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Performance Forensics

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An acute problem of production. They come to the scene and begin collecting evidence. The next question that arises is: What exactly happened? Then it sets out to solve the case. How can we draw from …

An acute problem of production. They come to the scene and begin collecting evidence. The next question that arises is: What exactly happened? Then it sets out to solve the case. How can we draw from the available data the correct conclusions? How to proceed best not to get on the wrong track? We are pursuing a case by case problem until dissolved.

Presented by Alois Reitbauer, Technology Strategist, dynaTrace-Compuware Center of Excellence, at the JAX Conference in Mainz, May 2011

Published in: Technology

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  • When there is a murder, suspicious fire or hit-and-run accident, police and rescue workers aren't the only ones in on the investigation. Forensic scientists also play an important part. They will take samples collected at the scene and analyze them in a forensics laboratory. With a little ingenuity and some very high-tech equipment, forensic scientists can help law enforcement catch even the wiliest perpetrator.Forensic science is a discipline that applies scientific analysis to the justice system, often to help prove the events of a crime. Forensic scientists analyze and interpret evidence found at the crime scene. That evidence can include blood, saliva, fibers, tire tracks, drugs,alcohol, paint chips and firearm residue.
  • http://www.istockphoto.com/stock-photo-13437760-over-a-dead-body.php?st=bc63d6b
  • http://euro-police.noblogs.org/2010/11/romanian-police-in-corruption-scandals/police_line_do_not_cross_8-jpg/
  • http://www.istockphoto.com/stock-photo-4608404-microscope.php?st=a02af80
  • Transcript

    • 1. Performance Forensics Alois ReitbauerdynaTrace Software
    • 2. Forensicsforensis adj - "of or before the forum." In Romantimes, a criminal charge meant presenting thecase before a group of public individuals in theforum. … The individual with the best argumentand delivery would determine the outcome ofthe case.
    • 3. Collecting Evidence
    • 4. What does this chart tell usResponse Time Time
    • 5. A- Response time problem?B - CPU problem?C - Sync problem?D - Database problem?
    • 6. We don‘t know
    • 7. We have to collect our evidence first
    • 8. Multi-Layered Measurement User-Level Application-Level Container/App Server-LevelSystem/OS/Virtualization-Level
    • 9. Understand your measurementsResponse Time only Response Time and GC
    • 10. How can this happen?A: Our response time is 2.3 secondsB: Our response time is 1.5 secondsC: Our response time is 6 seconds
    • 11. The beauty of measures ... Page Load Time HTTP Request Time 95 % Servlet Time Network Browser Firewall Sniffer Web Server Application Server Request Time (max) ... is that there are so many to choose from
    • 12. Types of Measurment• Cyclic Measurements – Are collected ar regular time intervals – Are time based – JMX, CPU, Memory• Event-based measurements – Are collected as a request occurs – Are transactional – Response Times, CPU consumption
    • 13. Typical Mesurements we work with• Memory – Consumption, GC• CPU – Usage, Load Average• Response Time – Transactions• Database – Statements, Pool Sizes• Communication – Calls, Latency, Size, Threads
    • 14. Types of Statistics• Min/Max• Average• Median• PercentilesUse percentiles for event-based measures andaverages for cyclical measures
    • 15. A Clearer Picture
    • 16. What is the problem?We haverResponse times of 6 seconds.We have response times of 6 seconds at a loadof 500 users with a CPU utilization of 10 percent.
    • 17. Increase Granularity
    • 18. 6 5Response Time (s) 4 3 2 1 0 100 200 300 400 500 600 700 800 900 Transactions
    • 19. 6 5Response Time (s) 4 3 Backend Java Frontend 2 Web Server 1 0 100 200 300 400 500 600 700 800 900 Transactions
    • 20. But before that ...
    • 21. Indentify the suspect
    • 22. Don‘t trust your assumptions
    • 23. Top 10 Optimizations are bad
    • 24. Seperate transaction types
    • 25. Baseline and Delta
    • 26. Understand the difference
    • 27. Make the problem reproducable. Otherwise you cannot check whether you fixed it.
    • 28. When are we done?
    • 29. ... when there are nomore why questions
    • 30. Solving the case
    • 31. Ensure you solved the real problem
    • 32. When a measure supports aproblem. Check for all measures affected by the problem.
    • 33. Have you tuned at the right place
    • 34. Fight problems not symptoms
    • 35. Watch out for side effects
    • 36. Alois Reitbaueralois.reitbauer@dynatrace.com @AloisReitbauer blog.dynatrace.com
    • 37. Download the latest Application Performance almanac:Web | Cloud | DevOps | Mobile | Automation | Tuningwww.dynatrace.com/almanac