When we run the aggregation wizard from Visual Studio for a MOLAP cube, no aggregations are created. The graph stays flat. We tried every different scenario and setting that we could think of with no luck. Any help would be appreciated.
Things to check:
Do you have natural hierarchies in your dimensions? (Needed for optimal aggregation)
Do you have the correct attribute relations for those hierarchies? (Also, needed for optimal aggregation and performance)
Do you have non parent-child dimensions available to aggregate? (Only the key attribute will aggregate in these)
Do you have any data in your cube? (the wizard needs data to work)
Do you have partitions? (aggregations can't be created for higher levels than the partition split e.g. you can't have a year aggregation on a monthly partition)
Ensure that at least some dimension attributes have their AttributeHierarchyOptimizedState set to FullyOptimized
Check the AggregationUsage cube dimension attribute property
Also read:
What are the natural hierarchies and why they are a good thing
Influencing Aggregation Candidates (This has much more detail than here)
Related
I would like to understand how OLAP-cube operations (i.e. drilling up/down, slicing/dicing and pivoting) and MDX are related.
My current guess is that OLAP-cube operations to MDX are like relational algebra to SQL. However, I do not see how some basic features of MDX correspond to OLAP-cube operations. For example, consider the following query on the demo "Sales" cube that comes with icCube:
SELECT {([Ottawa],[2009]), ([United States],[Feb 2010])} on Rows,
[Measures].members on Columns
FROM [Sales]
How does the use of tuples (e.g. ([Ottawa],[2009])) correspond to an OLAP-cube operation?
Yes, "OLAP-cube operations are what visualization tools are expected to implement". MDX is the query language that is executed against a cube that produces a result. OLAP clients generally run MDX against a cube. "OLAP cube operations" as described in that wikipedia are usually as a result of a person performing adhoc analysis against a cube in an client application.
Cube provide a structure and an access language that usually makes these types of operations easier (or at least faster)
How does MDX relate to a "drill down" operation? for example?
Firstly some MDX has already been run and yielded some kind of view of the cube (generally some rows, some columns, and a measure in the intersection although the MDX language syntax doesn't limit to two axes only).
So a person sees this information and decides to drill down on a single item in the row (this item was previously returned by some MDX). So the OLAP client generates some MDX that provides the drilled down view on the item
It might just add a children MDX function to the item in question. Or it might do it some other way. It depends on the client.
Heres some introductory info on how you can eavesdrop on the interactions between an OLAP client (which one? doesn't matter) and a SSAS cube
https://learn.microsoft.com/en-us/sql/analysis-services/instances/introduction-to-monitoring-analysis-services-with-sql-server-profiler
You can think of the MDX query specifying areas or regions of space within a cube - the tuple is a primary way of giving the processor co-ordinates which correspond to the part of the cube you are interested in.
It is the intersection of the co-ordinates and slices you specify that give you a result.
MDX is strongly related to set theory as the main types withing the cube are dimensions, sets, tuples, members etc.
An MDX query defines a table and for each of the table cell we've a tuple. In your scenario assuming we've two measures ( Meas1, Meas2 ) :
([Ottawa],[2009],[Meas1]) ([Ottawa],[2009],[Meas2])
([United States],[Feb 2010],[Meas1]) ([United States],[Feb 2010],[Meas2])
On this cell tuples you might add the WHERE clause, the SubQuery and the defaults that might be different than ALL (not advised). Remember all is a 'special' member that is ignored.
A tuple defines a single measure, Meas1 or Meas2, this will select the 'fact table' with a measure column, usually a numerical value. The other members are used to select rows in the table performing on them the aggregation defined by the measure ( sum , min, max.... ) on all rows defined by the tuple members, Ottawa and 2009 for example. As whytheq explains, you've a lot of transformations to 'play' with members as you would with sets.
This is the simple vision, as you can use calculated members that define a transformation instead of a simple row aggregation (e.g. Difference with previous year) and some aggregations are a bit more tricky ( open, close...).
But if you understand this well you got a perfect ground to understand MDX.
Though I have relatively good exposer in SQL Server, but I am still a newbie in SSAS.
We are to create set of reports in SSRS and have the Data source as SSAS CUBE.
Some of the reports involves data from atleast 3 or 4 tables and also would involve Grouping and all possible things from SQL Environment (like finding the max record for a day and joining that with 4 more tables and apply filtering logic on top of it)
So the actual question is should I need to have these logics implemented in Cubes or have them processed in SQL Database (using Named Query in SSAS) and get the result to be stored in Cube which would be shown in the report? I understand that my latter option would involve creation of more Cubes depending on each report being developed.
I was been told to create Cubes with the data from Transaction Tables and do entire logic creation using MDX queries (as source in SSRS). I am not sure if that is a viable solution.
Any help in this would be much appreciated; Thanks for reading my note.
Aru
EDIT: We are using SQL Server 2012 version for our development.
OLAP cubes are great at performing aggregations of data, effectively grouping over the majority of columns all at once. You should not strive to implement all the grouping at the named query or relational views level as this will prevent you from being able to drill down through the data in the cube and will result in unnecessary overhead on the relational database when processing the cube.
I would start off by planning to pull in the most granular data from your relational database into your cube and only perform filtering or grouping in the named queries or views if data volumes or processing time are a concern. SSAS will perform some default aggregations of the data to allow for fast queries at the most grouped level.
More complex concerns such as max(someColumn) for a particular day can still be achieved in the cube by using different aggregations, but you do get into complex scenarios if you want to aggregate by one function (MAX) only to the day level and then by another function across other dimensions (e.g. summing the max of each day per country). In that case it may well be worth performing the max-per-day calculation in a named query or view and loading that into its own measure group to be aggregated by SUM after that.
It sounds like you're at the beginning of the learning path for OLAP, so I'd encourage you to look at resources from the Kimball Group (no affiliation) including, if you have time, the excellent book "The Data Warehouse Toolkit". At a minimum, please look into Dimensional Modelling techniques as your cube design will be a good deal easier if you produce a dimensional model (likely a star schema) in either views or named queries.
I would look at BISM Tabular if your model is not complicated. It compresses and stores data in memory. As for data processing I would suggest to keep all calculations and grouping in database layer (create views).
All the calculations and grouping should be done at database level atleast in form of views.
There are mainly two ways to store data (MOLAP and ROLAP). Use MOLAP storage model for deal with tables that store transactions kind of data.
The customer's expectation from transaction data (from my experience) would be to understand the sales based upon time dimension. Something like Get total sales in last week or last quarter. etc.
MDX scripts are basically kind of SQL scripts that Cube can understand. No logic should be there. based upon what Parameters are chosen in SSRS report, MDX query should be prepared. Small analytical functions such as subtotal, average can be dome by MDX but not complex calculations.
I'm totally new to MDX language and curios if it capable of doing some sort of work.
There are calculated members that allows you to populate measure dimension with new data. But I have never met a reference to method that creates new dimensions with hierarchies.
For example, I have performed some cluster analysis and want to store result as hierarchy to make queries over calculated clusters. I'm searching for a function that takes predicate, apply it to all members in a slice and assign them new temporal dimension info. Something like the factor function in R.
Is this functionality available for MDX queries?
There is no standard way to create a dimension or hierarchies in MDX. Using calculated members is the usual way to go but this is not the same as creating a 'standard' hierarchy.
I don't know for others vendors but icCube introduced a couple of months Categories (also worth checking the SubCube functions family). They allow to dynamically, after the cube has been loaded, build new hierarchies.
This is really handy when you're doing complex calculations or have many-to-many relations, as calculated members might quickly become a mess.
Link to DCD: http://i48.tinypic.com/29de538.png
I have this DCD to work on, with SQL Server Business Intelligence Development Studio 2008.
I have made a facttable which is a view, because I need to have some measures..
Now how do I connect my facttable to the other tables? Dont know if the should be linked to selftest, instrument or network.
It would be great to hear the correct linking, and how to do it!
Thanks a bunch!
It looks like you have Instrument (first dimension), execution date (second dimension) and a resulting status (third dimension) for each Self Test. You may generate hierarchies of Instrument Type, Group and Network to group together Instruments within ssas.
The resulting counts (NumOk, NumWarning, NumError) may be your measures. Typically, you would group these together depending on the grain of your fact (i.e. daily) to a single fact table. There is no need to create separate facts per measures, you would only create separate fact tables if the measures relate to a different set of dimensions
Try to set your source up as follows (add additional attributes as required) and follow the model through to the DSV. Suggest that you ignore hierarchies until you get the basics working within SSAS.
What is the advantages of creating hierarchy in SSAS? What are the difference between drag and drop year,month one by one and single hierarchy on browser?
Mainly:
Aggregations: If you want the sum per year and you have
aggregations per month, SSAS will sum all the monthly aggregations
instead of summing all the values on that particuallar year
Its a Microsoft best practice to add big dimensions in some sort of
hierarchy and set the AttributeHierarchyVisible property to false so
the attribute is enable to be browsed through the hierarchy but not
alone. That prevents the user of dragging a attribute with 1 million rows to the cube, which would break it
The advantages of creating a Hierarchy is that you give your users (Cube Users) a pre defined hierarchy that will be common with all users. You will be saving them a lot of time instead of having to drag and drop 3 - 4 members every time. Additionally, if you are working with PPS or any other reporting layer, your filters can work directly on the Hierarchy to offer users 1 Filter with drill down options instead of having them choose from 3 to 4 Filters. Finally, if you use PPS, Hierarchies will allow the user to perform auto drill down in PPS charts.
These are just a few of the benefits of Hierarchies, there are also some benefits related to Performance of Aggregations, More flexibility when working with Date Hierarchies and creating custom measures.