This document discusses optimizing cloud applications using RightScale. It covers monitoring applications using tools like Collectd and New Relic RPM. It also discusses optimizing database performance on cloud infrastructure by scaling instances vertically and horizontally, using the right indices, and ensuring the working set fits in memory. The document provides an agenda and overview of these optimization techniques.

1. Optimizing Your Cloud Applications in RightScale Rafael H. Saavedra - VP Engineering, RightScale June 8th, 2011

2. Introduction 3-tier application architecture Vertical & horizontal scaling RightScale monitoring and cluster graphs New Relic RPM Support for optimizing DB performance Miscellaneous Agenda

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

4. No upfront investment Lowering operating costs Highly scalable Easy access Reduces business risk and maintenance costs Cloud computing advantages

5. 3-tier application architecture Load balancers A farm of application servers Master-slave

6. 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

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

8. Server arrays provide horizontal scaling

9. 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

10. 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

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

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

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

14. Heat map with 100 servers

15. Stacked graph of the same 100 servers

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. 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

18. New Relic RPM Direct access from RightScale dashboard

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

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

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

22. New Relic RPM Detailed response times by browser

23. An expensive query The N+1 query problem Finding patterns in similar requests New Relic RPM – 3 Examples

24. 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

25. 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

26. 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/mysql-slow.log file Identify slow queries Use MySQL EXPLAIN command to identify query plan

27. 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.

28. MySQLCollectdPlugin Uses MySQL SHOW STATUS command to collect statistics

29. Mysql-slow.log & explain command # Time: 101006 23:30:11 # User@Host: prod[prod] @ domU-12-31-39-0F-D0-C1.compute-1.internal [10.193.211.47] # Query_time: 7 Lock_time: 0 Rows_sent: 1 Rows_examined: 19785 SELECT * FROM `ec2_elastic_ips` WHERE (`ec2_elastic_ips`.ec2_instance_id = 6810144) LIMIT 1; mysql> explain select * FROM `ec2_elastic_ips` WHERE (`ec2_elastic_ips`.ec2_instance_id = 6810144) LIMIT 1 *************************** 1. row *************************** id: 1 select_type: SIMPLE table: ec2_elastic_ips type: ALL possible_keys: NULL key: NULL key_len: NULL ref: NULL rows: 33332 Extra: Using where 1 row in set (0.00 sec)

30. 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

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

32. Misc load testing using httperf RightScale provides ServerTemplates in the marketplace https://my.rightscale.com/library/server_templates/Httperf-Load-Tester-11H1/18316 Tutorial on httperf setup and configuration http://support.rightscale.com/03-Tutorials/02-AWS/E2E_Examples/E2E_Gaming_Deployment/Adding_Httperf_Load_Tester

33. Questions? Rafael Saavedra - VP Engineering, RightScale June 8th, 2011