Data warehouse administrators often want to give different users access to different subsets of the data. The OLAP model is a powerful paradigm for exploring data, and this paradigm also allows complex access-control rules to be expressed easily.

Mondrian already implements a rich set of access-control primitives (see mondrian schema guide), but we are considering some features to make mondrian more expressive in future releases.

Introduction: Access control in mondrian-2.4

In the current version of mondrian, roles are defined by <role> elements in the mondrian schema file. A role can have access granted, or denied, for cubes, hierarchies, and members of hierarchies.

The type of access granted to an object is inherited by its children. For example, if you deny access to a schema, then access to cubes in that schema will be denied by default, but you can override by granting access to specific cubes.

Most objects have just ‘all’ or ‘none’ access, but there is finer-grained access for hierarchies. If a role has ‘custom’ access to a hierarchy, it can spell out specific the members it has access to.

Mondrian does not perform authentication, or have a concept of a user. It is the responsibility of the container (usually a webserver or application server) to check that the user is allowed to access the mondrian instance, and to provide mondrian with a role under which the user should execute queries.

Deficiencies in access-control

There are several deficiencies in mondrian’s current access control model. Here are some of the major ones:

  • Partial cells. Need a policy where if a role can only see some of the children of a member, the total for that member includes only accessible children.
  • Hidden cells. Need a policy where if a role can only see some of the children of a member, the total for that member is not accessible.
  • Union roles. If a user belongs to more than one role, there is no easy way to run queries in the combined privileges of the roles.
  • Intrinsic roles. Need a way to define which members a role has access to using a rule rather than an explicit list of members in the schema file.
  • External roles. Need a way to define roles outside of the schema file, say in a database table.

Rollup policy

This proposed feature covers 1 ‘Partial cells’ and 2 ‘Hidden cells’ above.

Suppose that Fred belongs to a role that can see [USA].[CA] and [USA].[OR] but not [USA].[WA]. Fred runs the query

SELECT {[Measures].[Unit Sales]} ON COLUMNS,
  {[[Store].[USA], Store].[USA].Children} ON ROWS
FROM [Sales]

Under the current access-control policy, called ‘full’, the query returns

           | Unit Sales
[USA]      |    266,773
[USA].[CA] |     74,748
[USA].[OR] | 67,659

Note that [USA].[WA] is not returned, per the access-control policy, but the total includes the total from Washington (124,366) that Fred cannot see. For some applications, this is not appropriate. In particular, if the dimension has a small number of members, the end-user may be able to deduce the values of the members which they do not have access to.

To remedy this, a role can apply a ‘rollup policy’ to a hierarchy. The policy describes how a total is calculated for a particular member if the current role can only see some of that member’s children:

  • Full. The total for that member includes all children. This is the only policy today, and will remain the default policy.
  • Partial. The total for that member includes only accessible children.
  • *Hidden. If any of the children are inaccessible, the total is hidden.

Results under ‘partial’ policy:

           | Unit Sales
[USA]      |    142,407
[USA].[CA] |     74,748
[USA].[OR] |     67,659

Results under ‘hidden’ policy:

           | Unit Sales
[USA]      |          -
[USA].[CA] |     74,748
[USA].[OR] |     67,659

The policy is specified per role and hierarchy. In the following example, the role sees partial totals for the [Store] hierarchy but full totals for [Product].

<Role name="South Pacific manager">
  <SchemaGrant access="none">
    <CubeGrant cube="Sales" access="all">
      <HierarchyGrant hierarchy="[Store]" access="custom"
          rollupPolicy="partial" topLevel="[Store].[Store Country]">
        <MemberGrant member="[Store].[USA].[CA]" access="all"/>
        <MemberGrant member="[Store].[USA].[CA].[Los Angeles]" access="none"/>
      <HierarchyGrant hierarchy="[Customers]" access="custom"
          rollupPolicy="full" topLevel="[Customers].[State Province]"
        <MemberGrant member="[Customers].[USA].[CA]" access="all"/>
        <MemberGrant member="[Customers].[USA].[CA].[Los Angeles]"
      <HierarchyGrant hierarchy="[Gender]" access="none"/>

This example also shows existing features, such as how hierarchy grants can be restricted using topLevel and/or bottomLevel attributes, and how a role can be prevented from seeing a hierarchy using access="none". See the schema guide for a description of these features.

It is important that Mondrian applies access-control transparently. Within a particular role and access-control scheme, the value of a particular cell is always the same; it is not possible to circumvent access-control by defining calculations, for instance.

Intrinsic roles

Currently mondrian roles are extrinsic: they describe explicitly the members which they can see. If the number of members is large, or the set of members changes often, it is more convenient to define the members by their properties.

An intrinsic role defines which members it can see according to the properties of the member. For example, this role can see only products whose color is red.

<Role name="Red Part manager">
  <SchemaGrant access="none">
    <CubeGrant cube="Sales" access="all">
      <HierarchyGrant hierarchy="[Product]" access="custom">
        <MemberGrant level="[Product].[Product Name]" access="all"/>
            Filter([Product].[Product Name].Members,
                   [Product].Properties("Color") = 'red')

As before, parent members are visible if one of their children is visible, and the values of cells for those members are determined by the rollup policy.

Combining roles

Many systems have an access-control model where a user belongs to many roles simultaneously; when a user attempts an action, such as accessing a file or executing a query, the system either selects the appropriate role or uses the sum of the privileges of all of the roles. Mondrian does not work well with these systems, because we require that queries are executed in precisely one role.

The obvious solution is for mondrian to automatically combine roles roles when executing queries into one ‘super role’. The semantics are subtle. First each role figures out which members it can see (which, for extrinsic roles may involve rules which say they cannot see certain members), then the members visible to the roles are combined.

For example, suppose that role R1 can see all cities in California except San Francisco and Los Angeles, and role R2 can see all cities whose population is less than 1M. If Fred has roles R1 and R2, can he see San Francisco? Yes: R1 cannot see San Francisco, R2 can see it because it has fewer than 1M inhabitants, so it is in the union of the members in R1 and R2.


The partial and hidden rollup policies are more expensive to evaluate than full. Full requires just one point in multidimensional space to be fetched from disk, which can be satisfied using a SQL statement or a cache lookup, whereas partial and hidden require a more complex MDX expression, sometimes involving many cells.

Various implementations for partial and hidden are possible. One suggested scheme would add a special ‘role_id’ column to aggregate tables, which would contain a set of data for each role. This scheme would likely perform well, but because it is custom made for access control, it would tend to work against other strategies such as caching and semi-joins.

The preferred implementation is one which leverages the strengths of the existing MDX language. Some of these queries will require special ‘tricks’, not currently implemented in mondrian, to evaluate efficiently, but at least these tricks will benefit a wide range of queries beside access control, and therefore be thoroughly tested.

We plan to implement the partial and hidden policies by having the schema-reader introduce a calculated member for each partial total. For instance, under a ‘partial’ policy, above query would be executed as if Fred had written

WITH MEMBER [Store].[USA Partial]
  AS 'Aggregate([Store].[USA].Children)'
SELECT {[Measures].[Unit Sales]} ON COLUMNS,
  {[Store].[USA Partial], [Store].[USA].Children} ON ROWS
FROM [Sales]

In the naive evaluation strategy, the calculated member is expanded away as mondrian recursively evaluates expressions. In a more sophisticated strategy, the predicates which define the calculated member – including the access-control policies which filter the results of the expression [Store].[USA].Children – will be generated into a SQL query.

Evaluating expressions in memory is inefficient when there are many thousands of members. This is particularly easy to do when you can define a role according to attributes of members, for example ‘Red products’. We need to find ways to optimize those kinds of queries, by pushing the predicates down to SQL, and ideally by expressing the expressions in a dimensional manner so that the results can be cached.

Optimization #1: dimensional shift

This optimization involves recognizing that a set of members is being filtered by a property which is also dimension. The filtered set can be replaced by a member of the dimension.

For example,

       [Customer].CurrentMember.[Gender] = 'F')



This optimization saves a scan over a large set of members, replacing it with an access to a single cache cell.

Optimization #2, push predicates down to SQL

In some filter expressions the predicate is too complex to be converted a dimension. If the number of members in the set is large, it is worthwhile to let the DBMS evaluate the entire expression. For example,

  [Customer].[Gender] = 'F' or [Customer].[Age] > 50)

The ‘or’ prevents the dimensional shift from taking place. The best optimization would be to (a) translate the condition into the SQL WHERE clause

FROM customer
WHERE gender = 'F' and age > 50

The DBMS can use evaluation techniques such as indexes, and it incurs less I/O if we crunch the data as close to the disk as possible.

These kinds of predicates are very likely to occur when using intrinsic access-control on hierarchies.

Optimization #3, cache cells whose coordinates are complex members

After the above query has run, store the results in the cache. The should fabricate special members like ‘Red product’ so that these cells can be kept in the dimensional cache.

This optimization will benefit totals generated by access-controlled hierarchies, but also totals over calculated sets such as top 10 customers, and other commonly occurring patterns.


I have discussed some of the deficiencies in mondrian’s current access-control model, some features which could be introduced to address them, and sketches for how those features could be implemented. The ‘rollup policy’ feature is planned for mondrian-3.0 (see mondrian roadmap), but the other features are currently unplanned.