10 Dimensions
This chapter discusses using dimensions in a data warehouse: It contains the following topics:
10.1 What are Dimensions?
A dimension is a structure that categorizes data in order to enable users to answer business questions. Commonly used dimensions are customers, products, and time. For example, each sales channel of a clothing retailer might gather and store data regarding sales and reclamations of their Cloth assortment. The retail chain management can build a data warehouse to analyze the sales of its products across all stores over time and help answer questions such as:
-
What is the effect of promoting one product on the sale of a related product that is not promoted?
-
What are the sales of a product before and after a promotion?
-
How does a promotion affect the various distribution channels?
The data in the retailer's data warehouse system has two important components: dimensions and facts. The dimensions are products, customers, promotions, channels, and time. One approach for identifying your dimensions is to review your reference tables, such as a product table that contains everything about a product, or a promotion table containing all information about promotions. The facts are sales (units sold) and profits. A data warehouse contains facts about the sales of each product at on a daily basis.
A typical relational implementation for such a data warehouse is a star schema. The fact information is stored in what is called a fact table, whereas the dimensional information is stored in dimension tables. In our example, each sales transaction record is uniquely defined as for each customer, for each product, for each sales channel, for each promotion, and for each day (time).
In Oracle Database, the dimensional information itself is stored in a dimension table. In addition, the database object dimension helps to organize and group dimensional information into hierarchies. This represents natural 1:n
relationships between columns or column groups (the levels of a hierarchy) that cannot be represented with constraint conditions. Going up a level in the hierarchy is called rolling up the data and going down a level in the hierarchy is called drilling down the data. In the retailer example:
-
Within the
time
dimension, months roll up to quarters, quarters roll up to years, and years roll up to all years. -
Within the
product
dimension, products roll up to subcategories, subcategories roll up to categories, and categories roll up to all products. -
Within the
customer
dimension, customers roll up tocity
. Then cities roll up tostate
. Then states roll up tocountry
. Then countries roll up tosubregion
. Finally, subregions roll up toregion
, as shown in Figure 10-1.
Figure 10-1 Sample Rollup for a Customer Dimension
![Description of Figure 10-1 follows Description of Figure 10-1 follows](img/dwhsg072.gif)
Description of "Figure 10-1 Sample Rollup for a Customer Dimension"
Data analysis typically starts at higher levels in the dimensional hierarchy and gradually drills down if the situation warrants such analysis.
Dimension schema objects (dimensions) do not have to be defined. However, if your application uses dimensional modeling, it is worth spending time creating them as it can yield significant benefits, because they help query rewrite perform more complex types of rewrite. Dimensions are also beneficial to certain types of materialized view refresh operations and with the SQL Access Advisor. They are only mandatory if you use the SQL Access Advisor (a GUI tool for materialized view and index management) without a workload to recommend which materialized views and indexes to create, drop, or retain.
In spite of the benefits of dimensions, you must not create dimensions in any schema that does not fully satisfy the dimensional relationships described in this chapter. Incorrect results can be returned from queries otherwise.
See Also:
-
Data Warehousing Optimizations and Techniques for further details about schemas
-
Basic Query Rewrite for Materialized Views for further details regarding query rewrite
-
Oracle Database SQL Tuning Guide for further details regarding the SQL Access Advisor
10.1.1 Requirements for Dimensions in Data Warehouses
-
There must be a
1:n
relationship between a parent and children. A parent can have one or more children, but a child can have only one parent. -
There must be a
1:1
attribute relationship between hierarchy levels and their dependent dimension attributes. For example, if there is a columnfiscal_month_desc
, then a possible attribute relationship would befiscal_month_desc
tofiscal_month_name
. For skipNULL
levels, if a row of the relation of a skip level has aNULL
value for the level column, then that row must have aNULL
value for the attribute-relationship column, too. -
If the columns of a parent level and child level are in different relations, then the connection between them also requires a
1:n
join relationship. Each row of the child table must join with one and only one row of the parent table unless you use theSKIP
WHEN
NULL
clause. This relationship is stronger than referential integrity alone, because it requires that the child join key must be non-null, that referential integrity must be maintained from the child join key to the parent join key, and that the parent join key must be unique. -
You must ensure (using database constraints if necessary) that the columns of each hierarchy level are non-null unless you use the
SKIP
WHEN
NULL
clause and that hierarchical integrity is maintained. -
An optional join key is a join key that connects the immediate non-skip child (if such a level exists),
CHILDLEV
, of a skip level to the nearest non-skip ancestor (again, if such a level exists),ANCLEV
, of the skip level in the hierarchy. Also, this joinkey is allowed only whenCHILDLEV
andANCLEV
are defined over different relations. -
The hierarchies of a dimension can overlap or be disconnected from each other. However, the columns of a hierarchy level cannot be associated with more than one dimension.
-
Join relationships that form cycles in the dimension graph are not supported. For example, a hierarchy level cannot be joined to itself either directly or indirectly.
Note:
The information stored with a dimension objects is only declarative. The previously discussed relationships are not enforced with the creation of a dimension object. You should validate any dimension definition with the
DBMS_DIMENSION.VALIDATE_DIMENSION
procedure, as discussed in "Validating Dimensions".
10.2 Creating Dimensions
Before you can create a dimension object, the dimension tables must exist in the database possibly containing the dimension data. For example, if you create a customer dimension, one or more tables must exist that contain the city, state, and country information. In a star schema data warehouse, these dimension tables already exist. It is therefore a simple task to identify which ones will be used.
Now you can draw the hierarchies of a dimension as shown in Figure 10-1. For example, city
is a child of state
(because you can aggregate city-level data up to state), and country
. This hierarchical information will be stored in the database object dimension.
In the case of normalized or partially normalized dimension representation (a dimension that is stored in more than one table), identify how these tables are joined. Note whether the joins between the dimension tables can guarantee that each child-side row joins with one and only one parent-side row. In the case of denormalized dimensions, determine whether the child-side columns uniquely determine the parent-side (or attribute) columns. If you use constraints to represent these relationships, they can be enabled with the NOVALIDATE
and RELY
clauses if the relationships represented by the constraints are guaranteed by other means.
You may want the capability to skip NULL
levels in a dimension. An example of this is with Puerto Rico. You may want Puerto Rico to be included within a region of North America, but not include it within the state category. If you want this capability, use the SKIP
WHEN
NULL
clause. See the sample dimension later in this section for more information and Oracle Database SQL
Language Reference for syntax and restrictions.
You create a dimension using either the CREATE
DIMENSION
statement or the Dimension Wizard in Oracle Enterprise Manager. Within the CREATE
DIMENSION
statement, use the LEVEL
clause to identify the names of the dimension levels.
This customer dimension contains a single hierarchy with a geographical rollup, with arrows drawn from the child level to the parent level, as shown in Figure 10-1.
Each arrow in this graph indicates that for any child there is one and only one parent. For example, each city must be contained in exactly one state and each state must be contained in exactly one country. States that belong to more than one country violate hierarchical integrity. Also, you must use the SKIP
WHEN
NULL
clause if you want to include cities that do not belong to a state, such as Washington D.C. Hierarchical integrity is necessary for the correct operation of management functions for materialized views that include aggregates.
For example, you can declare a dimension products_dim
, which contains levels product
, subcategory
, and category
:
CREATE DIMENSION products_dim LEVEL product IS (products.prod_id) LEVEL subcategory IS (products.prod_subcategory) LEVEL category IS (products.prod_category) ...
Each level in the dimension must correspond to one or more columns in a table in the database. Thus, level product
is identified by the column prod_id
in the products table and level subcategory
is identified by a column called prod_subcategory
in the same table.
In this example, the database tables are denormalized and all the columns exist in the same table. However, this is not a prerequisite for creating dimensions. "Using Normalized Dimension Tables to Create Dimensions" shows how to create a dimension customers_dim
that has a normalized schema design using the JOIN
KEY
clause.
The next step is to declare the relationship between the levels with the HIERARCHY
statement and give that hierarchy a name. A hierarchical relationship is a functional dependency from one level of a hierarchy to the next level in the hierarchy. Using the level names defined previously, the CHILD
OF
relationship denotes that each child's level value is associated with one and only one parent level value. The following statement declares a hierarchy prod_rollup
and defines the relationship between products, subcategory, and category:
HIERARCHY prod_rollup (product CHILD OF subcategory CHILD OF category)
In addition to the 1:n
hierarchical relationships, dimensions also include 1:1
attribute relationships between the hierarchy levels and their dependent, determined dimension attributes. For example, the dimension times_dim
, as defined in Oracle Database Sample
Schemas, has columns fiscal_month_desc
, fiscal_month_name
, and days_in_fiscal_month
. Their relationship is defined as follows:
LEVEL fis_month IS TIMES.FISCAL_MONTH_DESC ... ATTRIBUTE fis_month DETERMINES (fiscal_month_name, days_in_fiscal_month)
The ATTRIBUTE
... DETERMINES
clause relates fis_month
to fiscal_month_name and days_in_fiscal_month
. Note that this is a unidirectional determination. It is only guaranteed, that for a specific fiscal_month
, for example, 1999-11
, you will find exactly one matching values for fiscal_month_name
, for example, November
and days_in_fiscal_month
, for example, 28. You cannot determine a specific fiscal_month_desc
based on the fiscal_month_name
, which is November
for every fiscal year.
In this example, suppose a query were issued that queried by fiscal_month_name
instead of fiscal_month_desc
. Because this 1:1
relationship exists between the attribute and the level, an already aggregated materialized view containing fiscal_month_desc
can be joined back to the dimension information and used to identify the data.
A sample dimension definition follows:
CREATE DIMENSION products_dim LEVEL product IS (products.prod_id) LEVEL subcategory IS (products.prod_subcategory) [SKIP WHEN NULL] LEVEL category IS (products.prod_category) HIERARCHY prod_rollup ( product CHILD OF subcategory CHILD OF category) ATTRIBUTE product DETERMINES (products.prod_name, products.prod_desc, prod_weight_class, prod_unit_of_measure, prod_pack_size, prod_status, prod_list_price, prod_min_price) ATTRIBUTE subcategory DETERMINES (prod_subcategory, prod_subcategory_desc) ATTRIBUTE category DETERMINES (prod_category, prod_category_desc);
Alternatively, the extended_attribute_clause
could have been used instead of the attribute_clause
, as shown in the following example:
CREATE DIMENSION products_dim LEVEL product IS (products.prod_id) LEVEL subcategory IS (products.prod_subcategory) LEVEL category IS (products.prod_category) HIERARCHY prod_rollup ( product CHILD OF subcategory CHILD OF category ) ATTRIBUTE product_info LEVEL product DETERMINES (products.prod_name, products.prod_desc, prod_weight_class, prod_unit_of_measure, prod_pack_size, prod_status, prod_list_price, prod_min_price) ATTRIBUTE subcategory DETERMINES (prod_subcategory, prod_subcategory_desc) ATTRIBUTE category DETERMINES (prod_category, prod_category_desc);
The design, creation, and maintenance of dimensions is part of the design, creation, and maintenance of your data warehouse schema. Once the dimension has been created, verify that it meets the requirements described in Requirements for Dimensions in Data Warehouses.
See Also:
-
Basic Query Rewrite for Materialized Views for further details of using dimensional information
-
Oracle Database SQL Language Reference for a complete description of the
CREATE
DIMENSION
statement
10.2.1 Dropping and Creating Attributes with Columns
You can use the attribute clause in a CREATE
DIMENSION
statement to specify one or multiple columns that are uniquely determined by a hierarchy level.
If you use the extended_attribute_clause
to create multiple columns determined by a hierarchy level, you can drop one attribute column without dropping them all. Alternatively, you can specify an attribute name for each attribute clause CREATE
or ALTER
DIMENSION
statement so that an attribute name is specified for each attribute clause where multiple level-to-column relationships can be individually specified.
The following statement illustrates how you can drop a single column without dropping all columns:
CREATE DIMENSION products_dim LEVEL product IS (products.prod_id) LEVEL subcategory IS (products.prod_subcategory) LEVEL category IS (products.prod_category) HIERARCHY prod_rollup ( product CHILD OF subcategory CHILD OF category) ATTRIBUTE product DETERMINES (products.prod_name, products.prod_desc, prod_weight_class, prod_unit_of_measure, prod_pack_size,prod_status, prod_list_price, prod_min_price) ATTRIBUTE subcategory_att DETERMINES (prod_subcategory, prod_subcategory_desc) ATTRIBUTE category DETERMINES (prod_category, prod_category_desc); ALTER DIMENSION products_dim DROP ATTRIBUTE subcategory_att LEVEL subcategory COLUMN prod_subcategory;
See Also:
Oracle Database SQL
Language Reference for a complete description of the CREATE
DIMENSION
statement
10.2.2 Using Multiple Hierarchies While Creating Joins
A single dimension definition can contain multiple hierarchies. Suppose our retailer wants to track the sales of certain items over time. The first step is to define the time dimension over which sales will be tracked. Figure 10-2 illustrates a dimension times_dim
with two time hierarchies.
Figure 10-2 times_dim Dimension with Two Time Hierarchies
![Description of Figure 10-2 follows Description of Figure 10-2 follows](img/dwhsg075.gif)
Description of "Figure 10-2 times_dim Dimension with Two Time Hierarchies"
From the illustration, you can construct the hierarchy of the denormalized time_dim
dimension's CREATE
DIMENSION
statement as follows.
CREATE DIMENSION times_dim LEVEL day IS times.time_id LEVEL month IS times.calendar_month_desc LEVEL quarter IS times.calendar_quarter_desc LEVEL year IS times.calendar_year LEVEL fis_week IS times.week_ending_day LEVEL fis_month IS times.fiscal_month_desc LEVEL fis_quarter IS times.fiscal_quarter_desc LEVEL fis_year IS times.fiscal_year HIERARCHY cal_rollup ( day CHILD OF month CHILD OF quarter CHILD OF year ) HIERARCHY fis_rollup ( day CHILD OF fis_week CHILD OF fis_month CHILD OF fis_quarter CHILD OF fis_year ) <attribute determination clauses>;
10.2.3 Using Normalized Dimension Tables to Create Dimensions
The tables used to define a dimension may be normalized or denormalized and the individual hierarchies can be normalized or denormalized. If the levels of a hierarchy come from the same table, it is called a fully denormalized hierarchy. For example, cal_rollup
in the times_dim
dimension is a denormalized hierarchy. If levels of a hierarchy come from different tables, such a hierarchy is either a fully or partially normalized hierarchy. This section shows how to define a normalized hierarchy.
Suppose the tracking of a customer's location is done by city, state, and country.
This data is stored in the tables customers and countries. The customer dimension
customers_dim
is partially normalized because the data entities
cust_id
and country_id
are taken from different
tables. The clause JOIN
KEY
within the dimension definition specifies how to join together the
levels in the hierarchy. The dimension statement is partially shown in the following.
This example does not use the Oracle-supplied sample schemas.
CREATE DIMENSION customers_dim LEVEL customer IS (customers.cust_id) LEVEL city IS (customers.cust_city) LEVEL state IS (customers.cust_state_province) LEVEL country IS (countries.country_id) LEVEL subregion IS (countries.country_subregion) LEVEL region IS (countries.country_region) HIERARCHY geog_rollup ( customer CHILD OF city CHILD OF state CHILD OF country CHILD OF subregion CHILD OF region JOIN KEY (customers.country_id) REFERENCES country);
If you use the SKIP
WHEN
NULL
clause, you can use the JOIN
KEY
clause to link levels that have a missing level in their hierarchy. For example, the following statement enables a state level that has been declared as SKIP
WHEN
NULL
to join city and country:
JOIN KEY (city.country_id) REFERENCES country;
This ensures that the rows at customer and city levels can still be associated with the rows of country, subregion, and region levels.
10.3 Viewing Dimensions
Dimensions can be viewed through one of two methods:
10.3.1 Viewing Dimensions With Oracle Enterprise Manager
All of the dimensions that exist in the data warehouse can be viewed using Oracle Enterprise Manager. Select the Dimension object from within the Schema icon to display all of the dimensions. Select a specific dimension to graphically display its hierarchy, levels, and any attributes that have been defined.
10.3.2 Viewing Dimensions With the DESCRIBE_DIMENSION Procedure
To view the definition of a dimension, use the DESCRIBE_DIMENSION
procedure in the DBMS_DIMENSION
package. For example, if a dimension is created in the sh
sample schema with the following statements:
CREATE DIMENSION channels_dim LEVEL channel IS (channels.channel_id) LEVEL channel_class IS (channels.channel_class) HIERARCHY channel_rollup ( channel CHILD OF channel_class) ATTRIBUTE channel DETERMINES (channel_desc) ATTRIBUTE channel_class DETERMINES (channel_class);
Execute the DESCRIBE_DIMENSION
procedure as follows:
SET SERVEROUTPUT ON FORMAT WRAPPED; --to improve the display of info EXECUTE DBMS_DIMENSION.DESCRIBE_DIMENSION('SH.CHANNELS_DIM');
You then see the following output results:
EXECUTE DBMS_DIMENSION.DESCRIBE_DIMENSION('SH.CHANNELS_DIM'); DIMENSION SH.CHANNELS_DIM LEVEL CHANNEL IS SH.CHANNELS.CHANNEL_ID LEVEL CHANNEL_CLASS IS SH.CHANNELS.CHANNEL_CLASS HIERARCHY CHANNEL_ROLLUP ( CHANNEL CHILD OF CHANNEL_CLASS) ATTRIBUTE CHANNEL LEVEL CHANNEL DETERMINES SH.CHANNELS.CHANNEL_DESC ATTRIBUTE CHANNEL_CLASS LEVEL CHANNEL_CLASS DETERMINES SH.CHANNELS.CHANNEL_CLASS
10.4 Using Dimensions with Constraints
Constraints play an important role with dimensions. Full referential integrity is sometimes enabled in data warehouses, but not always. This is because operational databases normally have full referential integrity and you can ensure that the data flowing into your data warehouse never violates the already established integrity rules.
It is recommended that constraints be enabled and, if validation time is a concern, then the NOVALIDATE
clause should be used as follows:
ENABLE NOVALIDATE CONSTRAINT pk_time;
Primary and foreign keys should be implemented also. Referential integrity constraints and NOT
NULL
constraints on the fact tables provide information that query rewrite can use to extend the usefulness of materialized views.
In addition, you should use the RELY
clause to inform query rewrite that it can rely upon the constraints being correct as follows:
ALTER TABLE time MODIFY CONSTRAINT pk_time RELY;
This information is also used for query rewrite. See Basic Query Rewrite for Materialized Views for more information.
If you use the SKIP
WHEN
NULL
clause, at least one of the referenced level columns should not have NOT
NULL
constraints.
10.5 Validating Dimensions
The information of a dimension object is declarative only and not enforced by the database. If the relationships described by the dimensions are incorrect, incorrect results could occur. Therefore, you should verify the relationships specified by CREATE
DIMENSION
using the DBMS_DIMENSION.VALIDATE_DIMENSION
procedure periodically.
This procedure is easy to use and has only four parameters:
-
dimension
: the owner and name. -
incremental
: set toTRUE
to check only the new rows for tables of this dimension. -
check_nulls
: set toTRUE
to verify that all columns that are not in the levels containing aSKIP
WHEN
NULL
clause are not null. -
statement_id
: a user-supplied unique identifier to identify the result of each run of the procedure.
The following example validates the dimension TIME_FN
in the sh
schema:
@utldim.sql EXECUTE DBMS_DIMENSION.VALIDATE_DIMENSION ('SH.TIME_FN', FALSE, TRUE, 'my first example');
Before running the VALIDATE_DIMENSION
procedure, you need to create a local table, DIMENSION_EXCEPTIONS
, by running the provided script utldim.sql
. If the VALIDATE_DIMENSION
procedure encounters any errors, they are placed in this table. Querying this table will identify the exceptions that were found. The following illustrates a sample:
SELECT * FROM dimension_exceptions WHERE statement_id = 'my first example';
STATEMENT_ID OWNER TABLE_NAME DIMENSION_NAME RELATIONSHIP BAD_ROWID ------------ ----- ---------- -------------- ------------ --------- my first example SH MONTH TIME_FN FOREIGN KEY AAAAuwAAJAAAARwAAA
However, rather than query this table, it may be better to query the rowid of the invalid row to retrieve the actual row that has violated the constraint. In this example, the dimension TIME_FN
is checking a table called month
. It has found a row that violates the constraints. Using the rowid, you can see exactly which row in the month table is causing the problem, as in the following:
SELECT * FROM month WHERE rowid IN (SELECT bad_rowid FROM dimension_exceptions WHERE statement_id = 'my first example'); MONTH QUARTER FISCAL_QTR YEAR FULL_MONTH_NAME MONTH_NUMB ------ ------- ---------- ---- --------------- ---------- 199903 19981 19981 1998 March 3
10.6 Altering Dimensions
You can modify a dimension using the ALTER
DIMENSION
statement. You can add or drop a level, hierarchy, or attribute from the dimension using this command.
Referring to the time dimension in Figure 10-2, you can remove the attribute fis_year
, drop the hierarchy fis_rollup
, or remove the level fiscal_year
. In addition, you can add a new level called f_year
as in the following:
ALTER DIMENSION times_dim DROP ATTRIBUTE fis_year; ALTER DIMENSION times_dim DROP HIERARCHY fis_rollup; ALTER DIMENSION times_dim DROP LEVEL fis_year; ALTER DIMENSION times_dim ADD LEVEL f_year IS times.fiscal_year;
If you used the extended_attribute_clause
when creating the dimension, you can drop one attribute column without dropping all attribute columns. This is illustrated in Dropping and Creating Attributes with Columns, which shows the following statement:
ALTER DIMENSION product_dim DROP ATTRIBUTE size LEVEL prod_type COLUMN Prod_TypeSize;
If you try to remove anything with further dependencies inside the dimension, Oracle Database rejects the altering of the dimension. A dimension becomes invalid if you change any schema object that the dimension is referencing. For example, if the table on which the dimension is defined is altered, the dimension becomes invalid.
You can modify a dimension by adding a level containing a SKIP
WHEN
NULL
clause, as in the following statement:
ALTER DIMENSION times_dim ADD LEVEL f_year IS times.fiscal_year SKIP WHEN NULL;
You cannot, however, modify a level that contains a SKIP
WHEN
NULL
clause. Instead, you need to drop the level and re-create it.
To check the status of a dimension, view the contents of the column invalid
in the ALL_DIMENSIONS
data dictionary view. To revalidate the dimension, use the COMPILE
option as follows:
ALTER DIMENSION times_dim COMPILE;
Dimensions can also be modified or deleted using Oracle Enterprise Manager.