Creating custom reports
The Advanced Reporting node on the Administration tab allows you to achieve two primary objectives:
- Provides a set of features for designing, scheduling, and publishing reports that have a custom structure and presentation. For more information, see Creating reports and report templates.
- Provides an interface to TrueSight Capacity Optimization data and model results to third-party reporting tools.
Overview of databases in TrueSight Capacity Optimization
Before discussing specific concepts, it is useful to recall some common definitions.
A relational database stores information in data structures called tables. More specifically, a data warehouse (such as the TrueSight Capacity Optimization DWH) uses two kinds of tables:
- Fact tables (FT): Contain all recorded data, also called observations.
- Dimension tables (DT): Contain all the information necessary to decode keys used in fact tables to identify data samples; decode keys are also called analysis dimensions.
Dimension tables have a simple primary key, while fact tables have a compound primary key consisting of the aggregate of relevant dimension keys.
Additional information
The data warehouse organizes tables in a star schema, in which a few fact tables reference a number of dimension tables. Data stored in fact tables is classified in different dimensions; dimension tables describe all available dimensions and can be joined to fact tables as needed.
An example of such a star schema is shown in the following figure:
Example of star schema
The TrueSight Capacity Optimization database model is a relational star schema organized with fact tables at different time resolutions and dimension tables.
All tables containing the suffix _DATA in their names are fact tables, wherease tables identified by the same prefix but not suffixed with _DATA are the corresponding dimension tables.
Another important concept is the difference between horizontal and vertical tables:
- A horizontal table stores all data related to a single fact in one row; this practice saves disk space but requires to modify the table structure, should the collected data types change.
- A vertical table enforces a fixed structure (typically: timestamp, fact ID, name, value) and uses one row for each data value. This is more space-consuming but allows for greater flexibility.
The following table is an example of a vertical table:
TS | ID | METRIC | VALUE |
---|---|---|---|
01/01/2007 00:00:00 | 12345 | CPU_UTIL | 60.75 |
01/01/2007 00:01:00 | 12345 | CPU_UTIL | 52.15 |
01/01/2007 00:00:00 | 12345 | MEM_UTIL | 80.20 |
01/01/2007 00:01:00 | 12345 | MEM_UTIL | 82.00 |
... | ... | ... | ... |
The following table is an example of a horizontal table:
TS | ID | CPU_UTIL | MEM_UTIL | ... |
---|---|---|---|---|
01/01/2007 00:00:00 | 12345 | 60.75 | 80.20 | ... |
01/01/2007 00:01:00 | 12345 | 2.15 | 82.00 | ... |
Vertical tables can be transformed into horizontal ones.
The TrueSight Capacity Optimization DWH schema is based on a vertical structure; should a horizontal structure be required for exporting and reporting, the AR module offers several ways to export data in a horizontal format.
There are two main kinds of fact tables, identified by the prefixes SYS and WKLD: one for system and the other for business driver observations. The main dimensions used by TrueSight Capacity Optimization are: entity, object, subobject, and location; for more details, refer to Maintaining ETL tasks.
The basic structure that BMC TrueSight Capacity Optimization uses to collect counters is shown in the following figure:
The TrueSight Capacity Optimization data model
Working with Advanced Reporting
The features of different Advanced Reporting components are described in the following sections:
Where to go from here
The Advanced Reporting module produces a number of public views that can be accessed using the CPIT_REP account. For more information on these public views, see Accessing data using public views.
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