feat: Add new feature formula for SO

content/docs/2.12/concepts/scaling-deployments.md

@@ -29,7 +29,7 @@ The only constraint is that the target `Custom Resource` must define `/scale` [s
 
 ## ScaledObject spec
 
-This specification describes the `ScaledObject` Custom Resource definition which is used to define how KEDA should scale your application and what the triggers are. The `.spec.ScaleTargetRef` section holds the reference to the target resource, ie. `Deployment`, `StatefulSet` or `Custom Resource`. 
+This specification describes the `ScaledObject` Custom Resource definition which is used to define how KEDA should scale your application and what the triggers are. The `.spec.ScaleTargetRef` section holds the reference to the target resource, ie. `Deployment`, `StatefulSet` or `Custom Resource`.
 
 [`scaledobject_types.go`](https://github.com/kedacore/keda/blob/main/apis/keda/v1alpha1/scaledobject_types.go)
 
@@ -49,7 +49,7 @@ spec:
     envSourceContainerName: {container-name}                # Optional. Default: .spec.template.spec.containers[0]
   pollingInterval:  30                                      # Optional. Default: 30 seconds
   cooldownPeriod:   300                                     # Optional. Default: 300 seconds
-  idleReplicaCount: 0                                       # Optional. Default: ignored, must be less than minReplicaCount 
+  idleReplicaCount: 0                                       # Optional. Default: ignored, must be less than minReplicaCount
   minReplicaCount:  1                                       # Optional. Default: 0
   maxReplicaCount:  100                                     # Optional. Default: 100
   fallback:                                                 # Optional. Section to specify fallback options
@@ -113,7 +113,7 @@ The `cooldownPeriod` only applies after a trigger occurs; when you first create
 #### idleReplicaCount
 
 ```yaml
-  idleReplicaCount: 0   # Optional. Default: ignored, must be less than minReplicaCount 
+  idleReplicaCount: 0   # Optional. Default: ignored, must be less than minReplicaCount
 ```
 
 > 💡 **NOTE:** Due to limitations in HPA controller the only supported value for this property is 0, it will not work correctly otherwise. See this [issue](https://github.com/kedacore/keda/issues/2314) for more details.
@@ -167,7 +167,7 @@ advanced:
   restoreToOriginalReplicaCount: true/false        # Optional. Default: false
 ```
 
-This property specifies whether the target resource (`Deployment`, `StatefulSet`,...) should be scaled back to original replicas count, after the `ScaledObject` is deleted. 
+This property specifies whether the target resource (`Deployment`, `StatefulSet`,...) should be scaled back to original replicas count, after the `ScaledObject` is deleted.
 Default behavior is to keep the replica count at the same number as it is in the moment of `ScaledObject's` deletion.
 
 For example a `Deployment` with `3 replicas` is created, then `ScaledObject` is created and the `Deployment` is scaled by KEDA to `10 replicas`. Then `ScaledObject` is deleted:
@@ -201,6 +201,29 @@ Starting from Kubernetes v1.18 the autoscaling API allows scaling behavior to be
 
 **Assumptions:** KEDA must be running on Kubernetes cluster v1.18+, in order to be able to benefit from this setting.
 
+---
+
+```yaml
+advanced:
+  scalingModifiers:                                    # Optional. Section to specify scaling modifiers
+    target: {target-value-to-scale-on}                    # Mandatory. New target if metrics are anyhow composed together
+    formula: {formula-for-fetched-metrics}                # Mandatory. Formula for calculation
+```
+
+**`scalingModifiers`**
+
+The `scalingModifiers` is optional and **experimental**. If defined, both `target` and `formula` are mandatory. Using this structure creates `composite-metric` for the HPA that will replace all requests for external metrics and handle them internally. With `scalingModifiers` each trigger used in the `formula` **must** have a name defined.
+
+**`scalingModifiers.target`**
+
+`target` defines new target value to scale on for the composed metric. All scaler metrics must be of the same type in order for ScaledObject to be successfully validated.
+
+**`scalingModifiers.formula`**
+
+  `formula` composes metrics together and allows them to be modified/manipulated. It accepts mathematical/conditional statements using [this external project](https://github.com/antonmedv/expr). If the `fallback` scaling feature is in effect, the `formula` will NOT modify its metrics (therefore it modifies metrics only when all of their triggers are healthy). Complete language definition of `expr` package can be found [here](https://expr.medv.io/docs/Language-Definition). Formula must return a single value (not boolean).
+
+For examples of this feature see section [Scaling Modifiers](#scaling-modifiers-experimental) below.
+
 ---
 #### triggers
 ```yaml
@@ -245,6 +268,65 @@ metadata:
 
 The presensce of this annotation will pause autoscaling no matter what number of replicas is provided. The above annotation will scale your current workload to 0 replicas and pause autoscaling. You can set the value of replicas for an object to be paused at to any arbitary number. To enable autoscaling again, simply remove the annotation from the `ScaledObject` definition.
 
+
+### Scaling Modifiers (Experimental)
+
+**Example: compose average value**
+
+```yaml
+advanced:
+  scalingModifiers:
+    formula: "(trig_one + trig_two)/2"
+    target: "2"
+...
+triggers:
+  - type: kubernetes-workload
+    name: trig_one
+    metadata:
+      podSelector: 'pod=workload-test'
+      value: '1'
+  - type: metrics-api
+    name: trig_two
+    metadata:
+      targetValue: "2"
+      url: "https://mockbin.org/bin/336a8d99-9e09-4f1f-979d-851a6d1b1423"
+      valueLocation: "tasks"
+```
+
+Formula composes 2 given metrics from 2 triggers `kubernetes-workload` named `trig_one` and `metrics-api` named `trig_two` together as an average value and returns one final metric which is used to make autoscaling decisions on.
+
+**Example: ternary operator**
+
+```yaml
+advanced:
+  scalingModifiers:
+    formula: "trig_one > 2 ? trig_one + trig_two : 1"
+```
+
+If metric value of trigger `trig_one` is more than 2, then return `trig_one` + `trig_two` otherwise return 1.
+
+**Example: count function**
+
+```yaml
+advanced:
+  scalingModifiers:
+    formula: "count([trig_one,trig_two,trig_three],{#>1}) > 1 ? 5 : 0"
+```
+
+If atleast 2 metrics (from the list `trig_one`,`trig_two`,`trig_three`) have value of more than 1, then return 5, otherwise return 0
+
+**Example: nested conditions and operators**
+
+```yaml
+advanced:
+  scalingModifiers:
+    formula: "trig_one < 2 ? trig_one+trig_two >= 2 ? 5 : 10 : 0"
+```
+
+Conditions can be used within another condition as well.
+If value of `trig_one` is less than 2 AND `trig_one`+`trig_two` is atleast 2 then return 5, if only the first is true return 10, if the first condition is false then return 0.
+
+Complete language definition of `expr` package can be found [here](https://expr.medv.io/docs/Language-Definition). Formula must return a single value (not boolean)
 ### Activating and Scaling thresholds
 
 To give a consistent solution to this problem, KEDA has 2 different phases during the autoscaling process.