You are looking at the documentation of a prior release. To read the documentation of the latest release, please
visit here.
New to KubeDB? Please start here.
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
kubectlcommand-line tool must be configured to communicate with your cluster.Install
KubeDBProvisioner, Ops-manager and Autoscaler operator in your cluster following the steps here.Install
Metrics Serverfrom hereYou should be familiar with the following
KubeDBconcepts:
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 4.4.26. 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/v1
kind: MongoDB
metadata:
name: mg-rs
namespace: demo
spec:
version: "4.4.26"
replicaSet:
name: "replicaset"
replicas: 3
storageType: Durable
storage:
resources:
requests:
storage: 1Gi
podTemplate:
spec:
containers:
- name: mongo
resources:
requests:
cpu: "200m"
memory: "300Mi"
limits:
cpu: "200m"
memory: "300Mi"
deletionPolicy: WipeOut
Let’s create the MongoDB CRO we have shown above,
$ kubectl create -f https://github.com/kubedb/docs/raw/v2024.11.18/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 4.4.26 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.containers[] | select(.name == "mongodb") | .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.namespecifies that we are performing compute resource scaling operation onmg-rsdatabase.spec.compute.replicaSet.triggerspecifies that compute autoscaling is enabled for this database.spec.compute.replicaSet.podLifeTimeThresholdspecifies the minimum lifetime for at least one of the pod to initiate a vertical scaling.spec.compute.replicaset.resourceDiffPercentagespecifies 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.minAllowedspecifies the minimum allowed resources for the database.spec.compute.replicaSet.maxAllowedspecifies the maximum allowed resources for the database.spec.compute.replicaSet.controlledResourcesspecifies the resources that are controlled by the autoscaler.spec.compute.replicaSet.containerControlledValuesspecifies which resource values should be controlled. The default is “RequestsAndLimits”.spec.opsRequestOptionscontains 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/v2024.11.18/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 PetSet: mg-rs
Normal UpdateReplicaSetResources 4m9s KubeDB Ops-manager Operator Successfully updated replicaset Resources
Normal Starting 4m9s KubeDB Ops-manager Operator Updating Resources of PetSet: 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.containers[] | select(.name == "mongodb") | .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