Tag Archives: security

How to restrict access to a given system or to specific data of that system

Hiding Dimension Details in Analysis Services

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An Analysis Services cube is usually accessed by a wide variety of people all of them having different roles in the business. For example Product Managers, Sales Representatives, Key Account Managers and so on. Sometimes it is necessary to hide detailed information of a given dimension or attribute from a certain user or role. In this post I will show you how this can be accomplished.

Lets take a simple example and assume we have the role “ProductManager” which must not be allowed to see any specific details of a single customer but must be able to see aggregation levels above the customer like Gender, Education, etc.

This can be accomplished quite easily by changing the security settings of our Product Manager role. We navigate to “Dimension Data” > Dimension “Customer” > Attribute “Customer”.
Next go to the “Advanced” tab and manually specify your Allowed Set as:
{[Customer].[Customer].[All Customers]}
Role_ProductManager

One important thing here is to uncheck “Enable Visual Totals” as shown above, otherwise you will not see any data at all! An end-user which owns the role ProductManager would see the following result in Excel:
PivotTable_ProductManager
He sees aggregated values on Education and Gender Level but can not see details of any single customer.

A scenario that’s a bit more complex to achieve is to hide not only one attribute but all attributes of a given dimension. Of course, we could use the approach as described above and do it for each and every attribute in the dimension. This would technically work, but its timely to implement and also hard to maintain (e.g. if new attributes are added). Ideally we only want to define it once in a single place and it should work for all attributes, existing and also new ones. We can make use auf Auto-Exists within our security role here. Auto-Exists basically reflect the security defined on one attribute to all other attributes in the same dimension. So if you are restricted to [Country]=”Austria” you can only see Cities in Austria and for Continents you would only see Europe but not America.

Having this in mind we could create a dummy-customer (which has no associated facts) on which we put our security. Instead of creating a dummy-customer manually in our relational DB we can also make use of the special UnknownMember which can be activated for any dimension. Doing this SSAS automatically creates a dummy-member for us which can be used for our security purposes:
Dimension_UnknownMember
Note that we have set the UnkownMemberName to “No Details Available” to inform the user that there are no details available for him/her.
The role itself would then specify the UnknownMember in the Allowed Set as:
{[Customer].[Customer].UNKNOWNMEMBER}
Role_UnknownMember
Again, make sure that “Enable Visual Totals” is unchecked!

Now our Excel looks as follows:
PivotTable_UnknownMember
(Note: “Show items with no data on rows” was enabled in PivotTable options here)

Using any attribute or hierarchy of our secured dimension in a filter would show this result:
Filter_UnkownMember

One last option you may consider is to set the Visibility of the UnknownMember to “Hidden” thus also hiding the UnknownMember and only revealing the All-members which was our initial requirement.  However, I think using the UnknownMember’s name to provide some further information for the end-user makes quite sense e.g. naming it “No Details Available due to Security Restrictions” or “Restricted by Security”.
This is of course not possible if you create your dummy-customer manually that’s why I prefer using the UnkownMember.

Note1:
During my tests I came across a bug which when you change the Visibility of the UnkownMember after you have already defined security makes you end up seeing all details. Even a redeploy or ProcessFull do not solve the problem. What you need to do is to explicitly change the definition of the role (e.g. by adding a blank to the Allowed Set) and redeploy the role afterwards.

Note2:
None of the above works for SSAS tabular as it does not have the concept of (disabled) VisualTotals which is essential for this solution to work!

Analysis Services Security: Multiple Roles in Tabular vs. Multidimensional – Part 2

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In one of my recent posts I highlighted how multiple security roles are handled in tabular opposed to multidimensional Analysis Services models. In this post I will go into more detail and focus on how the security is evaluated for both models.

I would like to continue using the same example as in the last post with the following roles:
“Bikes” – is restricted to [ProductCategory] = “Bikes”
“Brakes” – is restricted to [ProductSubCategory] = “Brakes”
“DE” – is restricted to [Country] = “DE”

I used the following MDX query to test both, the tabular and the multidimensional model:

  1. SELECT
  2. NON EMPTY {[Geography].[Country Region Name].[Country Region Name].MEMBERS} ON 0,
  3. NON EMPTY {[Product].[Product Category Name].[Product Category Name].MEMBERS} ON 1
  4. FROM [Model]
  5. WHERE (
  6. [Measures].[Reseller Total Sales]
  7. )

The last thing I mentioned was how the combination of roles “Bikes” and “DE” could be expressed in SQL:

  1. SELECT
  2.     [ProductCategory],
  3.     [Country],
  4.     SUM([Reseller Sales])
  5. FROM <table>
  6. WHERE [ProductCategory] = ‘Bikes’
  7.     OR [Country] = ‘Germany’
  8. GROUP BY
  9.     [Product Category],
  10.     [Country]

 

When running the previous MDX query on our tabular model using roles “Bikes” and “DE” we will see a very similar query being executed against our xVelocity Storage Engine:
(Note: There are also some SE engine queries executed to get the elements for rows and columns but the query below is the main query)

  1. SELECT
  2. [Geography_1fa13899-0770-4069-b7cb-5ddf22473324].[EnglishCountryRegionName],
  3. [Product_11920c93-05ae-4f1c-980e-466dfbcfca2a].[CalculatedColumn1 1],
  4. SUM([Reseller Sales_fc635e72-28dc-4156-80d5-43b805f8df1c].[SalesAmount])
  5. FROM [Reseller Sales_fc635e72-28dc-4156-80d5-43b805f8df1c]
  6. LEFT OUTER JOIN [Reseller_d52b9c6f-8d2d-4e23-ae4c-2fc57c1d968a]
  7.         ON [Reseller Sales_fc635e72-28dc-4156-80d5-43b805f8df1c].[ResellerKey]
  8.             =[Reseller_d52b9c6f-8d2d-4e23-ae4c-2fc57c1d968a].[ResellerKey]
  9. LEFT OUTER JOIN [Geography_1fa13899-0770-4069-b7cb-5ddf22473324]
  10.     ON [Reseller_d52b9c6f-8d2d-4e23-ae4c-2fc57c1d968a].[GeographyKey]
  11.         =[Geography_1fa13899-0770-4069-b7cb-5ddf22473324].[GeographyKey]
  12. LEFT OUTER JOIN [Product_11920c93-05ae-4f1c-980e-466dfbcfca2a]
  13.     ON [Reseller Sales_fc635e72-28dc-4156-80d5-43b805f8df1c].[ProductKey]
  14.         =[Product_11920c93-05ae-4f1c-980e-466dfbcfca2a].[ProductKey]
  15. WHERE
  16. (COALESCE((PFDATAID( [Product_11920c93-05ae-4f1c-980e-466dfbcfca2a].[CalculatedColumn1 1] ) = 6))
  17.     OR
  18. COALESCE((PFDATAID( [Geography_1fa13899-0770-4069-b7cb-5ddf22473324].[CountryRegionCode] ) = 5)));

Well, not really readable so lets make it a bit nicer by removing those ugly GUIDs, etc:

  1. SELECT
  2.     [Geography].[EnglishCountryRegionName],
  3.     [Product].[ProductCategory],
  4.     SUM([Reseller Sales].[SalesAmount])
  5. FROM [Reseller Sales]
  6. LEFT OUTER JOIN [Reseller]
  7.     ON [Reseller Sales].[ResellerKey] = [Reseller].[ResellerKey]
  8. LEFT OUTER JOIN [Geography]
  9.     ON [Reseller].[GeographyKey] = [Geography].[GeographyKey]
  10. LEFT OUTER JOIN [Product]
  11.     ON [Reseller Sales].[ProductKey] = [Product].[ProductKey]
  12. WHERE [Product].[ProductCategory] = "Bikes"
  13. OR [Geography].[CountryRegionCode] = "Germany";

This looks very similar to our SQL query. The special thing about it is the WHERE clause which combines the restrictions of both roles using OR which is then propagated also to our [Reseller Sales] fact table and that’s the reason why we see what we want and expect to see – all sales that were either made with “Bikes” OR made in “Germany”:
MultipleRoles_tabular

Another very important thing to note and remember here is that the security restrictions get propagated into and are evaluated within the query. This is done for each and every query (!) which is usually not a problem but may become crucial if you use dynamic security.
To test this with dynamic security I introduced a new role called “CustData” which is going to replace our “Bikes” for this test. It is restricted on table ‘Product’ as:

  1. =([Product Category Name] = IF( CUSTOMDATA() = "1", "Bikes", "Clothing"))

So instead of using the connectionstring “…;Roles=Bikes,DE; …” I now used “…;Roles=CustData,DE;CustomData=1; …” which results in the exact same results of course. However, the query now changed to the following (already beautified) xVelocity query:

  1. SELECT
  2.     [Geography].[EnglishCountryRegionName],
  3.     [Product].[ProductCategory],
  4.     SUM([Reseller Sales].[SalesAmount])
  5. FROM [Reseller Sales]
  6. LEFT OUTER JOIN [Reseller]
  7.     ON [Reseller Sales].[ResellerKey] = [Reseller].[ResellerKey]
  8. LEFT OUTER JOIN [Geography]
  9.     ON [Reseller].[GeographyKey] = [Geography].[GeographyKey]
  10. LEFT OUTER JOIN [Product]
  11.     ON [Reseller Sales].[ProductKey] = [Product].[ProductKey]
  12. WHERE [Product].$ROWFILTER IN '0x000000…000000000000000000000fffff00000000000ffffffff'));
  13. OR [Geography].[CountryRegionCode] = "Germany";

Instead of using a direct filter on [ProductCategory] we now see a filter on $ROWFILTER ?!?

### ASSUMPTIONS START ###
I have to admit that I am currently not entirely sure what this means but I assume the following:
Preceding the main query another xVelocity query is executed which is important for us:

  1. SELECT
  2. [Product].[RowNumber],
  3. [Product].[ProductCategory],
  4. COUNT()
  5. FROM [Product];

This query fetches each [RowNumber] and its associated [ProductCategory]. Internally the [RowNumber] column is created for every table. This is related to the columnar storage that xVelocity uses. Elaborating this in detail would go far beyond the context of this blog post. For more details on the RowNumber-column please refer too http://msdn.microsoft.com/en-us/library/hh622558(v=office.15).aspx which describes the Excel data model which is actually Power Pivot and therefore also applies to Tabular. (In general this link contains a lot of in-depth information on the tabular data model and the columnar storage concepts!)

I further assume that our security-expression is then evaluated against this temporary table to create an bitmap index of which rows match the security-expression and which don’t. This result is then applied to our main query which using the WHERE clause  [Product].$ROWFILTER IN ‘0x0000….’
For all subsequent queries the above query on [RowNumber] and [ProductCategory] is not executed again so I assume that the bitmap index gets cached internally by Analysis Services. I further assume that if the bitmap index gets cached it is also shared between users belonging to the same role which would be similar to multidimensional models.
### ASSUMPTIONS END ###

So the important take-away for tabular is that the security gets propagated into the query and down to the fact table. Combining multiple roles on this level using OR delivers the expected results.

 

For multidimensional models this is slightly different. You can define security on either the Database Dimension (which gets inherited down to all Cube Dimension) or you can define security on the Cube Dimension directly. Defining security on the Database Dimension already makes it very clear that the security restrictions are independent of any fact table/measure group. A Database Dimension may be used in several cubes so the engine cannot know in advance which measure group to use. Security for multidimensional models is defined on the multidimensional space defined by that dimension. If one role is not restricted on a dimension at all, the user will always see the whole dimension and its associated values, even if a second role would restrict that dimension. And this causes unexpected results as the user may see the whole cube.
In terms of SQL the query could be expressed as:

  1. SELECT
  2.     [ProductCategory],
  3.     [Country],
  4.     SUM([Reseller Sales])
  5. FROM <table>
  6. WHERE ( [ProductCategory] = 'Bikes' OR 1 = 1)
  7.     OR ( 1 = 1  OR [Country] = 'Germany')
  8. GROUP BY
  9.     [Product Category],
  10.     [Country]

The left side of the inner OR statements represents the role “Bikes” whereas the right part represents the “DE” role. It should be very obvious that due to the combination of both roles you finally see everything without any restriction!

Another important thing to point out is that security for multidimensional models is only evaluated once and not for every query. So even if you have complex dynamic security its evaluation only hits you once for the first user that connects to a role and is cached for all queries of that user and also shared with other users belonging to that role.

 

I hope this gives some more insights on how tabular and multidimensional handle multiple roles and their fundamental differences!

Analysis Services Security: Multiple Roles in Tabular vs. Multidimensional

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In terms of security tabular and multidimensional models of SQL Server Analysis Services are very similar. Both use Roles to handle specific security settings. Those Roles are then assigned to users and groups.  This is very trivial if a user only belongs to one Role – the user is allowed to see what is specified in the Role. But it can become very tricky if a user belongs to more than one role.

For both, tabular and multidimensional security settings of multiple roles are additive. This means that you will not see less if you are assigned multiple roles but only more. Lets assume we have the following Roles:
“Bikes” – is restricted to [ProductCategory] = “Bikes”
“Brakes” – is restricted to [ProductSubCategory] = “Brakes”
“DE” – is restricted to [Country] = “DE”

A user belonging to Roles “Bikes” and “Brakes” will see all products that belong to “Bikes” and all products that belong to “Brakes”. This is OK and returns the expected results as  both roles secure the same dimension/table. This applies to tabular and also multidimensional.

However, if roles secure different dimensions/tables it gets a bit more tricky. A user may belong to Roles “Bikes” and “DE”. For multidimensional this is a real problem as it finally result in the user seeing the whole cube! This is “by design” and can be explained as follows:
Role “Bikes” does not have any restriction on [Country] so all countries are visible, Role “DE” has no restriction on [ProductCategory] so all product categories are visible. As Roles are additive the user is allowed to see all countries and also all product categories, hence the whole cube:
MultipleRoles_multidimensional

Basically you would expect to see “Bikes”-sales for all countries and “Germany”-sales for all product categories but you end up seeing much more than this. If you have every faced this problem in real life you know that this is probably not the intended behavior your customers want to see. Usually Active Directory Groups are used and assigned to SSAS roles, so this can happen quite easily without anyone even noticing (except the user who is happy to see more Smile )!
Chris Webb wrote an excellent blog post on how to deal with those kinds of security requirements in multidimensional models here.

For tabular this behavior is somehow similar. A user belonging to both roles is also allowed to see all countries and all product categories – this is because security settings are additive, same as for multidimensional. Even though this is true in terms of the structure (rows, columns) of the query we still get a different result in terms of values!
Here is the same query on a tabular model with the same security settings:
MultipleRoles_tabular

This is exactly what we and our customers would expect to see – all sales for “Germany” and also every sale related to “Bikes”! In tabular models security applied to a given table cascades down to all related tables – in this case to our fact table. If a table is indirectly secured by different Roles which put security on different tables those restrictions are combined using OR. In terms of SQL this could be expressed as:

  1. SELECT
  2.     [ProductCategory],
  3.     [Country],
  4.     SUM([Reseller Sales])
  5. FROM <table>
  6. WHERE [ProductCategory] = 'Bikes'
  7.     OR [Country] = 'Germany'
  8. GROUP BY
  9.     [Product Category],
  10.     [Country]

Further pivoting the result would show the same as the MDX query.

Thinking back to some of my multidimensional cubes where I had to deal with multiple Roles this “slight difference” would have been really handy and would have saved me a lot of time that I put into custom security solutions using bridge tables, assemblies, etc.

In my next post I will go into much more detail on how the tabular security model works so stay tuned!

UPDATE: Part 2 can be found here