Enhancements to Aggregation Functionality

The performance of the Aggregation transformation is significantly improved through the use of hash objects. In addition, input data to aggregations are now sorted. This step improves the input and output processes, which are used by the underlying SAS and operating system technologies that aggregate IT data.
Moving averages and moving standard deviations are now available as statistics that can be included in aggregations. These moving statistics make SAS IT Resource Management more useful because they enable IT organizations to truly identify and establish baseline and threshold measurements for the many performance metrics that they want to measure. These statistics can also be used to monitor characteristics of the SAS IT Resource Management system. For example, they can help monitor the growth in the number of systems for which data is analyzed. They can also monitor the volume of reports created by each SAS IT Resource Management report job (if measures on those items are retained and managed using SAS IT Resource Management).
SAS IT Resource Management 3.3 delivers support for calculating and including percentiles for measurements within the IT data mart. Percentiles can be specified and calculated over designated time periods for those aggregation tables that are produced by the system. Percentile measurements in SAS IT Resource Management enable IT organizations to quantify and analyze utilization, availability, performance, and capacity characteristics of IT infrastructure components. These measurements can be compared with other components in the infrastructure so that IT organizations can prioritize and resolve current and potential problems.
Cloning is a new function that is available from the list of options that appears when you right-click on an existing aggregated table of a job that is open in the Diagram tab of the Job Editor window. Many IT organizations establish separate IT data marts for different IT resources based on their use by different business organizations or to support production, testing, or development. The cloning feature is useful because it allows Aggregation transformation definitions to be created once and then copied and reused across other IT data marts.
The Summarized Aggregation Table wizard is modified to simplify the specification of an aggregation.
  • The Select Analysis Column page is eliminated. In all the pages where an analysis column is requested, the list of all available columns is displayed. For example, to specify percent change columns, you can select the analysis columns from the list of class, ID, statistics, and percentile columns.
  • The pages that specify additional columns, such as statistics, percentiles, and so on, have a new, standardized appearance. The information about the columns is displayed in a grid format. The grid contains a row that describes the characteristics of each column (class, ID, statistic, percentile, and so on) that is specified. The row typically contains the name of the column, the target column name, the target column description, and the target column format. Additional information is displayed depending on the type of column being described. For example, a row that describes a statistic column displays its Weight By value, if weighting is specified. In addition, a row that describes a percentile column displays its Round To value.
    Adding and deleting columns from the aggregation table is accomplished by clicking the New and Delete buttons, respectively.
  • The MACHINE column is removed from the class list of these Key Metrics Disk aggregations:
    • SAR
    • HP Perf Agent
    • HP Reporter
    • BMC Perf Mgr
    Note: If the machines that are being monitored use the preceding adapters and are attached to a storage area network, then each disk within that storage area network appears as if it were installed locally on that machine. Aggregating such data from the perspective of the host system is of little, if any, value. Therefore, the MACHINE column has been removed from the class list of the Key Metric Disk aggregations for these data sources.
  • The retention period for the DayHour, DayShift, and DayShiftHour aggregations has changed from 92 to 45 days. The retention period remains the same for other aggregation tables.