Updating the data warehouse (DWH) is one of the most important activities of BMC TrueSight Capacity Optimization. The Data Warehouse administration menu lets you inspect and tune the mechanisms that control it. To access the Data Warehouse menus, click Data Warehouse from the Administration tab.
Near-Real-Time Warehousing is a process that stores, organizes and calculates statistics for the collected data. This service is always running inside the Data Hub.
The data warehousing activity consists of:
Data collected by ETL tasks is not available to the analysis module before it is processed by the warehousing engine.
Data flow reports allow you to keep BMC TrueSight Capacity Optimization imports under control, as the number of rows processed per day is a good indicator of the system's health.
When performing historical imports (for example, if you are planning to bulk load more than two million rows), it is strongly recommended to split the data in smaller chunks, limiting the amount of information processed at one time. This prevents congestion in the Near-Real-Time Warehouse engine.
The BMC TrueSight Capacity Optimization Data Warehouse can host different types of data:
For more information about data structure, Data format.
The primary purpose of the BMC TrueSight Capacity Optimization DWH is the collection of historical time series.
A time series is a sequence of samples or statistics for a certain measurement, each corresponding to a point in time. The BMC TrueSight Capacity Optimization DWH contains both time series samples and statistics, aggregated at different time resolution (hour, day, month).
All time series are associated with a measured object, described according to a reference model. In its most general form, the reference model for measured objects is displayed in the figure below.
The model comprises of the following components:
Each available object/metric has a standard name which adheres to the naming convention defined in the datasets. For a complete listing, refer to BMC TrueSight Capacity Optimization ETL Development Studio (also see Developing custom ETLs), or to the Datasets and metrics page in the Data Warehousing menu of the Administration section.
Each metric has a type which defines the measure unit and how statistics about that data should be collected. A complete listing is given below:
Generic counter, absolute value
A count of events, absolute number
A frequency, in events/sec
Positive accumulation counter
Configuration data (string)
Negative accumulation counter
Elapsed time, in seconds
Percentage counter, weighted
Peak Percentage counter
Peak frequency, in events/sec
Difference between subsequent samples
Generic counter, absolute value, weighted
Measurement units and formats also use common standards:
from 0 to 1
In summary, the reference model specifies:
BMC TrueSight Capacity Optimization uses ETL tasks to import data that is collected by third party applications or logged by the monitored entities themselves. This data, from BMC TrueSight Capacity Optimization perspective, is persistent because another application takes care of its collection and storage. In a second phase, BMC TrueSight Capacity Optimization accesses this data and imports it into its own data warehouse.
Standard data imported by BMC TrueSight Capacity Optimization is always in the form of a time series, which means that it is described by a value (numeric or textual, for configuration properties) which changes over time.
The Data Mover components are used by BMC TrueSight Capacity Optimization to deal with data which does not respect the two aforementioned properties. The Data Mover is able to access data which is not a time series and store it into BMC TrueSight Capacity Optimization.
The workflow of these components is displayed in the following image.
The available options of the Data Warehouse menu are described in the following topics: