This chapter discusses using dimensions in a data warehouse: It contains the following topics:
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:
time dimension, months roll up to quarters, quarters roll up to years, and years roll up to all years.
product dimension, products roll up to subcategories, subcategories roll up to categories, and categories roll up to all products.
customer dimension, customers roll up to
city. Then cities roll up to
state. Then states roll up to
country. Then countries roll up to
subregion. Finally, subregions roll up to
region, as shown in Figure 9-1.
Figure 9-1 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.
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 column
fiscal_month_desc, then a possible attribute relationship would be
fiscal_month_name. For skip
NULL levels, if a row of the relation of a skip level has a
NULL value for the level column, then that row must have a
NULL 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 the
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
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 when
ANCLEV 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.
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".
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 9-1. 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
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
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
DIMENSION statement or the Dimension Wizard in Oracle Enterprise Manager. Within the
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 9-1.
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
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
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
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
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
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)
DETERMINES clause relates
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,
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);
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.
Basic Query Rewrite for Materialized Views for further details of using dimensional information
Oracle Database SQL Language Reference for a complete description of the
You can use the attribute clause in a
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
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;
Oracle Database SQL Language Reference for a complete description of the
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 9-2 illustrates a dimension
times_dim with two time hierarchies.
Figure 9-2 times_dim Dimension with Two Time Hierarchies
From the illustration, you can construct the hierarchy of the denormalized
DIMENSION statement as follows. The complete
DIMENSION statement as well as the
TABLE statement are shown in Oracle Database Sample Schemas.
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>;
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
country_id are taken from different tables. The clause
KEY within the dimension definition specifies how to join together the levels in the hierarchy. The dimension statement is partially shown in the following. The complete
DIMENSION statement as well as the
TABLE statement are shown in Oracle Database 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
NULL clause, you can use the
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
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.
Dimensions can be viewed through one of two methods:
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.
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);
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
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
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
NULL clause, at least one of the referenced level columns should not have
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
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 to
TRUE to check only the new rows for tables of this dimension.
check_nulls: set to
TRUE to verify that all columns that are not in the levels containing a
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
@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
Referring to the time dimension in Figure 9-2, 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
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
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.