Getting "Attribute relationships do not exist between one or more levels in this hierarchy" message
Bill Pearson continues his overview of Attribute Relationships within his extended Rigid: The key values of the related attribute and the current attribute or other “unique” identifying code (this attribute does not exist in the. From this type of source table, you would build a parent child hierarchy in Analysis Services. The attribute relationships existing within the Product dimension of the work to determine that some combinations do not exist, among other.
In the present case we did not need to because Game Name is the leaf-level of the hierarchy, and the game names happen to be unique across all ancestry in our data. Another way of putting this is that the leaf level of the hierarchy — Game Name — has the same grain as the dimension key GameSK. In the real world this is often not the case — for example, over time, pinball machines with the same name but different ancestry are not unusual.
For reliability, the key of Game Name should be changed to include its ancestors also, even if not strictly necessary with our current data. If you defined a compound key using the KeyColumns property given earlier as option 1, you would have to pick some single column as the NameColumn property. If you used option 2 or 3 you would be exposing a new column for KeyColumns, and could potentially also use it for the NameColumn property, but usually you would not want to.
Calendar dimensions are usually an exception to this. As soon as you define the related relationships Choice Byou will get duplicate key errors on the Month and Quarter attributes, for reasons that should now be clear. However, in this particular case it would be more common to use option 2 or 3, defining a new column for each of the Month and Quarter attributes. In these cases the new columns can be used for both the KeyColumns and NameColumn properties, since these formats are usually not only fine for display but preferred.
Because this perfectly disambiguates the values when they appear in a report or pivot table. If you use Choice A this will not be the case.
Detect All Duplicate Keys At Once I deliberately contrived the examples such that after fixing the first case, another one is discovered.
Attribute Relationships: Settings and Properties - Page 2
This assumes the default error configuration for processing, where it stops upon the first error. It is possible to change this configuration so that it does not stop upon error, but continues through all attributes — this way you can discover all cases at once.
See Part 1 for how to do this. Duplicate Attribute Key Error and Analysis Services Tabular The short answer is that this situation cannot occur in Tabular, where the possibility is not even a concept.
Tabular has no notion of attributes though the columns of a table appear similar — more similar than they really areand still less a notion of attribute relationships — it has table relationships. In effect, a hierarchy in Tabular is analogous to an unnatural hierarchy in MD, as defined earlier.
As mentioned, Tabular columns really are not the same as attributes in MD, much as it appears they are in a pivot table.
Tabular does not allow multi-column relationships between tables. These facts have an important bearing on the sort of data model you can have in Tabular. Proactively Detecting Duplicate Key Error Cases When first developing a cube or making significant changes, it might be desirable to be able to analyze the source data for any desired dimension hierarchies where relationships will be used, to see where one of the above techniques will be required, rather than waiting for Analysis Services to tell us via the duplicate key error.
SQL queries to do this could easily be created in a data-driven fashion if a small amount of metadata were created to describe the desired hierarchies. As an example, here is a query that discloses the cases covered in this post: Wrapup Here are the key points of this post: Hierarchies may be defined and browsed without defining attribute relationships between the levels.
This is not recommended, but it works, and is more forgiving of the data than if relationships are created. Besides the causes described in Part 1the duplicate attribute key error may also be caused by attribute relationships.
Typically such relationships are created between the levels of a hierarchy, but the error, if it occurs, is driven by the relationship, not the existence of a hierarchy.
In other words, if relationships which surface the error are created but there is no corresponding hierarchy, the error s will still occur. 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.
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. For example, in the Product dimension we have cited, the Subcategory attribute has a many-to-one relationship with the Category attribute, as shown in Illustration 2. This is the purpose of attribute relationships: To optimize our dimension designs, we must first understand how our attributes are related to each other, and then we must take steps to let Analysis Services know, via attribute relationships, what those relationships are.
With the default scenario in place, anytime a query to the data source includes an attribute within the dimension, Analysis Services will summarize data from the primary key and then group it by the attribute under consideration.
SQL Server Analysis Services Attribute Relationships
Unsurprisingly, inefficiencies can mount in cases where the totals of a given attribute might be easily derived from the totals of another attribute. We can see the attribute relationships within the Customer dimension that we have discussed in our geographical attribute examples, appearing as depicted in Illustration 3.
Since Analysis Services cannot identify which attribute combinations naturally exist in the dimension, the server must use the fact data to identify meaningful member combinations, as we intimated earlier. As an illustration, at query time, if an information consumer requests data by Country and State-Province, Analysis Services will be forced to perform additional processing to ascertain, for example, that the combination of Country: Arizona does not exist.
postgresql - Error 42P relation does not exist - Database Administrators Stack Exchange
We can turn to the browser for the Customer dimension within the Adventure Works cube to see the structure underlying the aforementioned example attributes in our Customer geography example, as shown in Illustration 4. Using the Browser for the Customer Dimension to See the Results of the Underlying Attribute Relationships Here we can see that the Analysis Server has used the attribute relationships depicted in Illustration 3 to align the appropriate State-Provinces to their respective Countries, and therefore to make the example we have offered, Country: Arizona, a physically invalid combination.
Even though, in the example above, Customer Key is no longer directly related to Country and State-Province, it remains indirectly related to these attributes through chained attribute relationships. Such a chain of attribute relationships is also known as a cascading attribute relationship.