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Autoscaling the Compute Resource of a MongoDB Sharded Database
This guide will show you how to use KubeDB to autoscale compute resources i.e. cpu and memory of a MongoDB sharded 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 Sharded Database
Here, we are going to deploy a MongoDB sharded database using a supported version by KubeDB operator. Then we are going to apply MongoDBAutoscaler to set up autoscaling.
Deploy MongoDB Sharded Database
In this section, we are going to deploy a MongoDB sharded 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/v1alpha2
kind: MongoDB
metadata:
name: mg-sh
namespace: demo
spec:
version: "4.4.26"
storageType: Durable
shardTopology:
configServer:
storage:
resources:
requests:
storage: 1Gi
replicas: 3
podTemplate:
spec:
resources:
requests:
cpu: "200m"
memory: "300Mi"
mongos:
replicas: 2
podTemplate:
spec:
resources:
requests:
cpu: "200m"
memory: "300Mi"
shard:
storage:
resources:
requests:
storage: 1Gi
replicas: 3
shards: 2
podTemplate:
spec:
resources:
requests:
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/v2024.2.14/docs/examples/mongodb/autoscaling/compute/mg-sh.yaml
mongodb.kubedb.com/mg-sh created
Now, wait until mg-sh has status Ready. i.e,
$ kubectl get mg -n demo
NAME VERSION STATUS AGE
mg-sh 4.4.26 Ready 3m57s
Let’s check a shard Pod containers resources,
$ kubectl get pod -n demo mg-sh-shard0-0 -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-sh -o json | jq '.spec.shardTopology.shard.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 the shard pod of the 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-sh
namespace: demo
spec:
databaseRef:
name: mg-sh
opsRequestOptions:
timeout: 3m
apply: IfReady
compute:
shard:
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-shdatabase.spec.compute.shard.triggerspecifies that compute autoscaling is enabled for the shard pods of this database.spec.compute.shard.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.shard.minAllowedspecifies the minimum allowed resources for the database.spec.compute.shard.maxAllowedspecifies the maximum allowed resources for the database.spec.compute.shard.controlledResourcesspecifies the resources that are controlled by the autoscaler.spec.compute.shard.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.
Note: In this demo we are only setting up the autoscaling for the shard pods, that’s why we only specified the shard section of the autoscaler. You can enable autoscaling for mongos and configServer pods in the same yaml, by specifying the
spec.compute.mongosandspec.compute.configServersection, similar to thespec.comput.shardsection we have configured in this demo.
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.shard.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/v2024.2.14/docs/examples/mongodb/autoscaling/compute/mg-as-sh.yaml
mongodbautoscaler.autoscaling.kubedb.com/mg-as-sh 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-sh 102s
$ kubectl describe mongodbautoscaler mg-as-sh -n demo
Name: mg-as-sh
Namespace: demo
Labels: <none>
Annotations: <none>
API Version: autoscaling.kubedb.com/v1alpha1
Kind: MongoDBAutoscaler
Metadata:
Creation Timestamp: 2022-10-27T09:46:48Z
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:shard:
.:
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: 2022-10-27T09:46:48Z
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: 2022-10-27T09:47:08Z
Resource Version: 654853
UID: 36878e8e-f100-409e-aa76-e6f46569df76
Spec:
Compute:
Shard:
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-sh
Ops Request Options:
Apply: IfReady
Timeout: 3m0s
Status:
Checkpoints:
Cpu Histogram:
Bucket Weights:
Index: 1
Weight: 5001
Index: 2
Weight: 10000
Reference Timestamp: 2022-10-27T00:00:00Z
Total Weight: 0.397915611757652
First Sample Start: 2022-10-27T09:46:43Z
Last Sample Start: 2022-10-27T09:46:57Z
Last Update Time: 2022-10-27T09:47:06Z
Memory Histogram:
Reference Timestamp: 2022-10-28T00:00:00Z
Ref:
Container Name: mongodb
Vpa Object Name: mg-sh-shard0
Total Samples Count: 3
Version: v3
Cpu Histogram:
Bucket Weights:
Index: 1
Weight: 10000
Reference Timestamp: 2022-10-27T00:00:00Z
Total Weight: 0.39793263724156597
First Sample Start: 2022-10-27T09:46:50Z
Last Sample Start: 2022-10-27T09:46:56Z
Last Update Time: 2022-10-27T09:47:06Z
Memory Histogram:
Reference Timestamp: 2022-10-28T00:00:00Z
Ref:
Container Name: mongodb
Vpa Object Name: mg-sh-shard1
Total Samples Count: 3
Version: v3
Conditions:
Last Transition Time: 2022-10-27T09:47:08Z
Message: Successfully created mongoDBOpsRequest demo/mops-vpa-mg-sh-shard-ml75qi
Observed Generation: 1
Reason: CreateOpsRequest
Status: True
Type: CreateOpsRequest
Vpas:
Conditions:
Last Transition Time: 2022-10-27T09:47:06Z
Status: True
Type: RecommendationProvided
Recommendation:
Container Recommendations:
Container Name: mongodb
Lower Bound:
Cpu: 400m
Memory: 400Mi
Target:
Cpu: 400m
Memory: 400Mi
Uncapped Target:
Cpu: 35m
Memory: 262144k
Upper Bound:
Cpu: 1
Memory: 1Gi
Vpa Name: mg-sh-shard0
Conditions:
Last Transition Time: 2022-10-27T09:47:06Z
Status: True
Type: RecommendationProvided
Recommendation:
Container Recommendations:
Container Name: mongodb
Lower Bound:
Cpu: 400m
Memory: 400Mi
Target:
Cpu: 400m
Memory: 400Mi
Uncapped Target:
Cpu: 25m
Memory: 262144k
Upper Bound:
Cpu: 1
Memory: 1Gi
Vpa Name: mg-sh-shard1
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-vpa-mg-sh-shard-ml75qi VerticalScaling Progressing 19s
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-vpa-mg-sh-shard-ml75qi VerticalScaling Successful 5m8s
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-vpa-mg-sh-shard-ml75qi
Name: mops-vpa-mg-sh-shard-ml75qi
Namespace: demo
Labels: <none>
Annotations: <none>
API Version: ops.kubedb.com/v1alpha1
Kind: MongoDBOpsRequest
Metadata:
Creation Timestamp: 2022-10-27T09:47:08Z
Generation: 1
Managed Fields:
API Version: ops.kubedb.com/v1alpha1
Fields Type: FieldsV1
fieldsV1:
f:metadata:
f:ownerReferences:
.:
k:{"uid":"36878e8e-f100-409e-aa76-e6f46569df76"}:
f:spec:
.:
f:apply:
f:databaseRef:
f:timeout:
f:type:
f:verticalScaling:
.:
f:shard:
.:
f:limits:
.:
f:memory:
f:requests:
.:
f:cpu:
f:memory:
Manager: kubedb-autoscaler
Operation: Update
Time: 2022-10-27T09:47:08Z
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: 2022-10-27T09:49:49Z
Owner References:
API Version: autoscaling.kubedb.com/v1alpha1
Block Owner Deletion: true
Controller: true
Kind: MongoDBAutoscaler
Name: mg-as-sh
UID: 36878e8e-f100-409e-aa76-e6f46569df76
Resource Version: 655347
UID: c44fbd53-40f9-42ca-9b4c-823d8e998d01
Spec:
Apply: IfReady
Database Ref:
Name: mg-sh
Timeout: 3m0s
Type: VerticalScaling
Vertical Scaling:
Shard:
Limits:
Memory: 400Mi
Requests:
Cpu: 400m
Memory: 400Mi
Status:
Conditions:
Last Transition Time: 2022-10-27T09:47:08Z
Message: MongoDB ops request is vertically scaling database
Observed Generation: 1
Reason: VerticalScaling
Status: True
Type: VerticalScaling
Last Transition Time: 2022-10-27T09:49:49Z
Message: Successfully Vertically Scaled Shard Resources
Observed Generation: 1
Reason: UpdateShardResources
Status: True
Type: UpdateShardResources
Last Transition Time: 2022-10-27T09:49:49Z
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 3m27s KubeDB Ops-manager Operator Pausing MongoDB demo/mg-sh
Normal PauseDatabase 3m27s KubeDB Ops-manager Operator Successfully paused MongoDB demo/mg-sh
Normal Starting 3m27s KubeDB Ops-manager Operator Updating Resources of StatefulSet: mg-sh-shard0
Normal Starting 3m27s KubeDB Ops-manager Operator Updating Resources of StatefulSet: mg-sh-shard1
Normal UpdateShardResources 3m27s KubeDB Ops-manager Operator Successfully updated Shard Resources
Normal Starting 3m27s KubeDB Ops-manager Operator Updating Resources of StatefulSet: mg-sh-shard0
Normal Starting 3m27s KubeDB Ops-manager Operator Updating Resources of StatefulSet: mg-sh-shard1
Normal UpdateShardResources 3m27s KubeDB Ops-manager Operator Successfully updated Shard Resources
Normal UpdateShardResources 46s KubeDB Ops-manager Operator Successfully Vertically Scaled Shard Resources
Normal ResumeDatabase 46s KubeDB Ops-manager Operator Resuming MongoDB demo/mg-sh
Normal ResumeDatabase 46s KubeDB Ops-manager Operator Successfully resumed MongoDB demo/mg-sh
Normal Successful 46s KubeDB Ops-manager Operator Successfully Vertically Scaled Database
Now, we are going to verify from the Pod, and the MongoDB yaml whether the resources of the shard pod of the database has updated to meet up the desired state, Let’s check,
$ kubectl get pod -n demo mg-sh-shard0-0 -o json | jq '.spec.containers[].resources'
{
"limits": {
"memory": "400Mi"
},
"requests": {
"cpu": "400m",
"memory": "400Mi"
}
}
$ kubectl get mongodb -n demo mg-sh -o json | jq '.spec.shardTopology.shard.podTemplate.spec.resources'
{
"limits": {
"memory": "400Mi"
},
"requests": {
"cpu": "400m",
"memory": "400Mi"
}
}
The above output verifies that we have successfully auto scaled the resources of the MongoDB sharded database.
Cleaning Up
To clean up the Kubernetes resources created by this tutorial, run:
kubectl delete mg -n demo mg-sh
kubectl delete mongodbautoscaler -n demo mg-as-sh