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 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! 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 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 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 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 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 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 Cubes, dimensions and 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 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 etc etc etc

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 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 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 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 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 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 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? 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 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 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 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 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 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 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 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 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 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 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 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

Consequences: Unprocessed Aggregations Slow queries! (Again)

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

Thanks!

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 #SQLBITS