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Oracle® OLAP User's Guide
12c Release 1 (12.1)

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4 Querying Dimensional Objects

Oracle OLAP adds power to your SQL applications by providing extensive analytic content and fast query response times. A SQL query interface enables any application to query cubes and dimensions without any knowledge of OLAP.

The OLAP option automatically generates a set of relational views on cubes, dimensions, and hierarchies. SQL applications query these views to display the information-rich contents of these objects to analysts and decision makers. You can also create custom views that follow the structure expected by your applications, using the system-generated views like base tables.

In this chapter, you learn the basic methods for querying dimensional objects in SQL. It contains the following topics:

See Also:

Exploring the OLAP Views

The system-generated views are created in the same schema as the analytic workspace. Oracle OLAP provides three types of views:

  • Cube views

  • Dimension views

  • Hierarchy views

These views are related in the same way as fact and dimension tables are in a star schema. Cube views serve the same function as fact tables, and hierarchy views and dimension views serve the same function as dimension tables. Typical queries join a cube view with either a hierarchy view or a dimension view.

Cube Views

Each cube has a cube view that presents the data for all the measures and calculated measures in the cube. You can use a cube view like a fact table in a star or snowflake schema. However, the cube view contains all the summary data in addition to the detail level data.

Discovering the Names of the Cube Views

The default name for a cube view is cube_VIEW. To find the view for UNITS_CUBE in your schema, you might issue a query like this one:

SELECT view_name FROM user_views WHERE view_name LIKE 'UNITS_CUBE%';

VIEW_NAME
------------------------------
UNITS_CUBE_VIEW

The next query returns the names of all the cube views in your schema from USER_CUBE_VIEWS:

SELECT view_name FROM user_cube_views;
 
VIEW_NAME
------------------------------
UNITS_CUBE_VIEW
PRICE_CUBE_VIEW

Discovering the Columns of a Cube View

Like a fact table, a cube view contains a column for each measure, calculated measure, and dimension in the cube. In the following example, UNITS_CUBE_VIEW has columns for the SALES, UNITS, and COST measures, for several calculated measures on SALES, and for the TIME, CUSTOMER, PRODUCT, and CHANNEL dimensions.

DESCRIBE units_cube_view
Name                                      Null?    Type
----------------------------------------- -------- ----------------------------
SALES                                              NUMBER
UNITS                                              NUMBER
COST                                               NUMBER
SALES_PP                                           NUMBER
SALES_CHG_PP                                       NUMBER
SALES_PCTCHG_PP                                    NUMBER
SALES_PROD_SHARE_PARENT                            NUMBER
SALES_PROD_SHARE_TOTAL                             NUMBER
SALES_PROD_RANK_PARENT_PP                          NUMBER
TIME                                               VARCHAR2(100)
CUSTOMER                                           VARCHAR2(100)
PRODUCT                                            VARCHAR2(100)
CHANNEL                                            VARCHAR2(100)

The USER_CUBE_VIEW_COLUMNS data dictionary view describes the columns of a cube view, as shown by the following query.

SELECT column_name, column_type FROM user_cube_view_columns
     WHERE view_name = 'UNITS_CUBE_VIEW';
 
COLUMN_NAME                    COLUMN_TYPE
------------------------------ --------------
SALES                          MEASURE
UNITS                          MEASURE
COST                           MEASURE
SALES_PP                       MEASURE
SALES_CHG_PP                   MEASURE
SALES_PCTCHG_PP                MEASURE
SALES_PROD_SHARE_PARENT        MEASURE
SALES_PROD_SHARE_TOTAL         MEASURE
SALES_PROD_RANK_PARENT_PP      MEASURE
TIME                           KEY
CUSTOMER                       KEY
PRODUCT                        KEY
CHANNEL                        KEY
 
13 rows selected.

Displaying the Contents of a Cube View

You can display the contents of a cube view quickly with a query like this one. All levels of the data are contained in the cube, from the detail level to the top.

SELECT sales, units, time, customer, product, channel
     FROM units_cube_view WHERE ROWNUM < 15;

     SALES      UNITS TIME       CUSTOMER   PRODUCT    CHANNEL
---------- ---------- ---------- ---------- ---------- --------
1120292752    4000968 TOTAL      TOTAL      TOTAL      TOTAL
 134109248     330425 CY1999     TOTAL      TOTAL      TOTAL
 130276514     534069 CY2003     TOTAL      TOTAL      TOTAL
 100870877     253816 CY1998     TOTAL      TOTAL      TOTAL
 136986572     565718 CY2005     TOTAL      TOTAL      TOTAL
 140138317     584929 CY2006     TOTAL      TOTAL      TOTAL
 144290686     587419 CY2004     TOTAL      TOTAL      TOTAL
 124173522     364233 CY2000     TOTAL      TOTAL      TOTAL
  92515295     364965 CY2002     TOTAL      TOTAL      TOTAL
 116931722     415394 CY2001     TOTAL      TOTAL      TOTAL
31522409.5      88484 CY2000.Q1  TOTAL      TOTAL      TOTAL
27798426.6      97346 CY2001.Q2  TOTAL      TOTAL      TOTAL
29691668.2     105704 CY2001.Q3  TOTAL      TOTAL      TOTAL
32617248.6     138953 CY2005.Q3  TOTAL      TOTAL      TOTAL
 
14 rows selected.

Dimension and Hierarchy Views

Each dimension has one dimension view plus a hierarchy view for each hierarchy associated with the dimension. For example, a Time dimension might have these three views:

  • Time dimension view

  • Calendar hierarchy view

  • Fiscal hierarchy view

You can use dimension views and hierarchy views like dimension tables in a star schema.

Discovering the Names of Dimension and Hierarchy Views

USER_CUBE_DIM_VIEWS identifies the dimension views for all dimensions. The default name for a dimension view is dimension_VIEW.

SELECT * FROM user_cube_dim_views;
 
DIMENSION_NAME                 VIEW_NAME
------------------------------ ------------------------------
PRODUCT                        PRODUCT_VIEW
CUSTOMER                       CUSTOMER_VIEW
CHANNEL                        CHANNEL_VIEW
TIME                           TIME_VIEW

USER_CUBE_HIER_VIEWS identifies the hierarchy views for all the dimensions. For a hierarchy view, the default name is dimension_hierarchy_VIEW. The following query returns the dimension, hierarchy, and view names.

SELECT * FROM user_cube_hier_views ORDER BY dimension_name;
 
DIMENSION_NAME  HIERARCHY_NAME  VIEW_NAME
--------------- --------------- ------------------------------
CHANNEL         PRIMARY         CHANNEL_PRIMARY_VIEW
CUSTOMER        MARKET          CUSTOMER_MARKET_VIEW
CUSTOMER        SHIPMENTS       CUSTOMER_SHIPMENTS_VIEW
PRODUCT         PRIMARY         PRODUCT_PRIMARY_VIEW
TIME            FISCAL          TIME_FISCAL_VIEW
TIME            CALENDAR        TIME_CALENDAR_VIEW

Discovering the Columns of a Dimension View

Like a dimension table, a dimension view contains a key column, level name, level keys for every level of every hierarchy associated with the dimension, and attribute columns. In the following example, TIME_VIEW has a column for the dimension keys, the level name, and the dimension attributes.

DESCRIBE time_view
Name                                      Null?    Type
----------------------------------------- -------- ----------------------------
DIM_KEY                                            VARCHAR2(100)
LEVEL_NAME                                         VARCHAR2(30)
DIM_ORDER                                          NUMBER
END_DATE                                           DATE
LONG_DESCRIPTION                                   VARCHAR2(100)
SHORT_DESCRIPTION                                  VARCHAR2(100)
TIME_SPAN                                          NUMBER

USER_CUBE_DIM_VIEW_COLUMNS describes the information in each column, as shown in this query.

SELECT column_name, column_type FROM user_cube_dim_view_columns
     WHERE view_name ='TIME_VIEW';
 
COLUMN_NAME                    COLUMN_TYPE
------------------------------ --------------------
DIM_KEY                        KEY
LEVEL_NAME                     LEVEL_NAME
DIM_ORDER                      DIM_ORDER
END_DATE                       ATTRIBUTE
TIME_SPAN                      ATTRIBUTE
LONG_DESCRIPTION               ATTRIBUTE
SHORT_DESCRIPTION              ATTRIBUTE

Displaying the Contents of a Dimension View

The following query displays the level and attributes of each dimension key.

SELECT dim_key, level_name, long_description description, time_span, end_date
     FROM time_view WHERE dim_key LIKE '%2005%';
 
DIM_KEY      LEVEL_NAME           DESCRIPTION   TIME_SPAN END_DATE
------------ -------------------- ------------ ---------- ---------
CY2005       CALENDAR_YEAR        2005                365 31-DEC-05
CY2005.Q2    CALENDAR_QUARTER     Q2.05                91 30-JUN-05
CY2005.Q4    CALENDAR_QUARTER     Q4.05                92 31-DEC-05
CY2005.Q3    CALENDAR_QUARTER     Q3.05                92 30-SEP-05
CY2005.Q1    CALENDAR_QUARTER     Q1.05                90 31-MAR-05
2005.01      MONTH                JAN-05               31 31-JAN-05
2005.05      MONTH                MAY-05               31 31-MAY-05
2005.07      MONTH                JUL-05               31 31-JUL-05
2005.03      MONTH                MAR-05               31 31-MAR-05
2005.04      MONTH                APR-05               30 30-APR-05
2005.08      MONTH                AUG-05               31 31-AUG-05
2005.09      MONTH                SEP-05               30 30-SEP-05
2005.02      MONTH                FEB-05               28 28-FEB-05
2005.11      MONTH                NOV-05               30 30-NOV-05
2005.06      MONTH                JUN-05               30 30-JUN-05
2005.10      MONTH                OCT-05               31 31-OCT-05
2005.12      MONTH                DEC-05               31 31-DEC-05
FY2005       FISCAL_YEAR          FY2005              365 30-JUN-05
FY2005.Q4    FISCAL_QUARTER       Q4 FY-05             91 30-JUN-05
FY2005.Q1    FISCAL_QUARTER       Q1 FY-05             92 30-SEP-04
FY2005.Q2    FISCAL_QUARTER       Q2 FY-05             92 31-DEC-04
FY2005.Q3    FISCAL_QUARTER       Q3 FY-05             90 31-MAR-05
 
22 rows selected.

Discovering the Columns of a Hierarchy View

Like the dimension views, the hierarchy views also contain columns for the dimension key, level name, and level keys. However, all of the rows and columns are associated with the dimension keys that belong to the hierarchy.

DESCRIBE time_calendar_view
Name                                      Null?    Type
----------------------------------------- -------- ----------------------------
DIM_KEY                                            VARCHAR2(100)
LEVEL_NAME                                         VARCHAR2(30)
DIM_ORDER                                          NUMBER
HIER_ORDER                                         NUMBER
LONG_DESCRIPTION                                   VARCHAR2(100)
SHORT_DESCRIPTION                                  VARCHAR2(100)
END_DATE                                           DATE
TIME_SPAN                                          NUMBER
PARENT                                             VARCHAR2(100)
TOTAL                                              VARCHAR2(100)
CALENDAR_YEAR                                      VARCHAR2(100)
CALENDAR_QUARTER                                   VARCHAR2(100)
MONTH                                              VARCHAR2(100)

Displaying the Contents of a Hierarchy View

The following query displays the dimension keys, parent key, and the full ancestry for calendar year 2005.

SELECT dim_key, long_description description, parent, calendar_year year,
       calendar_quarter quarter, month FROM time_calendar_view
       WHERE calendar_year='CY2005'
       ORDER BY level_name, end_date;

DIM_KEY      DESCRIPTION  PARENT       YEAR         QUARTER      MONTH
------------ ------------ ------------ ------------ ------------ ------------
CY2005.Q1    Q1.05        CY2005       CY2005       CY2005.Q1
CY2005.Q2    Q2.05        CY2005       CY2005       CY2005.Q2
CY2005.Q3    Q3.05        CY2005       CY2005       CY2005.Q3
CY2005.Q4    Q4.05        CY2005       CY2005       CY2005.Q4
CY2005       2005         TOTAL        CY2005
2005.01      JAN-05       CY2005.Q1    CY2005       CY2005.Q1    2005.01
2005.02      FEB-05       CY2005.Q1    CY2005       CY2005.Q1    2005.02
2005.03      MAR-05       CY2005.Q1    CY2005       CY2005.Q1    2005.03
2005.04      APR-05       CY2005.Q2    CY2005       CY2005.Q2    2005.04
2005.05      MAY-05       CY2005.Q2    CY2005       CY2005.Q2    2005.05
2005.06      JUN-05       CY2005.Q2    CY2005       CY2005.Q2    2005.06
2005.07      JUL-05       CY2005.Q3    CY2005       CY2005.Q3    2005.07
2005.08      AUG-05       CY2005.Q3    CY2005       CY2005.Q3    2005.08
2005.09      SEP-05       CY2005.Q3    CY2005       CY2005.Q3    2005.09
2005.10      OCT-05       CY2005.Q4    CY2005       CY2005.Q4    2005.10
2005.11      NOV-05       CY2005.Q4    CY2005       CY2005.Q4    2005.11
2005.12      DEC-05       CY2005.Q4    CY2005       CY2005.Q4    2005.12
 
17 rows selected.

Creating Basic Queries

Querying a cube is similar to querying a star schema. In a star schema, you join a fact table to a dimension table. The fact table provides the numeric business measures, and the dimension table provides descriptive attributes that give meaning to the data. Similarly, you join a cube view with either a dimension view or a hierarchy view to provide fully identified and meaningful data to your users.

For dimensions with no hierarchies, use the dimension views in your queries. For dimensions with hierarchies, use the hierarchy views, because they contain more information than the dimension views.

When querying a cube, remember these guidelines:

  • Apply a filter to every dimension.

    The cube contains both detail level and aggregated data. A query with an unfiltered dimension typically returns more data than users need, which negatively impacts performance.

  • Let the cube aggregate the data.

    Because the aggregations are calculated in the cube, a typical query does not need a GROUP BY clause. Simply select the aggregations you want by using the appropriate filters on the dimension keys or attributes.

Applying a Filter to Every Dimension

To create a level filter, you must know the names of the dimension levels. You can easily acquire them by querying the dimension or hierarchy views:

SELECT DISTINCT level_name FROM time_calendar_view;
 
LEVEL_NAME
------------------------------
CALENDAR_YEAR
CALENDAR_QUARTER
MONTH
TOTAL

Several data dictionary views list the names of the levels. The following example queries USER_CUBE_HIER_LEVELS.

SELECT level_name FROM user_cube_hier_levels
     WHERE dimension_name = 'TIME' AND hierarchy_name ='CALENDAR';
 
LEVEL_NAME
--------------------
TOTAL
CALENDAR_YEAR
CALENDAR_QUARTER
MONTH

To see the importance of applying a filter to every dimension, consider the query in Example 4-1, which has no filter on the time dimension.

Example 4-1 Displaying Aggregates at All Levels of Time

/* Select key descriptions and facts */
SELECT t.long_description time,
     ROUND(f.sales) sales
/* From dimension views and cube view */
  FROM time_calendar_view t,
     product_primary_view p,
     customer_shipments_view cu,
     channel_primary_view ch,
     units_cube_view f
/* No filter on Time */
  WHERE p.level_name = 'TOTAL' 
     AND cu.level_name = 'TOTAL'
     AND ch.level_name = 'TOTAL'
/* Join dimension views to cube view */
     AND t.dim_key = f.time
     AND p.dim_key = f.product
     AND cu.dim_key = f.customer
     AND ch.dim_key = f.channel
  ORDER BY t.end_date;

Without a filter on the Time dimension, the query returns values for every level of time. This is more data than users typically want to see, and the volume of data returned can negatively impact performance.

TIME            SALES
---------- ----------
JAN-98        8338545
FEB-98        7972132
Q1.98        24538588
MAR-98        8227911
APR-98        8470315
MAY-98        8160573
JUN-98        8362386
Q2.98        24993273
JUL-98        8296226
AUG-98        8377587
SEP-98        8406728
Q3.98        25080541
OCT-98        8316169
NOV-98        8984156
Q4.98        26258474
1998        100870877
                .
                .
                .

Now consider the results when a filter restricts Time to yearly data.

Example 4-2 shows a basic query. It selects the Sales measure from UNITS_CUBE_VIEW, and joins the keys from the cube view to the hierarchy views to get descriptions of the keys.

Example 4-2 Basic Cube View Query

/* Select key descriptions and facts */
SELECT t.long_description time,
     ROUND(f.sales) sales
/* From dimension views and cube view */
  FROM time_calendar_view t,
     product_primary_view p,
     customer_shipments_view cu,
     channel_primary_view ch,
     units_cube_view f
/* Create level filters */
  WHERE t.level_name = 'CALENDAR_YEAR'
     AND p.level_name = 'TOTAL'
     AND cu.level_name = 'TOTAL'
     AND ch.level_name = 'TOTAL'
/* Join dimension views to cube view */
     AND t.dim_key = f.time
     AND p.dim_key = f.product
     AND cu.dim_key = f.customer
     AND ch.dim_key = f.channel
  ORDER BY t.end_date;

Example 4-2 selects the following rows. For CUSTOMER, PRODUCT, and CHANNEL, only one value is at the top level. TIME has a value for each calendar year.

TIME          SALES
-------- ----------
1998      100870877
1999      134109248
2000      124173522
2001      116931722
2002       92515295
2003      130276514
2004      144290686
2005      136986572
2006      140138317

Dimension attributes also provide a useful way to select the data for a query. The WHERE clause in Example 4-3 uses attributes values to filter all of the dimensions.

Example 4-3 Selecting Data with Attribute Filters

/* Select key descriptions and facts */
SELECT t.long_description time,
     p.long_description product,
     cu.long_description customer,
     ch.long_description channel,
     ROUND(f.sales) sales
/* From dimension views and cube view */
FROM time_calendar_view t,
     product_primary_view p,
     customer_shipments_view cu,
     channel_primary_view ch,
     units_cube_view f
/* Create attribute filters */
WHERE t.long_description in ('2005', '2006')
    AND p.package = 'Laptop Value Pack'
    AND cu.long_description LIKE '%Boston%'
    AND ch.long_description = 'Internet'
/* Join dimension views to cube view */
    AND t.dim_key = f.time
    AND p.dim_key = f.product
    AND cu.dim_key = f.customer
    AND ch.dim_key = f.channel
ORDER BY time, customer;

The query selects two calendar years, the products in the Laptop Value Pack, the customers in Boston, and the Internet channel.

TIME   PRODUCT                        CUSTOMER              CHANNEL       SALES
------ ------------------------------ --------------------- -------- ----------
2005   Laptop carrying case           KOSH Entrpr Boston    Internet       5936
2005   56Kbps V.92 Type II Fax/Modem  KOSH Entrpr Boston    Internet      45285
2005   Internal 48X CD-ROM            KOSH Entrpr Boston    Internet       2828
2005   Standard Mouse                 KOSH Entrpr Boston    Internet        638
2005   Envoy Standard                 Warren Systems Boston Internet      19359
2005   Laptop carrying case           Warren Systems Boston Internet      13434
2005   Standard Mouse                 Warren Systems Boston Internet        130
2006   Standard Mouse                 KOSH Entrpr Boston    Internet        555
2006   Laptop carrying case           KOSH Entrpr Boston    Internet       6357
2006   56Kbps V.92 Type II Fax/Modem  KOSH Entrpr Boston    Internet      38042
2006   Internal 48X CD-ROM            KOSH Entrpr Boston    Internet       3343
2006   Envoy Standard                 Warren Systems Boston Internet      24198
2006   Laptop carrying case           Warren Systems Boston Internet      13153
2006   Standard Mouse                 Warren Systems Boston Internet         83
 
14 rows selected.

Allowing the Cube to Aggregate the Data

A cube contains all of the aggregate data. As shown in this chapter, a query against a cube just selects the aggregate data. It does not calculate the values.

The following is a basic query against a fact table:

/* Querying a fact table */
SELECT t.calendar_year_dsc time,
     SUM(f.sales) sales
  FROM time_dim t, units_fact f
  WHERE t.calendar_year_dsc IN ('2005', '2006')
     AND t.month_id = f.month_id
  GROUP BY t.calendar_year_dsc;

The next query fetches the exact same results from a cube using filters:

/* Querying a cube */
SELECT t.long_description time, f.sales sales
  FROM time_calendar_view t, 
     product_primary_view p,
     customer_shipments_view cu,
     channel_primary_view ch,
     units_cube_view f
/* Apply filters to every dimension */
  WHERE t.long_description IN ('2005', '2006')
     AND p.level_name = 'TOTAL'
     AND cu.level_name = 'TOTAL'
     AND ch.level_name = 'TOTAL'
/* Join dimension views to cube view */
     AND t.dim_key = f.TIME
     AND p.dim_key = f.product
     AND cu.dim_key = f.customer
     AND ch.dim_key = f.channel
  ORDER BY time;

Both queries return these results:

TIME       SALES
----- ----------
2005   136986572
2006   140138317

The query against the cube does not compute the aggregate values with a SUM operator and GROUP BY clause. Because the aggregates exist in the cube, this would re-aggregate previously aggregated data. Instead, the query selects the aggregates directly from the cube and specifies the desired aggregates by applying the appropriate filter to each dimension.

Query Processing

The most efficient queries allow the OLAP engine to filter the data, so that the minimum number of rows required by the query are returned to SQL.

The following are among the WHERE clause operations that are pushed into the OLAP engine for processing:

  • =

  • !=

  • >

  • !>

  • <

  • !<

  • IN

  • NOT IN

  • IS NULL

  • LIKE

  • NOT LIKE

The OLAP engine also processes nested character functions, including INSTR, LENGTH, NVL, LOWER, UPPER, LTRIM, RTRIM, TRIM, LPAD, RPAD, and SUBSTR.

SQL processes other operations and functions in the WHERE clause, and all operations in other parts of the SELECT syntax.

Creating Hierarchical Queries

Drilling is an important capability in business analysis. In a dashboard or an application, users click a dimension key to change the selection of data. Decision makers frequently want to drill down to see the contributors to a data value, or drill up to see how a particular data value contributes to the whole. For example, the Boston regional sales manager might start at total Boston sales, drill down to see the contributions of each sales representative, then drill up to see how the Boston region contributes to the New England sales total.

The hierarchy views include a PARENT column that identifies the parent of every dimension key. This column encapsulates all of the hierarchical information of the dimension: If you know the parent of every key, then you can derive the ancestors, the children, and the descendants.

For level-based hierarchies, the LEVEL_NAME column supplements this information by providing a convenient way to identify all the keys at the same depth in the hierarchy, from the top to the base. For value-based hierarchies, the PARENT column provides all the information about the hierarchy.

See Also:

Chapter 6, "Developing Reports and Dashboards" about using bind variables to support drilling

Drilling Down to Children

You can use the PARENT column of a hierarchy view to select only the children of a particular value. The following WHERE clause selects the children of calendar year 2005.

/* Select children of calendar year 2005 */
WHERE  t.parent = 'CY2005'
   AND p.dim_key = 'TOTAL'
   AND cu.dim_key = 'TOTAL'
   AND ch.dim_key = 'TOTAL'

The query drills down from Year to Quarter. The four quarters Q1-05 to Q4-05 are the children of year CY2005 in the Calendar hierarchy.

TIME          SALES
-------- ----------
Q1.05      31381338
Q2.05      37642741
Q3.05      32617249
Q4.05      35345244

Drilling Up to Parents

The PARENT column of a hierarchy view identifies the parent of each dimension key. Columns of level keys identify the full heritage. The following WHERE clause selects the parent of a Time key based on its LONG_DESCRIPTION attribute.


/* Select the parent of a Time key*/
WHERE t.dim_key = 
   (SELECT DISTINCT parent
      FROM time_calendar_view
      WHERE long_description='JAN-05')
   AND p.dim_key= 'TOTAL'
   AND cu.dim_key = 'TOTAL'
   AND ch.dim_key = 'TOTAL'

The query drills up from Month to Quarter. The parent of month JAN-05 is the quarter Q1-05 in the Calendar hierarchy.

TIME          SALES
-------- ----------
Q1.05      31381338

Drilling Down to Descendants

The following WHERE clause selects the descendants of calendar year 2005 by selecting the rows with a LEVEL_NAME of MONTH and a CALENDAR_YEAR of CY2005.

/* Select Time level and ancestor */
WHERE t.level_name = 'MONTH'
    AND t.calendar_year = 'CY2005'
    AND p.dim_key = 'TOTAL'
    AND cu.dim_key = 'TOTAL'
    AND ch.dim_key = 'TOTAL'

The query drills down two levels, from year to quarter to month. The 12 months Jan-05 to Dec-05 are the descendants of year 2005 in the Calendar hierarchy.

TIME          SALES
-------- ----------
JAN-05     12093518
FEB-05     10103162
MAR-05      9184658
APR-05      9185964
MAY-05     11640216
JUN-05     16816561
JUL-05     11110903
AUG-05      9475807
SEP-05     12030538
OCT-05     11135032
NOV-05     11067754
DEC-05     13142459

Drilling Up to Ancestors

The hierarchy views provide the full ancestry of each dimension key, as shown in "Displaying the Contents of a Hierarchy View". The following WHERE clause uses the CALENDAR_YEAR level key column to identify the ancestor of a MONTH dimension key.

/* Select the ancestor of a Time key based on its Long Description attribute */
WHERE t.dim_key =
   (SELECT calendar_year
      FROM time_calendar_view
      WHERE long_description = 'JAN-05')
   AND p.dim_key = 'TOTAL'
   AND cu.dim_key = 'TOTAL'
   AND ch.dim_key = 'TOTAL'

The query drills up two levels from month to quarter to year. The ancestor of month Jan-05 is the year 2005 in the Calendar hierarchy.


TIME          SALES
-------- ----------
2005      136986572

Using Calculations in Queries

A DBA can create calculated measures in Analytic Workspace Manager, so they are available to all applications. This not only simplifies application development, but ensures that all applications use the same name for the same calculation.

Nonetheless, you may want to develop queries that include your own calculations. In this case, you can use an inner query to select aggregate data from the cube, then perform calculations in an outer query. You can select data from cubes that use any type of aggregation operators, and you can use any functions or operators in the query. You must ensure only that you select the data from the cube at the appropriate levels for the calculation, and that the combination of operators in the cube and in the query create the calculation you want.

Example 4-4 shows a query that answers the question, What was the average sales of Sentinel Standard computers to Government customers for the third quarter of fiscal year 2005. UNITS_CUBE is summed over all dimensions, so that FY2005.Q3 is a total for July, August, and September. The inner query extracts the data for these months, and the outer query uses the MIN, MAX, and AVG operators and a GROUP BY clause to calculate the averages.

Example 4-4 Calculating Average Sales Across Customers

SELECT customer, ROUND(MIN(sales)) minimum, ROUND(MAX(sales)) maximum, 
   ROUND(AVG(sales)) average
FROM
   (SELECT cu.long_description customer,
       t.month_long_description time
       f.sales sales
   FROM time_fiscal_view t,
     product_primary_view p,
     customer_market_view cu,
     channel_primary_view ch,
     units_cube_view f
   WHERE t.parent = 'FY2005.Q3'
     AND p.dim_key = 'SENT STD'
     AND cu.parent = 'GOV'
     AND ch.level_name = 'TOTAL'
     AND t.dim_key = f.time
     AND p.dim_key = f.product
     AND cu.dim_key = f.customer
     AND ch.dim_key = f.channel
   )
GROUP BY customer
ORDER BY customer;

This is the data extracted from the cube by the inner query:

CUSTOMER                                 TIME          SALES
---------------------------------------- -------- ----------
Dept. of Labor                           JAN-05      1553.26
Dept. of Labor                           MAR-05       1555.6
Ministry of Intl Trade                   JAN-05      1553.26
Ministry of Intl Trade                   FEB-05      1554.56
Ministry of Intl Trade                   MAR-05       1555.6
Royal Air Force                          JAN-05      1553.26
Royal Air Force                          FEB-05      6218.23
UK Environmental Department              JAN-05      4659.78
UK Environmental Department              FEB-05      3109.12

The outer query calculates the minimum, maximum, and average sales for each customer:

CUSTOMER                          MINIMUM    MAXIMUM    AVERAGE
------------------------------ ---------- ---------- ----------
Dept. of Labor                       1553       1556       1554
Ministry of Intl Trade               1553       1556       1554
Royal Air Force                      1553       6218       3886
UK Environmental Department          3109       4660       3884

Using Attributes for Aggregation

An OLAP cube aggregates the data within its hierarchies, using the parent-child relationships revealed in the hierarchy views. The OLAP engine does not calculate aggregates over dimension attribute values.

Nonetheless, you may want to aggregate products over color or size, or customers by age, zip code, or population density. This is the situation when you can use a GROUP BY clause when querying a cube. Your query can extract data from the cube, then use SQL to aggregate by attribute value.

The cube must use the same aggregation operator for all dimensions, and the aggregation operator in the SELECT list of the query must match the aggregation operator of the cube. You can use a GROUP BY clause to query cubes that use these operators:

  • First Non-NA Value

  • Last Non-NA Value

  • Maximum

  • Minimum

  • Sum

Aggregating Measures Over Attributes

Example 4-5 shows a query that aggregates over an attribute named Package. It returns these results:

TIME   PACKAGE                 SALES
------ ------------------ ----------
2005   All                1809157.64
2005   Multimedia         18083256.3
2005   Executive            19836977
2005   Laptop Value Pack  9547494.81

Units Cube uses the SUM operator for all dimensions, and the query uses the SUM operator to aggregate over Sales. The Package attribute applies only to the Item level of the Product dimension, so the query selects the Item level of Product. It also eliminates nulls for Package, so that only products that belong to a package are included in the calculation. The GROUP BY clause breaks out Total Sales by Time and Package.

Example 4-5 Aggregating Over an Attribute

SELECT t.long_description time,
     p.package package,
     SUM(f.sales) sales
  FROM time_calendar_view t,
     product_primary_view p,
     customer_shipments_view cu,
     channel_primary_view ch,
     units_cube_view f
/* Select Product by level and attribute */
  WHERE p.level_name = 'ITEM'
     AND p.package IS NOT NULL
     AND t.long_description = '2005'
     AND cu.level_name = 'TOTAL'
     AND ch.level_name = 'TOTAL'
/* Join dimensions and cube */
     AND t.dim_key = f.time
     AND p.dim_key = f.product
     AND cu.dim_key = f.customer
     AND ch.dim_key = f.channel
  GROUP BY t.long_description, p.package;

Aggregating Calculated Measures Over Attributes

Before using the technique described in "Aggregating Measures Over Attributes", ensure that the calculation is meaningful. For example, the common calculation Percent Change might be defined as a calculated measure in a cube. Summing over Percent Change would produce unexpected results, because the calculation for Percent Change ((a-b)/b,) is not additive.

Consider the following rows of data. The correct Total Percent Change is .33, whereas the sum of the percent change for the first two rows is .75.

Row Sales Sales Prior Period Percent Change
1 15 10 .50
2 25 20 .25
Total 40 30 .33

Example 4-6 shows a query that aggregates over the Package attribute and calculates Percent Change From Prior Period. The inner query aggregates Sales and Sales Prior Period over the attributes, and the outer query uses the results to compute the percent change. These are the results of the query, which show the expected results for PCT_CHG_PP:

TIME   PACKAGE                 SALES PRIOR_PERIOD PCT_CHG_PP
------ ------------------ ---------- ------------ ----------
2005   All                1809157.64   1853928.06 -.02414895
2006   All                1720399.03   1809157.64 -.04906074
2005   Executive            19836977   20603879.8 -.03722128
2006   Executive          19580638.4     19836977 -.01292226
2005   Laptop Value Pack  9547494.81   10047298.6 -.04974509
2006   Laptop Value Pack  9091450.58   9547494.81 -.04776585
2005   Multimedia         18083256.3   19607675.5 -.07774604
2006   Multimedia         18328678.7   18083256.3 .013571806
 
8 rows selected.

Example 4-6 Querying Over Attributes Using Calculated Measures

/* Calculate Percent Change */
SELECT TIME, package, sales, prior_period,
     ((sales - prior_period) / prior_period) pct_chg_pp
FROM
/* Fetch data from the cube and aggregate over Package */
     (SELECT t.long_description time,
          p.package package,
          SUM(f.sales) sales,
          SUM(f.sales_pp) prior_period
       FROM time_calendar_view t,
          product_primary_view p,
          customer_shipments_view cu,
          channel_primary_view ch,
          units_cube_view f
/* Create filters */
       WHERE p.level_name = 'ITEM'
          AND p.package IS NOT NULL
          AND t.long_description IN ('2005', '2006')
          AND cu.level_name = 'TOTAL'
          AND ch.level_name = 'TOTAL'
/* Join dimension views to cube view */
          AND t.dim_key = f.time
          AND p.dim_key = f.product
          AND cu.dim_key = f.customer
          AND ch.dim_key = f.channel
       GROUP BY t.long_description, p.package
       ORDER BY p.package);

Joining Cubes to Tables and Views

You can join cubes to other cubes and to relational objects such as:

  • Tables

  • Views including external tables and PL/SQL table functions

  • Other row source types, like other joins

Typically, you do not need a fully aggregated cube when joining it to a table or view, and a CUBE JOIN operation limits the number of fetched values to improve performance automatically. The cube must be on the right side of the equation. If the query does not support CUBE JOIN, then the more expensive HASH JOIN, MERGE JOIN, or NESTED LOOPS are commonly used.

You can use hints in the query to influence the use of CUBE JOIN:

  • USE_CUBE forces a CUBE JOIN if possible.

  • NO_USE_CUBE prevents a CUBE JOIN.

See "Viewing Execution Plans" for more information about CUBE JOIN.

Example 4-7 joins a table that contains French descriptions of the Customer dimension to a cube that supports only English. The query returns these results:

CUSTOMER                                        SALES
---------------------------------------- ------------
La Marine des USA Washington                   600.34
Monolith Motor Co. Chattanooga               17946.51
Piedmont, Inc. San Jose                      24874.41
Ministere du Commerce Int. Nagano            27595.97
Depart. des commun. - Stuttgart              30706.10
Min. Env. Brit. Londres                      38125.77
Departement de travail Nouvelle-Orleans      42507.50
Ministere des Finances Sorbonne              43607.58
Monolith Motor Co. Knoxville                 50874.53
Serv. des USA de recherche Wyo               54497.19
Depart. des commun. - Bonn                   58944.97
     .
     .
     .

Example 4-7 Joining a Cube and a Table

SELECT cu.ship_to_dsc_french customer,
     f.sales sales
FROM time_calendar_view t,
     product_primary_view p,
     customer_dim cu,
     channel_primary_view ch,
     units_cube_view f
WHERE t.dim_key = 'CY2006'
    AND p.level_name = 'TOTAL'
    AND ch.level_name = 'TOTAL'
    AND t.dim_key = f.TIME
    AND p.dim_key = f.product
    AND cu.ship_to_id = f.customer
    AND ch.dim_key = f.channel
ORDER BY f.sales;

Viewing Execution Plans

You can generate and view execution plans for queries against cubes and dimensions the same as for those against relational tables.

The SQL EXPLAIN PLAN command creates a table with the content of the explain plan. The default table name is PLAN_TABLE.

See Also:

Oracle Database SQL Tuning Guide for a complete discussion of execution plans

Generating Execution Plans

The following command creates an execution plan for a basic query on a cube:

EXPLAIN PLAN FOR
  SELECT t.long_description time,
     p.long_description product,
     cu.long_description customer,
     ch.long_description channel,
     f.sales sales
  FROM time_calendar_view t,
     product_primary_view p,
     customer_shipments_view cu,
     channel_primary_view ch,
     units_cube_view f
  WHERE t.level_name = 'CALENDAR_YEAR'
     AND p.level_name = 'TOTAL'
     AND cu.level_name = 'TOTAL'
     AND ch.level_name = 'TOTAL'
     AND t.dim_key = f.TIME
     AND p.dim_key = f.product
     AND cu.dim_key = f.customer
     AND ch.dim_key = f.channel
  ORDER BY t.end_date;

The DISPLAY table function of the DBMS_XPLAN PL/SQL package formats and displays information from an execution plan, as shown in Example 4-8.

Example 4-8 Execution Plan for a Cube Query

SQL> SELECT plan_table_output FROM TABLE(dbms_xplan.display());
 
PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------
Plan hash value: 1667678335
 
----------------------------------------------------------------------------------------------
| Id  | Operation                       | Name       | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                |            |     1 |   100 |   104   (3)| 00:00:02 |
|   1 |  SORT ORDER BY                  |            |     1 |   100 |   104   (3)| 00:00:02 |
|   2 |   JOINED CUBE SCAN PARTIAL OUTER|            |       |       |            |          |
|   3 |    CUBE ACCESS                  | UNITS_CUBE |       |       |            |          |
|   4 |    CUBE ACCESS                  | CHANNEL    |       |       |            |          |
|   5 |    CUBE ACCESS                  | CUSTOMER   |       |       |            |          |
|   6 |    CUBE ACCESS                  | PRODUCT    |       |       |            |          |
|*  7 |    CUBE ACCESS                  | TIME       |     1 |   100 |   103   (2)| 00:00:02 |
----------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
 
   7 - filter(SYS_OP_ATG(VALUE(KOKBF$),12,13,2)='CALENDAR_YEAR' AND
              SYS_OP_ATG(VALUE(KOKBF$),43,44,2)='TOTAL' AND
              SYS_OP_ATG(VALUE(KOKBF$),33,34,2)='TOTAL' AND
              SYS_OP_ATG(VALUE(KOKBF$),23,24,2)='TOTAL')
 
22 rows selected.

Example 4-9 shows an execution plan for a query that joins a cube and a table. See "Joining Cubes to Tables and Views" for the query.

Example 4-9 Execution Plan for a Cube Join

PLAN_TABLE_OUTPUT
-------------------------------------------------------------------------------------------------
Plan hash value: 3634608218
-------------------------------------------------------------------------------------------------
| Id  | Operation                        |Name        |Rows | Bytes |TempSpc|Cost (%CPU)| Time    |
--------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                 |            |1464 |   128K|       | 1524  (94)| 00:00:19|
|   1 |  SORT ORDER BY                   |            |1464 |   128K|   152K| 1524  (94)| 00:00:19|
|*  2 |   CUBE JOIN                      |            |1464 |   128K|       | 1422 (100)| 00:00:18|
|   3 |    TABLE ACCESS FULL             |CUSTOMER_DIM|  61 |  2379 |       |    4   (0)| 00:00:01|
|   4 |    JOINED CUBE SCAN PARTIAL OUTER|            |     |       |       |           |         |
|   5 |     CUBE ACCESS                  |UNITS_CUBE  |     |       |       |           |         |
|   6 |     CUBE ACCESS                  |CHANNEL     |     |       |       |           |         |
|   7 |     CUBE ACCESS                  |PRODUCT     |     |       |       |           |         |
|*  8 |     CUBE ACCESS                  |TIME        |2520 |   125K|       | 1417 (100)| 00:00:18|
--------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - access("CU"."SHIP_TO_ID"=SYS_OP_ATG(VALUE(KOKBF$),76,77,2))
   8 - filter(SYS_OP_ATG(VALUE(KOKBF$),32,33,2)='CY2006' AND
              SYS_OP_ATG(VALUE(KOKBF$),85,86,2)='TOTAL' AND 
              SYS_OP_ATG(VALUE(KOKBF$),65,66,2)='TOTAL')

22 rows selected.

Types of Execution Plans

Table 4-1 describes the types of execution plans for cubes.

Table 4-1 Descriptions of Execution Plans for Cubes and Dimensions

Operation Option Description

CUBE JOIN

--

Joins a table or view on the left and a cube on the right.

CUBE JOIN

ANTI

Uses an antijoin for a table or view on the left and a cube on the right.

CUBE JOIN

ANTI SNA

Uses an antijoin (Single-sided Null Aware) for a table or view on the left and a cube on the right. The join column on the right (cube side) is NOT NULL. For example:

SELECT cols FROM table 
   WHERE table.c1 NOT IN 
      (SELECT col FROM cube 
         WHERE cube.col IS NOT NULL)

CUBE JOIN

OUTER

Uses an outer join for a table or view on the left and a cube on the right.

CUBE JOIN

RIGHT SEMI

Uses a right semijoin for a table or view on the left and a cube on the right.

CUBE SCAN

--

Uses inner joins for all cube access.

CUBE SCAN

PARTIAL OUTER

Uses an outer join for least one dimension, and inner joins for the other dimensions.

CUBE SCAN

OUTER

Uses outer joins for all cube access.


See Also:

Oracle Database SQL Language Reference for descriptions of these join types.

Querying the Data Dictionary

If you are developing a generic application -- that is, one where the names of the dimensional objects are not known -- then your application can retrieve this information from the data dictionary.

Among the static views of the database data dictionary are those that provide information about dimensional objects. All OLAP metadata is stored in the data dictionary. A few of the data dictionary views were introduced previously in this chapter.

Table 4-2 provides brief descriptions of the ALL views. There are corresponding DBA and USER views.

Table 4-2 Static Data Dictionary Views for OLAP

View Description

ALL_CUBE_ATTR_VISIBILITY

Describes the visibility of the attributes for cube dimensions.

ALL_CUBE_ATTRIBUTES

Describes the attributes for cube dimensions.

ALL_CUBE_BUILD_PROCESSES

Describes the cube build processes and maintenance scripts.

ALL_CUBE_CALCULATED_MEMBERS

Describes the calculated members (keys) for cube dimensions.

ALL_CUBE_DIM_LEVELS

Describes the cube dimension levels.

ALL_CUBE_DIM_MODELS

Describes the models for cube dimensions.

ALL_CUBE_DIM_VIEW_COLUMNS

Describes the columns of the system-generated relational views of cube dimensions.

ALL_CUBE_DIM_VIEWS

Describes the system-generated relational views of OLAP dimensions.

ALL_CUBE_DIMENSIONALITY

Describes the dimension order of the OLAP cubes.

ALL_CUBE_DIMENSIONS

Describes the cube dimensions.

ALL_CUBE_HIER_LEVELS

Describes the hierarchy levels for cube dimensions.

ALL_CUBE_HIER_VIEW_COLUMNS

Describes the columns of relational hierarchy views of cube dimensions.

ALL_CUBE_HIER_VIEWS

Describes the hierarchies for cube dimensions.

ALL_CUBE_HIERARCHIES

Describes the OLAP dimension hierarchies.

ALL_CUBE_MEASURES

Describes the measures in the OLAP cubes.

ALL_CUBE_VIEW_COLUMNS

Describes the columns of the relational views of OLAP cubes.

ALL_CUBE_VIEWS

Describes the system-generated relational views of OLAP cubes.

ALL_CUBES

Describes the OLAP cubes.

ALL_MEASURE_FOLDER_CONTENTS

Describes the contents of OLAP measure folders.

ALL_MEASURE_FOLDERS

Describes the OLAP measure folders.


See Also:

Oracle Database Reference for full descriptions of data dictionary views.