppFabric Caching Client AppFabric Caching Client Routing Table Routing Table Cache1 Cache2 Cache3 Primary for Primary for Primary for Replication Agent K3, V3 K1, V1 K2, V2 (K2, V2) K2, V2 Secondary for Secondary for Secondary for K2, V2 K3, V3 K1, V1

36. Resource Data - Optimistic Locking • GetCacheItem returns a version object • Every update to an object internally increments it's version • Supply the version obtained along with the Put/Remove • Put/Remove will succeed only if the passed in version matches the version in the cache Two clients access the same item Both update the item Second Client gets in first; put succeeds because item version matches; atomically increments the version First client tries put; Fails because the versions don’t match

37. Resource Data - Pessimistic Locking Client1: GetAndLock ("k1") Client3: Get ("k1") Client2: GetAndLock ("k1") • New in Beta – Take locks on non-existent keys • Allows you to co-ordinate creating new object amongst multiple clients GetAndLock gets lock handle Regular Get succeeds Other GetAndLock on same item fails K1

38. Resource/Activity Data – Tracking Changes • Cache Event notifications • Register on any client to notify changes • Batched Notifications – (New in Beta) DataCache.RegisterCacheLevelCallback( int filter, DataCacheChangeCallback delegate); DataCache.RegisterRegionLevelCallback( String region, int filter, DataCacheChangeCallback delegate); DataCache.RegisterKeyLevelCallback( String region, String key, int filter, DataCacheChangeCallback delegate);

39. Scalable Notifications Register Notification for Key “K3" Call Delegate Store Last LSN Map Keys to Partition (say P2) Application AppFabric Caching Client Partition: P2 Last LSN: 19 Routing Table Poll Required Nodes Nodes Return List of Changes LSN Order Cache2 Cache3 Cache1 K2, V2 Primary for Primary for Primary for Change Log (Partition P2) Del K32 Del K43 Change Log Partition P1 Add K2 Del K32 Change Log 33 Add K1 34 Del K22 K1, V1 K3, V3

40. Outline • The Problem • AppFabric Caching – The Solution • Understanding Cache Data • Overview and Programming AppFabric Caching • Deployment & Access API • How does AppFabric Caching Scale? • Features • Demo • AppFabric Caching Futures • Conclusions

41. Outline • The Problem • AppFabric Caching – The Solution • Understanding Cache Data • Overview and Programming AppFabric Caching • AppFabric Caching Futures • AppFabric Caching and Azure Platform • ASP.Net and Persistance • Conclusions

42. Application Growth • You are a popular Grocer!! • Lots of Users! • Irregular & Elastic Demands • Solution - Move to the Services World! • Application on-premise with data on SQL Azure • Application on Windows Azure with data on SQL Azure • Application on Windows Azure with on-premise database connected by AppFabric Service Bus

43. App on-premise; Data on SQLAzure … ASP.Net Web Tier Application Application Application AppFabric Caching Access Layer AppFabric Caching Access Layer AppFabric Caching Access Layer Application & AppFabric Caching deployed On-premise Caching Service Caching Service Caching Service Caching Worker Role Data on SQL Azure

44. App on Windows Azure; Data on SQL Azure Web Role … Application Application Application AppFabric Caching Access Layer AppFabric Caching Access Layer AppFabric Caching Access Layer Application & AppFabric Caching on Windows Azure Caching Service Caching Service Caching Service Caching Worker Role AppFabric Caching Worker Role Data on SQL Azure

45. App on Windows Azure; Data on-premise Web Role … Application Application Application AppFabric Caching Access Layer AppFabric Caching Access Layer AppFabric Caching Access Layer AppFabric Service Bus Application & AppFabric Caching on Windows Azure Caching Service Caching Service Caching Service Caching Worker Role AppFabric Caching Worker Role Data on-premises

46. Outline • The Problem • AppFabric Caching – The Solution • Understanding Cache Data • Overview and Programming AppFabric Caching • AppFabric Caching Futures • AppFabric Caching and Azure Platform • ASP.Net and Persistance • Conclusions

47. Integration with ASP.Net • ASP.Net extends cache surface .Net 4.0 • Integrate with the Application Cache • System.Runtime.Caching namespace • AppFabric Caching will be a provider for this namespace • Granular Session Updates • IPartialSessionState lists keys changed • Extensible Output Cache Provider • Output Caching not limited to single node

48. Future – Cache Through • Callback for read-through, write-behind • Specified at Named Cache Level • Read-Through • Called when item not present in cache • Callback returns the object/serialized bytes • Write-Behind • Writes to cache are queued • Callback called asynchronously in batches • Re-tries upon failure • Bulk Access APIs

49. HPC Integration Final Results Store AppFabric Caching Central Market Data Store (~1 TB Tick Data) Market Data Final Results AppFabric Caching Intermediate Store Market Data Scratch Job Input Rollup Operation Split Method Scratch Keys Calculation Operation GridDynamics testing on CTP1 50 times improvement in some cases

50. Outline • The Problem • AppFabric Caching – The Solution • Understanding Cache Data • Overview and Programming AppFabric Caching • Deployment and Maintenance • AppFabric Caching Futures • Conclusions