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Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks
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Performance monitoring - Adoniram Mishra, Rupesh Dubey, ThoughtWorks

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  • 1. ANALYSING PERFORMANCE METRICS Steps beyond performance testing - Adoniram Mishra and Rupesh Dubey, ThoughtWorks
  • 2. Agenda:  How is it related and different than performance testing  What parameters to monitor  What are the tools we use for performance monitoring  How to analyze live data
  • 3.  Basics are the same 9 Most important performance monitoring parameters Uptime Page Speed Full page load time Geographic performance Disk Free space Memory Utilization ( Heap memory, overall memory) Database performance CPU usage Internal Jobs / cron jobs
  • 4. • Simpler Architecture • Centralised Monitoring • Limited Bottlenecks
  • 5.  Distributed Architecture  Multi technologies  Multi vendors  Scattered Information  Rich UI
  • 6.  Daily Traffic : 2 million+page views  7 back end systems  1 Front end system  Rich UI + responsiveness  25 Servers
  • 7. Client gmetad gmetad gmetad gmond Node gmond Node gmond Node gmond Node Poll Poll Poll Poll CLUSTER 1 Poll PollPoll CLUSTER 2
  • 8. Splunk Server Data Source 1 Data Source 2 Data Source 3 Forwarders Forwarders Forwarders Alerts Search Dashboard, reports
  • 9.  Case Study War Room Notes: Apr 25 9:00 AM -- [Agent SPLUNK]The Memory on 3 Backend servers have crossed 90% -- May hem ! May Hem!
  • 10. • Should we add more memory. - This will be a temp fix, without even investigating a root cause • Let's see what SPLUNK gives us.Does it had additional information • Real Fix lies with .... Let's refer to our architecture
  • 11.  Recent integration with a 3rd party system  We wrote our custom UI  In performance testing the page load time was about 7 sec.  What's the root cause? Solution: • Is our API call slow? • Is our custom UI slow? • Or is it? ....
  • 12.  For most heavily used db queries  Need to check which are the slow queries
  • 13.  This is an important aspect to check while doing a functional testing  How and what message is logged not just the log level
  • 14.  Eg: 127.0.0.1 [01/jan/2014:16:38:24 -0600] "GET /api/books_details HTTP/1.1" 200 476 127.0.0.1 [01/jan/2014:16:39:24 -0600] "GET /api/magazines_details HTTP/1.1" 404 500 We can query the GET calls which is giving 404 as status code from the log server

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