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Caching in Distributed Environment


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Based on my article published in the Microsoft Architecture Journal : Issue 17Available on-line at

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Caching in Distributed Environment

  1. 1. Caching in the Distributed Environment Abhijit Gadkari Based on the article published in the Microsoft Architecture Journal : Issue 17 Available on-line at 1
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  3. 3. Agenda Background info and basics Different types of cache like temporal , spatial , primed and demand cache Some Examples Caching in the ORM world! Transactional cache and Shared cache Managing the interaction Size of a cache and its impact on application performance Five minute introduction of “Velocity” – Microsoft ‘s Distributed Caching platform Open Forum ! 3
  4. 4. Basics Storage Size Cost per byte On Board Latency RAM Persistence Hard Disk Cloud Data is stored in memory – i.e. L1, L2, L3 etc. known as cache. This concept is extensively used in the von Neumann Architecture. Memory Access time is measured in access time. Given an address , the memory presents the data at some other time Memory Access Time = Latency + Transfer Size / Transfer Rate [2] 4
  5. 5. Types of Data Data Activity Resource Reference Data Data Data Understanding the different types of data and their semantics helps to understand the different caching needs that comes with usage of that data type. [1] 5
  6. 6. Why ? – For Performance and Availability Data Type [1] Caching Strategy [1] Reference Data Practically immutable, non-volatile and long lasting in nature - ideal candidate for caching. Can be shared across processes / application. For example, zip code, state list, department list, etc. Activity Data Activity data is generated by the currently executing activity as part of a business transaction. Only good for the life on the transaction. Short lived in nature. For example, shopping cart on e-commerce web site. Resource Data Highly dependent on domain logic and volatile in nature. Cache only when required. [a.k.a. don’t cache unless and until absolutely required]. Commonly associated keywords – concurrency , locking, ACID, dirty read, corrupt cache, business logic, etc. For example, quantity information in an inventory application. Unknown DO NOT CACHE [ME] “Keep a data item in electronic memory if its access frequency is five minutes or higher, otherwise keep it in magnetic memory”[2] Wikipedia defines cache as “a temporary area where frequently accessed data can be stored for rapid access”[3] 6
  7. 7. Principle of Locality Based on work done in 1959 on Atlas System’s Virtual Memory [4] Temporal Cache Good for frequently accessed , relatively nonvolatile data. For example, drop-down list on a web page Spatial Cache Data adjacent to recently referenced data will be requested in near future. For example, GridView paging 7
  8. 8. Temporal Cache public sealed class Cache : IEnumerable using System.Web.Caching 8
  9. 9. Spatial Cache In .NET, cache can be synchronized using SqlCacheDependency 9
  10. 10. Primed and Demand Cache [5,6] Primed and Demand cache is based on the future use of the data. Predating future is not easy and should be based on sound engineering principals The primed cache pattern is applicable when the cache or the part of the cache can be predicted in advance. For example, a web browser cache The demand cache pattern is useful when cache can not be predicted in advance. For example, a cached copy of user credentials The primed cache is populated at the beginning of the application, whereas the demand cache is populated during the execution of the application 10
  11. 11. Primed Cache In .NET ICachedReport interface can be used to store the pre-populated reports. The primed cache results in an almost constant size cache structure 11
  12. 12. Demand Cache 1 user can have many roles 1 role can have many permissions Managing demand cache Minimize memory leak Maximize hit-ratio Effective eviction policy In dynamic environment Adaptive Caching Strategies can be very effective 12
  13. 13. Caching in the ORM World! Customer I M cust_id type credit_allowed P 3456 gold 1 E D 7890 bronze 0 A N C E M I Gold Silver Bronze S RDBMS M A T RDBMS – persistent storage In memory object graph C H Ms Entity Framework /LINQ JDO, TopLink, Hibernate, NHibernate The ORM manager populates the data stored in persistent storage like database in the form of an object graph. An object graph is a good caching candidate 13
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  15. 15. Layered Cache Architecture The layering principle is based on the explicit Separation of responsibilities Cache layering is prevalent in many ORM solutions. For Example, Velocity, Hibernate The first layer represents the transactional cache and the Second layer is the shared cache designed as a process or clustered cache 15
  16. 16. Transactional Cache Objects formed in a valid state and participating in a transaction can be stored in the transactional cache Strictly bounded by the ACID rules Transactional cache size is small size and short lived Thrashing , cache corruption and caching conflicts should be strictly avoided  Many caching frameworks offer out of the box prepackaged transactional cache solution 16
  17. 17. Shared Cache Can be implemented as a process cache or clustered cache. The clustered cache introduces resource replication overhead Shared cache is a read-only cache Distributed caching solutions typically implements a shared cache solution Can be implemented as an identity map. For example, caching read-only, static reports using ICachedReport 17
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  19. 19. Chasing the Right Size Cache Remember the 80-20 rule a.k.a. Pareto principle and the bell shaped graph 19
  20. 20. Microsoft project code named Velocity [1] Distributed in-memory application cache platform  Can store any serializable CLR object Allows clustering and provides ASP.NET session provider object so that ASP.NET session objects can be stored in the distributed cache without having to write to database 20
  21. 21. Conventional Stack Stack with Distributed Cache Application Application Application Application Web Server[s] / App Server[s] Web Server[s] / App Server[s] Database Distributed Cache Application Application Database One Logical View Velocity Physical implementation Named Cache Regions Regions Named Cache 21 Regions
  22. 22. Features [1] Machine -> Cache Host -> Named Cache -> Regions -> Cache Items -> objects Cache Operations Get [select]– Returns object or entire Cache item Add [insert]- Creates new entry else exception if entry exists Put[update] - Replaces existing entry or creates a new one Remove [delete]- Removes existing entry Expiration and Eviction Policy is based on time-to-live [TTL] logic Concurrency model supports optimistic version based updates and pessimistic locking “Velocity” can be deployed as a service or embedded within the application. For example, host application can be ASP.NET / .NET application 22
  23. 23. Example [1] // Create instance of cachefactory (reads appconfig) CacheFactory fac = new CacheFactory(); // Get a named cache from the factory Cache catalog = fac.GetCache(quot;catalogcachequot;); // Simple Get/Put catalog.Put(quot;toy-101quot;, new Toy(quot;thomasquot;, .,.)); // From the same or a different client Toy toyObj = (Toy)catalog.Get(quot;toy-101quot;); // Region based Get/Put catalog.CreateRegion(quot;toyRegionquot;); // Both toy and toyparts are put in the same region catalog.Put(quot;toyRegionquot;, quot;toy-101quot;, new Toy( .,.)); Catalog.Put(quot;toyRegionquot;, quot;toypart-100quot;, new ToyParts(…)); Toy toyObj = (Toy)catalog.Get(quot;toyRegionquot;, quot;toy-101quot;); 23
  24. 24. Resources Based on the paper “Caching in the Distributed Environment” published in the Microsoft Architecture Journal : Issue 17 1. Microsft Project Code Named “Velocity” by N. Sampathkumar, M Krishnaprasad and A. Nori 2.Transaction Processing : Concepts and Techniques by Jim Gray and Andreas Reuter [ISBN: 1558601902] 3. 4. “The Locality Principle” by Peter J. Denning , Communications of the ACM”, July 2005, Vol 48, No 7 5. “Caching Patterns and Implementation”, by Octavian Paul Rotaru, Leonardo Journal of Sciences LJS: 5:8 , January-June 2006 6. Data Access Patterns: Database Interactions in Object-Oriented Applications, by Clifton Nock, Addision Wesley 24
  25. 25. Open Forum ! Abhijit Gadkari Blog : 25