Version: 0.1.6

Scaling

Autoscaling monitors your Pods and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost. With autoscaling, it is easy to set up Pods scaling for resources in minutes. The service provides a simple, powerful user interface that lets you build scaling plans for resources.

This document describes how to scale Pods (Pulsar instances) which are used for running functions, sources, and sinks.

How it works

With Kubernetes Horizontal Pod Autoscaler (HPA), Function Mesh supports automatically scaling the number of Pods (Pulsar instances) that are required to run for Pulsar functions, sources, and sinks.

For resources with HPA configured, the HPA controller monitors the resource's Pods to determine if it needs to change the number of Pod replicas. In most cases, where the controller takes the mean of a per-pod metric value, it calculates whether adding or removing replicas would move the current value closer to the target value.

scaling

Manual scaling

In CRDs, the replicas parameter is used to specify the number of Pods (Pulsar instances) that are required for running Pulsar functions, sources, or sinks. You can set the number of Pods based on the CPU threshold. When the target CPU threshold is reached, you can scale the Pods manually through either of the two ways:

  • Use the kubectl scale --replicas command. The CLI command does not change the replicas configuration in the CRD. If you use the kunectl apply -f command to re-submit the CRD file, the CLI configuration may be overwritten.

    kubectl scale --replicas="" pod/POD_NAME
  • Update the value of the replicas parameter in the CRD and re-submit the CRD with the kubectl apply -f command.

Autoscaling

Function Mesh supports scaling Pods (Pulsar instances) based on the CPU utilization automatically. By default, autoscaling is disabled (The value of the maxReplicas parameter is set to 0). To enable autoscaling, you can specify the maxReplicas parameter and set a value for it in the CRD. This value should be greater than the value of the replicas parameter.

Auto-scale Pulsar Functions

This example shows how to auto-scale the number of Pods running Pulsar Functions to 8.

  1. Specify the maxReplicas to 8 in the Pulsar Functions CRD. The maxReplicas refers to the maximum number of Pods that are required for running the Pulsar Functions.

    apiVersion: cloud.streamnative.io/v1alpha1
    kind: Function
    metadata:
    name: java-function-sample
    namespace: default
    spec:
    className: org.apache.pulsar.functions.api.examples.ExclamationFunction
    forwardSourceMessageProperty: true
    maxPendingAsyncRequests: 1000
    replicas: 1
    maxReplicas: 8
    logTopic: persistent://public/default/logging-function-logs
    input:
    topics:
    - persistent://public/default/java-function-input-topic
    typeClassName: java.lang.String
    output:
    topic: persistent://public/default/java-function-output-topic
    typeClassName: java.lang.String
    # Other function configs
  2. Apply the configurations.

    kubectl apply -f path/to/source-sample.yaml

Auto-scale Pulsar connectors

This example shows how to auto-scale the number of Pods for running a Pulsar source connector to 5.

  1. Specify the maxReplicas to 5 in the Pulsar source CRD. The maxReplicas refers to the maximum number of Pods that are required for running the Pulsar source connector.

    Example

    apiVersion: compute.functionmesh.io/v1alpha1
    kind: Source
    metadata:
    name: source-sample
    spec:
    className: org.apache.pulsar.io.debezium.mongodb.DebeziumMongoDbSource
    replicas: 1
    maxReplicas: 5
    output:
    producerConf:
    maxPendingMessages: 1000
    maxPendingMessagesAcrossPartitions: 50000
    useThreadLocalProducers: true
    topic: persistent://public/default/destination
    typeClassName: org.apache.pulsar.common.schema.KeyValue
    resources:
    limits:
    cpu: "0.2"
    memory: 1.1G
    requests:
    cpu: "0.1"
    memory: 1G
    # Other configurations
  2. Apply the configurations.

    kubectl apply -f path/to/source-sample.yaml