Skip to main content

Star schemas and aggregate (or summary) fact tables


Aggregate tables can further improve query performance by reducing the number of rows over which higher-level metrics must be aggregated. 
However, the use of aggregate tables with dimension tables is not a valid physical modeling strategy. Whenever aggregation is performed over fact data, it is a general requirement that tables joined to the fact table must be at the same attribute level or at a higher level. If the auxiliary table is at a lower level, fact rows will be replicated prior to aggregation and this will result in inflated metric values (also known as "multiple counting").

With the above Time dimension table, a fact table at the level of Day functions correctly because there is exactly one row in DIM_TIME for each day. To aggregate the facts to the level of Quarter, it is valid to join the fact table to the dimension table and group by the quarter ID from the dimension table.

Sql
select DT.QUARTER_ID,
   max(DT.QUARTER_DESC) Quarter_Desc
   sum(FT.REVENUE) Revenue
from DAY_FACT_TABLE FT
   join DIM_TIME DT
      on (FT.DAY_ID = DT.DAY_ID)
group by DT.QUARTER_ID

If, however, there is an aggregate fact table already at the level of Quarter, the results will not be correct. This is because the query must join on Quarter ID, but the quarter ID is not a unique key of the dimension table. Because any given quarter of a year contains 90, 91 or 92 days, the dimension table will contain that many rows with the same quarter ID. Thus fact rows will be replicated prior to taking the sum, and the sum will be too high.

Sql
select FT.QUARTER_ID,
   max(DT.QUARTER_DESC) Quarter_Desc
   sum(FT.REVENUE) Revenue
from QTR_FACT_TABLE FT
   join DIM_TIME DT
      on (FT.QUARTER_ID = DT.QUARTER_ID)
group by FT.QUARTER_ID

This is a generally recognized problem with star schemas, and is not strictly a MicroStrategy limitation.

Star schemas will function correctly with MicroStrategy SQL Generation Engine 8.x as long as they obey the general data warehousing principle that fact tables should not be at a higher level than the dimension tables to which they are joined.

If aggregate tables are required, it is necessary to provide higher-level lookup tables with unique rows corresponding to each aggregate table's key. Logical views are a way to do this without adding tables or views to the warehouse. For example, LWV_LU_QUARTER may be defined using the following SQL statement:

Sql
select distinct QUARTER_ID, QUARTER_DESC, YEAR_ID
from DIM_TIME

 

With this logical view, it becomes possible for MicroStrategy SQL Generation Engine 8.x to query the quarter-level fact table as follows. Since the logical view has distinct rows per quarter, multiple counting will not occur in this query.

Sql
select FT.QUARTER_ID,
   max(LQ.QUARTER_DESC) Quarter_Desc
   sum(FT.REVENUE) Revenue
from QTR_FACT_TABLE FT
   join (select distinct QUARTER_ID, QUARTER_DESC, YEAR_ID
         from DIM_TIME) LQ
      on (FT.QUARTER_ID = LQ.QUARTER_ID)
group by FT.QUARTER_ID

For more information on the use of logical views in MicroStrategy SQL Generation Engine 8.1.x and 9.x, consult the MicroStrategy Project Design Guide manual, Appendix B: Logical Tables, "Creating logical tables."

Aggregate tables store pre-summarized totals at a higher level of aggregation than the most granular fact table. They allow reports to be generated from small, rather than large, tables; therefore, performance is enhanced. A successful aggregation strategy seeks to choose aggregate tables that will have the most impact while taking the least amount of space.

Aggregation decisions are driven by the following factors:

  • Usage patterns: Build aggregate tables that are likely to be used the most.
  • Compression ratios: The compression ratio between two tables is defined as the size of the aggregate compared to the size of the smallest table from which the aggregate can be derived.
  • Volatility: Changes in hierarchies over time impact the accuracy of aggregate tables. Sometimes aggregate tables must be rebuilt as a result of changes in dimensions.
A good candidate for aggregation should have at most 10-15 percent of the size of the smallest table from which it is derived.

EXAMPLE:
The MicroStrategy Tutorial project uses aggregate tables by default. A simple metric sum (Revenue) will go to different aggregate tables depending on the attributes on the template.

  1. Create a report with Year on the rows and Revenue on the columns.
  2. Execute the report and view the SQL:

  3. Drill from Year to Item and view the SQL:

The query will go from using ORDER_FACT to ORDER_DETAIL. When Year is on the template, the engine selects the smaller table and the fact is calculated as:

sum(a11.ORDER_AMT)
    instead of:

    sum((a11.QTY_SOLD * (a11.UNIT_PRICE - a11.DISCOUNT)))
    Labels:

    Comments

    1. hinaFebruary 5, 2021 at 12:19 PM

      Is there any solution now to use aggregate tables with star schema without creating logical tables?

      ReplyDelete

    Post a Comment

    Popular posts from this blog

    URL API connection mode parameters

    URL API connection mode parameters When using the URL API, to denote which type of Authentication to use, developers can use the connMode parameter.  The following values are used for this parameter: Standard = 1 LDAP Authentication = 16 Database Authentication = 32 Guest = 8 Windows Authentication = 2 Trusted Authentication = 64 Integrated Authentication = 128
    Post a Comment
    Read more

    Level metrics in Microstartegy/MSTR

    Level Metrics Level metrics allows the users to choose the right combination of level target, filtering, and grouping (referred to as elements) to achieve your desired results.  The elements of a metric level are described below: • Target : The target is the attribute level at which the metric calculation groups. For a more detailed description, see  Target: The context of a calculation . • Grouping : Grouping determines how the metric aggregates. For a more detailed description, see  Grouping: How aggregation is performed . • Filtering : Filtering governs how the report filter interacts with the metric calculation. For a more detailed description, see  Filtering: Interaction with report filters . The level is indicated between the curly braces ({ }) in the metric definition shown below: Sum(Revenue) {~, Product} The tilde (~) represents the report level with standard filtering, denoted by the plus sign (+). If you add item as a level, th...
    Post a Comment
    Read more

    Create a transaction services photo uploader

    Create a transaction services photo uploader   1.  Create a new table "photo_upload" in Tutorial warehouse database (the default location: C:\Program Files\MicroStrategy\Tutorial Reporting\TUTORIAL_DATA_7200.mdb), as shown below:    2. The 'photo_upload' table has to be pre-populated with *exactly* 10 rows of data, the values for the 'ID' column should be 1-10 and the values for the 'uploaded' column should all be 0 3.  In MicroStrategy Desktop, create a freeform report "R1" based on the new table "photo_upload" in Tutorial data created at step 1, as shown below:   SELECT Location, Description, ID, uploaded, numbers FROM PHOTO_UPLOAD 4.  Create another table for transaction insert SQL. Make sure to create an 'autonumber' type ID as primary key for this table, or auto_increment ID for different DBs.                     5. Create...
    Post a Comment
    Read more

    Apply or Pass-through functions in Microstrategy

    Ap ply (Pass-Through) functions MSTR Apply functions provide access to functions or syntactic constructs that are not standard in MicroStrategy but are provided by various RDBMS systems.. Syntax common to Apply functions Apply Function Name   ("expression with placeholders", Arg1, Arg2, Arg3, …ArgN) where: Apply Function Name  – is a generic name used for the predefined pass-through functions described above expression with placeholders  – is the string describing the actual expression or syntax that the engine uses while generating the SQL and which is sent to the RDBMS. The placeholders are represented by #0, #1, and so on. "#" is a reserved character for MicroStrategy. Arg  – is an argument that replaces the parameter markers in the pattern. Arg1 replaces #0, Arg2 replaces #1, and so on. There are   five  pre-defined Apply functions to replace regular, predefined functions of the same type. For more details, cli...
    6 comments
    Read more

    Microstrategy Removing sections that do not have metric data

    Removing sections that do not have metric data This is an interesting feature which might not be explored by many of us and it comes us handy. A  cross join between datasets can result in rows or Group Header/Footer sections that do not have metric data. For example, a document contains two datasets. Dataset 1 contains Year and Revenue, with data for three years (2007-2009). Dataset 2 contains Year and Profit, filtered to return data for only two years (2008 and 2009). If you place Year and Profit in the Details and execute the document, it displays three rows, although no profit data exists for 2007. This is a product of the cross join between the two datasets. You do not want to see the blank line for 2007 since it does not give you any data for profit. You can select the  Trim sections for which no metric value data is available  check box. This removes the row for 2007, since no metric data for Profit is available for 2007. The results are shown below: ...
    Post a Comment
    Read more

    Tableau Dataset Connector to Microstrategy REST API URL

    Tableau Dataset Connector to Microstrategy REST API URL The MicroStrategy Dataset Connector is an out‑of‑the box feature that allows you to fetch data from reports or cubes that reside in MicroStrategy projects into Tableau Public (also known as Tableau Desktop). You can import datasets with up to four million rows and 30+ columns. Tableau Public 10.4 or later is needed. Open Tableau Desktop Public Edition. Under Connect, click  Web Data Connector . If it is not there, click  More > Web Data Connector . Enter the following URL: https:// <MSTR Domain> /CommunityConnectors/mstr/ If the connector does not automatically detect Tableau, add the query  ?mode=tableau  to the end of the URL and refresh your browser. Enter your API Server URL and environment credentials. Your REST API URL is your environment URL with  /MicroStrategyLibrary/  added at the end. Click  Connect Select the dataset you want to import. Select the...
    Post a Comment
    Read more

    Case functions Microstrategy

    Ca se functions Microstrategy Case functions return specified data in a SQL query based on the evaluation of user-defined conditions. In general, a user specifies a list of conditions and corresponding return values. Case This function evaluates multiple expressions until a condition is determined to be true, then returns a corresponding value. If all conditions are false, a default value is returned.  Case  can be used for categorizing data based on multiple conditions. This is a single-value function. Syntax Case ( Condition1 ,  ReturnValue1 ,  Condition2 , ReturnValue2 ,...,  DefaultValue ) Example Case(([Total Revenue] < 300000), 0, ([Total Revenue] < 600000), 1, 2) sum(Case (Day@DESC in (“Sat”,”Sun”), Sales, 0) {~+} Sum(Case(Category@DESC In("Books","Electronics"),Revenue,0)){~+} CaseV (case vector) CaseV  evaluates a single metric and returns different values according to the results. It can be used to perfo...
    3 comments
    Read more

    Microstrategy Custom number formatting symbols

    Custom number formatting symbols If none of the built-in number formats meet your needs, you can create your own custom format in the Number tab of the Format Cells dialog box. Select  Custom  as the Category and create the format using the number format symbols listed in the table below. Each custom format can have up to four optional sections, one each for: Positive numbers Negative numbers Zeros Text Each section is optional. Separate the sections by semicolons, as shown in the example below: #,###;(#,###);0;"Error: Entry must be numeric" For more examples, see  Custom number formatting examples . To jump to a section of the formatting symbol table, click one of the following: Numeric symbols Character/text symbols Date and time symbols Text color symbols Currency symbols Conditional symbols Numeric symbols For details on how numeric symbols apply to the Big Decimal data type, refer to the  Project Design Guide . ...
    3 comments
    Read more

    Prompt-in-prompt (Nested prompt) feature in a FreeForm SQL Report

    Prompt-in-prompt (Nested prompt) feature in a FreeForm SQL Report In some business scenarios, it is required to implement prompt-in-prompt (nested prompt) feature to use the answer of one prompt to qualify on the elements of another prompt in Freefrom SQL reports in MicroStrategy Developer 9.4.x-10.x. The following procedure describes how to achieve prompt-in-prompt in a Freeform SQL report in MicroStrategy Tutorial project: Create a new filter, select Add an Attribute qualification and choose the highest level attribute Country. Make sure to Qualify On: Elements. Click Prompt as the image shown below. Accept all the default values without any changes in the popped up window after clicking on "Prompt" as shown below. Save the filter as "Country Filter". Click "Prompt", choose "Use a filter to reduce the number of elements" option and select Country Filter created in last step, as shown below. Save the filter as "Region Filter...
    1 comment
    Read more

    HyperIntelligence Training Videos

    HyperIntelligence  Training Videos           Design and build hyper cards Optimizing Datasets for HyperIntelligence Using the HyperIntelligence for Office Outlook Add-In Building HyperIntelligence Cards Using HyperIntelligence for Mobile on Android Deploying HyperIntelligence for Outlook Insights On-The-Go: HyperIntelligence for Mobile Building HyperIntelligence Profile Cards Designing Custom HyperIntelligence Cards Using the Calendar with HyperIntelligence for Mobile
    4 comments
    Read more