RightScale Webinar: Performance tuning applications in the public cloud is both easier and harder than on your own server hardware. It's much easier to scale up and scale out in the cloud but you generally don't have much (if any) control over the hardware. With public cloud, you take the building blocks offered by the cloud infrastructure and design the application architecture to scale based on the capacity planning requirements and scalability testing results. In this session, we'll talk through our experiences scaling and performance tuning the RightScale platform in the cloud and share tips for sizing, auto-scaling, monitoring, and troubleshooting large-scale cloud deployments.

1. Optimizing Your Cloud Applications in RightScale October 13, 2011 Watch the video of this webinar

3. Raphael Simon, Sr. Systems Architect, RightScaleQ&A Jordan Evans, Account Manager, RightScale Please use the “Questions” window to ask questions any time!

4. Agenda Introduction 3-tier application architecture Vertical & horizontal scaling RightScale monitoring and cluster graphs New Relic RPM Support for optimizing DB performance Load testing Please use the “Questions” window to ask questions any time!

5. Multi-tenancy Shared resource pooling Geo-distribution and ubiquitous network access Service oriented Dynamic resource provisioning Self-organizing Utility based pricing Cloud computing characteristics

6. No upfront investment Lowering operating costs Highly scalable Easy access Reduces business risk and maintenance costs Enables process automation Cloud computing advantages

7. 3-tier application architecture Load balancers An array of application servers Master-slave

8. Optimizing Your Cloud Applications in RightScale Vertical & Horizontal Scaling

9. Instance size (vertical scaling) Instance autoscaling (horizontal scaling) Server arrays RightScale support for performance optimization ServerTemplates are configured to capture performance data CollectdRightScripts Hardware & OS monitoring data Specialized plugins – MySQL, HAProxy, Apache, NgInx, IIS, etc Monitoring graphs: individual, cluster, stacked, heat maps Alerts & escalations New Relic RPM Cloud performance optimization

10. Compute units vs memory Scaling up – spectrum of instance sizes

11. Server arrays provide horizontal scaling

12. The array scales up or down based on performance votes Tags allow scaling on an arbitrary decision set Decision threshold controls reaction time Sleep time allows new resources to have an impact Scaling can be time dependent Detailed setup instructions: http://bit.ly/c1oLr2 Fast response to changes in load conditions using alerts Allocation of servers to availability zones based on weights Deployment-based so configuration is consistent Arrays can be pre-scaled to support anticipated demand Server arrays provide horizontal scaling

13. Optimizing Your Cloud Applications in RightScale Monitoring & Cluster Graphs with RightScale

14. Server monitoring graphs

15. Cluster monitoring Individual graphs Good for a dozen servers Displays all standard graphs with full detail Stacked graphs Displays the contribution of many servers to a total Great to see the sum and variability of activity in a cluster Difficult to make out individual servers Examples: requests/sec, cpu busy cycles, I/O bytes/sec Heat maps Displays a bar for each server Great to see uneven distribution across servers Great to quickly spot performance problems across many servers Difficult to read absolute values or see the total cluster activity

16. Cluster monitoring architecture Architecture Monitoring front-end serverspull data from storage servers Up to 100 servers on one graph(to be increased) monitoring storage servers monitoring front-end servers your servers

17. Cluster monitoring Current cluster monitoring: one graph per server

18. Stacked graphs Each color band shows contribution of one server Servers are stacked on top of one another

19. Heat maps Each horizontal strip shows one server The color shows how “hot” the server is running

20. Heat map with 100 servers

21. Stacked graph of the same 100 servers

22. Optimizing Your Cloud Applications in RightScale Application Performance Analytics with New Relic

23. Real-Time App Performance Analytics Supports Ruby, PHP, Java & .Net SQL & NoSQL performance Web transaction tracing Performance notifications Availability monitoring Scalability analysis New Relic RPM

24. New Relic RPM Direct access from RightScale dashboard

25. New Relic RPM Historical statistics over a period of time

26. New Relic RPM Distribution of the most time consuming requests

27. New Relic RPM Statistics about response times from different countries

28. New Relic RPM Detailed response times by browser

29. An expensive query The N+1 query problem New Relic RPM – 2 Examples

30. Optimizing Your Cloud Applications in RightScale Optimizing Database Performance

31. Optimizing DB performance RightScale MySQLServerTemplates Configuration files tailored to instance size innodb_buffer_pool_size key_buffer_size thread_size sort_buffer_size The never ending task of identifying current bottlenecks Disk seeks Performance of disk operations Scale up when working set cannot fit in memory – avoid active swapping Constant monitoring of performance graphs, logs and query Schema considerations

32. Schema considerations Lookups need to be indexed Sorting requires an index Joins need to be done on indices Become slower as tables grow Compounded indices should be used consistently Do not abuse indices Each index requires a disk write Compact tables if they become fragmented Deleted rows do not remove the corresponding index entries

33. Monitoring DB performance Standard collectd statistics User vs wait time (disk operations) Performance of disk operations Scale up when working set cannot fit in memory MySQLcollectdplugin Monitor INSERT, SELECT, UPDATE operations The breakdown of read operations can indicate missing indices Monitoring /var/log/mysqlslow.log file Identify slow queries Use MySQL EXPLAIN command to identify query plan

34. MySQLCollectdPlugin Uses MySQL SHOW STATUS command to collect statistics A large set of counters that are divided into 10 categories Connections IO Requests Select Rates Read Rates Key Rates Commands Rates Query Cache Tables Memory Misc.

35. MySQLCollectdPlugin Uses MySQL SHOW STATUS command to collect statistics

36. Mysqlslow.log & explain command

37. MySQL performance depends on locality Wait time should be minimum when working set fits in memory Performance degrades once wait time is significant wait time insignificant user time dominates

38. MySQL reads graphs Read-random-next represents a table scan Read-next represents an index scan

39. Optimizing Your Cloud Applications in RightScale Load Testing

40. Load testing using httperf RightScale provides ServerTemplates in the marketplace https://my.rightscale.com/library/server_templates/Httperf-Load-Tester/24714 Tutorial on httperf setup and configuration http://support.rightscale.com/03-Tutorials/02-AWS/E2E_Examples/E2E_Gaming_Deployment/Adding_Httperf_Load_Tester

42. Talk with RightScale customers

43. Ask questions at the Genius Bar