SQL Cat have a list of tips titled Top 10 Best Practices for Building a Large Scale Relational Data Warehouse.

Under section 4 - Design dimension tables appropriately they state:

Avoid partitioning dimension tables.

They don't mention why this should not be done, nor can I find anything on the web that explicilty points out why it's something to be avoided.

Why should I avoid partitioning dimension tables?

A more concrete example is provided below to help facilitate an answer, and hold a discussion about why partitioning should not be done in large relational data warehouses. I'm not looking for advice on improving the data model that is specific to the concrete example. If the example doesn't help in providing any extra insight into why partitioning dimensions should not be done, then please ignore it.

Example: you can use to reference in your answer as to why partitioned dimensions are a bad/suboptimal idea (if it helps you) ...

In our environment we have an Account dimension, this is partitioned on DateEffective and is loaded monthly. Some of our queries involve WHERE DateEffective >= @ReportDate, which seems to be a good candidate for partition elimination. Also if we need to reload the month's data we will delete an entire month's worth of data, which also seems it would benefit from table partitioning.

Update about our environment since posting question ...

The table mentioned above has non-aligned non-clustered indexes (investigated with the following Brent Ozar code).

    [db_name]               = isnull(db_name(s.database_id),db_name())
    ,[schema_name]          = object_schema_name(i.object_id,db_id())
    ,[object_name]          = o.name
    ,index_name             = i.name
    ,index_type_desc        = i.type_desc
    ,data_space_name        = ds.name
    ,data_space_type_desc   = ds.type_desc
    sys.objects as o

    inner join sys.indexes as i 
        on o.object_id = i.object_id

    inner join sys.data_spaces as ds 
        on ds.data_space_id = i.data_space_id

    left join sys.dm_db_index_usage_stats as s 
        on  i.object_id     = s.object_id 
        and i.index_id      = s.index_id
        and s.database_id   = db_id()
        o.type      = 'u'
    and i.type      in (1, 2)
    and o.object_id in
         select filter.object_id 
             select ob.object_id, ds.type_desc 
                sys.objects ob 
                inner join sys.indexes ind on ind.object_id = ob.object_id 
                inner join sys.data_spaces ds on ds.data_space_id = ind.data_space_id
             group by ob.object_id, ds.type_desc 
         ) as filter 
         group by filter.object_id 
         having count(*) > 1
order by
    [object_name] desc

This showed:

  • the clustered index on the partition scheme
  • 5 of 8 non-clustered indexes on the partition scheme
  • 3 of 8 non-clustered indexes on primary, the rows_filegroup
    • 1 of these was a unique, non-clustered index (for the sake of completeness: defined as a primary key non-clustered in the create table script in source control)

Another update

I found this answer by Remus Rusanu which shed some light on the complications with partitioned tables that would be relevant for dimensions.

His statements are block quoted with my interpretation using my example above

non-aligned indexes prevent efficient partition switch operations

So, we should attempt to align indexes when a table is partitioned. Partition switching is not even used (?possibly prevented?) to load the table in my example, as there are non-aligned indexes.

Using aligned indexes solves these issues, but brings its own set of problems, because this physical, storage design, option ripples into the data model

This would certainly seem to be the case with the example I've provided, and some changes would be required to implement aligned indexes.

Due to dimensions typically using surrogate keys as the primary key (a unique clustered index), this provides an ever increasing narrow key (i.e. small data size on disk). This is important because the B-tree seeks that occur when joining between dimensions and facts can occur faster. Further, the clustered index will be part of any non-clustered indexes created, which also prevents bloating the non-clustered index, creating more efficient index seeks/scans here also.

Why is this important?

aligned indexes mean unique constrains can no longer be created/enforced (except for the partitioning column)


all foreign keys referencing the partitioned table must include the partitioning key


this in turn requires that all tables referencing the partitioned table contain partitioning key column value ... in order to properly declare the foreign key constraint.

The impacts are ...

  • A DateEffective column would need to be added to every table referencing the account dimension in our environment. Implementing a DateEffective column on the fact tables we have is redundant, as this lookup is taken care of by our ETL process that loads the correct AccountID key value. Further, some facts are declared at a grain that is more selective than a date data type, of which DateEffective clearly is, making it more nonsensical to include this column in the fact tables (data model ripple effects).
  • A number of the non-clustered indexes would need to be changed to include the DateEffective column

However ...

  • Data warehouses typically don't have foreign key constraints implemented. A good answer on SO covers this.
  • Also, since 2008 edition, Sql Server has parallel bitmap filtered hash-joins available to optimize star joins (see: Optimizing Data Warehouse Query Performance Through Bitmap Filtering), and foreign keys aren't required for this optimization.
  • This would seem to point to it being ok to partition a dimension table, as the changes required are now "only" having to include the partition key into the non-aligned indexes, because the foreign key constraints issue is non existent in our environment (our ETL process manages this integrity).
  • Maybe a 'highly justify partitioning dimension tables' would be better stated. How big does the dimension table get? Keep in mind that article was written at a time when ram and CPU IOPS weren't cheap like today. Commented Nov 24, 2015 at 1:59
  • Care to elaborate why RAM and CPU would prevent one from partitioning a dimension specifically? Given it was only written 2 years ago I would assume there's a very good chance data warehouses out there are running on older hardware. Commented Nov 24, 2015 at 2:03
  • Regarding your acct dimension. Can an account have many rows? If not the way i read your post if the effective date could change that may lead to row migration. Or a delete and insert rather than an update. What is driving you partition your acct dimension? Size? Im struggling to grasp a dimension that's so big that it requires partitioning. Commented Nov 24, 2015 at 6:18
  • The account dimension is actually a financial contract (banking industry), the contract attributes are high in count, and many attributes have wide data types, and given the nature of the changes available to certain attributes it does change fairly often (almost every contract will have an update every month). Could the physical and logical data model be better? Absolutely, on both counts. But it is what it is, and given the central nature of it within the data model it would take a major work effort to change. It's just a concrete example for the purposes of facilitating an answer. Commented Nov 24, 2015 at 9:04
  • 2
    Here are just a couple of the potential downsides of partitioned tables. If you are not taking advantage of the primary benefits, such as partition switching, it might make sense to re-phrase the question as "are there any reasons that I should partition my specific dimension table in this case?". The only reason I see in the question is that you may have a lot of queries with WHERE DateEffective >= @ReportDate. Are these queries that you can't use a normal index for? Commented Mar 15, 2016 at 17:56

1 Answer 1


I suspect the advice is predicated on the likely utility of partitioning a dimension table. In a data warehouse, fact tables are good example of the adage, big data is medium data, plus time. Dimension tables don't have time (not really), and as a rule don't have useful partitioning properties.

Yours seems like a good example. Why is Accounts partitioned on DateEffective? "Because some reports select on that column" is not a sufficient answer. An index on that column would be the conventional solution, and has the advantage of not biasing the physical data structure.

However many accounts you have, your fact tables are at least 1-3 orders of magnitude larger. Your server is scaled to that proportion. Looking up accounts is a relatively trivial operation. On the face of it, it doesn't seem like a candidate for partitioning.

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.