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Perfmon and Profiler 101

Perfmon and Profiler 101. About Me: Brent Ozar. SQL Server Expert for Quest Software Former SQL DBA Managed >80tb SAN, VMware Dot-com-crash experience Specializes in performance tuning. Today’s Agenda. The Honda Civic and the Nissan GT-R Metrics, Trace, Mitigations A New Secret Weapon

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Perfmon and Profiler 101

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  1. Perfmon and Profiler 101

  2. About Me: Brent Ozar • SQL Server Expert for Quest Software • Former SQL DBA • Managed >80tb SAN, VMware • Dot-com-crash experience • Specializes in performance tuning

  3. Today’s Agenda • The Honda Civic and the Nissan GT-R • Metrics, Trace, Mitigations • A New Secret Weapon • Resources and Q&A

  4. If You Don’t Need to Go Fast… From http://flickr.com/photos/stevekeys/2755142278/

  5. But The Faster You Want To Go

  6. The More You Have To Measure

  7. Windows “Check Engine” Light

  8. And If You Wanna Go Fast:

  9. Two Approaches to Detection • Exceptions Monitoring: Check Engine • Proactive Monitoring: Detailed Gauges

  10. Where Do We Start?

  11. Capture Metrics With Perfmon • Performance Monitor, aka Perfmon • Ships with all Windows versions • Polls any server from your desktop • Pulls performance metrics • Writes them to a file • Requires some OS permissions • Does not include alerts or analytics

  12. 11

  13. Memory Counters • Memory – Available Mbytes • Paging File - % Usage • SQLServer:Buffer Manager – • Buffer cache hit ratio • Page life expectancy • SQLServer:Memory Manager – Memory Grants Pending

  14. Storage Metrics: Physical Disk • % Disk Time • Avg. Disk Queue Length • Avg. Disk sec/Read • Avg. Disk sec/Write • Disk Reads/sec • Disk Writes/sec

  15. CPU Metrics • Processor - % Processor Time • System – Processor Queue Length • SQLServer:General Statistics – User Connections (not CPU, just “other”)

  16. The Raw Output: CSV Files 16

  17. Adding Analytical Formulas 17

  18. That’s a Lot of Zeroes! 18

  19. Sorting High to Low 19

  20. What To Look For, In Order • System – Processor Queue Length • Memory – Available Mbytes • Lock pages in memory!

  21. What To Look For Next • Disk metrics on the page file drive • Disk metrics on the log file drive • Disk metrics on the data file drive • Disk metrics on the TempDB drive

  22. Got Everything on One Drive? • Narrow it down with the DMV sys.dm_io_virtual_file_stats

  23. What If We Could Data Mine It?

  24. Table Analysis Tools For The Cloud

  25. Detecting Categories of Load

  26. Capture Queries with a Trace

  27. Columns to Capture What’s Going On What The Impact Was CPU Reads Writes Duration Start Time End Time • Text Data • DatabaseID and/or DatabaseName • Login Name • Host Name • Application Name

  28. Profiler’s Results: A Trace Table

  29. Order By Duration Descending

  30. Casting and Grouping

  31. Another Cloud Analytics Candidate

  32. Another Way: Perf Dashboard

  33. Correlate Metrics & Trace • Show a cause and effect relationship • Fields to mentally “join” on: • Date/Time ranges • CPU • Reads/Writes • Duration

  34. Sample Problem #1 • Metrics tell us: • Very high disk queue lengths on data drive • Trace tells us: • Report queries doing table scans w/o indexes • Many scheduled reports run simultaneously

  35. Ways We Can Mitigate It • Add covering indexes • Modify existing indexes • Run reports serially, not all at once • Add hard drives to the data file array • Add memory to cache scanned tables

  36. Sample Problem #2 • Metrics tell us: • Page file drive queue lengths average >20 • Page file use averages >1% • Available memory averages <200mb • Buffer cache hit ratio andpage life expectancy are high • Trace tells us: • No unusual queries

  37. Memory Configuration

  38. Ways We Can Mitigate It • Add memory and enable AWE/PAE • Add memory and upgrade to 64-bit • Reduce SQL’s min/max memory sizes • Move the app to its own server

  39. Sample Problem #3 • Metric looks OK, but every 15 minutes: • Long drive queues on the log file drive • Page life expectancy drops near zero • Network traffic jumps • Trace tells us: • Transaction log backups are running

  40. Ways We Can Mitigate It • Stop doing log backups • Put the databases in simple mode • Add drives to the transaction log array • Throttle the transaction log backups

  41. Sample Problem #4 • Metrics tell us: • CPU average is high • Disk, memory look OK • Trace tells us: • Queries are using cursors • Operating on individual records, not sets

  42. How We Can Mitigate It • Change cursor to set-based query • Buy really fast processors • Spend a lot on licensing

  43. Wrapping Things Up • Double-check the event log first • Don’t get overwhelmed: focus with the Metric – Trace – Mitigation process • Show a clear cause and effect • Use cloud-based BI to get an edge

  44. Resources On The Web • My posts about Perfmon and analytics:www.BrentOzar.com/perfmonwww.BrentOzar.com/perfmoncloud • Excel Table Analysis Tools for the Cloud:www.SQLServerDataMining.com/cloud • SQL Server community:SQLServerPedia.com

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