In This Section:
Essbase contains multidimensional databases that support analysis and management reporting applications. This chapter discusses multidimensional concepts and terminology.
Note:
The information in this chapter is designed for block storage databases. Some of the information is not relevant to aggregate storage databases. For detailed information on the differences between aggregate and block storage, see Comparison of Aggregate and Block Storage.
Online analytical processing (OLAP) is a multidimensional, multi-user, client-server computing environment for users who need to analyze enterprise data. OLAP applications span a variety of organizational functions. Finance departments use OLAP for applications such as budgeting, activity-based costing (allocations), financial performance analysis, and financial modeling. Sales departments use OLAP for sales analysis and forecasting. Among other applications, marketing departments use OLAP for market research analysis, sales forecasting, promotions analysis, customer analysis, and market/customer segmentation. Typical manufacturing OLAP applications include production planning and defect analysis.
Important to all of the applications mentioned in the previous paragraph is the ability to provide managers with the information they need to make effective decisions about an organization's strategic directions. A successful OLAP application provides information as needed, that is, it provides “just-in-time” information for effective decision-making.
Providing “just-in-time” information requires more than a base level of detailed data. “Just-in-time” information is computed data that usually reflects complex relationships and is often calculated on the fly. Analyzing and modeling complex relationships are practical only if response times are consistently short. In addition, because the nature of data relationships may not be known in advance, the data model must be flexible. A truly flexible data model ensures that OLAP systems can respond to changing business requirements as needed for effective decision making.
Although OLAP applications are found in widely divergent functional areas, they all require the following key features:
Key to OLAP systems are multidimensional databases. Multidimensional databases not only consolidate and calculate data; they also provide retrieval and calculation of a variety of data subsets. A multidimensional database supports multiple views of data sets for users who need to analyze the relationships between data categories. For example, a marketing analyst might want answers to the following questions:
How did Product A sell last month? How does this figure compare to sales in the same month over the last five years? How did the product sell by branch, region, and territory?
Did this product sell better in particular regions? Are there regional trends?
Did customers return Product A last year? Were the returns due to product defects? Did the company manufacture the products in a specific plant?
Did commissions and pricing affect how salespeople sold the product? Did particular salespeople do a better job of selling the product?
With a multidimensional database, the number of data views is limited only by the database outline, the structure that defines all elements of the database. Users can pivot the data to see information from a different viewpoint, drill down to find more detailed information, or drill up to see an overview.
This section introduces the concepts of outlines, dimensions and members within a multidimensional database. If you understand dimensions and members, you are well on your way to understanding the power of a multidimensional database.
A dimension represents the highest consolidation level in the database outline. The database outline presents dimensions and members in a tree structure to indicate a consolidation relationship. For example, in Figure 2, Hierarchical Structure, Time is a dimension and Qtr1 is a member.
Essbase has two types of dimensions: standard dimensions and attribute dimensions.
Standard dimensions represent the core components of a business plan and often relate to departmental functions. Typical standard dimensions are Time, Accounts, Product Line, Market, and Division. Dimensions change less frequently than members.
Attribute dimensions are a special type of dimension that are associated with standard dimensions. Through attribute dimensions, you group and analyze members of standard dimensions based on the member attributes (characteristics). For example, you can compare the profitability of non-caffeinated products that are packaged in glass to the profitability of non-caffeinated products that are packaged in cans.
Members are the individual components of a dimension. For example, Product A, Product B, and Product C might be members of the Product dimension. Each member has a unique name. Essbase can store the data associated with a member (referred to as a stored member in this chapter) or it can dynamically calculate the data when a user retrieves it.
All Essbase database development begins with creating a database outline. A database outline accomplishes the following:
Essbase uses the concept of members to represent data hierarchies. Each dimension consists of one or more members. The members, in turn, may consist of other members. When you create a dimension, you tell Essbase how to consolidate the values of its individual members. Within the tree structure of the database outline, a consolidation is a group of members in a branch of the tree.
For example, many businesses summarize their data monthly, rolling up monthly data to obtain quarterly figures, and rolling up quarterly data to obtain annual figures. Businesses may also summarize data by zip code, by city, state, and country. Any dimension can be used to consolidate data for reporting purposes.
In the Sample Basic database included with Essbase Server, for example, the Year dimension consists of five members: Qtr1, Qtr2, Qtr3, and Qtr4, each storing data for an individual quarter, plus Year, storing summary data for the entire year. Qtr1 consists of four members: Jan, Feb, and Mar, each storing data for an individual month, plus Qtr1, storing summary data for the entire quarter. Likewise, Qtr2, Qtr3, and Qtr4 consist of the members that represent the individual months plus the member that stores the quarterly totals.
The database outline in Figure 2, Hierarchical Structure uses a hierarchical structure to represent the data consolidations and relationships in Qtr1.
Some dimensions consist of relatively few members, while others may have hundreds or even thousands of members. Essbase does not limit the number of members within a dimension and enables the addition of new members as needed.
Essbase uses the terms defined in the following sections to describe a database outline. These terms are used throughout Essbase documentation.
Essbase uses hierarchical and family history terms to describe the roles and relationships of the members in an outline. You can describe the position of the members of the branches in Figure 3, Member Generation and Level Numbers in several ways.
Figure 3, Member Generation and Level Numbers illustrates the following parent, child, and sibling relationships:
A parent is a member that has a branch below it. For example, Margin is a parent member for Sales and Cost of Goods Sold.
A child is a member that has a parent above it. For example, Sales and Cost of Goods Sold are children of the parent Margin.
Siblings are child members of the same immediate parent, at the same generation. For example, Sales and Cost of Goods Sold are siblings (they both have the parent Margin), but Marketing (at the same branch level) is not a sibling because its parent is Total Expenses.
Figure 3, Member Generation and Level Numbers illustrates the following descendant and ancestral relationships:
Descendants are members in branches below a parent. For example, Profit, Inventory, and Ratios are descendants of Measures. The children of Profit, Inventory, and Ratios are also descendants of Measures.
Ancestors are members in branches above a member. For example, Margin, Profit, and Measures are ancestors of Sales.
Figure 3, Member Generation and Level Numbers illustrates the following root and leaf member relationships:
The root is the top member in a branch. Measures is the root for Profit, Inventory, Ratios, and the children of Profit, Inventory, and Ratios.
Leaf members have no children. They are also referred to as level 0 members. For example, Opening Inventory, Additions, and Ending Inventory are leaf members.
Figure 3, Member Generation and Level Numbers illustrates the following generations levels:
Generation refers to a consolidation level within a dimension. A root branch of the tree is generation 1. Generation numbers increase as you count from the root toward the leaf member. In Figure 3, Member Generation and Level Numbers, Measures is generation 1, Profit is generation 2, and Margin is generation 3. All siblings of each level belong to the same generation; for example, both Inventory and Ratios are generation 2.
Figure 4, Generations shows part of the Product dimension with its generations numbered.
Level also refers to a branch within a dimension; levels reverse the numerical ordering used for generations. Levels count up from the leaf member toward the root. The root level number varies depending on the depth of the branch. In the example in Figure 3, Member Generation and Level Numbers, Sales and Cost of Goods Sold are level 0. All other leaf members are also level 0. Margin is level 1, and Profit is level 2. Notice that the level number of Measures varies depending on the branch. For the Ratios branch, Measures is level 2. For the Total Expenses branch, Measures is level 3.
Figure 5, Levels shows part of the Product dimension with its levels numbered.
To make reports easier to maintain, you can assign a name to a generation or level and then use the name as a shorthand for all members in that generation or level. Because changes to an outline are automatically reflected in a report, when you use generation and level names, you do not need to change the report if a member name is changed or deleted from the database outline.
Essbase has two types of dimensions: standard dimensions and attribute dimensions. This chapter primarily considers standard dimensions because Essbase does not allocate storage for attribute dimension members. Instead it dynamically calculates the members when the user requests data associated with them.
An attribute dimension is a special type of dimension that is associated with a standard dimension. See Working with Attributes.
Most data sets of multidimensional databases have two characteristics:
Essbase maximizes performance by dividing the standard dimensions of an application into two types: dense dimensions and sparse dimensions. This division allows Essbase to cope with data that is not smoothly distributed, without losing the advantages of matrix-style access to the data. Essbase speeds up data retrieval while minimizing the memory and disk requirements.
Most multidimensional databases are inherently sparse: they lack data values for the majority of member combinations. A sparse dimension is a dimension with a low percentage of available data positions filled.
For example, the Sample Basic database shown in Figure 6, Sample Basic Database Outline includes the Year, Product, Market, Measures, and Scenario dimensions. Product represents the product units, Market represents the geographical regions in which the products are sold, and Measures represents the accounts data. Because not every product is sold in every market, Market and Product are chosen as sparse dimensions.
Most multidimensional databases also contain dense dimensions. A dense dimension is a dimension with a high probability that one or more cells is occupied in every combination of dimensions. For example, in the Sample Basic database, accounts data exists for almost all products in all markets, so Measures is chosen as a dense dimension. Year and Scenario are also chosen as dense dimensions. Year represents time in months, and Scenario represents whether the accounts values are budget or actual values.
Note:
In Figure 6, Caffeinated, Intro Date, Ounces, Pkg Type and Population are attribute dimensions. See Working with Attributes.
In most data sets, existing data tends to follow predictable patterns of density and sparsity. If you match patterns correctly, you can store the existing data in a reasonable number of fairly dense data blocks, rather than in many highly sparse data blocks.
By default, a new dimension is set sparse. To help you determine whether dimensions should be dense or sparse, Essbase provides an automatic configuration feature.
To select automatic configuration of dense and sparse dimensions, see “Setting Dimensions as Dense or Sparse” in the Essbase Administration Services Online Help.
Essbase can make recommendations for the sparse-dense configuration of dimensions based on the following factors:
You can apply a recommended configuration or you can turn off automatic configuration and manually set the sparse or dense property for each dimension. Attribute dimensions are always sparse dimensions. Keep in mind that you can associate attribute dimensions only with sparse standard dimensions.
Note:
The automatic configuration of dense and sparse dimensions provides only an estimate. It cannot take into account the nature of the data you will load into your database or multiple user considerations.
Consider the Sample Basic database that is provided with Essbase. The Sample Basic database represents data for The Beverage Company (TBC).
TBC does not sell every product in every market; therefore, the data set is reasonably sparse. Data values do not exist for many combinations of members in the Product and Market dimensions. For example, if Caffeine Free Cola is not sold in Florida, then data values do not exist for the combination Caffeine Free Cola (100-30)->Florida.So, Product and Market are sparse dimensions. Therefore, if no data values exist for a specific combination of members in these dimensions, Essbase does not create a data block for the combination.
However, consider combinations of members in the Year, Measures, and Scenario dimensions. Data values almost always exist for some member combinations on these dimensions. For example, data values exist for the member combination Sales->January->Actual because at least some products are sold in January. Thus, Year and, similarly, Measures and Scenario are dense dimensions.
The sparse-dense configuration of the standard dimensions in the Sample Basic database may be summarized as follows:
Essbase creates a data block for each unique combination of members in the Product and Market dimensions (for more information on data blocks, see Data Storage). Each data block represents data from the dense dimensions. The data blocks are likely to have few empty cells.
For example, consider the sparse member combination Caffeine Free Cola (100-30), New York, illustrated by Figure 7, Dense Data Block for Sample Basic Database:
If accounts data (represented by the Measures dimension) exists for this combination for January, it probably exists for February and for all members in the Year dimension.
If a data value exists for one member on the Measures dimension, it is likely that other accounts data values exist for other members in the Measures dimension.
If Actual accounts data values exist, it is likely that Budget accounts data values exist.
The following scenarios show how a database is affected when you select different standard dimensions. Assume that these scenarios are based on typical databases with at least seven dimensions and several hundred members:
If you make all dimensions sparse, Essbase creates data blocks that consist of single data cells that contain single data values. An index entry is created for each data block and, therefore, in this scenario, for each existing data value.
This configuration produces an index that requires a large amount of memory. The more index entries, the longer Essbase searches to find a specific block.
If you make all dimensions dense, as shown in Figure 9, Database with All Dense Standard Dimensions, Essbase creates one index entry and one very large, very sparse block. In most applications, this configuration requires thousands of times more storage than other configurations. Essbase needs to load the entire memory when it searches for a data value, which requires enormous amounts of memory.
Based upon your knowledge of your company’s data, you have identified all your sparse and dense standard dimensions. Ideally, you have approximately equal numbers of sparse and dense standard dimensions. If not, you are probably working with a non-typical data set and you need to do more tuning to define the dimensions.
Essbase creates dense blocks that can fit into memory easily and creates a relatively small index as shown in Figure 10, An Ideal Configuration with Combination of Dense and Sparse Dimensions. Your database runs efficiently using minimal resources.
Consider a database with four standard dimensions: Time, Accounts, Region, and Product. In the following example, Time and Accounts are dense dimensions, and Region and Product are sparse dimensions.
The two-dimensional data blocks shown in Figure 11, Two-dimensional Data Block for Time and Accounts represent data values from the dense dimensions: Time and Accounts. The members in the Time dimension are J, F, M, and Q1. The members in the Accounts dimension are Rev, Exp, and Net.
Essbase creates data blocks for combinations of members in the sparse standard dimensions (providing at least one data value exists for the member combination). The sparse dimensions are Region and Product. The members of the Region dimension are East, West, South, and Total US. The members in the Product dimension are Product A, Product B, Product C, and Total Product.
Figure 12, Data Blocks Created for Sparse Members on Region and Product shows 11 data blocks. No data values exist for Product A in the West and South, for Product B in the East and West, and for Product C in the East. Therefore, Essbase has not created data blocks for these member combinations. The data blocks that Essbase has created have very few empty cells.
This example effectively concentrates all sparseness into the index and concentrates all data into fully utilized blocks. This configuration provides efficient data storage and retrieval.
Next consider a reversal of the dense and sparse dimension selections. In the following example, Region and Product are dense dimensions, and Time and Accounts are sparse dimensions.
As shown in Figure 13, Two-Dimensional Data Block for Region and Product, the two-dimensional data blocks represent data values from the dense dimensions: Region and Product.
Essbase creates data blocks for combinations of members in the sparse standard dimensions (providing at least one data value exists for the member combination). The sparse standard dimensions are Time and Accounts.
Figure 14, Data Blocks Created for Sparse Members on Time and Accounts shows 12 data blocks. Data values exist for all combinations of members in the Time and Accounts dimensions; therefore, Essbase creates data blocks for all the member combinations. Because data values do not exist for all products in all regions, the data blocks have many empty cells. Data blocks with many empty cells store data inefficiently.
Each data value in a multidimensional database is stored in a single cell. A particular data value is referenced by specifying its coordinates along each standard dimension.
Note:
Essbase does not store data for attribute dimensions. Essbase dynamically calculates attribute dimension data when a user retrieves the data.
Consider the simplified database shown in Figure 15, A Multidimensional Database Outline.
This database has three dimensions: Accounts, Time, and Scenario:
The intersection of one member from one dimension with one member from each of the other dimensions represents a data value. The example in Figure 16, Three-Dimensional Database has three dimensions; thus, the dimensions and data values in the database can be represented in a cube.
The shaded cells in Figure 17, Sales Slice of the Database illustrate that when you specify Sales, you are specifying the portion of the database containing eight Sales values.
Slicing a database amounts to fixing one or more dimensions at a constant value while allowing the other dimensions to vary.
When you specify Actual Sales, you are specifying the four Sales values where Actual and Sales intersect as shown by the shaded area in Figure 18, Actual, Sales Slice of the Database.
A data value is stored in a single cell in the database. To refer to a specific data value in a multidimensional database, you specify its member on each dimension. In Figure 19, Sales -> Jan -> Actual Slice of the Database, the cell containing the data value for Sales, Jan, Actual is shaded. The data value can also be expressed using the cross-dimensional operator (->) as Sales -> Actual -> Jan.
Essbase uses two types of internal structures to store and access data: data blocks and the index system.
Essbase creates a data block for each unique combination of sparse standard dimension members (providing that at least one data value exists for the sparse dimension member combination). The data block represents all the dense dimension members for its combination of sparse dimension members.
Essbase creates an index entry for each data block. The index represents the combinations of sparse standard dimension members. It contains an entry for each unique combination of sparse standard dimension members for which at least one data value exists.
For example, in the Sample Basic database outline shown in Figure 20, Product and Market Dimensions from the Sample Basic Database, Product and Market are sparse dimensions.
If data exists for Caffeine Free Cola in New York, Essbase creates a data block and an index entry for the sparse member combination of Caffeine Free Cola (100-30) -> New York. If Caffeine Free Cola is not sold in Florida, Essbase does not create a data block or an index entry for the sparse member combination: Caffeine Free Cola (100-30) -> Florida.
The data block Caffeine Free Cola (100-30) -> New York represents all the Year, Measures, and Scenario dimensions for Caffeine Free Cola (100-30) -> New York.
Each unique data value can be considered to exist in a cell in a data block. When Essbase searches for a data value, it uses the index to locate the appropriate data block. Then, within the data block, it locates the cell containing the data value. The index entry provides a pointer to the data block. The index handles sparse data efficiently because it includes only pointers to existing data blocks.
Figure 21, Part of a Data Block for the Sample Basic Database shows part of a data block for the Sample Basic database. Each dimension of the block represents a dense dimension in the Sample Basic database: Time, Measures, and Scenario. A data block exists for each unique combination of members of the Product and Market sparse dimensions (providing that at least one data value exists for the combination).
Each data block is a multidimensional array that contains a fixed, ordered location for each possible combination of dense dimension members. Accessing a cell in the block does not involve sequential or index searches. The search is almost instantaneous, resulting in optimal retrieval and calculation speed.
Essbase orders the cells in a data block according to the order of the members in the dense dimensions of the database outline.
A (Dense)
a1
a2
B (Dense)
b1
b11
b12
b2
b21
b22
C (Dense)
c1
c2
c3
D (Sparse)
d1
d2
d21
d22
E (Sparse)
e1
e2
e3The block in Figure 22, Data Block Representing Dense Dimensions for d22 -> e3 represents the three dense dimensions from within the combination of the sparse members d22 and e3 in the preceding database outline. In Essbase, member combinations are denoted by the cross-dimensional operator. The symbol for the cross-dimensional operator is ->. Thus, d22 -> e3 denotes the block for d22 and e3. The intersection of A, b21, and c3 is written A -> b21 -> c3.
Essbase creates a data block for every unique combination of the members of the sparse dimensions D and E (providing that at least one data value exists for the combination).
Data blocks, such as the one shown in Figure 22, Data Block Representing Dense Dimensions for d22 -> e3, may include cells that do not contain data values. A data block is created if at least one data value exists in the block. Essbase compresses data blocks with missing values on disk, expanding each block fully as it brings the block into memory. Data compression is optional, but is enabled by default. For more information, see Data Compression.
By carefully selecting dense and sparse standard dimensions, you can ensure that data blocks do not contain many empty cells, minimizing disk storage requirements and improving performance. In Essbase, empty cells are known as #MISSING data
A multidimensional database supports multiple views of data sets for users who need to analyze the relationships between data categories. Slicing the database in different ways gives you different perspectives of the data. The slice of January in Figure 23, Data for January, for example, examines all data values for which the Year dimension is fixed at Jan.
The slice in Figure 24, Data for February shows data for the month of February:
The slice in Figure 25, Data for Profit Margin shows data for profit margin:
To create an optimized Essbase database, consider the following questions:
How does your company use the data?
How do you plan to build and order the dimensions?
Which data compression scheme will you use?
How do you want to create and order calculations?
For more information on:
Planning the development of your multidimensional database, see Case Study: Designing a Single-Server, Multidimensional Database.
Selecting dense and sparse dimensions, see Selection of Dense and Sparse Dimensions.
Loading data, see Understanding Data Loading and Dimension Building.
Compressing data and optimizing your database, see Data Compression.
Calculating your database, see Calculating Essbase Databases.