Autoscaling¶

Numaflow is able to run with both Horizontal Pod Autoscaling and Vertical Pod Autoscaling.

Horizontal Pod Autoscaling¶

Horizontal Pod Autoscaling approaches supported in Numaflow include:

Numaflow Autoscaling
Kubernetes HPA
Third Party Autoscaling (such as KEDA)

Numaflow Autoscaling¶

Numaflow provides 0 - N autoscaling capability out of the box, it's available for all the UDF, Sink and following Source vertices.

Kafka
Redis Streams

Numaflow autoscaling is enabled by default, there are some parameters can be tuned to achieve better results.

apiVersion: numaflow.numaproj.io/v1alpha1
kind: Pipeline
metadata:
  name: my-pipeline
spec:
  vertices:
    - name: my-vertex
      scale:
        disabled: false # Optional, defaults to false.
        min: 0 # Optional, minimum replicas, defaults to 0.
        max: 20 # Optional, maximum replicas, defaults to 50.
        lookbackSeconds: 180 # Optional, defaults to 180.
        cooldownSeconds: 90 # Optional, defaults to 90.
        zeroReplicaSleepSeconds: 180 # Optional, defaults to 180.
        targetProcessingSeconds: 20 # Optional, defaults to 20.
        targetBufferAvailability: 50 # Optional, defaults to 50.
        replicasPerScale: 2 # Optional, defaults to 2.

disabled - Whether to disable Numaflow autoscaling, defaults to false.
min - Minimum replicas, valid value could be an integer >= 0. Defaults to 0, which means it could be scaled down to 0.
max - Maximum replicas, positive integer which should not be less than min, defaults to 50. if max and min are the same, that will be the fixed replica number.
lookbackSeconds - How many seconds to lookback for vertex average processing rate (tps) and pending messages calculation, defaults to 180. Rate and pending messages metrics are critical for autoscaling, you might need to tune this parameter a bit to see better results. For example, your data source only have 1 minute data input in every 5 minutes, and you don't want the vertices to be scaled down to 0. In this case, you need to increase lookbackSeconds to cover all the 5 minutes, so that the calculated average rate and pending messages won't be 0 during the silent period, to prevent scaling down to 0 from happening.
cooldownSeconds - After a scaling operation, how many seconds to wait before doing another scaling on the same vertex. This is to give some time for a vertex to stabilize, defaults to 90 seconds.
zeroReplicaSleepSeconds - How many seconds it will wait after scaling down to 0, defaults to 180. Numaflow autoscaler periodically scales up a vertex pod to "peek" the incoming data, this is the period of time to wait before peeking.
targetProcessingSeconds - It is used to tune the aggressiveness of autoscaling for source vertices, it measures how fast you want the vertex to process all the pending messages, defaults to 20. It is only effective for the Source vertices which support autoscaling, typically increasing the value leads to lower processing rate, thus less replicas.
targetBufferAvailability - Targeted buffer availability in percentage, defaults to 50. It is only effective for UDF and Sink vertices, it determines how aggressive you want to do for autoscaling, increasing the value will bring more replicas.
replicasPerScale - Maximum number of replicas change happens in one scale up or down operation, defaults to 2. For example, if current replica number is 3, the calculated desired replica number is 8; instead of scaling up the vertex to 8, it only does 5.

To disable Numaflow autoscaling, set disabled: true as following.

apiVersion: numaflow.numaproj.io/v1alpha1
kind: Pipeline
metadata:
  name: my-pipeline
spec:
  vertices:
    - name: my-vertex
      scale:
        disabled: true

Notes

Numaflow autoscaling does not apply to reduce vertices, and following source vertices which do not have a way to calculate their pending messages.

Generator
HTTP
Nats

Kubernetes HPA¶

Kubernetes HPA is supported in Numaflow for any type of Vertex. To use HPA, remember to point the scaleTargetRef to the vertex as below, and disable Numaflow autoscaling in your Pipeline spec.

apiVersion: autoscaling/v2beta1
kind: HorizontalPodAutoscaler
metadata:
  name: my-vertex-hpa
spec:
  minReplicas: 1
  maxReplicas: 3
  metrics:
    - resource:
        name: cpu
        targetAverageUtilization: 50
      type: Resource
  scaleTargetRef:
    apiVersion: numaflow.numaproj.io/v1alpha1
    kind: Vertex
    name: my-vertex

With the configuration above, Kubernetes HPA controller will keep the target utilization of the pods of the Vertex at 50%.

Kubernetes HPA autoscaling is useful for those Source vertices not able to count pending messages, such as HTTP.

Third Party Autoscaling¶

Third party autoscaling tools like KEDA are also supported in Numaflow, which can be used to autoscale any type of vertex with the scalers it supports.

To use KEDA for vertex autoscaling, same as Kubernetes HPA, point the scaleTargetRef to your vertex, and disable Numaflow autoscaling in your Pipeline spec.

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: my-keda-scaler
spec:
  scaleTargetRef:
    apiVersion: numaflow.numaproj.io/v1alpha1
    kind: Vertex
    name: my-vertex
  ... ...

Vertical Pod Autoscaling¶

Vertical Pod Autoscaling can be achieved by setting the targetRef to Vertex objects as following.

spec:
  targetRef:
    apiVersion: numaflow.numaproj.io/v1alpha1
    kind: Vertex
    name: my-vertex