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Performance Designs

Performance Designs. Designing for Specific Performance and Testing. Brendon Higgins CEng MBCS CITP NetApp User Group June 2009. Presenter. Brendon Higgins CEng MBCS CITP NetApp Certified Data Management Administrator Data Centre Services Engineer DLA Piper UK LLP Brendon.Higgins@dla.com.

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Performance Designs

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  1. Performance Designs Designing for Specific Performance and Testing Brendon Higgins CEng MBCS CITP NetApp User Group June 2009

  2. Presenter Brendon Higgins CEng MBCS CITP NetApp Certified Data Management Administrator Data Centre Services Engineer DLA Piper UK LLP Brendon.Higgins@dla.com Author Images used on web forums Insert filename here

  3. Why Bother Attending Learn about storage performance at the design stage based on a case study example. • How is performance measured • What performance should be expected • What performance is being delivered Insert filename here

  4. Introduction to DLA Piper Global Legal Firm • More than 8,000 people across more than 65 offices in 28 countries • $2,262,000,000 annual revenue • DLA Piper represents more than 140 of the top 250 Fortune 500 clients and nearly half of the FTSE 350 or their subsidiaries Insert filename here

  5. Overview of units of performance Storage Systems Throughput Capacity (GB) IOPS Latency Cost Cars Maximum Speed Capacity (cc) BHP 0-60 time Cost Insert filename here

  6. Warning – Maths! Insert filename here

  7. Storage - Mebibyte The megabyte (MB) unit is used in NetApp storage as a measure of capacity and it is based on the binary system 220 or 1,048,576 bytes Kilobyte 210 Megabyte 220 Gigabyte 230 Terabyte 240 Petabyte 250 Multiples of 1024 Insert filename here

  8. Throughput Throughput is a measure of the total data moved through a channel in a given time • Megabytes per second (MB/s) are the units used in this presentation • Standard S.I. prefixes indicating multiplication by 1,000 • 1,000 kilo • 1,000,000 mega • 1,000,000,000 giga • Duplex communication is assumed between the devices {two way street} Insert filename here

  9. Quoted Throughput It is important to understand the difference throughput quoted • maximum {theoretical | achievable | sustained} • peak measured • ‘good’ Guideline Figures – But what type of measure? Insert filename here

  10. Moving Data 10Gb 10 Gb 5 Minutes Insert filename here

  11. Moving Data 10 Gb = 10,240 MB 10,240 ÷ 300 = 34 MB per sec moved 10Gb 10 Gb 5 Minutes 34 MB/s Insert filename here

  12. Moving Data 10 Gb = 10,240 MB 10,240 ÷ 300 = 34 MB per sec moved 10Gb 10 Gb 5 Minutes 34 or 36 MB/s? But throughput is measured as MB/s ‘106’ 10,737,418,240 bytes ÷ 300 = 35,791,394 36 MB/s 288Mbps (8x for bits) Insert filename here

  13. Maths Error The classic mistake is to interchange throughput MB/s with binary MB 1,000,000 bytes vs 1,048,576 bytes 4.9% error margin Insert filename here

  14. Maths Error The classic mistake is to interchange throughput MB/s with binary MB 1,000,000 bytes vs 1,048,576 bytes 4.9% error margin Or worse, network megabits (Mb/s) when describing megabytes 131,072 vs 1,048,576 1/8 required value 87.5% error margin! Remember NASA’s Mars Polar Lander? - $125m craft destroyed after a mix-up between imperial and metric measurements Insert filename here

  15. Input/Output Operations Per Second Data Insert filename here

  16. ‘IOPS’ The measurement is taken as • Total number of IOPS • Average number of random read IOPS • Average number of random write IOPS • Average number of sequential read IOPS • Average number of sequential write IOPS Each IOP in a NetApp system is • between 4 KB (4,096 bytes) and 128KB • but can peak to 256KB Insert filename here

  17. Rotational latency Time in milliseconds (ms) required to move the head to the data Sequential vs Random Insert filename here

  18. Cost “What would you do if it was your money?” Insert filename here

  19. What performance should be expected Insert filename here

  20. Real World Performance Predicting the future and assumptions… • Guestimation for specification • Multiplying averages in design • Performance metrics of little value Beware FUD - Fear, Uncertainty, and Doubt Insert filename here

  21. iSCSI vs FCP A classic ‘conversation’ point for block level performance and a good illustration of FUD. Ask the person next to you, which they think is the best… There is no correct answer as it depends on the other components which make up the system and external factors such as cost. Calm down, calm down! Insert filename here

  22. Design Requirement As fast as possible… What else Insert filename here

  23. Case Study: Design Requirement The application supplier’s required specifications for the SQL server based on observed history using similar sized deployments. • 100 Gb of storage in the first year • Then growth to 500Gb over the next 5 years • At least 400 IOPS • At least 425 MB/s throughput • Latency not greater than 12 ms Insert filename here

  24. Theoretical performance characteristics of devices Competitive Advantage http://www.spec.org/ http://en.wikipedia.org/wiki/List_of_device_bandwidths Top Secret Insert filename here

  25. Graph of Disk Performance Random 4KB Workload - Can Vary 120 and below for 10k 220 and below for 15k 50-60 and below for SATA. Insert filename here

  26. Case Study: Options Single Filer 3 Shelves 42 Disks Dual Filers (Act./Act.) 3 Shelves - S/W Ownership 21 Disks per Filer Insert filename here

  27. NetApp Storage Recapitulation • All NetApp operations are made with 4Kb blocks • Data write latency is host to filer memory • Back to back CPs (CP generated CP) • Aggregate is physical unit • Only data disks affect performance Insert filename here

  28. Following Example Disk Configuration 1 Spare disk per filer 20 Disks in each aggregate RAID Group Size of 16 with 2 groups RAID – DP so a parity and double parity disk per raid group 16 Data disks available in each aggregate Insert filename here

  29. Calculating Data Rates in Aggregates 16x 300Gb 15K FC data disks in the aggregate  Each NetApp disk IOP is equal to either 4, 64 or 128 KB of storage The throughput for each IOP/s (1024 x 4) ÷ 1000 = 4.096KB/s Insert filename here

  30. Calculating Data Rates in Aggregates 16x 300Gb 15K FC data disks in the aggregate  Each NetApp disk IOP is equal to either 4, 64 or 128 KB of storage The throughput for each IOP/s (1024 x 4) ÷ 1000 = 4.096KB/s 16x data disks @ 220 IOPS = 3,520 IOPS Each IOPS @ 4.096, 65.536 or 131.072 KB/s Insert filename here

  31. Calculating Data Rates in Aggregates 16x 300Gb 15K FC data disks in the aggregate  Each NetApp disk IOP is equal to either 4, 64 or 128 KB of storage The throughput for each IOP/s (1024 x 4) ÷ 1000 = 4.096KB/s 16x data disks @ 220 IOPS = 3,520 IOPS Each IOPS @ 4.096, 65.536 or 131.072 KB/s 3,520 x 4.096 = 14,418 KBps or 14 MB/s 3,520 x 65.536 = 230,686.72 KBps or 231 MB/s 3,520 x 131.072 = 461,373.44 KBps or 461 MB/s Insert filename here

  32. What performance is being delivered ? Insert filename here

  33. Stress test the design and get host based IO reports A realistic simulation of the IO pattern of the application can be created using 3rd party tools. Two of the free tools available are: Microsoft’s SQLIO.exe from www.microsoft.com/downloads Intel’s IOMeter from www.iometer.org/ IOMeter also has a GUI Insert filename here

  34. SQLIO.exe: 8KB Sequential - Report 8 threads reading for 300 secs from file E:\SQLIOtest\sqlio_1v1f.dat using 8KB sequential IOs using specified size: 50000 MB for file: E:\SQLIOtest\sqlio_1v1f.dat CUMULATIVE DATA: throughput metrics: IOs/sec: 6837.39 MBs/sec: 53.41 latency metrics: Min_Latency(ms): 0 Avg_Latency(ms): 9 Max_Latency(ms): 1449 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2324+ %: 59 3 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 14 Insert filename here

  35. SQLIO.exe: 8KB Random - Report 8 threads reading for 300 secs from file E:\SQLIOtest\sqlio_1v1f.dat using 8KB random IOs using specified size: 50000 MB for file: E:\SQLIOtest\sqlio_1v1f.dat CUMULATIVE DATA: throughput metrics: IOs/sec: 3386.12 MBs/sec: 26.45 latency metrics: Min_Latency(ms): 0 Avg_Latency(ms): 18 Max_Latency(ms): 774 histogram: ms: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24+ %: 5 0 1 2 3 5 6 6 5 5 4 4 4 3 3 3 3 3 2 2 2 2 2 2 26 Insert filename here

  36. Filer Statistic Filer Sysstat Lun Status Statit FilerView Host Perfstat System Manager Operations Manager Others… Insert filename here

  37. NetApp System Manager Insert filename here

  38. Statit Disks – During 8k Stress Test disk ut% xfers ureads--chain-usecs writes--chain-usecs cpreads-chain-usecs /aggr4/plex0/rg0: 2c.16 1 3.59 0.69 1.00 6692 1.71 3.37 932 1.19 2.98 373 2c.17 2 4.14 0.69 1.00 15962 2.40 2.98 760 1.06 2.75 282 2c.18 95 205.80 203.85 1.98 9107 1.37 2.40 4256 0.58 2.09 7261 2c.19 93 202.93 201.56 1.98 9121 0.69 3.81 4051 0.69 2.42 3984 2c.20 95 206.12 204.91 1.98 8987 0.66 4.00 4890 0.55 2.00 7095 1b.21 94 207.78 206.54 1.98 9216 0.69 3.85 4980 0.55 2.67 5750 2c.22 93 203.09 201.77 1.98 8973 0.74 3.57 4180 0.58 2.23 6531 1b.32 95 208.44 206.81 1.99 9397 0.79 3.33 6350 0.84 1.78 5140 2c.33 95 209.55 207.78 1.98 9446 0.79 3.30 5768 0.98 2.08 6974 1b.34 94 203.64 202.08 1.98 9038 0.76 3.41 5677 0.79 1.90 6263 2c.35 95 207.94 206.44 1.98 9513 0.76 3.41 6566 0.74 2.11 7593 2c.36 95 208.60 207.10 1.98 9463 0.82 3.19 6636 0.69 2.54 5712 1b.37 96 212.27 210.97 1.98 9585 0.74 3.50 5500 0.55 1.81 9895 2c.38 95 208.68 207.17 1.98 9348 0.76 3.38 6143 0.74 2.00 6357 1b.48 95 207.17 206.01 1.98 9572 0.69 3.81 7949 0.47 2.06 7243 1b.49 94 206.09 204.83 1.98 9206 0.76 3.45 6440 0.50 2.00 5289 /aggr4/plex0/rg1: 1b.50 0 0.63 0.00 .... . 0.50 5.63 1150 0.13 4.80 833 1b.51 0 0.63 0.00 .... . 0.50 5.63 1243 0.13 6.60 576 1b.52 94 207.20 206.38 1.98 9032 0.50 5.21 4485 0.32 2.42 8759 1b.53 94 206.04 205.33 1.98 9297 0.42 6.13 3347 0.29 3.00 3152 Insert filename here

  39. Statit Throughput The sum of (Read Xfers x (Chain length x IOP Size)) + (Write Xfers x (Chain length x IOP Size)) + (CP Read Xfers x (Chain length x IOP Size)) For each data disk gives the throughput in KBps Insert filename here

  40. Measuring Data Rates Read Xfers = 203; Chains = 1.98 Write Xfers = 1.37; Chains = 2.4 CP Read Xfers = 0.58; Chains = 2.09 CP Read Xfer / Chains disk ut% xfers ureads--chain-usecs writes--chain-usecs cpreads-chain-usecs /aggr4/plex0/rg0: 2c.16 1 3.59 0.69 1.00 6692 1.71 3.37 932 1.19 2.98 373 2c.17 2 4.14 0.69 1.00 15962 2.40 2.98 760 1.06 2.75 282 2c.18 95 205.80 203.85 1.98 9107 1.37 2.40 4256 0.58 2.09 7261 2c.19 93 202.93 201.56 1.98 9121 0.69 3.81 4051 0.69 2.42 3984 2c.20 95 206.12 204.91 1.98 8987 0.66 4.00 4890 0.55 2.00 7095 1b.21 94 207.78 206.54 1.98 9216 0.69 3.85 4980 0.55 2.67 5750 2c.22 93 203.09 201.77 1.98 8973 0.74 3.57 4180 0.58 2.23 6531 1b.32 95 208.44 206.81 1.99 9397 0.79 3.33 6350 0.84 1.78 5140 2c.33 95 209.55 207.78 1.98 9446 0.79 3.30 5768 0.98 2.08 6974 1b.34 94 203.64 202.08 1.98 9038 0.76 3.41 5677 0.79 1.90 6263 2c.35 95 207.94 206.44 1.98 9513 0.76 3.41 6566 0.74 2.11 7593 2c.36 95 208.60 207.10 1.98 9463 0.82 3.19 6636 0.69 2.54 5712 1b.37 96 212.27 210.97 1.98 9585 0.74 3.50 5500 0.55 1.81 9895 2c.38 95 208.68 207.17 1.98 9348 0.76 3.38 6143 0.74 2.00 6357 1b.48 95 207.17 206.01 1.98 9572 0.69 3.81 7949 0.47 2.06 7243 1b.49 94 206.09 204.83 1.98 9206 0.76 3.45 6440 0.50 2.00 5289 /aggr4/plex0/rg1: 1b.50 0 0.63 0.00 .... . 0.50 5.63 1150 0.13 4.80 833 1b.51 0 0.63 0.00 .... . 0.50 5.63 1243 0.13 6.60 576 1b.52 94 207.20 206.38 1.98 9032 0.50 5.21 4485 0.32 2.42 8759 1b.53 94 206.04 205.33 1.98 9297 0.42 6.13 3347 0.29 3.00 3152 Write Xfer / Chains Read Xfer / Chains Insert filename here

  41. Measuring Data Rates Read Xfers = 203; Chains = 1.98 Write Xfers = 1.37; Chains = 2.4 CP Read Xfers = 0.58; Chains = 2.09 (203 x (1.98 x 4.096)) + (1.37 x (2.4 x 4.096)) + (0.58 x (2.09 x 4.096)) Gives the output per disk then multiply by 16 data disks Insert filename here

  42. Measuring Random Data Rates Read Xfers = 203; Chains = 1.98 Write Xfers = 1.37; Chains = 2.4 CP Read Xfers = 0.58; Chains = 2.09 (203 x (1.98 x 4.096)) + (1.37 x (2.4 x 4.096)) + (0.58 x (2.09 x 4.096)) Gives the output per disk then multiply by 16 data disks Throughput = 26,636 KBps or 26 MB/s sqlio v1.5.SG throughput metrics: IOs/sec: 3386.12 MBs/sec: 26.45 Insert filename here

  43. Measuring Serial Data Rates Read Xfers = 256; Chains = 3.7 Write Xfers = 0.74; Chains = 3.82 CP Read Xfers = 0.86; Chains = 2.48 (256 x (3.7 x 4.096)) + (0.74 x (3.82 x 4.096)) + (0.86 x (2.48 x 4.096)) Gives the output per disk then multiply by 16 data disks = 62,400 KBps or 62 MB/s* sqlio v1.5.SG throughput metrics: IOs/sec: 6837.39 MBs/sec: 53.41* NB: System was not idle during the test Insert filename here

  44. Calculating Worse Case Data Rate 16x data disks @ 220 IOPS = 3,520 IOPS Each IOPS @ 4.096KB/s 3,520 x 4.096 = 14,418 KBps or 14 MB/s A conservative numbers have been used for the IOPS and no estimate of ‘cache/chaining advantage’ has been made. Performance can only be higher if nothing has failed on the system. Insert filename here

  45. Case Study: Observed Results Insert filename here

  46. SQL IOPs Types • Sequential writes to Transaction Logs • Random reads from the Data Files Insert filename here

  47. A Week of Live Data Insert filename here

  48. Production System Once the new SQL server entered production the applications team began reporting performance issues which they believed to be caused by the storage. The previous slide showed the typical performance and throughput, which did not look like a storage bottleneck. This issue was resolved with another tool. Insert filename here

  49. SQL Server 2005 Waits and Queues Insert filename here

  50. Getting Help NetApp Forums and Communities sites Insert filename here

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