Common Sense Performance Indicators in the Cloud
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This document discusses common performance indicators for monitoring systems in the cloud. It recommends monitoring system characteristics like CPU, memory, disks, and network usage as well as application stack metrics like frontend performance, caching, and database and API usage. Tools like collectd, rrdtool, and drraw can be used to collect, store, and visualize performance data over time to help tune performance, investigate issues, and understand system architectures.
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Common Sense Performance Indicators in the Cloud
- 1. Common Sense Performance Indicators Nick Gerner June 24, 2010
- 2. Goals Common Sense in the Cloud same as outside the cloud 1. Tune performance 2. Investigate issues 3. Visualize architecture
- 3. Nick Gerner www.nickgerner.com @gerner • Formerly senior engineer at SEOmoz • Linkscape: index of the web for SEO • Lead data services • Developer • Back-end ops guy
- 4. SEOmoz • Seattle-based Startup (~7 engineers) • SEO Blog and Community • Toolset and Platform OpenSiteExplorer.org • 300TB/month processing pipeline • 5 mil req/day API hits
- 5. SEOmoz Engineering • 50 < nodes < 500 • AWS based since 2008 – EC2 – linux root access to bare VM – S3 – networked disk – EBS – local disk I/O – ELB – load balancing as a service
- 6. SEOmoz Architecture Processing The Raw Web Crawlers Crawlers Storage Process Prepare Data Pipeline
- 7. SEOmoz Architecture API Memcache App Lighttpd Partners Memcache App Lighttpd ELB S3 SEOmoz Memcache App Lighttpd Apps
- 8. End-to-End Performance Indicators Latency Conversion Rate DNS Time to On-load Web Object Count
- 9. Great ...but not the focus of this talk Latency Conversion Rate DNS Time to On-load Web Object Count
- 10. Performance Indicators System App Characteristics Stack Front-End CPU Mem Drives Middleware Caching Net Disk Competes Back-end For Database WS-API http://www.flickr.com/photos/dnisbet/3118888630/
- 11. Performance Indicators System Characteristics App Stack CPU Mem Front-End Drives Middleware Caching Competes For Back-end Net Disk Database WS-API http://www.flickr.com/photos/dnisbet/3118888630/
- 12. /proc • System stats • Per-process stats • It all comes from here ...but use tools to see it
- 13. System Characteristics Load Average CPU Memory Disk Network
- 14. Load Average • Combines a few things • Good place to start • Explains nothing http://www.flickr.com/photos/maple03/4176389418/
- 15. CPU • Break out by process • Break out user vs system • User, System, I/O wait, Idle http://www.flickr.com/photos/pacdog/213442876/
- 16. Why watch it? • Who's doing work • Is CPU maxed? • Blocked on I/O? • Compare to Load Average http://www.flickr.com/photos/pacdog/213442876/
- 17. Memory • Break out by Process • Free, cached, used http://www.flickr.com/photos/williamhook/3118248600/
- 18. Why watch it? • Cached + Free = Available • Do you have spare memory? – App uses – Memcache – DB cache http://www.flickr.com/photos/williamhook/3118248600/
- 19. Disk • Read bytes/sec • Write bytes/sec • Disk utilization http://www.flickr.com/photos/robfon/2174992215/
- 20. Why watch it? • Is disk busy? • When? • Who's using it? http://www.flickr.com/photos/robfon/2174992215/
- 21. Network • Read bytes/sec • Write bytes/sec • Established connections http://www.flickr.com/photos/ahkitj/20853609/
- 22. Why watch it? • Max connections (~1024 is magic) • Bandwidth is $$$ • When are you busy? • SOA considerations http://www.flickr.com/photos/ahkitj/20853609/
- 23. v Perf Monitoring Solution FREE, in Apt 1. data collection (collectd) 2. data storage (rrdtool) 3. dashboard management (drraw)
- 24. Perf Monitoring Architecture Multiple Clusters Multiple Applications Nodes come up and go down Cluster Cluster
- 25. Perf Monitoring Architecture collectd agents new nodes get Cluster generic config Cluster node names follow convention according to role
- 26. Perf Monitoring Architecture On its own server: collectd server Perf Monitoring Web server drraw.cgi Server allows connections from new nodes perf data backed up daily Cluster Cluster
- 27. Perf Monitoring Architecture Happy Sysadmin Visibility into system history of performance Perf Monitoring Server Cluster Cluster
- 28. Perf Dashboard Featurs 1. Summarize nodes/systems 2. Visualize data over time 3. Stack measurements – Per-process – Per-node 4. Handle new nodes –
- 30. CPU
- 31. Memory
- 32. Disk
- 33. Network
- 35. Web Requests
- 36. mod_status
- 38. Per-request
- 39. Graph Summary • cpu, mem, disk, net • over time • per node • per process • Through in relevant app measures e.g. per request stats: • req/sec • median latency/req
- 40. Ad-hoc Tools • $ dstat -cdnml system characteristics • $ iotop per-process disk I/O • $ iostat -x 3 detailed disk stats • $ netstat -tnp fast, per-process TCP connection stats
- 41. Resources • Perf Testing: What, How, Why http://www.nickgerner.com/2010/02/performance-testing- what-andhow-why/ • Perf Testing Case Study: OSE http://www.nickgerner.com/2010/01/performance-testing- case-study-ose/ • S3 Benchmarks http://twopieceset.blogspot.com/2009/06/s3- performance-benchmarks.html • Perf Measurement – http://twopieceset.blogspot.com/2009/03/performance- measurement-for-small-and.html –
- 42. More Resources • http://www.collectd.org • http://oss.oetiker.ch/rrdtool/ • http://web.taranis.org/drraw/ • http://dag.wieers.com/home-made/dstat/ • $ man proc –
- 43. Q: Why? A: Perf Tuning Test Validate Measure Improve Interpret
- 44. Q: Why? A: System Arch • Better Devs/Ops • Identify Bottlenecks • Scaling Considerations
- 45. Q: Why? A: Issue Investigation • Machine Specific? • System Wide? • Which Component? • Timeline? • Cascading Failures?