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Common Analysis Services Design Mistakes and How to Avoid Them. Chris Webb www.crossjoin.co.uk. Who Am I?. Chris Webb chris@crossjoin.co.uk Independent Analysis Services and MDX consultant and trainer SQL Server MVP Blogger: http://cwebbbi.spaces.live.com. Agenda.

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Presentation Transcript
who am i
Who Am I?
  • Chris Webb chris@crossjoin.co.uk
  • Independent Analysis Services and MDX consultant and trainer
  • SQL Server MVP
  • Blogger: http://cwebbbi.spaces.live.com
agenda
Agenda
  • 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

P/X001

The Developer Side of the Microsoft Business Intelligence stack

Sascha Lorenz

P/L001

Understanding SARGability (to make your queries run faster)

Rob Farley

P/L002

Notes from the field: High Performance storage for SQL Server

Justin Langford

P/L005

Service Broker: Message in a bottle

Klaus Aschenbrenner

P/T007

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

Tim Kent

Coming up…

  • #SQLBITS