Threshold Strategy Plugin
The threshold
strategy defines a range of metric values with a lower and an
upper bound, and an action to take when the current metric value is considered
to be within bounds.
Multiple tiers can be defined by declaring more than one check
in the
same scaling policy. If there is any overlap between the bounds, the safest
check
will be used.
Note: When using the threshold
strategy with multiple checks make sure
they all have the same group
value, otherwise your target
may not be able to scale down.
Agent Configuration Options
Policy Configuration Options
lower_bound
(float: <optional>)
- The minimum value a metric must have to be considered with bounds. This value is always inclusive, meaning a metric with the exact same value aslower_bound
is considered within bounds.upper_bound
(float: <optional>)
- The maximum value a metric must have to be considered within bounds. This value is always exclusive, meaning a metric with the exact same value asupper_bound
is considered out of bounds.delta
(int: <optional>)
- Specifies the relative amount to add (positive value) or remove (negative value) from the current target count. Conflicts withpercentage
andvalue
.percentage
(float: <optional>)
- Specifies a percentage value by which the current count should be increased (positive value) or decreased (negative value). Conflicts withdelta
andvalue
.value
(int: <optional>)
- Specifies an absolute value that should be set as the new target count. Conflicts withdelta
andpercentage
.within_bounds_trigger
(int: 5)
- The number of data points in the query result time series that must be within the bound values to trigger the action.
At least one of lower_bound
or upper_bound
must be defined. If
lower_bound
is not defined, any value below upper_bound
is considered
within bounds. Similarly, if upper_bound
is not defined, any value above or
equal to lower_bound
will be considered within bounds.
One, and only one, of delta
, percentage
, or value
must be defined.