Analytics Entities

BIG-IQ monitors the BIG-IPs for information about system activity. You can then use the Analytic Metric Query API with this information to gain statistical insight into system activity. Many types of activities are monitored and each has its own type of information. The analytics service provides entities to help you use this information. An entity is defined by a set of Dimensions and Metrics, and is associated with an identifying module name, product name, and one or more metricset groups.

There are two kinds of metrics. The first kind is collected by the BIG-IP and stored. The second kind is calculated at query time. Depending upon the module and metricSet, the following metrics can be stored.

Metric Type Method Description
Count Sum over dimension and time The total number of events or the total number of events relevant to a property. The value of these can be different. For example, the Page-load-time property can only be counted for the subset of HTTP transaction events which loads a page. Aggregation of Dimension and time by Sum.
Max Max over dimension and time The maximum value of property in the time frame.
Min Min over dimension and time The minimum value of property in the time frame.
Sum Sum over dimension and time Sum of the values from all of the events. For example, server latency would be the total latency for all events in the time frame.
Sum of Squares Sum over dimension and time Sum of the squares of the property values from all the events in the specific time frame.

The following metrics can be calculated.

Metric Type Method Description
avg-count-per-sec sum(Count)/sum(TimeFrame) Average number of events per second in the time frame.
avg-value-per-event sum(Sum)/sum(Count) Average value of a property for the events that had the property.
avg-value-per-sec sum(Count)/sum(TimeFrame) Average value of the property per second.
stddev sqrt(SumOfSquares - (Sum/Count)^2) The standard deviation for the average value per event.

Entity reference

The following is a list of entities provided by BIG-IQ. These topics give the defining Dimensions set and Metrics and also give the module name, product name, and metricset groups. Dimensions are characteristic extents of the system related to activity, such as the “Client IP”, “URL” or “Virtual Server”. Metrics are measures, such as “count”, “sum”, “min” or “max”. A MetricSet is a set of closely related Metrics which are often used together, such as “Average Server Latency”, “Max Server Latency”, and “Min Server Latency”. You must specify the product, module and metricset to identify the entity when using the Analytic Metric Query API.

Entities