Size of a dimension

Can someone explain the following
1. A Dimension table with 10 characteristics consists of 900,000 records
2. The multiple of the SID for all of the characteristics i.e. SID1 * SID2 * ... * SID10
is much greater than 900,000
How is this figure of 900,000 derived? Is this just based on the actual SID values which are loaded via the transactions and the multiple of the SID represents the maximum value that the Dimension table can consist of?
Could anyone through some light onto this? Thanks
If this is the case is there a tool/transaction which I can use to derive which SID values have been used in the cube? Thanks
Message was edited by:
        Niten Shah

hi,
have you run the program ? se38 not se39
you will see a list of cubes like
cube1 /bic/d... (dimension table name) rows : xxx ratio : xx %
cube2 /bic/d... (dimension table name) rows : xxx ratio : xx %
cube3 /bic/d... (dimension table name) rows : xxx ratio : xx %
what you need is the ratio
hope this helps.
regards
harikrishna N

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    Hi
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  • Follow-up on u0093Fact Table VS. Dimension relative sizeu0094

    You don’t have to read the background info. below, it is only for reference. I have already gone though several links as such your touch in your own words will be much appreciated.
    Can you help me understand these which came from the many links the Experts referred me .
    1. For better performance: “Use line item dimension where applicable, but use the "high cardinality" option with extreme care.”  Any clarification on “high cardinality”? And why with “extreme care”?
    2.  For better performance:  “1. Make the Index perfect and create secondary index based on requirement..”  I don’t seem to follow this index issue being raised here.
    Thanks.
    --Background: References from these past postings ---
    There is much to be said about performance in general, but I will try to answer your specific question regarding fact and dimension tables.
    First of all try to compress as many requests as possible in the fact table and do that regularily.
    Partition your compressed fact table physically based on for example 0CALMONTH. In the infocube maintenance, in the Extras menu, choose partitioning.
    Partition your cube logically into several smaller cubes based on for example 0CALYEAR. Combine the cubes with a multiprovider.
    Use constants on infocube level (Extras->Structure Specific Infoobject properties) and/or restrictions on specific cubes in your multiprovider queries if needed.
    Create aggregates of subsets of your characteristics based on your query design. Use the debug option in RSRT to investigate which objects you need to include.
    To investigate the size of the dimension tables, first use the test in transaction RSRV (Database Information about InfoProvider Tables). It will tell you the relative sizes of your dimensions in comparison to your fact table. Then go to transaction DB02 and conduct a detailed analysis on the large dimension tables. You can choose "table columns" in the detailed analysis screen to see the number of distinct values in each column (characteristic). You also need to understand the "business logic" behind these objects. The ones that have low cardinality, that is relate to each other shoule be located together. With this information at hand you can understand which objects contribute the most to the size of the dimension and separate the dimension.
    Use line item dimension where applicable, but use the "high cardinality" option with extreme care.
    Generate database statistics regularily using process chains or (if you use Oracle) schedule BRCONNECT runs using transaction DB13.
    Good luck!
    Kind Regards
    Andreas
    There are some simple things by which we can maintain the performance.
    1> Make the Index perfect and create secondary index based on requirement.
    2> Make statistics perfect.
    3> Based on data size in dimenssion table use lineitem dimenssion.
    HOpe this will help you.
    Suneel

    Hi Caud..
    1] High Cardinality..
    High Cardinality means that this dimension contains a large number of characteristic values. This information is used in accordance with the individual database platform in order to optimize performance. For example, an index type other than the standard may be used. Generally a dimension is perceived to have high cardinality when the dimension is at least 20% the size of the fact tables in terms of the number of entries each contain. Avoid marking a dimension as having high cardinality if you are in any doubt.
    Cardinality creates indexes on the Dimension table entries and there by you would see an improvement in performance.
    With Oracle DB, setting the Cardinality option causes a b-tree index to be built instead of a bitmap index even if it is not a line item dim.
    Setting it as a Line Item dim also causes a b-tree index to be built instead of a bitmap index, but also embeds SID directly in the fact table, eliminating the dimension table.
    As it changes from bit map to B-tree, u have to be careful as the SAP recommends the Bit map.
    2]As indices increased the performance tremendously, u have to consider when ever the performance is main factor. it is very easy for query to access the data in the data target if it already indexed.
    Refer this link..
    http://help.sap.com/saphelp_nw04/helpdata/en/a7/d50f395fc8cb7fe10000000a11402f/frameset.htm
    Hope it helps-
    Regards-
    MM
    Assign points if it is useful, it is right way to say thanks.
    Message was edited by: vishnuC

  • Should my dimension table be this big

    Im in the process of building my first product dimension for a star schema and not sure if im doing this correctly. A brief explanation of our setup
    We have products (dresses) made by designers for specific collections each of which has a range of colors and each color can come in many sizes. For our UK market this equates to some 1.9 million
    product variants. Flattening the structure out to Product+designer+collection gives about 33,000 records but when you add all the colors and then all the colors sizes it pumps that figure up to 1.9million. My “rolled own” incremental ETL load runs
    ok just now, but we are expanding into the US market and our projections indicate that our products variants could multiple 10 fold. Im a bit worried about performance of the ETL just now and in the future.
    Is 1.9m records not an awful lot of records to incrementally load (well analyse) for a dimension table nightly as it is never mind what that figure may grow to when we go to US?
    I thought of somehow reducing this by using a snowflake but would this not just reduce the number of columns in the dimensions and not the row count?
    I then thought of separating the colors and size into their own dimension, but this doesn’t seem right as they are attributes of products and also I would lose the relationship between products,  size & color I.e. I would have to go through the
    fact table (which ive read is not a good choice.) for any analysis.
    Am I correct in thinking these are big numbers for a dimension table? Is it even possible to reduce the number somhow?
    Still learning so welcome any help.
    Thanks

    Hi Plip71,
    In my opinion, It is always good to reduce the Dimension Volume as much as possible for better performance.
    Is there any Hierarchy in you product Dimension?.. Going for a snowflake for this problem is a bad idea..
    Solution 1:
    From the details given by, It is good to Split the Colour and Size as seperate dimension. This will reduce the vloume of dimension and increase the column count in the fact(seperate WID has to be maintained in the fact table). but, this will improve the
    performance of the cube. before doint this please check the layout requirement from the business.
    Solution 2:
    Check the distinct count of Item varient used in fact table. If it is very less, they try creating a linear product dimension. i.e, create an view for the product dimension doing a inner join with the fact table. so that only the used Dimension member will
    be loaded in the Cube Product Dimension. hence volume is reduced with improvement in performance and stability of the cube.
    Thanks in advance, Sorry for the delayed reply ;)
    Anand
    Please vote as helpful or mark as answer, if it helps Regards, Anand

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