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Export data point data by creating aliases

This section provides information about how Analytics and Resource Manager share performance data, and how to manually modify data points.

All monitored data points are available for performance data extraction. However, this configuration is something that needs to be done knowingly, proactively, and maintained carefully ON A PER DATA POINT BASIS and on an ongoing basis using configuration management processes appropriate for your organization. The initial configuration in this section is required before you start to try to report on any performance data in Analytics.

Data point ETL configuration is attained by adding "Aliases" to data points. It is EXACTLY the presence of a least one alias on a monitored data point, that causes it to be ETLed to the Analytics data warehouse. In other words, if a data point does not have an alias, that data is not being extracted and therefore is not available for Analytics. Note that it is perfectly valid to have more than one alias on a data point, and indeed, you should do so if you want that one value available in different units in the data warehouse. Duplicating data in the data warehouse is strongly encouraged instead of having to unit convert data at reporting time in real time.

Resource Manager data points represent raw, collected metrics. For example, view the /Server/Linux monitoring template. The ssCpuIdle data point is collected by the ssCpuIdle data source. ssCpuIdle represents the percentage of idle time for all CPUs on this box.

Edit the data point, and you can examine its defined aliases. The cpu__pct alias represents the percentage of CPU used by the box. The alias value is calculated by using the data point and a reverse Polish notation (RPN) expression. Aliases are used to normalize raw data point values across data point types.


To avoid issues with percentile and projections, limit alias names to 31 characters. Do not use periods in alias names.

You can provide a formula to be used during extraction of data. This allows you to convert data points with different units to the same units during extraction. For example, if you are monitoring disk space on a Unix system (bytes) and a Windows system (bits), you can use a formula to convert both to kilobytes and have them use the same alias but different RPNs.

On the /Server/Windows monitoring template, open the ProcessorTotalProcessorTime data point in the ProcessorTotalProcessorTime data source. An alias "cpu__pct" is defined with no RPN formula. In this case, the raw data point represents the desired quantity ("Percentage of Consumed CPU Time"). The alias, cpu__pct, allows you to compare two different data sources.

The same principle applies to other data points. Some devices give network bandwidth by using kilobytes; others use megabits. Aliases provide a method to normalize these data points to a common denominator.

There are two ways you can add aliases to a data point:

  • Manually adding and modifying them a data point at a time using the Resource Manager UI data point screen as mentioned previously. This is highly discouraged. Not only is it tedious and error prone, but there is no record of what was done.
  • Running the scripts provided in the ZenETL ZenPack to manage them in bulk. This is highly recommended.


Periods are not allowed in aliases since the alias names are used to name the database tables in Analytics that store them. Indeed, while the structure of individual tables will be identical on all Analytics installs, the schema in total for performance data in Analytics is dynamically controlled by exactly these alias definitions and therefore never the same on any two installs. Tables are dynamically created in the data warehouse as needed if new aliases are added.