HFM Application Design for Performance


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HFM application performance is a complex function of application design, data volume and quality, user process, and infrastructure design. Chris Barbieri reviews each of these aspects of system performance, explaining the relationship between application design and performance, along with updated application statistics to answer the question “what’s normal?” More than just statistics, Chris describes how HFM behaves according to variations in design.

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HFM Application Design for Performance

  1. 1. z al an rR Hyperion Financial Management ateApplication Design ew Performance g5,for Ed of November 2012 rty ro pe P Chris Barbieri Practice Director
  2. 2. Personal Background • Established HFM performance tuning techniques and statistics widely used today z al • 4+ years as Sr. Product Issues Manager atn a Hyperion rR ate – HFM, Smart View, Shared Services, MDM 2001 HFM launch team 2001 w ge • • Certified HFM, HyperionEd Enterprise of Boston College rty • B.S. Finance & Accounting, • pe roHFM Performance Tuning Lab at Ranzal MBA, Babson College • P Established •Copyright 2012 Edgewater Ranzal
  3. 3. Foundation of Performance • Hyperion Financial Management l • Metadata designaas it a nz R impacts performance at er members – Volume of –gew of structures Impact d E o f• Data rty – Content ro pe – Density P • Rules • EnvironmentCopyright 2012 Edgewater Ranzal
  4. 4. z al an rR Metadata ate gew f Ed y o rt ro pe PCopyright 2012 Edgewater Ranzal
  5. 5. 12 Standard Dimensions Application Profile User controlled Entity al nz 1. Year 5. a rR 6. Account 2. Period 7. te a ICP 3. View ew Scenario g 8. Ed User defined of System rty pe 4. Value dimension, 9. Custom 1 P ro includes 10. Custom 2 currencies 11. Custom 3 12. Custom 4Copyright 2012 Edgewater Ranzal
  6. 6. Application Profile Year – No inherent impact on performance – Can be increased after the application is built l n za – Impacts database table volume a Period rR ate whether you use them or notew – Base periods comprise column structure of every table, dgit is key to your entire fE – Avoid weekly profiles unless application’s designo rty pe – Yearly is inadvisable ro View P – No impact, but only YTD is stored – Other views are on-the-fly derivations • Consider the UI click volumeCopyright 2012 Edgewater Ranzal
  7. 7. System Dimension Value Dimension – Can not directly modify this al – “<Entity Currency>” points to entity’s default currency z an – “<Parent Currency>” points to default currency of the entity’s parent rR • Anything above “<Entity Curr Total>” must be Parent.Child format ate Currencies w ge – Don’t add currencies you aren’t using d • Sets of calc status records for (every entity * every currency) o fE • Impact of loading metadata with entity or currency changes rty – Normally translate from the entity’s currency only into it’s parent’s currency ro pe translations P – Beware of non-default • Impacted calc status • Data explosion • Adds to cycle timeCopyright 2012 Edgewater Ranzal
  8. 8. User Controlled Dimensions Entity – Sum of the data of the children al a nz each – Avoid Consolidate All or All With Data on rR ate hierarchy – Assign Adj flags sparingly w e dg fE • Don’t disable if you ever had journals on entity o rty ICP – “Hidden” pe o dimension Scenario Pr – Number of tablesCopyright 2012 Edgewater Ranzal
  9. 9. Impact of Account Depth z al an rR ate 4- Net Income 6- Net Income w 5- EBIT ge 3- Optg Income 2- Gross Margin f Ed 4- Optg Income y o rt 3- Gross Profit e 1- Sales Pr op 2- Gross Margin 1- Sales Effect is multiplied when you consider the custom dimensions Parent accounts don’t lockCopyright 2012 Edgewater Ranzal
  10. 10. User Defined Dimensions Custom 1..4 al – Think dozens or hundreds, but not thousands z an • If Thousands are necessary, 64 bit makes this possible rR ate • Rules remain a major factor in performance w ge – Avoid: d fE • Employees • Products o • Anything that y very dynamic, changing greatly from year rt is to year p e P roone relationship with the entities • One to Configurable dimensions in 2012 Edgewater Ranzal
  11. 11. Metadata Efficiency Ratio What does the average entity have in common with the top entity? al nz – Density measurement of re-use of the accounts and customs across all entities a rR ate ew top entity dg o fE rty ro pe base PCopyright 2012 Edgewater Ranzal
  12. 12. Metadata Volumes (Americas) 80 Applications Median +1 Std Deviation High Accounts 1,383 2,814 7,491 ICP Accounts With Plug 17 291 2,273 Accounts With Data Audit 26 1,356 7,490 Consolidation Rules 45% z al OrgBy Period 16% an Phased Submission 19% rR Consolidation Methods - ate 3 16 ew dg Currencies 25 57 150 fE Custom1 177 3,248 23,897 Custom2 o 67 2,397 20,484 rty pe Custom3 46 919 5,681 ro Custom4 19 184 1,199 Entity Hierarchies Entities (unique) P 672 4 12 4,242 44 21,199 ICP Members 200 1,161 7,770 Scenarios 10 27 81 Scenarios Using Process Management - 6 37 Scenarios Using Data Audit - 11 78Copyright 2012 Edgewater Ranzal
  13. 13. z al an rR Data ate gew f Ed y o rt ro pe PCopyright 2012 Edgewater Ranzal
  14. 14. What’s a Subcube? • HFM data storage unit • Database tables stored by z al – Each record contains all periods for the an [Year] e rR at – All records for a subcube are loaded into memory together w d ge o fE erty Parent subcube, op stored in DCN tables Pr Currency subcubes, stored in DCE tablesCopyright 2012 Edgewater Ranzal
  15. 15. Take it to the Limit Reports, Grids, or Forms that: – Pull lots of entities z al – Lots of years an rR – Lots of scenarios ate ew dg Not so problematic: – Lots of accounts f E o rty pe – Or Custom dimension members Smart Viewro P – Cell volume impacts bandwidth – Subcubes impact server performanceCopyright 2012 Edgewater Ranzal
  16. 16. Data Design al “Metadata volume is interesting, but it’s a nz how you it that er R matters most” at ew dg • Density o fE rty zeros • Content pe – Specifically: ro P – Tiny numbers – Invalid RecordsCopyright 2012 Edgewater Ranzal
  17. 17. Data Volume Measurement • No perfect method al nz Simple, easy to see Ra Method How- How-To Pros Cons er <Entity Currency> Data Extract Extract all data, Can only extract at count per entity input from calculated g ew average Can’t identify FreeLRU Parse HFM event E deasy to monitor individual cubes, Good sense of logs fcube, growth o monthly rty harder to understand ro pe Database Analysis P Query DCE, DCN tables and count Easy for a DBA, see all subcubes Doesn’t count dynamic members, includes invalid recordsCopyright 2012 Edgewater Ranzal
  18. 18. Data Density Using FreeLRU • Survey of data density using FreeLRU al method Number of applications reviewed: Median Min nz Max a +1 Std Dev rR 44 ate ew NumCubesInRAM 1,369 72 15,152 5,068 dg NumDataRecordsInRAM of E1,170,908 247,900 23,019,754 4,574,074 rty pe NumRecordsInLargestCube 53,089 2,508 593,924 169,272 Records per cube Pro 1,352 24 91,418 15,832 Metadata efficiency 3.4% 0.3% 39.7% 12.3%Copyright 2012 Edgewater Ranzal
  19. 19. Loaded vs. Consolidated Data • What percent of the loaded data is a zero value? – <5% is reasonable z al – No zeros are best an rR – Watch ZeroView settings on scenarios ate • Watch out for tiny values, from allocations ew dg • How much does the data expand from Sub fE Calculate? • How many zeros are oy generated by the rt pe consolidation process? ro P – Intercompany eliminations Consolidated 19.6% – Allocations Calculated 9.4% – Empty variables Loaded 0.9%Copyright 2012 Edgewater Ranzal
  20. 20. Growth Up the Entity Chain Level Number Records Top Entity 1 261,593 Average Subcube 814 z al 5,193 an rR e 516 at Base entities including calculated data 680 Base entities input data only ew 443 421 dg o fE rty ro pe Top 261,593 P Average 5,193 Base 421Copyright 2012 Edgewater Ranzal
  21. 21. Loaded, Calculated, and Consolidated • Rough stats: median from 10 applications Monthly l za Zeros Rules Monthly % an Growth Growth e rR Loaded Records 153,826 wat 4.1% 3.3% ge Ed of Loaded + Calculated Records rty 353,122 19.7% 2.7% 2.0 ro pe P Consolidated Records 63,432 6.9% 3.2% Total data for all base (or top) entities Can be easily managed by better rule writing!Copyright 2012 Edgewater Ranzal
  22. 22. Invalid Records • Type 1: Orphaned records from metadata that has been deleted al z an – Member is removed from dimension_Item table, but not rR ate from the data tables ew – These can be removed by Database > Delete Invalid Records g • Type 2: the member still Ed f exists, but is no longer in a valid o intersection ty r account Pro pe – Most often from changing CustomX Top Member on an – These cannot be removed by HFM, but are filtered out in memoryCopyright 2012 Edgewater Ranzal
  23. 23. How Much Memory Do I Need? Plan A Plan B Number of entities 814 814 al * 2 cubes: entity currency + contribution 1,628 1,628 z Non-USD entities Non- 483 483 an currency** add another cube for parent currency** 483 483 r R2 Entity_value cubes 2,111 2,111 ate ew Actual 2011, 2012 2 dg 4 Currency scenarios, 3 Estimate, 3 Forecast scenarios 3 10 fE Total Year_scenarios 5 12 o Total cubes 10,555 25,332 rty pe Average records per cube 5,193 5,193 ro Optimal MaxRecordsInRAM setting 54,812,115 131,549,076 bytes per record P 120 120 Records * bytes converted to MB = MaxDataCacheSizeInMB 6,273 15,055 low. ** Many entities are translated into other currencies as well, making this value low.Copyright 2012 Edgewater Ranzal
  24. 24. z al an rR Rules Timing ate gew f Ed y o rt ro pe PCopyright 2012 Edgewater Ranzal
  25. 25. Data Density <> Calc Time Average Rule Execution Time in Contrast with Data Volume 900 2.500 800 z al an 2.000 700 rR ate 600 1.500 Seconds Records 500 ew dg 400 1.000 fE 300 o rty 200 0.500 pe 100 ro - - P Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec correlation between density and calc times • Most applications are rules boundCopyright 2012 Edgewater Ranzal
  26. 26. S econds 0 50 100 150 200 250 300 350 400 450 83820.83820_D FR .FR _N B M A L_R E GION S .U S P Copyright 2012 Edgewater Ranzal U S C A .U S ro E ME A .D E pe A P .C N C Z .C Z_N B M rty D E _N B M.83704 o FR _N B M.83519 fE But Some are I/O Bound TH .83899 U S .U S GO U S .80808 ew B R .83545 elapsed ate3820.83820_1801 dg totalrecords OTH A P .82828 rR3820.83820_1851 an Time vs. Volume z E ME A .B E al LA .B R U S .80820 A R .83856 0 10,000 20,000 30,000 40,000 50,000 60,000
  27. 27. How Long Should Rules Take? al nz • Total consolidation time for all entities, 12 periods a rR • Divide by 12 periods and total number of entities w ate ge Ed Seconds Per of Entity rty pe 0 0.25 ro 2.0 P 4.0 10.0Copyright 2012 Edgewater Ranzal
  28. 28. Rules Impact Ratio • Total consolidation time al with rules a nz rR ate • Divided by time with Blank Rules ew E dg y of rt pe • Typically 2- 5 times o • More than rthat is an P opportunity for improvementCopyright 2012 Edgewater Ranzal
  29. 29. z al an rR Reference ate ew dg fE Application o rty ro pe PCopyright 2012 Edgewater Ranzal
  30. 30. Small but Constant Application 0:04:19 al Full Rules Blank Rules z 0:03:36 an rR ate 0:02:53 ew 0:02:10 dg 0:01:26 o fE rty pe 0:00:43 0:00:00 Pro physical physical virtual virtual virtual virtual virtual virtual HFM lab Cust E Ranzal dev T-61 laptop Cust A Cust B Cust C Cust D • Applied across multiple environmentsCopyright 2012 Edgewater Ranzal
  31. 31. z al an rR ate ew dg o fE rty ro pe PCopyright 2012 Edgewater Ranzal
  32. 32. z al an Chris Barbieri R at er cbarbieri@ranzal.com ew dg Needham, MA Needham, o fE rty USA ro pe +1.617.480.6173 P www.ranzal.comCopyright 2012 Edgewater Ranzal