1. 1. Optimal Azure Database Development Karel Coenye Tweet and win an Ignite 2016 ticket #itproceed
2. 2. About Me
3. 3. As a Database Architect
4. 4. The Cloud
5. 5. New Application, New possibility Lets go to the cloud!
6. 6. First thing First, lets talk about
7. 7. Things that cost money Database Troughput Unit (DTU) TPS -> 1 DTU 3320 TPH (Basic) 51,4 TPM (Standard) 0,92 TPS (Premium) Size Time Download Response Time Basic : 80 percentile in 2 sec Standard: 90 percentile in 1 sec Premium: 95 percentile in ,5 sec
8. 8. DTU Average - Rules 1 User = 5 DTU (if he would use all the power) User pacing estimated at 1sec 1 User => 1 DTU realistically Tiers are size, Response Time and DTU based Performance has to fit in the tier, otherwise it becomes to expensive
9. 9. DTU Explained Read Lite SELECT; in-memory; read-only 35 % Read Medium SELECT; mostly in-memory; read-only 20 % Read Heavy SELECT; mostly not in-memory; read-only 5 % Update Lite UPDATE; in-memory; read-write 20 % Update Heavy UPDATE; mostly not in-memory; read-write 3 % Insert Lite INSERT; in-memory; read-write 3 % Insert Heavy INSERT; mostly not in-memory; read-write 2 % Delete DELETE; mix of in-memory and not in-memory; read-write 2 % CPU Heavy SELECT; in-memory; relatively heavy CPU load; read-only 10 % The overall mix has a read/write ratio of approximately 2:1
10. 10. How design can save cost Limit heavy reads Limit heavy writes Limit CPU intensive queries Limit Useless Cycles Limit the result sets Limit Contention lets focus on architecture
11. 11. Stevie Wonder goes to a concert
12. 12. Common Cloud Issues Availability Data Management Design And Implementation Messaging Management and Monitoring Performance And Scalability Resiliency Security
13. 13. Management and Monitoring
14. 14. External Configuration Store
15. 15. Metering
16. 16. Stevie Wonder wants to sing
17. 17. Performance And scalability
18. 18. Stateful vs. Stateless
19. 19. Think outside the box
20. 20. Segregation Of Data
21. 21. Small High-Performance OLTP Small Stateful Strictly Consistent High Performance Scales via Elastic Databases Contains minimal operational dataset
22. 22. Large Single Version of the truth Datastore Contains the truth Contains all the data Highly Normalized (3Rd NF or Higher) Stateful Transactionally Consistent Mix of Strict and eventually consistent Master of the Data
23. 23. Read-Only / Reporting Warehouse / Sync DB (semi)-Stateless Contains subset of data Has eventual consistency Scales via sync/replication
24. 24. Competing Consumers
25. 25. Cache-Aside
26. 26. Remote Computing Data Data Cache Stored Procedures Data Cache Stored Procedures Data Cache Stored Procedures
27. 27. Design and Implementation
28. 28. Command and Query Responsibility Segregation
29. 29. Materialized Views
30. 30. Managing Data Consistency
31. 31. Strong Consistency
32. 32. Eventual Consistency
33. 33. Replicating And Synchronizing
34. 34. Replicating Data(bases)
35. 35. Master - Master
36. 36. Master - Slave
37. 37. Replication
38. 38. Synchronizing Data(bases)
39. 39. Sync Works on
40. 40. Data Sync
41. 41. How does Stevie sound at the Metallica Concert?
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44. 44. Thank you!
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