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Common Analysis Services Design Mistakes and How to Avoid Them. Chris Webb Who Am I?. Chris Webb Independent Analysis Services and MDX consultant and trainer SQL Server MVP Blogger: Agenda.

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Presentation Transcript
who am i
Who Am I?
  • Chris Webb
  • Independent Analysis Services and MDX consultant and trainer
  • SQL Server MVP
  • Blogger:
  • Why good cube design is a Good Thing
  • Using built-in best practices in BIDS
  • ETL in your DSV
  • User-unfriendly names
  • Unnecessary attributes
  • Parent/child pain
  • One cube or many?
  • Over-reliance on MDX
  • Unused and/or unprocessed aggregations
why good design is important
Why Good Design is Important!
  • As if you needed reasons…?
  • Good design = good performance = faster initial development = easy further development = simple maintenance
  • This is not an exhaustive list, but a selection of design problems and mistakes I’ve seen on consultancy engagements
best practices in bids
Best Practices in BIDS
  • Don’t ignore the blue squiggly lines in BIDS!
    • They sometimes make useful recommendations about what you’re doing
  • Actively dismissing them, with comments, is a useful addition to documentation
  • As always, official ‘best practices’ aren’t always best practices in all situations
common design mistakes
Common Design Mistakes
  • Three questions need to be asked:
    • What’s the problem?
    • What bad things will happen as a result?
    • What can I do to fix it (especially after I’ve gone into production)?
  • This is not a name-and-shame session!
problem etl in your dsv
Problem: ETL in your DSV
  • It’s very likely, when you are working in SSAS, that you need changes to the underlying relational structures and data
    • Eg you need a new column in a table
  • You then have two options:
    • Go back to the relational database and/or ETL and make the change
    • Hack something together in the DSV using named queries and named calculations
  • The DSV is the easy option, but…
consequences etl in your dsv
Consequences: ETL in your DSV
  • It could slow down processing performance
    • No way to influence the SQL that SSAS generates
    • Expensive calculations/joins are better done once then persisted in the warehouse; you may need to process more than once
  • It makes maintenance much harder
    • DSV UI is not great for writing SQL
    • Your DBA or warehouse developer certainly won’t be looking at it
fix etl in your dsv
Fix: ETL in your DSV
  • Bite the bullet and either:
    • Do the necessary work in the underlying tables or ETL packages
    • Create a layer of views instead of using named queries and calculations
  • Use the Replace Table With option to point the table in the DSV at your new view/table
  • No impact on the rest of the cube!
problem unfriendly names
Problem: Unfriendly Names
  • Cubes, dimensionsand hierarchies need to have user-friendly names
  • However names are often user-unfriendly
    • Unchanged from what the wizard suggests, or
    • Use some kind of database naming convention
  • Designing a cube is like designing a UI
  • Who wants a dimension called something like “Dim Product”….?
consequences unfriendly names
Consequences: Unfriendly Names
  • Unfriendly names put users off using the cube
    • These are the names that users will see in their reports, so they must be ‘report ready’
    • Users need to understand what they’re selecting
  • Also encourage users to export data out of cube to ‘fix’ the names
    • And so you end up with stale data, multiple versions of the truth etcetcetc
fix unfriendly names
Fix: Unfriendly Names
  • You can rename objects easily, but:
    • This can break calculations on the cube
    • It can also break existing queries and reports, which will need rewriting/rebuilding
    • IDs will not change, which makes working with XMLA confusing
  • You should agree the naming of objects with end users before you build them!
problem unnecessary attributes
Problem: Unnecessary Attributes
  • Wizards often generate attributes on dimensions that users don’t want or need
  • Classic example is an attribute built from a surrogate key column
    • Who wants to show a surrogate key in a report?
consequences unnecessary attributes
Consequences: Unnecessary Attributes
  • The more attributes you have:
    • The more cluttered and less useable your UI
    • The slower your dimension processing
    • The harder it is to come up with an effective aggregation design
fix unnecessary attributes
Fix: Unnecessary Attributes
  • Delete any attributes that your users will never use
  • Merge attributes based on key and name columns into a single attribute
  • Set AttributeHierarchyEnabled to false for ‘property’ attributes like email addresses
  • Remember that deleting attributes that are used in reports or calculations can cause more problems
problem parent child hierarchies
Problem: Parent Child Hierarchies
  • Parent Child hierarchies are the only way to model hierarchies where you don’t know the number of levels in advance
  • They are also very flexible, leading some people to use them more often than they should
consequences parent child
Consequences: Parent Child
  • Parent Child hierarchies can lead to slow query performance
    • No aggregations can be built at levels inside the hierarchy
    • Slow anyway
  • They can also be a nightmare for
    • Scoping advanced MDX calculations
    • Dimension security
fix parent child
Fix: Parent Child
  • If you know, or can assume, the maximum depth of your hierarchy, there’s an alternative
  • Normal user hierarchies can be made ‘Ragged’ with the HideMemberIf property
    • Hides members if their parent has no name, or the same name as them
  • Still has performance issues, but less than parent/child
  • You can use the BIDS Helper “parent/child naturaliser” to convert the underlying relational table to a level-based structure
problem one cube or many
Problem: One Cube or Many?
  • When you have multiple fact tables do you create:
    • One cube with multiple measure groups?
    • Multiple cubes with one measure group?
  • Each has its own pros and cons that need to be understood
consequences one cube
Consequences: One Cube
  • Monster cubes containing everything can be intimidating and confusing for users
  • Also tricky to develop, maintain and test
    • Often changing one thing breaks another
    • Making changes may take the whole cube offline
  • Securing individual measure groups is a pain
  • If there are few common dimensions between measure groups and many calculations, query performance can suffer
consequences multiple cubes
Consequences: Multiple Cubes
  • If you need to analyse data from many cubes in one query, options are very limited
  • A single cube is the only way to go if you do need to do this
  • Even if you don’t think you need to do it now, you probably will do in the future!
fix one cube to multiple
Fix: One Cube to Multiple
  • If you have Enterprise Edition, Perspectives can help overcome usability issues
  • Linked measure groups/dimensions can also be used to split out more cubes for security purposes
  • If you have one cube, you probably don’t want to split it up though
fix multiple cubes to one
Fix: Multiple Cubes to One
  • Start again from scratch!
  • LookUpCube() is really bad for performance
  • Linked measure groups and dimensions have their own problems:
    • Duplicate MDX code
    • Structural changes require linked dimensions to be deleted and recreated
problem over reliance on mdx
Problem: Over-reliance on MDX
  • As with the DSV, it can be tempting to use MDX calculations instead of making structural changes to cubes and dimensions
  • A simple example is to create a ‘grouping’ calculated member instead of creating a new attribute
  • Other examples include pivoting measures into a dimension, or doing m2m in MDX
consequences over reliance on mdx
Consequences: Over-reliance on MDX
  • MDX should always be your last resort:
  • Pure MDX calculations are always going to be the slowest option for query performance
  • They are also the least-easily maintainable part of a cube
  • The more complex calculations you have, the more difficult it is to make other calculations work
fix over reliance on mdx
Fix: Over-reliance on MDX
  • Redesigning your cube is a radical option but can pay big dividends in terms of performance
  • Risks breaking existing reports and queries but your users may be ok with this to get more speed
problem unused aggregations
Problem: Unused Aggregations
  • Aggregations are the most important SSAS feature for performance
  • Most people know they need to build some and run the Aggregation Design Wizard…
  • …but don’t know whether they’re being used or not
consequences unused aggregations
Consequences: Unused Aggregations
  • Slow queries!
  • If you haven’t built the right aggregations, then your queries won’t get any performance benefit
  • You’ll waste time processing these aggregations, and waste disk space storing them
fix unused aggregations
Fix: Unused Aggregations
  • Design some aggregations!
  • Rerun the Aggregation Design Wizard and set the Aggregation Usage property appropriately
  • Perform Usage-Based Optimisation
  • Design aggregations manually for queries that are still slow and could benefit from aggregations
problem unprocessed aggregations
Problem: Unprocessed Aggregations
  • Even if you’ve designed aggregations that are useful for your queries, you need to ensure they’re processed
  • Running a Process Update on a dimension will drop all Flexible aggregations
fix unprocessed aggregations
Fix: Unprocessed Aggregations
  • Run a Process Default or a Process Index on your cube after you have run a Process Update on any dimensions
  • Note that this will result in:
    • Longer processing times overall
    • More disk space used
  • But it will at least mean that your queries run faster
coming up


The Developer Side of the Microsoft Business Intelligence stack

Sascha Lorenz


Understanding SARGability (to make your queries run faster)

Rob Farley


Notes from the field: High Performance storage for SQL Server

Justin Langford


Service Broker: Message in a bottle

Klaus Aschenbrenner


Save the Pies for Lunch - Data Visualisation Techniques with SSRS 2008

Tim Kent

Coming up…