Focus on what’s important
Performance analytics driven by machine learning
Why we built illuminate
• To diagnose performance issues
– Without human intervention
– Machine scalable performance diagn...
Why illuminate is different!
• Lightweight
– Small memory footprint, small CPU footprint, small network footprint
• Intell...
illuminate analysis
• Machine Learning finds largest bottleneck
– Points you in the right direction quickly
– Concentrate ...
A sample of the bottlenecks that we find
Category Bottleneck Description
High Pause Times Shows you high pause times due t...
illuminate - How it works
Some technical details
• Supports RedHat/Debian Linux systems
– Including Amazon AWS, MS Azure, Google Cloud Compute
– Run...
Sample Overview
Sample Diagnosis Result
Sample Action Plan & Explanation
Sample - Historical Timeline
Sample SLA Metrics
illuminate roadmap
• 2015+ - Extensive Roadmap…
– Trigger diagnosis on breach of message throughput SLA
– Deeper analysis ...
Focus on what’s important
Get your free trial at www.jclarity.com
Follow us on Twitter @jclarity
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Illuminate - Performance Analystics driven by Machine Learning

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This is an introduction slide deck which gives you the motivation of why we built illuminate and why it is so very different to the traditional APMs out there!

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Illuminate - Performance Analystics driven by Machine Learning

  1. 1. Focus on what’s important Performance analytics driven by machine learning
  2. 2. Why we built illuminate • To diagnose performance issues – Without human intervention – Machine scalable performance diagnostics and analysis • Industry not happy with existing tools – Current state of the art is about raw metrics, no analysis – “Collect everything and hope for the best” • We could build on a proven methodology – Methodology proven through years of customer engagements – Required the use of Machine Learning
  3. 3. Why illuminate is different! • Lightweight – Small memory footprint, small CPU footprint, small network footprint • Intelligent – illuminate’s Machine Learned algorithm interprets the data for you! • Adaptive – Scales up or down with your application • Pervasive – Fills out the server piece of the performance puzzle
  4. 4. illuminate analysis • Machine Learning finds largest bottleneck – Points you in the right direction quickly – Concentrate on biggest problem! • illuminate looks at the overall server – The problem may not be caused by the Java application! • illuminate aggregates across servers – If X servers have a similar problem we wrap that up in one report • Auto-triggers on SLA breaches – Performance/business trigger points, e.g. login page within 2 secs
  5. 5. A sample of the bottlenecks that we find Category Bottleneck Description High Pause Times Shows you high pause times due to Garbage Collection (GC) Too much time in GC Shows you if your application not progressing due to GC Running out of memory Shows you if your application is close to an OOME Heavy Disk I/O Shows you if you are reading or writing too much from disk Waiting on external system Shows you threads that are waiting for a response from an external system, e.g. Database, Webservice etc Blocked threads Shows you threads that are blocked (with profiles) Deadlocked threads Shows you deadlocked threads (with profiles) Sleeping threads Shows you threads that are sleeping (with profiles) Hot Loop Shows you if your code is in a hot (infinite loop) Context Switching Shows you if your application is battling others for CPU time
  6. 6. illuminate - How it works
  7. 7. Some technical details • Supports RedHat/Debian Linux systems – Including Amazon AWS, MS Azure, Google Cloud Compute – Run via init.d or simple run-headless.sh shell script – /proc should be available to read from • Daemon Comms over SSL'd Websockets – SaaS hosted dashboard (secure public, or secure in-house) – Self Updating Daemons / Dashboard as a virtual appliance • Supports modern web browsers – IE8+ (10 preferred), FF, Chrome, Safari, Opera
  8. 8. Sample Overview
  9. 9. Sample Diagnosis Result
  10. 10. Sample Action Plan & Explanation
  11. 11. Sample - Historical Timeline
  12. 12. Sample SLA Metrics
  13. 13. illuminate roadmap • 2015+ - Extensive Roadmap… – Trigger diagnosis on breach of message throughput SLA – Deeper analysis for popular APIs/Libraries/Frameworks/App Servers – .NET, Javascript, Ruby et al – Embedded Devices – Capacity planning – PaaS and IDE integrations – Self Healing applications…
  14. 14. Focus on what’s important Get your free trial at www.jclarity.com Follow us on Twitter @jclarity

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