Alex mang patterns for scalability in microsoft azure application
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The document discusses patterns for scalability in Microsoft Azure applications. It covers queue-based load leveling, competing consumers, and priority queue patterns for handling application load and message processing. It also discusses materialized view and sharding patterns for scaling databases, where materialized views optimize queries and sharding partitions data horizontally across multiple servers. The talk includes demos of priority queue and sharding patterns to illustrate their implementations.
Alex mang patterns for scalability in microsoft azure application
- 1. Patterns for Scalability in Microsoft Azure Applications (starting at 17.00) Alex Mang alexmang.ro | @mangalexandru 25thof October 2014
- 3. •Alex Mang •CEO @ KeyTicket Solutions •Event management and on-line ticketing, powered by Azure •Microsoft BizSpark Plus (1/4 nation-wide) •Azure Advisor •Husband & father Speaker.Bio.ToString()
- 4. •They promised crazyscaling capacity •… & they fulfilled their promise •… but my apps are still under-performing Context of cloud apps
- 6. Queue-Based Load Pattern Queue Based Load Leveling Pattern Source: Microsoft Patterns & Practices -Cloud Design Patterns Possible bottlenecks
- 7. Competing-Consumers Pattern Source: Microsoft Patterns & Practices -Cloud Design Patterns What about important messages?
- 8. Priority Queue Pattern Competing Consumers Pattern Source: Microsoft Patterns & Practices -Cloud Design Patterns
- 9. Recap Queue-based Load: -Removes app failure caused by 3rdparty services Competing-Consumers: -Ensures messages (= tasks) are processed faster, even under high load Priority Queue: -Ensures important tasks run first
- 10. DEMO PRIORITY QUEUE PATTERN
- 11. •Most developers think about how data is stored in OOP manner •In NoSQL, commonly store everything in a single entity •In SQL, we have size constraints •End-up in: •Performance impact •High prices What about data repo. limit.?
- 12. •Can generate views in advance, containing data on a per-requirement basis •Only contain data required by query •Include current values of calculated columns or data items •May be optimized for a single queries •Updated a.s.a.p. (schedule / triggered) What about data repo. limit.? (cont’)
- 13. Materialized-View Pattern Source: Microsoft Patterns & Practices -Cloud Design Patterns
- 14. Materialized-View Pattern Materialized View Pattern Example Source: Microsoft Patterns & Practices -Cloud Design Patterns
- 15. Materialized-View Pattern When to use Queries are complex Data difficult to query directly Temporary views dramaticallyimprove perf. Temporary views act as DTOs for UI, reporting etc. Data store not always available (SQL Azure) Security or privacy reasons
- 16. Recap So far, I’ve talked about: •Taking pieces of code out of the ‘front facing’ apps •Messaging methods between apps and consumers •Simple queue •Priority queue •Response queues •Partitioning data into views •Materialized views! Hip-hip, Hurray! WE CAN SCALE OUR COMPUTE ROLES!
- 17. But what about data? •Thought: If we scale-out our compute roles… … why not scale out our databases too?
- 18. Sharding Pattern •Scale-up vs. scale-out applies to dbs. too! •Shard = horizontal partition of overall data •Must scale out data because: •Storage limitations •Concurrent requests •Customer isolation •Geography
- 19. Sharding Pattern (cont’ed) •Horizontal partitions of data •(a.k.a. shards) •Same schema, different data •Runs on its own server •Benefits: •Use commodity hardware •Better performance •Closely located geographically
- 20. Sharding Pattern (cont’ed) Source: Microsoft Patterns & Practices -Cloud Design Patterns
- 21. Strategy Advantages Considerations Lookup •Morecontrol •Easy shard rebalance •Shard lookup may create additional overhead Range •Easy to implement •Workswell on range queries •Easy management •Suboptimal balance •Shard rebalance is difficult Hash •Best balance •Requestrouting directly via hashing alg. •Calculating hash may createadditional overhead •Rebalance is difficult Sharding Pattern (cont’ed)
- 23. •Microsoft Patterns & Practices: •Cloud Design Patterns http://msdn.microsoft.com/en- us/library/dn568099.aspx •Developing Multi-tenant Apps. for the Cloud: http://msdn.microsoft.com/en- us/library/ff966499.aspx •Moving Applications to the Cloud: http://msdn.microsoft.com/en- us/library/ff728592.aspx References
- 24. References (cont’ed) •Get Started with Azure SQL Database Elastic Scale Preview •http://azure.microsoft.com/en- us/documentation/articles/sql-database-elastic- scale-get-started/ •Elastic Scale Documentation Map •http://azure.microsoft.com/en- us/documentation/articles/sql-database-elastic- scale-documentation-map/ •Elastic Scale Sample App
- 25. MVA •Microsoft Virtual Academy •On-linecurricula •Getting started courses •Deep dives •“How To”s •Live Events •Compete against friends & colleagues
- 26. MVA (cont’ed) •Personal learning paths •Earn points for each course •Romania TOP 10/ week / month / all-time •WIN PRIZES (phones, tablets etc.) www.microsoftvirtualacademy.com •Did I mention PRIZES?
- 27. Q & A
- 28. Thank you!
- 30. Patterns for Scalability in Microsoft Azure Applications Alex Mang alexmang.ro | @mangalexandru 25thof October 2014 Please fill the online evaluation form after event