Common Analysis Services Design Mistakes and How to Avoid Them Chris Webb www.crossjoin.co.uk

Transcription

Common Analysis Services Design Mistakes and How to Avoid Them Chris Webb www.crossjoin.co.uk
Common Analysis Services Design
Mistakes and How to Avoid Them
Chris Webb
www.crossjoin.co.uk
Who Am I?
• Chris Webb
[email protected]
• 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