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Autoscaling the Compute Resource of a MongoDB Replicaset Database

This guide will show you how to use KubeDB to autoscale compute resources i.e. cpu and memory of a MongoDB replicaset database.

Before You Begin

At first, you need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster.
Install KubeDB Provisioner, Ops-manager and Autoscaler operator in your cluster following the steps here.
Install Metrics Server from here
You should be familiar with the following KubeDB concepts:

To keep everything isolated, we are going to use a separate namespace called demo throughout this tutorial.

$ kubectl create ns demo
namespace/demo created

Note: YAML files used in this tutorial are stored in docs/examples/mongodb directory of kubedb/docs repository.

Autoscaling of Replicaset Database

Here, we are going to deploy a MongoDB Replicaset using a supported version by KubeDB operator. Then we are going to apply MongoDBAutoscaler to set up autoscaling.

Deploy MongoDB Replicaset

In this section, we are going to deploy a MongoDB Replicaset database with version 4.4.6. Then, in the next section we will set up autoscaling for this database using MongoDBAutoscaler CRD. Below is the YAML of the MongoDB CR that we are going to create,

apiVersion: kubedb.com/v1alpha2
kind: MongoDB
metadata:
  name: mg-rs
  namespace: demo
spec:
  version: "4.2.3"
  replicaSet:
    name: "replicaset"
  replicas: 3
  storageType: Durable
  storage:
    resources:
      requests:
        storage: 1Gi
  podTemplate:
    spec:
      resources:
        requests:
          cpu: "200m"
          memory: "300Mi"
        limits:
          cpu: "200m"
          memory: "300Mi"
  terminationPolicy: WipeOut

Let’s create the MongoDB CRO we have shown above,

$ kubectl create -f https://github.com/kubedb/docs/raw/v2023.01.17/docs/examples/mongodb/autoscaling/compute/mg-rs.yaml
mongodb.kubedb.com/mg-rs created

Now, wait until mg-rs has status Ready. i.e,

$ kubectl get mg -n demo
NAME    VERSION    STATUS    AGE
mg-rs   4.2.3      Ready     2m53s

Let’s check the Pod containers resources,

$ kubectl get pod -n demo mg-rs-0 -o json | jq '.spec.containers[].resources'
{
  "limits": {
    "cpu": "200m",
    "memory": "300Mi"
  },
  "requests": {
    "cpu": "200m",
    "memory": "300Mi"
  }
}

Let’s check the MongoDB resources,

$ kubectl get mongodb -n demo mg-rs -o json | jq '.spec.podTemplate.spec.resources'
{
  "limits": {
    "cpu": "200m",
    "memory": "300Mi"
  },
  "requests": {
    "cpu": "200m",
    "memory": "300Mi"
  }
}

You can see from the above outputs that the resources are same as the one we have assigned while deploying the mongodb.

We are now ready to apply the MongoDBAutoscaler CRO to set up autoscaling for this database.

Compute Resource Autoscaling

Here, we are going to set up compute resource autoscaling using a MongoDBAutoscaler Object.

Create MongoDBAutoscaler Object

In order to set up compute resource autoscaling for this replicaset database, we have to create a MongoDBAutoscaler CRO with our desired configuration. Below is the YAML of the MongoDBAutoscaler object that we are going to create,

apiVersion: autoscaling.kubedb.com/v1alpha1
kind: MongoDBAutoscaler
metadata:
  name: mg-as-rs
  namespace: demo
spec:
  databaseRef:
    name: mg-rs
  opsRequestOptions:
    timeout: 3m
    apply: IfReady
  compute:
    replicaSet:
      trigger: "On"
      podLifeTimeThreshold: 5m
      resourceDiffPercentage: 20
      minAllowed:
        cpu: 400m
        memory: 400Mi
      maxAllowed:
        cpu: 1
        memory: 1Gi
      controlledResources: ["cpu", "memory"]
      containerControlledValues: "RequestsAndLimits"

Here,

spec.databaseRef.name specifies that we are performing compute resource scaling operation on mg-rs database.
spec.compute.replicaSet.trigger specifies that compute autoscaling is enabled for this database.
spec.compute.replicaSet.podLifeTimeThreshold specifies the minimum lifetime for at least one of the pod to initiate a vertical scaling.
spec.compute.replicaset.resourceDiffPercentage specifies the minimum resource difference in percentage. The default is 10%. If the difference between current & recommended resource is less than ResourceDiffPercentage, Autoscaler Operator will ignore the updating.
spec.compute.replicaSet.minAllowed specifies the minimum allowed resources for the database.
spec.compute.replicaSet.maxAllowed specifies the maximum allowed resources for the database.
spec.compute.replicaSet.controlledResources specifies the resources that are controlled by the autoscaler.
spec.compute.replicaSet.containerControlledValues specifies which resource values should be controlled. The default is “RequestsAndLimits”.
spec.opsRequestOptions contains the options to pass to the created OpsRequest. It has 3 fields. Know more about them here : readinessCriteria, timeout, apply.

If it was an InMemory database, we could also autoscaler the inMemory resources using MongoDB compute autoscaler, like below.

Autoscale inMemory database

To autoscale inMemory databases, you need to specify the spec.compute.replicaSet.inMemoryStorage section.

  ...
  inMemoryStorage:
    usageThresholdPercentage: 80
    scalingFactorPercentage: 30
  ...

It has two fields inside it.

usageThresholdPercentage. If db uses more than usageThresholdPercentage of the total memory, memoryStorage should be increased. Default usage threshold is 70%.
scalingFactorPercentage. If db uses more than usageThresholdPercentage of the total memory, memoryStorage should be increased by this given scaling percentage. Default scaling percentage is 50%.

Note: To inform you, We use db.serverStatus().inMemory.cache["bytes currently in the cache"] & db.serverStatus().inMemory.cache["maximum bytes configured"] to calculate the used & maximum inMemory storage respectively.

Let’s create the MongoDBAutoscaler CR we have shown above,

$ kubectl apply -f https://github.com/kubedb/docs/raw/v2023.01.17/docs/examples/mongodb/autoscaling/compute/mg-as-rs.yaml
mongodbautoscaler.autoscaling.kubedb.com/mg-as-rs created

Verify Autoscaling is set up successfully

Let’s check that the mongodbautoscaler resource is created successfully,

$ kubectl get mongodbautoscaler -n demo
NAME        AGE
mg-as-rs    102s

$ kubectl describe mongodbautoscaler mg-as-rs -n demo
Name:         mg-as-rs
Namespace:    demo
Labels:       <none>
Annotations:  <none>
API Version:  autoscaling.kubedb.com/v1alpha1
Kind:         MongoDBAutoscaler
Metadata:
  Creation Timestamp:  2022-10-27T06:56:34Z
  Generation:          1
  Managed Fields:
    API Version:  autoscaling.kubedb.com/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:metadata:
        f:annotations:
          .:
          f:kubectl.kubernetes.io/last-applied-configuration:
      f:spec:
        .:
        f:compute:
          .:
          f:replicaSet:
            .:
            f:containerControlledValues:
            f:controlledResources:
            f:maxAllowed:
              .:
              f:cpu:
              f:memory:
            f:minAllowed:
              .:
              f:cpu:
              f:memory:
            f:podLifeTimeThreshold:
            f:resourceDiffPercentage:
            f:trigger:
        f:databaseRef:
        f:opsRequestOptions:
          .:
          f:apply:
          f:timeout:
    Manager:      kubectl-client-side-apply
    Operation:    Update
    Time:         2022-10-27T06:56:34Z
    API Version:  autoscaling.kubedb.com/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:status:
        .:
        f:checkpoints:
        f:conditions:
        f:vpas:
    Manager:         kubedb-autoscaler
    Operation:       Update
    Subresource:     status
    Time:            2022-10-27T07:01:05Z
  Resource Version:  640314
  UID:               ab03414a-67a2-4da4-8960-6e67ae56b503
Spec:
  Compute:
    Replica Set:
      Container Controlled Values:  RequestsAndLimits
      Controlled Resources:
        cpu
        memory
      Max Allowed:
        Cpu:     1
        Memory:  1Gi
      Min Allowed:
        Cpu:                     400m
        Memory:                  400Mi
      Pod Life Time Threshold:   5m0s
      Resource Diff Percentage:  20
      Trigger:                   On
  Database Ref:
    Name:  mg-rs
  Ops Request Options:
    Apply:    IfReady
    Timeout:  3m0s
Status:
  Checkpoints:
    Cpu Histogram:
      Bucket Weights:
        Index:              2
        Weight:             10000
        Index:              3
        Weight:             5000
      Reference Timestamp:  2022-10-27T00:00:00Z
      Total Weight:         0.3673624107285783
    First Sample Start:     2022-10-27T07:00:42Z
    Last Sample Start:      2022-10-27T07:00:55Z
    Last Update Time:       2022-10-27T07:01:00Z
    Memory Histogram:
      Reference Timestamp:  2022-10-28T00:00:00Z
    Ref:
      Container Name:     mongodb
      Vpa Object Name:    mg-rs
    Total Samples Count:  3
    Version:              v3
    Cpu Histogram:
      Bucket Weights:
        Index:              0
        Weight:             10000
      Reference Timestamp:  2022-10-27T00:00:00Z
      Total Weight:         0.3673624107285783
    First Sample Start:     2022-10-27T07:00:42Z
    Last Sample Start:      2022-10-27T07:00:55Z
    Last Update Time:       2022-10-27T07:01:00Z
    Memory Histogram:
      Reference Timestamp:  2022-10-28T00:00:00Z
    Ref:
      Container Name:     replication-mode-detector
      Vpa Object Name:    mg-rs
    Total Samples Count:  3
    Version:              v3
  Conditions:
    Last Transition Time:  2022-10-27T07:01:05Z
    Message:               Successfully created mongoDBOpsRequest demo/mops-mg-rs-cxhsy1
    Observed Generation:   1
    Reason:                CreateOpsRequest
    Status:                True
    Type:                  CreateOpsRequest
  Vpas:
    Conditions:
      Last Transition Time:  2022-10-27T07:01:00Z
      Status:                True
      Type:                  RecommendationProvided
    Recommendation:
      Container Recommendations:
        Container Name:  mongodb
        Lower Bound:
          Cpu:     400m
          Memory:  400Mi
        Target:
          Cpu:     400m
          Memory:  400Mi
        Uncapped Target:
          Cpu:     49m
          Memory:  262144k
        Upper Bound:
          Cpu:     1
          Memory:  1Gi
    Vpa Name:      mg-rs
Events:            <none>

So, the mongodbautoscaler resource is created successfully.

you can see in the Status.VPAs.Recommendation section, that recommendation has been generated for our database. Our autoscaler operator continuously watches the recommendation generated and creates an mongodbopsrequest based on the recommendations, if the database pods are needed to scaled up or down.

Let’s watch the mongodbopsrequest in the demo namespace to see if any mongodbopsrequest object is created. After some time you’ll see that a mongodbopsrequest will be created based on the recommendation.

$ watch kubectl get mongodbopsrequest -n demo
Every 2.0s: kubectl get mongodbopsrequest -n demo
NAME                    TYPE              STATUS       AGE
mops-mg-rs-cxhsy1       VerticalScaling   Progressing  10s

Let’s wait for the ops request to become successful.

$ watch kubectl get mongodbopsrequest -n demo
Every 2.0s: kubectl get mongodbopsrequest -n demo
NAME                    TYPE              STATUS       AGE
mops-mg-rs-cxhsy1       VerticalScaling   Successful   68s

We can see from the above output that the MongoDBOpsRequest has succeeded. If we describe the MongoDBOpsRequest we will get an overview of the steps that were followed to scale the database.

$ kubectl describe mongodbopsrequest -n demo mops-mg-rs-cxhsy1
Name:         mops-mg-rs-cxhsy1
Namespace:    demo
Labels:       <none>
Annotations:  <none>
API Version:  ops.kubedb.com/v1alpha1
Kind:         MongoDBOpsRequest
Metadata:
  Creation Timestamp:  2022-10-27T07:01:05Z
  Generation:          1
  Managed Fields:
    API Version:  ops.kubedb.com/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:metadata:
        f:ownerReferences:
          .:
          k:{"uid":"ab03414a-67a2-4da4-8960-6e67ae56b503"}:
      f:spec:
        .:
        f:apply:
        f:databaseRef:
        f:timeout:
        f:type:
        f:verticalScaling:
          .:
          f:replicaSet:
            .:
            f:limits:
              .:
              f:cpu:
              f:memory:
            f:requests:
              .:
              f:cpu:
              f:memory:
    Manager:      kubedb-autoscaler
    Operation:    Update
    Time:         2022-10-27T07:01:05Z
    API Version:  ops.kubedb.com/v1alpha1
    Fields Type:  FieldsV1
    fieldsV1:
      f:status:
        .:
        f:conditions:
        f:observedGeneration:
        f:phase:
    Manager:      kubedb-ops-manager
    Operation:    Update
    Subresource:  status
    Time:         2022-10-27T07:02:31Z
  Owner References:
    API Version:           autoscaling.kubedb.com/v1alpha1
    Block Owner Deletion:  true
    Controller:            true
    Kind:                  MongoDBAutoscaler
    Name:                  mg-as-rs
    UID:                   ab03414a-67a2-4da4-8960-6e67ae56b503
  Resource Version:        640598
  UID:                     f7c6db00-dd0e-4850-8bad-5f0855ce3850
Spec:
  Apply:  IfReady
  Database Ref:
    Name:   mg-rs
  Timeout:  3m0s
  Type:     VerticalScaling
  Vertical Scaling:
    Replica Set:
      Limits:
        Cpu:     400m
        Memory:  400Mi
      Requests:
        Cpu:     400m
        Memory:  400Mi
Status:
  Conditions:
    Last Transition Time:  2022-10-27T07:01:05Z
    Message:               MongoDB ops request is vertically scaling database
    Observed Generation:   1
    Reason:                VerticalScaling
    Status:                True
    Type:                  VerticalScaling
    Last Transition Time:  2022-10-27T07:02:30Z
    Message:               Successfully Vertically Scaled Replicaset Resources
    Observed Generation:   1
    Reason:                UpdateReplicaSetResources
    Status:                True
    Type:                  UpdateReplicaSetResources
    Last Transition Time:  2022-10-27T07:02:31Z
    Message:               Successfully Vertically Scaled Database
    Observed Generation:   1
    Reason:                Successful
    Status:                True
    Type:                  Successful
  Observed Generation:     1
  Phase:                   Successful
Events:
  Type    Reason                     Age    From                         Message
  ----    ------                     ----   ----                         -------
  Normal  PauseDatabase              4m9s   KubeDB Ops-manager Operator  Pausing MongoDB demo/mg-rs
  Normal  PauseDatabase              4m9s   KubeDB Ops-manager Operator  Successfully paused MongoDB demo/mg-rs
  Normal  Starting                   4m9s   KubeDB Ops-manager Operator  Updating Resources of StatefulSet: mg-rs
  Normal  UpdateReplicaSetResources  4m9s   KubeDB Ops-manager Operator  Successfully updated replicaset Resources
  Normal  Starting                   4m9s   KubeDB Ops-manager Operator  Updating Resources of StatefulSet: mg-rs
  Normal  UpdateReplicaSetResources  4m9s   KubeDB Ops-manager Operator  Successfully updated replicaset Resources
  Normal  UpdateReplicaSetResources  2m44s  KubeDB Ops-manager Operator  Successfully Vertically Scaled Replicaset Resources
  Normal  ResumeDatabase             2m43s  KubeDB Ops-manager Operator  Resuming MongoDB demo/mg-rs
  Normal  ResumeDatabase             2m43s  KubeDB Ops-manager Operator  Successfully resumed MongoDB demo/mg-rs
  Normal  Successful                 2m43s  KubeDB Ops-manager Operator  Successfully Vertically Scaled Database
  Normal  UpdateReplicaSetResources  2m43s  KubeDB Ops-manager Operator  Successfully Vertically Scaled Replicaset Resources

Now, we are going to verify from the Pod, and the MongoDB yaml whether the resources of the replicaset database has updated to meet up the desired state, Let’s check,

$ kubectl get pod -n demo mg-rs-0 -o json | jq '.spec.containers[].resources'
{
  "limits": {
    "cpu": "400m",
    "memory": "400Mi"
  },
  "requests": {
    "cpu": "400m",
    "memory": "400Mi"
  }
}

$ kubectl get mongodb -n demo mg-rs -o json | jq '.spec.podTemplate.spec.resources'
{
  "limits": {
    "cpu": "400m",
    "memory": "400Mi"
  },
  "requests": {
    "cpu": "400m",
    "memory": "400Mi"
  }
}

The above output verifies that we have successfully auto scaled the resources of the MongoDB replicaset database.

Cleaning Up

To clean up the Kubernetes resources created by this tutorial, run:

kubectl delete mg -n demo mg-rs
kubectl delete mongodbautoscaler -n demo mg-as-rs

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