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Measuring What Matters
© AKAMAI - EDGE 2016
Introductions
Cliff Crocker
Akamai
Buddy Brewer
SOASTA
© AKAMAI - EDGE 2016
Three eras of modern performance measurement
2001-2006 Basically one browser
2007-2010 Transitional period
2011-2016 WTF just happened
© AKAMAI - EDGE 2016
2001-2006: Internet Explorer 6
2001 - 2006

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© AKAMAI - EDGE 2016
2007-2010: Transitional period
2007
IE7
iPhone
Firefox 2
2008 2009 2010
© AKAMAI - EDGE 2016
2007-2010: Transitional period
2007
IE7
iPhone
Firefox 2
2008
IE7
iPhone 3G
Firefox 3
Android
Chrome 1
2009 2010
© AKAMAI - EDGE 2016
2007-2010: Transitional period
2007
IE7
iPhone
Firefox 2
2008
IE7
iPhone 3G
Firefox 3
Android
Chrome 1
2009
IE8
iPhone 3GS
Firefox 3
Android 2.1
Chrome 1
2010
© AKAMAI - EDGE 2016
2007-2010: Transitional period
2007
IE7
iPhone
Firefox 2
2008
IE7
iPhone 3G
Firefox 3
Android
Chrome 1
2009
IE8
iPhone 3GS
Firefox 3
Android 2.1
Chrome 1
2010
IE8
iPhone 3GS
Firefox 3
Android 2.1
Chrome 1
iPad
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© AKAMAI - EDGE 2016
2011-2016: WTF just happened
?
How many browsers in a
typical Thanksgiving
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2011-2016: WTF just happened
572
How many browsers in a
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© AKAMAI - EDGE 2016
So what can I measure?
© AKAMAI - EDGE 2016
What is available today - Synthetic Tooling
Pros
CPU metrics
Test environments & other places without real users
Access to everything including headers
Cons
Gaps in: Geos, browsers, pages
Impossible to precisely match user demo- & technographics
No way to realistically measure browser cache impact
© AKAMAI - EDGE 2016
What is available today - RUM
Pros
Near perfect coverage of browsers, geos, pages
Ability to understand impact to performance
Realistic measurement of browser cache impact
Cons
Gaps in: Geos, browsers, pages
Impossible to precisely match user demo- & technographics
No access to HTTP header data
© AKAMAI - EDGE 2016
Engagement
Revenue
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Caching
CPU
Pre-prod
Availability
Page details
Resources
Headers
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ia/images/edge/akamai-edge-conference-s
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1
2
3
4
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Measuring what matters

  • 2. © AKAMAI - EDGE 2016 Introductions Cliff Crocker Akamai Buddy Brewer SOASTA
  • 3. © AKAMAI - EDGE 2016 Three eras of modern performance measurement 2001-2006 Basically one browser 2007-2010 Transitional period 2011-2016 WTF just happened
  • 4. © AKAMAI - EDGE 2016 2001-2006: Internet Explorer 6 2001 - 2006
  • 5. © AKAMAI - EDGE 2016 2007-2010: Transitional period 2007 IE7 iPhone Firefox 2 2008 2009 2010
  • 6. © AKAMAI - EDGE 2016 2007-2010: Transitional period 2007 IE7 iPhone Firefox 2 2008 IE7 iPhone 3G Firefox 3 Android Chrome 1 2009 2010
  • 7. © AKAMAI - EDGE 2016 2007-2010: Transitional period 2007 IE7 iPhone Firefox 2 2008 IE7 iPhone 3G Firefox 3 Android Chrome 1 2009 IE8 iPhone 3GS Firefox 3 Android 2.1 Chrome 1 2010
  • 8. © AKAMAI - EDGE 2016 2007-2010: Transitional period 2007 IE7 iPhone Firefox 2 2008 IE7 iPhone 3G Firefox 3 Android Chrome 1 2009 IE8 iPhone 3GS Firefox 3 Android 2.1 Chrome 1 2010 IE8 iPhone 3GS Firefox 3 Android 2.1 Chrome 1 iPad Nav Timing
  • 9. © AKAMAI - EDGE 2016 2011-2016: WTF just happened ? How many browsers in a typical Thanksgiving weekend?
  • 10. © AKAMAI - EDGE 2016 2011-2016: WTF just happened 572 How many browsers in a typical Thanksgiving weekend?
  • 11. © AKAMAI - EDGE 2016 Meanwhile...
  • 12. © AKAMAI - EDGE 2016
  • 13. © AKAMAI - EDGE 2016 So what can I measure?
  • 14. © AKAMAI - EDGE 2016 What is available today - Synthetic Tooling Pros CPU metrics Test environments & other places without real users Access to everything including headers Cons Gaps in: Geos, browsers, pages Impossible to precisely match user demo- & technographics No way to realistically measure browser cache impact
  • 15. © AKAMAI - EDGE 2016 What is available today - RUM Pros Near perfect coverage of browsers, geos, pages Ability to understand impact to performance Realistic measurement of browser cache impact Cons Gaps in: Geos, browsers, pages Impossible to precisely match user demo- & technographics No access to HTTP header data
  • 16. © AKAMAI - EDGE 2016 Engagement Revenue Coverage Caching CPU Pre-prod Availability Page details Resources Headers SyntheticReal
  • 17. © AKAMAI - EDGE 2016
  • 18. © AKAMAI - EDGE 2016 https://www.w3.org/wiki/Web_Performance/Publications W3C - Performance Working Group
  • 19. © AKAMAI - EDGE 2016 Navigation Timing API
  • 20. © AKAMAI - EDGE 2016 Resource Timing API
  • 21. © AKAMAI - EDGE 2016 Coming soon to a browser near you! Measure the size of assets in addition to performance. Resource Timing (2.0) - Transfer Size
  • 22. © AKAMAI - EDGE 2016 ● Measure performance using high-precision timestamps ● Consists of “marks” and “measures” ● PerformanceMark: Timestamp ● PerformanceMeasure: Duration between two given marks Browser Support - User Timing API
  • 23. © AKAMAI - EDGE 2016 Browser Support - Navigation Timing API
  • 24. © AKAMAI - EDGE 2016 Browser Support - Resource Timing API
  • 25. © AKAMAI - EDGE 2016 Browser Support - User Timing API
  • 26. © AKAMAI - EDGE 2016 So what should I measure?
  • 27. © AKAMAI - EDGE 2016
  • 28. © AKAMAI - EDGE 2016 What matters depends on you
  • 29. © AKAMAI - EDGE 2016 What matters to an ops engineer? ● Preference for binary metrics ● Looks for indicators of system performance ● Affinity to network and back-end metrics MTTR
  • 30. © AKAMAI - EDGE 2016 What matters to an ops engineer? Network Server
  • 31. © AKAMAI - EDGE 2016 What matters to an ops engineer? Network Server function getPerf() { var timing = window.performance.timing; return { dns: timing.domainLookupEnd - timing.domainLookupStart, connect: timing.connectEnd - timing.connectStart }; } ttfb: timing.responseStart - timing.connectEnd};
  • 32. © AKAMAI - EDGE 2016 Help answers questions like... ● Do I have an issue with an upstream DNS resolver? ● Am I having Tier1 connectivity issues? ● How fast is my application server responding?
  • 33. © AKAMAI - EDGE 2016 What matters to an ops engineer? Don’t be misled by “super fast” responses ● DNS Caching ● Prefetching ● Connection reuse ● Pages served from browser cache
  • 34. © AKAMAI - EDGE 2016 What matters to an ops engineer? Object Level Timing ● Resource timing API ● Binary metric for testing infrastructure ● Measure performance of a third-party ● CDN healthcheck
  • 35. © AKAMAI - EDGE 2016 What matters to an ops engineer? Object Timing var rUrl = ‘https://edge.akamai.com/ec/us/multimed ia/images/edge/akamai-edge-conference-s peaker-buddy-brewer.jpg’; var me = performance.getEntriesByName(rUrl)[0]; var timings = { loadTime: me.duration, dns: me.domainLookupEnd - me.domainLookupStart, tcp: me.connectEnd - me.connectStart, waiting: me.responseStart - me.requestStart, fetch: me.responseEnd - me.responseStart }
  • 36. © AKAMAI - EDGE 2016 What matters to a front-end developer? Optimized Load Times ● “Do no harm” ● Looks for indicators of application performance ● Looks to measure a “fully loaded” page ● Affinity for front-end metrics
  • 37. © AKAMAI - EDGE 2016 What matters to a front-end developer? Client
  • 38. © AKAMAI - EDGE 2016 What matters to a front-end developer?
  • 39. © AKAMAI - EDGE 2016 What matters to a front-end developer? function getPerf() { var timing = window.performance.timing; return { Basepage: timing.responseEnd - timing.responseStart, DLoading: timing.domLoading – timing.navigationStart, DInt: timing.domIneractive – timing.navigationStart, DContLoaded: timing.domContentLoadedEventEnd – timing.navigationStart, DContLoadTime: timing.domContentLoadedEventEnd – timing.domContentLoadedEventStart, DComplete: timing.domComplete - timing.navigationStart, PLoad: timing.loadEventStart - timing.navigationStart }; } Client
  • 40. © AKAMAI - EDGE 2016 Be wary of onload... ~1s OnLoad ~3.5s visibly complete
  • 41. © AKAMAI - EDGE 2016 What matters to a designer? UX ● Performance by design (D4P) ● Looks for indicators of human perception ● How quickly the page renders ● Affinity for custom metrics and render metrics
  • 42. © AKAMAI - EDGE 2016 RUM first paint and start render Chrome - “First Paint” Visual Start Render
  • 43. © AKAMAI - EDGE 2016 User perception Courtesy of the filament group: https://www.filamentgroup.com/lab/weight-wait.html
  • 44. © AKAMAI - EDGE 2016 Custom Metrics
  • 45. © AKAMAI - EDGE 2016 Custom Metrics - User Timing API performance.mark(“startTask”); //Some stuff you want to measure happens here performance.mark(“stopTask”); //Measure the duration between the two marks performance.measure(“taskDuration”,“startTask”,“stopTask”); //Measure the load of an image <img src=”image1.gif” onload=”performance.mark(‘image1’)”>
  • 46. © AKAMAI - EDGE 2016 What matters to a business executive? $$$$$$$ ● Business impact analysis ● Simplified/lean metrics ● Actionable data
  • 47. © AKAMAI - EDGE 2016 What matters to a business executive?
  • 48. © AKAMAI - EDGE 2016 What matters to a business executive?
  • 49. © AKAMAI - EDGE 2016 What matters to a business executive?
  • 50. © AKAMAI - EDGE 2016 Don’t create unintended silos Biz Ops Dev Design
  • 51. © AKAMAI - EDGE 2016 Don’t create unintended silos The success of my business is a reflection of... The digital experience I provide, which depends on... how quickly I can render content in the browser, which is impacted by... the performance and stability of my infrastructure. 1 2 3 4 Biz Design Ops Dev
  • 52. © AKAMAI - EDGE 2016 “I just want to buy my Mom’s birthday present.” “I just want to know if it will rain today.” “I just want to share pictures of my vacation”
  • 53. © AKAMAI - EDGE 2016 How do I collect it?
  • 54. © AKAMAI - EDGE 2016
  • 55. © AKAMAI - EDGE 2016 Real users are not normal What do I do with it?
  • 56. © AKAMAI - EDGE 2016 What do I do with it?
  • 57. © AKAMAI - EDGE 2016 2.9s - p50 (median) 4.4s - Average 10.4s - p95 What do I do with it?
  • 58. © AKAMAI - EDGE 2016 What should I throw out?
  • 59. © AKAMAI - EDGE 2016 Load Time = 3500 years
  • 60. © AKAMAI - EDGE 2016 Load Time = -(10years)
  • 61. © AKAMAI - EDGE 2016 What should I keep?
  • 62. © AKAMAI - EDGE 2016
  • 63. © AKAMAI - EDGE 2016
  • 64. © AKAMAI - EDGE 2016 Other opportunities for bots... 1. How does performance impact bot behavior? 2. Are bots skewing the numbers I’m reporting? 3. Am I providing an optimal experience for search bots? 4. Does any of this impact SEO?
  • 65. © AKAMAI - EDGE 2016 Summary ● We have amazing tools at our disposal - your users are wired for sound! ● Measuring what matters is situation independent ● Important to look at all metrics from the same context ● Use robust statistics when looking at your data