Entities and metrics

The TrueSight Capacity Optimization Data Warehouse offers capabilities to store metrics of different sub-types. In addition, the product offers various out-of-the-box datasets and predefined metrics that can be populated either by ETLs or Agents.

For more information, refer to the following sections: 



Metrics in TrueSight Capacity Optimization are definitions of time-varying information about entities, either domains, systems, or business drivers. (See Entity Types for the various types of entities in TrueSight Capacity Optimization.) The actual information, in the form of metric instances, is loaded into TrueSight Capacity Optimization by connectors.

Metrics are of three sub-types:

  • Time series metrics: An instance of this sub-type contains a series of numeric values over regular time intervals, for example, one value every five minutes, or one value every hour, etc. Each value represents something measured over that time interval (five minutes, one hour, etc.). Each interval of time can have at most one value in the series.
  • Configuration series metrics: An instance of this sub-type contains a series of values that have variable-length validity periods. Each value represents something that holds true during a time period t1 to t2. The validity periods in the series are non-overlapping, so that at any given instant in time, there can be at most one value valid. This kind of representation is suitable for configuration information, for example, hardware description or total amount of memory in a system, which does not change very often. Each value is represented as a string.
  • Events: An instance of this sub-type denotes a planned change or incident occurring about an entity at a specific time. To understand how these are used, see Events.

TrueSight Capacity Optimization contains definitions of thousands of metrics, sometimes called resource counters, and in addition, a connector may define its own custom metrics. Every metric known to a TrueSight Capacity Optimization instance must have a unique string name called the "object" or "resource" identifier, conventionally in an all-capitals and underscores format, for example, CPU_UTILCPU_MODEL, and BYDISK_UTIL. As explained in Viewing datasets and metrics by dataset and ETL module, metrics in TrueSight Capacity Optimization are arranged in homogeneous groups called Datasets. For example:

  • The dataset named SYSCNF contains over a hundred different Configuration series metric definitions, including CPU_MODEL and CPU_NUM.
  • The dataset named SYSGLB contains over a hundred and fifty Time series metric definitions, including CPU_UTIL.
  • The dataset named EVDAT contains a single definition for all Events.

One particular dataset, named OBJREL, contains a single definition for all relationships. For more information about relationships, see Entity relationships.

Custom metrics created by users or connectors are distinguished by a _C suffix in their name.

The entities and metrics that are imported by the ETLs are documented in the respective ETL topics. For more information, see the topics under Collecting data via ETL modules.

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Metric instances and sub-objects

Datasets contain only the definitions of metrics. Instances of metrics are created by a connector based on the connector's data source.

Each instance of a metric is associated with only one entity. For example, a connector may create an instance of the time series metric CPU_UTIL and associate it with a particular system named Host1. Usually, there can be only one instance of a metric associated with an entity, as in the example of CPU_UTIL.

But some metrics are sub-object metrics. For such a metric, multiple metric instances may be associated with a single entity, one per sub-object of an entity. By convention, such metrics have names starting with the prefix BY. For example, the time series metric BYDISK_UTIL can have multiple instances for a system, one per disk. The connector that creates the metric instances assigns strings to each disk of each system, based on the data source. For example, if a system Host1 has two disks named diskA and diskB, then the connector may choose to create two instances of the time series metric BYDISK_UTIL, one instance for a sub-object of Host1 called diskA, and another instance for a sub-object of Host1 called diskB.

When looking at the metric instances of an entity in the TrueSight Capacity Optimization console workspace, you will see the metric and the sub-object of each metric instance identified in columns named Resource and Subresource, respectively.

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LOCATION field in metrics used for business drivers

There is another way to associate multiple metric instances with a single entity: every metric also has a LOCATION field, by default filled with the string UNKNOWN. But when creating an instance, a connector may fill this field with arbitrary strings like Boston or New York. Then each such LOCATION value added, creates a separate instance of the metric associated with the same entity.

The intent of this field is to enable the connector to associate separate instances with geographic locations. Such a scheme might make sense for business drivers, for example, BYPAGE_RESPONSE_TIME could denote response times broken down by web page as well as by the location from where the page request was made. In our example, these two instances would be created for a single business driver entity:

  • BYPAGE_RESPONSE_TIME for sub-object ftp.bmc.com, for location Boston.
  • BYPAGE_RESPONSE_TIME for sub-object ftp.bmc.com, for location New York.

This use of the LOCATION field in metrics is rare, but it is available for when there are an unpredictable number of geographic locations that need to be tracked separately for a single business driver. You can achieve the same effect by creating separate business drivers, one for each location.

When looking at the metric instances of an entity in the TrueSight Capacity Optimization console workspace, you will see the LOCATION value identified in a column named Location.

Assigning a LOCATION field creates a new instance of the metric on the same entity! Do not use the LOCATION field if you simply need to associate a location with a system. Use a configuration metric for that purpose.

Metric instances associated with domains

Domains, i.e., entities of category APP, may have only configuration series metrics associated with them. Otherwise, domains behave just like the other two categories of metrics, namely systems (SYS) and business drivers (WKLD), for the purposes of metric instance creation.

The dataset named APPCNF contains definitions for a handful of built-in configuration series metrics, and users or connectors can define additional custom configuration series metrics.

Structure of a metric instance

A metric instance is identified by the following tuple:




Unique identifier for an entity, for example, a System ID for a system


Whether the entity is a domain ("APP"), a system ("SYS"), or a business driver ("WKLD")


Metric unique name, for example, BYPAGE_RESPONSE_TIME


Sub-object name, for example, www.foo.com/index.html


Location field, for example, "Boston"

Each of these elements can be specified by a connector. The most typical case is for a system time series or configuration series metric, where the SUBOBJECT is GLOBAL and LOCATION is UKNOWN.

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Metric values and summarization

Time series metric values are summarized by the TrueSight Capacity Optimization data warehouse according to the meanings of the metrics. The meanings are denoted by metric type codes, as follows:

Metric type ID

Metric type






Time series; number of events in interval


Pages downloaded



Time series; time taken for a typical activity


Response time



Time series; events per second during interval





Time series; typical quantity normalized against a maximum


CPU utilization



Configuration series; something true during the validity period


Number of CPUs



Time series; a quantitative increment over the previous interval


Disk space used



Time series; a negative increment (decrement) over previous interval


Disk space free



Time series; an absolute value


CPU queue length

The TrueSight Capacity Optimization data warehouse uses the indicated summarization methods to calculate hourly, daily, and monthly values from the raw numbers loaded by connectors. No summarization is done for CONF metrics.

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Size metric unit prefixes

 TrueSight Capacity Optimization uses the JEDEC standards for unit prefixes for representing binary data in TrueSight Capacity Optimization. These standards are uniformly applied when presenting binary data in the user interface and while defining thresholds. 

The JEDEC specification contains definitions of commonly used prefixes such as kilo, mega, and giga, which are usually combined with the units byte and/or bit to designate multiples of the units.

The specification lists three prefixes as follows:

  • kilo (K): A multiplier equal to 1024 (210).
  • mega (M): A multiplier equal to 1048576 (220 or K2, where K = 1024).
  • giga (G): A multiplier equal to 1073741824 (230 or K3, where K = 1024).

These prefixes are also the de facto standard in the industry, and are used to match and cross-verify metrics in TrueSight Capacity Optimization against similar metrics from other monitoring and capacity management tools.

For example, Linux operating system uses K from the JEDEC standard when reporting KB/s in commands such as iostat and Microsoft Windows uses KB/GB from the JEDEC standards when reporting disk space available.

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Where to go from here

For more information on entities, metrics and datasets available in  TrueSight Capacity Optimization, see the following topics:

See also

  • To learn about how  TrueSight Capacity Optimization collects data, see Collecting data.
  • To learn about all entity categories present in  TrueSight Capacity Optimization, see Entity types.

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