I have a large dimension in my cube which has 5 million rows. when I drag that dimension column in excel to view metric value it times out. Is there any way I can limit the data dynamically or selecting only top 10K rows when that large dimension is pulled in excel? or any other best approch?
you shouldn't allow the attribute to be browsed. Think about, who will look for a particular row among 5 million others?
The best practice in this case, and that's not me saying I heard from a Microsoft MVP, its to add the attribute to a Hierarchy and set the AttributeHierarchyVisible property to false. It will hide the attribute from the end user. You can still use it on MDX and it still appears in hierarchies, you just cant browse it alone.
Related
As of now in my fact table i have around 6 Date Keys which needs to be linked to one Date Dimension.
Possible solution on table with me is:
Creating multiple date dimension and linking it with Fact Table. As this involves duplicating the data. I wanted to avoid this solution as i have around 10 Date Columns in my fact Table.
I am looking for a solution in order to reduce the redundancy of number of Tables in my Model.
Something like User-Role
Without increasing the number of tables, we can use the relationship which is relevant to the measures. This can be achieved by enforcing the data model to activate the relationship we need. The relationship can be moved from inactive to active in DAX using USERELATIONSHIP function. Below are the snaps showing one active and 2 inactive relationships and usage of "USERELATIONSHIP" while implementing the measures and results later.
I am trying to somehow link two dimensions together in SSAS.
I have the following Dimensions:
Location
Price Scheme
Product
I have a fact and measure group that is linked through Product and Price Scheme (there are only about 3 price scheme members).
Price Scheme is also an attribute of Location.
I want my measure group to show up against each location. If in my source query I do a join to Location based on the Price Scheme, then I get about 100 million records which makes the cube processing take a long time. There is less than 1 million rows in the measure group when at the Sales Price Scheme level.
I suppose my question is: how do I write an MDX query that will get the measures from my measure group based on the Price Scheme of the attribute against the Location dimension?
I know I can do a referenced dimension.. but doesn't that just modify the query by performing an inner join onto the Location dimension and as such will still give me 100 million rows to process?
Simple OLAP rule: The less you process, the slower MDX you get.
There are two ways:
Use many-to-many relation: which is also slow and you need one more
extra fact table to join two dimensions.
Use Slow-changing dimension: which is super fast on your cube, but
will take some time to process. You may speed it up, by setting
indexes and ProcessingGroup property to ByTable. Roughly saying it's what you described above.
In order to say more we want to see your data scheme.
I've got a simple cube with a fact table which has a date field among others and connected it with a time dimension which has 2 hierarchies.
What I want to do is create one measure that will be filtered only by the one time hierarchy and a second one for the second time hierarchy.
Basically this:
Measure1 ----> Cannot be affected by filtering of time_hierarchy2 and gets filtered only by time_hierarchy1
And the same for Measure2.
With what I've tried so far I can't do this because whenever I add a time hierarchy in the cube browser filter area, it affects both measures while I want them to be independent.
Is this possible?
The idea is to create two instances (i.e Cube Dimensions) of your Database Dimension and put one Hierarchy in each of them. This concept is also known as a Role-Playing Dimension.
You can then add filters using these role-playing dimensions to filter your Measures.
In the way you have described your current data model this is not possible. Within Analysis Services if you were to review the Dimension Usage tab you will notice the dimension to measure group usage. For a single measure to dimension relationship the measure will be affected by all attributes/hierarchies of the related dimension when browsing the cube.
If a viable option would be to have a separate TimeKey in your fact you may establish a Role Playing Dimension and have multiple constraints from the your fact to Time dimension.
Another option could be similar to where I recently split this setup into multiple facts each with a single reference to the Time dimension so that I could the plot separate measures to the same graph on the same time axis. How to avoid Role Playing Dimension
We have an "age" dimension in our SSAS Cube. It's basically just the one attribute that's the person's whole number age at the time an event happened. We've had a requirement to further break it down into adult/child with a sub group of adult/geriatric and pediatric/neonatal.
When adding these new attributes to the dimension and a hierarchy, do I have to go into the aggregation designs and rebuild the ones that reference the dimension?
We aren't changing the key of the patient age, just adding the extra data.
Unfortunately, you aggregations won't include new level automatically, but they will help anyway: engine can use lower than your new level aggregations of the same dimension as it's faster than retrieving from data files.
Please also remember '1/3 rule': aggregations should be less than 1/3 the size of the fact table.
You can see details in amazing white paper 'Analysis Services 2008 R2 Performance Guide' http://download.microsoft.com/download/6/5/6/6567C845-FC8D-4D62-920F-C027A349C889/SSASPerfGuide2008R2.pdf (3.4 Aggregations, page 60).
I have a SSAS cube in which one of my dimension has 5 million recrods. When I try to view data for the dimension, report or excel pivot becomes lengthy and also the performance is poor. I cant categorize that particular dimension data. Only way I can think of to restrict data is select top 10K rows from the dimension which has metric values. Apart from restricting it to top 10K dimension records can anyone please suggest other possibilities?
Have you set up aggregations? I would venture to guess that the majority of the time being spent getting your data to a viewing point has to do with your measures. If I was you I would try adding in aggregations or upping the aggregation percent in order to alleviate some of the pressure at querytime by passing this workload to the processing time of the dimension/cube.
Generally, people set their aggregation levels at about 30% to start.
If you have done this already, I would think about upgrading your hardware on the server that your cube sits on. (depending on what you already have)
These are just suggestions as it could also be an issue in your cube design that is causing a lengthy runtime.
I would suggest you to create a hierarchy for showing 5 million records. Group by substring in Level 1,( if required some characters in Level 2), then the data falling under that group. For example :
Level 1 Value
A Apple
A Ant
This would mean that you wont be showing all 5 million records at once and it is very effective now to use aggregations too.