Many-to-many in attribute relationship | Data Warehousing, BI and Data Science
Attribute relationships inside of Analysis Services are a critical piece to the design of your dimensions. The benefits of creating attribute. SQL Server | SQL Server Building your time attribute relationship you have to consider the following questions: How will I normally. Join BI Architect Bill Pearson as he introduces Attribute Relationships into his extended examination of the dimensional model within the.
Unfortunately, there is no hard and fast rule. Use Regular Dimension Relationship Whenever Possible Latest versions of MSAS give us the flexibility of using referenced, many-to-many, fact, and other types of relationships between fact and dimension tables. However, this does not mean that star and snowflake schemas are obsolete or that you should be building a data warehouse using the third normal form.
If you must use a referenced relationship snowflake schemathen be sure to check the Materialize check box in the Dimension Relationship dialog. MSAS would have to join partition records to referenced dimension dynamically during query execution for un-materialized dimensions. Fact dimensions sometimes referred to as degenerate dimensions have a valid business use. For example, we often need to expose invoice numbers or customer identifiers on reports. I suggest you test performance of such reports with Analysis Services and compare to the same report generated directly from the relational data source.
How to create time attribute relationship (2) | ORAYLIS
For huge transaction-level reports, you may find that SQL queries will perform just as well or better than reports created with Analysis Services. See the next recommended practice for more tuning advice concerning fact-level dimensions. Many-to-many dimensions are necessary for several common business scenarios; however, this flexibility comes with a performance penalty. Investigate the possibility of alternative implementation without using many-to-many relationships.
If you have to use a many-to-many relationship, be sure to read and follow optimization guidelines in this Microsoft article. Although you could use string data types for key columns, they do not perform nearly as well as the numeric data types. At times you may need to use multiple columns for the composite key for a dimension attribute. Although this practice is almost always acceptable, for large dimensions and particularly for fact level dimensions you may have to add an alternate key to the dimension table to ensure processing is efficient.
While processing this dimension, MSAS must retrieve a unique combination of three string columns, which can be very time-consuming for a dimension that contains millions of records. Instead, you can add an identity column or another integer column uniquely identifying each transaction to the dimension table and use it as the attribute key.SSAS 109 Attribute Relationships
Reading a single integer column will be considerably faster than reading three string columns. Storing integer keys will also be much more efficient. Use Natural Hierarchies When Possible I trust that you have already heard this piece of advice because it has been heavily stressed in a number of white papers and presentations.
By William Pearson Procedure: Define Attribute Relationships and Examine Attribute Relationship Property Settings in Analysis Services In the practice procedures that follow, we will select and examine a representative dimension within the sample cube, and then focus upon the attribute relationship property settings that reference select dimension attributes.
In Dimensional Model Components: Introduction and Overview Parts I through V, respectively, we overviewed the properties underpinning Database and Cube dimensions, and then examined the properties supporting dimension attributes.
Recommended Practices for SQL Server Analysis Services 2005/2008 Design
Composite Keys we focused upon those properties for a simple attribute key and a composite attribute key, respectively. Just as we did for the respective subject matter objects in those articles, we will examine the detailed settings for representative attribute relationships here.
As I noted earlier, we can organize attribute hierarchies into levels within user hierarchies to provide navigation paths for users in a cube.
A user hierarchy can represent a natural hierarchy, such as city, state, and country, as we shall see in our practice session or can simply represent a navigation path that fits a local business scenario, such as employee name, title, and department name.
Moreover, as we also mentioned earlier, to the information consumer navigating a hierarchy, these two types of user hierarchies are identical. As a part of my discussion in Introduction to Attribute Relationships in MSSQL Server Analysis Services, I stated that, with a natural hierarchy, if we define attribute relationships between the attributes that make up the levels, Analysis Services can use an aggregation from one attribute to obtain the results from a related attribute.
If there are no defined relationships between attributes, Analysis Services will aggregate all non-key attributes from the key attribute. Within our practice session we will work within the Customer Geography natural hierarchy of the Customer dimension. Define Attribute Relationships for Attributes in the Customer Geography Hierarchy We will begin our practice with attribute relationships within the Customer Geography hierarchy of the Customer dimension.
Within the Solution Explorer, right-click the Customer dimension expand the Dimensions folder as necessary. Click Open on the context menu that appears, as shown in Illustration 3. Opening the Dimension via the Dimension Designer The tabs of the Dimension Designer open. Click the Dimension Structure tab, if we have not already arrived there by default.
The attributes belonging to the Customer dimension appear as depicted in Illustration 4. We can also see, within the Hierarchies and Levels pane, four levels in the Customer Geography user hierarchy. This hierarchy currently exists simply as a drill down path for information consumers, and appears as shown in Illustration 5. Hierarchies and Levels Pane, Customer Dimension 4.
We note that four member properties link the non-key attributes from the Geography table to the key attribute from the Geography table, as depicted in Illustration 6.
- Introduction to Attribute Relationships in MSSQL Server Analysis Services - Page 2
- Specifying Attribute Relationships Between Attributes in a User-Defined Hierarchy
- Attribute Relationships: Settings and Properties - Page 2
In the Attributes pane, expand the Full Name attribute. We see that the Geography attribute is related to the Full Name attribute. We also note that the Postal Code attribute is indirectly related to the Full Name attribute through the Geography attribute, because Postal Code is linked to the Geography attribute and the Geography attribute is linked to the Full Name attribute.
These relationships are circled in Illustration 7. In the Properties window which appears for the highlighted Postal Code attribute, by default in the right bottom corner of the design environmentwe can observe that the RelationshipType property for this attribute is set to Flexible.
This is appropriate because the relationship between a customer and a postal code may change over time. The RelationshipType property defines rules for the modification of the key value of the members of the related, dependent attribute in our example, the Full Name attribute is the current attribute, whereas the Postal Code attribute is the related attribute.
When we define an attribute relationship, we use the RelationshipType property to specify that the relationship is one of the following types: The key values of the related attribute and the current attribute have a fixed association, and cannot change without a full reprocessing of the dimension. The key of the related, dependent attribute, and therefore the entire member of the dependent attribute, can be changed anytime. In our example above, the Postal Code attribute is dependent upon the Full Name Customer attribute with a Flexible relationship, since the postal code can change anytime a customer moves to another location.
If we define a relationship as Rigid, Analysis Services retains aggregations when the dimension is updated. If a relationship that is defined as Rigid actually changes, Analysis Services generates an error during processing unless the dimension is fully processed. Specifying the appropriate relationships and relationship properties increases query and processing performance, as we noted in Introduction to Attribute Relationships in MSSQL Server Analysis Services.
We noted that the RelationshipType property in our example is set to Flexible: In our example above, the Postal Code attribute is dependent upon the Full Name Customer attribute with a Flexible relationship, since the postal code can change anytime a customer moves. While we will not make modifications here, we also see that the Cardinality setting for the Postal Code attribute relationship is set to Many. Cardinality defines the nature of the relationship of the key of related attributes and their members when those members are used as member properties of the current attribute and its members.