Ssas 2005 null processing

Thanks for your respond. Unfortunatelly, SSAS does not properly recognize keys in my view. I have one main table and 3 lookup tables. Primary key supposed to be just from main table.

That is not correct. Simply SSAS just does not recognize keys properly. I have large SSAS model - about 40 measure groups. Biggest problems with logical primary keys I have are in facts tables that have certain joins. ID1, A. ID2, B. FID1, B. FField, A. Is this possible? Thank you for your update. I was not able to find other workaround on this issue at present. Please rest assured this has been routed to the proper channel. When responding to posts, please "Reply to Group" via your newsreader so that others may learn and benefit from your issue.

As fact and dimension tables grow over time, query performance can slow down. There are many techniques for improving query performance - good partition design for measure groups, implementing natural hierarchies for dimensions etc. However, there are also other techniques that often get overlooked.

One such technique is to look at the source data and decide if it is really necessary to import it into the cube. A smaller cube will generally outperform a larger one all other factors being equal. I recently worked on an SSAS database with a dimension that had grown to contain over 30 million members at the leaf level. For any given day a subset of these members would be used - around fifty thousand would actually have data in the cube, and this was massively impacting query performance time for users — around 3 minutes were required to complete the query.

On examining the data returned by the SSAS queries, most of the measure values for these members was zero. Only a few hundred records per day contained non-zero values. In the source system relational tables the values were null, but in the cube they were displayed as zero. As a result, to resolve the cube queries SSAS was having to read and aggregate all these zero values when in actual fact they were of no interest to the users.

To continue processing when null values are found in foreign keys in a snowflaked dimension, handle the null values first by setting NullProcessing on the KeyColumn of the dimension attribute. This discards or converts the record, before the KeyNotFound error can occur. This produces the NullKeyNotAllowed error, which is logged and counts toward the key error limit. Nulls can be problem for non-key fields, in that MDX queries return different results depending on whether null is interpreted as zero or empty.

For this reason, Analysis Services provides null processing options that let you predefine the conversion behavior you want. It is set to Automatic by default, which converts nulls to zeroes for fields containing numeric data.

Change the value to either Error or UnknownMember. This modification removes the underlying conditions that trigger KeyNotFound by either discarding or converting the record before it is checked for errors.

Depending on error configuration, either of these actions can result in an error that is reported and counted. You might need to adjust additional properties, such as setting KeyNotFound to ReportAndContinue or KeyErrorLimit to a non-zero value, to allow processing to continue when these errors are reported and counted.

By default, the presence of a duplicate key does not stop processing, but the error is ignored and the duplicate record is excluded from the database. You can then examine the error to determine potential flaws in dimension design. You can raise the error limit to allow more errors through during processing.

There is no guidance for raising the error limit; the appropriate value will vary depending on your scenario. Once the error limit is reached, you can specify that processing stops or that logging stops. For example, suppose you set the action to StopLogging on an error limit of On the st error, processing continues, but errors are no longer logged or counted. You can specify a file to store key-related error messages that are reported during processing.

By default, errors are visible during interactive processing in the Process window and then discarded when you close the window or session. The log will only contain error information related to keys, identical to the errors you see reported in the processing dialog boxes. Errors will be logged to a text file and must have. The file will be empty unless errors occur. By default, a file will be created in the DATA folder. You can specify another folder as long as the Analysis Services service account can write to that location.

Decide whether errors will stop processing or be ignored. Remember that only the error is ignored. The record that caused the error is not ignored; it is either discarded or converted to unknown member. Records that violate data integrity rules are never added to the database.

By default, processing stops when the first error occurs, but you can change this by raising the error limit. In cube development, it can be useful to relax error configuration rules, allowing processing to continue, so that there is data to test with.

Decide whether to change default null processing behaviors. By default, nulls in a string column are processed as empty values, while nulls in a numeric column are processed as zero. Skip to main content. This browser is no longer supported. Download Microsoft Edge More info.

Contents Exit focus mode. Please rate your experience Yes No. Any additional feedback? In this article. IgnoreError neither logs nor counts the error; processing continues as long as the error count is under the maximum limit.