Karpenter: Kubernetes Node Autoscaling

By /

Okay, here’s a comprehensive article on Karpenter, focusing on its role in Kubernetes Node Autoscaling, totaling approximately 5000 words.

Karpenter: Kubernetes Node Autoscaling Redefined

Introduction

In the dynamic world of container orchestration, Kubernetes has established itself as the de facto standard. However, managing the underlying infrastructure that powers these containerized applications remains a crucial, and often complex, challenge. One of the most critical aspects of this management is node autoscaling – the ability to automatically provision and deprovision compute resources (nodes) in response to the fluctuating demands of your workloads.

Traditional approaches to Kubernetes autoscaling, primarily the Cluster Autoscaler, have served the community well. But as Kubernetes deployments grow in scale and complexity, and as applications become increasingly diverse in their resource requirements, limitations begin to surface. This is where Karpenter steps in.

Karpenter is an open-source, flexible, high-performance node autoscaler built for Kubernetes. Developed by AWS but designed to be cloud-agnostic, Karpenter dramatically simplifies and optimizes the process of node provisioning. It directly observes pod resource requests and rapidly launches the right-sized nodes to meet those needs, minimizing waste and maximizing efficiency. This article provides an in-depth exploration of Karpenter, covering its architecture, features, benefits, configuration, and practical use cases.

1. The Need for Advanced Node Autoscaling

Before diving into Karpenter’s specifics, it’s essential to understand the challenges it addresses and why traditional solutions sometimes fall short.

1.1. Limitations of the Kubernetes Cluster Autoscaler (CA)

The Cluster Autoscaler (CA) is the built-in autoscaling solution within Kubernetes. It operates by monitoring unschedulable pods (pods that cannot be placed on existing nodes due to resource constraints). When it detects unschedulable pods, it interacts with a cloud provider’s API (e.g., AWS Auto Scaling Groups, Azure Virtual Machine Scale Sets, Google Compute Engine Managed Instance Groups) to increase the number of nodes in the cluster. Conversely, when nodes are underutilized for a defined period, the CA can terminate them.

While functional, the CA has several limitations:

  • Indirect Provisioning: The CA relies on cloud provider-specific constructs like Auto Scaling Groups (ASGs). This introduces an extra layer of abstraction and configuration. You must predefine ASGs with specific instance types, AMIs, and other settings. This can lead to a proliferation of ASGs to accommodate diverse workload needs, making management cumbersome.
  • Slow Provisioning Times: Launching new nodes through ASGs can be relatively slow. The CA must first request an increase in the ASG’s desired capacity, then the cloud provider must provision the instance, and finally, the instance must join the Kubernetes cluster. This process can take several minutes, impacting application responsiveness during scaling events.
  • Bin Packing Inefficiencies: The CA often struggles with efficient bin packing – the process of optimally placing pods onto nodes to maximize resource utilization. Because it’s constrained by the predefined instance types within ASGs, it may launch larger-than-necessary instances to accommodate a single pod, leading to wasted resources. Or, it might not be able to find an ASG with an instance type that exactly matches a pod’s requirements.
  • Limited Flexibility: The CA’s reliance on pre-configured ASGs limits its flexibility. Adapting to rapidly changing workload requirements or leveraging spot instances (for cost savings) can be complex and require significant manual configuration.
  • Cloud Provider Lock-in: Although technically, the CA can work with multiple clouds, the core of its design is inherently coupled to the concept of cloud-provider managed groups of instances. This creates a degree of lock-in, making portability more challenging.

1.2. The Rise of Just-in-Time Node Provisioning

Karpenter represents a shift towards a more dynamic and efficient approach to node autoscaling: just-in-time (JIT) node provisioning. Instead of relying on pre-configured groups of instances, Karpenter directly provisions nodes on demand based on the immediate needs of unschedulable pods. This offers several advantages:

  • Speed: Karpenter can provision new nodes much faster than the CA, often in seconds rather than minutes. This is because it bypasses the intermediary layer of ASGs and directly interacts with the cloud provider’s API to launch instances.
  • Efficiency: Karpenter can precisely match instance types to pod resource requests, minimizing waste and optimizing resource utilization. It considers a wide range of instance types and selects the best fit.
  • Flexibility: Karpenter is highly configurable and can adapt to a variety of workload requirements. It supports advanced scheduling constraints, spot instances, and various instance types.
  • Simplified Management: Karpenter eliminates the need to manage numerous ASGs, simplifying cluster administration. You define provisioning rules, and Karpenter handles the rest.
  • Cloud Agnostic Design: While originating from AWS, Karpenter’s architecture is designed to be extensible to other cloud providers. The core logic is decoupled from cloud-specific implementations.

2. Karpenter Architecture and Components

Karpenter’s architecture is elegant and efficient, comprising a few key components that work together seamlessly:

2.1. The Karpenter Controller

The heart of Karpenter is the controller, a Kubernetes deployment that runs within your cluster. The controller is responsible for:

  • Watching Pods: The controller continuously monitors the Kubernetes API server for unschedulable pods. It analyzes the pod’s resource requests (CPU, memory, GPU, etc.) and scheduling constraints (node selectors, affinities, tolerations).
  • Provisioning Decisions: Based on the observed pod requirements and the configured Provisioner (described below), the controller determines the optimal instance type and other launch parameters. It considers factors like cost, availability, and any specified constraints.
  • Node Creation: The controller interacts with the cloud provider’s API (e.g., AWS EC2, Azure VMs, GCP Compute Engine) to launch the necessary instances. It uses a cloud provider-specific implementation (an “instance provider”) to handle the details of instance creation.
  • Node Deletion (Deprovisioning): Karpenter also manages node lifecycle by terminating underutilized or empty nodes. It considers factors like node age, utilization, and any defined consolidation policies.
  • Event Emission: The controller emits Kubernetes events to provide visibility into its actions. These events can be monitored to track provisioning and deprovisioning activities.

2.2. Provisioners (CRD)

Provisioners are the core configuration mechanism in Karpenter. They are defined as Kubernetes Custom Resource Definitions (CRDs). A Provisioner defines a set of rules and constraints that Karpenter uses to make provisioning decisions. Key aspects of a Provisioner include:

  • Provider-Specific Settings: This section contains configuration details specific to the cloud provider. For example, in AWS, this might include subnet selectors, security group selectors, and AMI selectors.
  • Requirements: This is where you define the constraints that Karpenter should consider when selecting instance types. You can specify:
    • Instance Types: Explicitly allow or deny specific instance types.
    • Instance Families: Allow or deny entire instance families (e.g., c5, m6g).
    • CPU/Memory Ranges: Define minimum and maximum CPU and memory requirements.
    • Architectures: Specify supported architectures (e.g., amd64, arm64).
    • Operating Systems: Restrict to specific operating systems (e.g., linux).
    • Purchase Options: Control whether to use on-demand or spot instances (and configure spot instance interruption behavior).
    • Labels and Taints: Karpenter will automatically add labels and, if appropriate, taints to provisioned nodes. These can be used to further influence pod scheduling.
    • Startup Taints: Allows you to define taints that are present when the node first joins the cluster, and which are removed only once all pods on the node are ready. This is useful, for example, for preventing a node from accepting new workloads until its specialized hardware has been initialized.
  • Limits: You can set limits on the total resources (CPU, memory) that a Provisioner can manage. This prevents runaway provisioning.
  • TTL (Time-to-Live): Karpenter supports two TTL-related mechanisms for node management:
    • ttlSecondsAfterEmpty: This setting specifies how long (in seconds) a node should remain idle before Karpenter considers it for termination. This helps consolidate workloads and reduce costs.
    • ttlSecondsUntilExpired: This setting specifies an absolute expiration time for nodes. After this time, Karpenter will actively terminate the node, even if it’s not empty. This is useful for ensuring node hygiene (e.g., regular AMI updates) and for managing spot instances.
  • Consolidation: Karpenter can actively consolidate workloads, meaning it can identify opportunities to move pods from underutilized nodes to other nodes (including newly provisioned ones), and then terminate the empty nodes. This is enabled by default and can be fine-tuned.

2.3. Instance Provider (Cloud Provider Specific)

The instance provider is the component that bridges the gap between Karpenter’s generic provisioning logic and the specific API of a cloud provider. Each supported cloud provider has its own instance provider implementation. For example:

  • AWS: The AWS instance provider interacts with the EC2 API to launch instances, manage security groups, and handle other AWS-specific tasks.
  • Azure: The Azure instance provider interacts with the Azure Compute API to manage Virtual Machines.
  • GCP: The GCP instance provider uses the Google Compute Engine API.

The instance provider is responsible for:

  • Instance Launch: Creating new instances according to the parameters provided by the Karpenter controller.
  • Instance Termination: Deleting instances when they are no longer needed.
  • Instance Status: Monitoring the status of launched instances and reporting back to the controller.
  • Pricing Information: Fetching pricing data (e.g., on-demand and spot prices) to inform provisioning decisions.

2.4. Interruption Handling (Spot Instances)

Karpenter has built-in support for handling spot instance interruptions. Spot instances are spare compute capacity offered by cloud providers at significantly reduced prices. However, they can be reclaimed by the cloud provider with short notice (typically a two-minute warning on AWS).

When using spot instances, Karpenter’s instance provider listens for interruption notices. Upon receiving a notice, Karpenter takes the following actions:

  1. Taint the Node: Karpenter immediately taints the node with karpenter.sh/interruption to prevent new pods from being scheduled on it.
  2. Evict Pods: Karpenter gracefully evicts the pods running on the interrupted node. This gives the pods time to shut down cleanly (respecting terminationGracePeriodSeconds).
  3. Provision Replacement: Karpenter immediately attempts to provision a replacement node to accommodate the evicted pods.

This interruption handling mechanism ensures that your workloads are resilient to spot instance interruptions, minimizing downtime.

3. Key Features and Benefits of Karpenter

Karpenter offers a compelling set of features that translate into significant benefits for Kubernetes users:

3.1. Rapid Node Provisioning

Karpenter’s JIT provisioning approach dramatically reduces the time it takes to launch new nodes. By directly interacting with the cloud provider’s API and bypassing the complexities of ASGs, Karpenter can provision nodes in seconds, often an order of magnitude faster than the Cluster Autoscaler. This speed is critical for:

  • Handling Spikes in Demand: Quickly scale up resources to accommodate sudden increases in traffic or workload.
  • Improving Application Responsiveness: Reduce latency and improve user experience by ensuring that resources are available when needed.
  • Faster Deployments: Accelerate deployment times for new applications or updates.

3.2. Optimized Resource Utilization (Bin Packing)

Karpenter excels at efficient bin packing. It considers a wide range of instance types and selects the best fit for the pending pods’ resource requests. This minimizes wasted resources and reduces infrastructure costs. Karpenter’s approach contrasts sharply with the CA, which is often constrained by the predefined instance types within ASGs.

Karpenter uses a sophisticated algorithm to evaluate potential instance types, taking into account:

  • Resource Requests: CPU, memory, GPU, and other resources.
  • Scheduling Constraints: Node selectors, affinities, tolerations.
  • Instance Pricing: On-demand and spot prices.
  • Availability Zones: Ensuring high availability and fault tolerance.

3.3. Flexible Configuration (Provisioners)

The Provisioner CRD provides a powerful and flexible way to configure Karpenter’s behavior. You can define multiple Provisioners to handle different workload types or environments. This level of control allows you to:

  • Tailor Provisioning to Specific Needs: Create Provisioners for different teams, applications, or environments, each with its own set of constraints.
  • Leverage Spot Instances Effectively: Define Provisioners that prioritize spot instances for cost savings, while ensuring resilience through interruption handling.
  • Enforce Resource Limits: Prevent runaway provisioning by setting limits on the total resources that a Provisioner can manage.
  • Control Node Lifecycle: Use TTL settings to manage node expiration and consolidation, optimizing resource utilization and ensuring node hygiene.
  • Adapt to a wide range of Kubernetes scheduling features: Karpenter is designed from the bottom up to be compatible with core Kubernetes scheduling primitives.

3.4. Simplified Cluster Management

Karpenter simplifies cluster management by eliminating the need to manage numerous ASGs. You define provisioning rules through Provisioners, and Karpenter handles the rest. This reduces operational overhead and allows you to focus on your applications rather than infrastructure management.

3.5. Cloud-Agnostic Design

While initially developed by AWS, Karpenter’s architecture is designed to be cloud-agnostic. The core logic is decoupled from cloud-specific implementations, making it relatively straightforward to add support for new cloud providers. This portability is a significant advantage in a multi-cloud or hybrid-cloud world.

3.6. Active Consolidation

Karpenter’s consolidation feature actively optimizes resource utilization by identifying underutilized nodes and moving pods to other nodes (including newly provisioned ones). This process helps:

  • Reduce Costs: Terminate unnecessary nodes to minimize infrastructure spending.
  • Improve Efficiency: Maximize resource utilization by packing pods more tightly onto fewer nodes.
  • Reduce Fragmentation: Prevent the cluster from becoming fragmented with many partially utilized nodes.

Consolidation is enabled by default and can be configured through the Provisioner’s consolidationPolicy field. You can choose between WhenEmpty (consolidate only when a node is completely empty) and WhenUnderutilized (consolidate when a node is below a certain utilization threshold – Karpenter determines this threshold automatically).

3.7. Built-in Spot Instance Interruption Handling

Karpenter’s seamless integration with spot instance interruption handling provides resilience and cost savings. By automatically detecting interruptions, tainting nodes, evicting pods, and provisioning replacements, Karpenter minimizes downtime and allows you to leverage spot instances confidently.

3.8. Extensibility

Karpenter’s architecture is designed for extensibility. The use of CRDs and a modular design makes it possible to add new features and cloud provider integrations relatively easily. The community is actively developing new instance providers and enhancing Karpenter’s capabilities.

3.9. Observability

Karpenter emits Kubernetes events that provide detailed information about its actions. These events can be monitored using standard Kubernetes tools (e.g., kubectl get events, monitoring dashboards) to track provisioning and deprovisioning activities. Karpenter also exposes Prometheus metrics for monitoring its performance and resource usage.

4. Installing and Configuring Karpenter

Installing and configuring Karpenter involves several steps:

4.1. Prerequisites

  • Kubernetes Cluster: You need a running Kubernetes cluster (version 1.19 or later is recommended).
  • IAM Permissions (AWS Example): If you’re using AWS, you need to create an IAM role that grants Karpenter the necessary permissions to provision and manage EC2 instances. This role will be associated with the Karpenter controller’s pod. The required permissions include:
    • ec2:RunInstances
    • ec2:CreateFleet
    • ec2:TerminateInstances
    • ec2:DescribeInstances
    • ec2:DescribeInstanceTypes
    • ec2:DescribeImages
    • ec2:DescribeSubnets
    • ec2:DescribeSecurityGroups
    • iam:PassRole (to pass the instance profile to launched instances)
    • And other related permissions. The Karpenter documentation provides a detailed IAM policy example.
  • Cloud Provider CLI: Ensure the cloud provider CLI is configured.
  • Helm: Helm is highly recommended for installing Karpenter.

4.2. Installation using Helm

The recommended way to install Karpenter is using Helm:

  1. Add the Karpenter Helm Repository:

    bash
    helm repo add karpenter https://charts.karpenter.sh
    helm repo update

  2. Create a Namespace:
    bash
    kubectl create namespace karpenter

  3. Create a KarpenterNode IAM role, Instance Profile, and Security Group. Refer to AWS Karpenter documentation for detailed steps for AWS, and similar steps can be taken for other providers.

  4. Install the Karpenter Helm Chart:

    “`bash
    helm install karpenter karpenter/karpenter -n karpenter \
    –set serviceAccount.annotations.”eks.amazonaws.com/role-arn”=arn:aws:iam:::role/ \
    –set clusterName= \
    –set clusterEndpoint= \
    –set aws.defaultInstanceProfile=
    –version v0.32.1 #Use the latest stable version

    “`

    • serviceAccount.annotations: This annotation associates the Karpenter controller’s service account with the IAM role you created. Replace placeholders with actual values.
    • clusterName: The name of your Kubernetes cluster.
    • clusterEndpoint: Your cluster endpoint.
    • aws.defaultInstanceProfile: Instance Profile to be used by Karpenter.
    • --version: Always use a specific version for production deployments to avoid unintended upgrades.
      4.3. Configuring a Provisioner

After installing Karpenter, you need to create at least one Provisioner to define how Karpenter should provision nodes. Here’s an example of a simple Provisioner for AWS:

yaml
apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
name: default
spec:
requirements:
- key: karpenter.sh/capacity-type
operator: In
values: ["spot"] # Prefer Spot instances
- key: kubernetes.io/arch
operator: In
values: ["amd64", "arm64"]
- key: karpenter.k8s.aws/instance-family
operator: In
values: ["c5", "c6g", "m5", "m6g", "r5", "r6g"] # Allow these instance families.
provider:
subnetSelector:
Name: "*-private-*" # Select subnets with names matching this pattern. Replace this with your specific criteria
securityGroupSelector:
kubernetes.io/cluster/<your_cluster_name>: "owned" # Select security groups owned by your cluster.
instanceProfile: <instance_profile_name> #Use instance profile created earlier.
ttlSecondsAfterEmpty: 30 # Terminate empty nodes after 30 seconds.
ttlSecondsUntilExpired: 2592000 # Expire nodes after 30 days (example)
limits:
resources:
cpu: "1000" # Limit total CPU provisioned by this Provisioner to 1000 cores.

Explanation of the Provisioner:

  • metadata.name: A unique name for the Provisioner.
  • spec.requirements: Defines constraints for instance selection:
    • karpenter.sh/capacity-type: Prefers spot instances. You could also use "on-demand".
    • kubernetes.io/arch: Allows both amd64 and arm64 architectures.
    • karpenter.k8s.aws/instance-family: Allows instances of specified families.
  • spec.provider: AWS-specific settings:
    • subnetSelector: Selects the subnets where instances can be launched. This uses a label selector. You’ll need to adapt this to match your VPC configuration.
    • securityGroupSelector: Selects the security groups to associate with the instances. Again, adapt this to your setup.
    • instanceProfile: The IAM instance profile to be used.
  • spec.ttlSecondsAfterEmpty: Terminates nodes that have been empty for 30 seconds.
  • spec.ttlSecondsUntilExpired: Expires nodes after 30 days (2,592,000 seconds). This is useful for node rotation and upgrades.
  • spec.limits: Sets a limit of 1000 CPU cores that this Provisioner can provision.

Apply the Provisioner:

bash
kubectl apply -f provisioner.yaml

4.4. Deploying a Sample Workload

To test Karpenter, you can deploy a sample workload that requests more resources than are currently available in your cluster. This will trigger Karpenter to provision a new node.

yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: inflate
spec:
replicas: 3
selector:
matchLabels:
app: inflate
template:
metadata:
labels:
app: inflate
spec:
containers:
- name: inflate
image: public.ecr.aws/eks-distro/kubernetes/pause:3.7
resources:
requests:
cpu: "1" # Request 1 CPU core per pod.

Apply the Deployment:

bash
kubectl apply -f inflate.yaml

Monitoring Karpenter:

You can monitor Karpenter’s activity using:

  • kubectl get nodes: Observe new nodes being created.
  • kubectl get events -n karpenter: View Karpenter’s events.
  • kubectl logs -n karpenter -l app.kubernetes.io/name=karpenter: View Karpenter controller logs.
  • Prometheus Metrics: If you have Prometheus set up, you can monitor Karpenter’s metrics.

5. Advanced Karpenter Configuration and Use Cases

5.1. Multiple Provisioners

A key strength of Karpenter is the ability to define multiple Provisioners, each tailored to different workload requirements. This allows you to create a highly customized and efficient autoscaling strategy. For example:

  • GPU Workloads: Create a Provisioner that specifically targets GPU-enabled instance types (e.g., p3, g4dn) for machine learning or other GPU-intensive tasks.

    yaml
    apiVersion: karpenter.sh/v1alpha5
    kind: Provisioner
    metadata:
    name: gpu-provisioner
    spec:
    requirements:
    - key: karpenter.k8s.aws/instance-family
    operator: In
    values: ["p3", "g4dn"]
    - key: nvidia.com/gpu
    operator: Exists # Require nodes with GPUs
    # ... other provider settings ...

  • High-Memory Workloads: Define a Provisioner that favors instance types with large amounts of memory (e.g., r5, x1).

  • Spot vs. On-Demand: Create separate Provisioners for spot and on-demand instances, allowing you to prioritize spot for cost savings while using on-demand for critical workloads.

  • Different Teams/Environments: Create Provisioners for different teams or environments (e.g., development, staging, production), each with its own resource limits and constraints.

5.2. Node Affinity and Anti-Affinity

Karpenter fully respects Kubernetes node affinity and anti-affinity rules. You can use these features to control where pods are scheduled, even when Karpenter is provisioning the nodes.

  • Node Affinity: Use nodeSelector or nodeAffinity in your pod specifications to require pods to be scheduled on nodes with specific labels. Karpenter will consider these requirements when selecting instance types.

    “`yaml
    apiVersion: v1
    kind: Pod

    …

    spec:
    affinity:
    nodeAffinity:
    requiredDuringSchedulingIgnoredDuringExecution:
    nodeSelectorTerms:
    – matchExpressions:
    – key: my-custom-label
    operator: In
    values: [“value1”]
    containers:
    # …
    “`

    You would then configure your Karpenter Provisioner to add the my-custom-label=value1 label to the nodes it provisions.

  • Node Anti-Affinity: Use nodeAntiAffinity to prevent pods from being scheduled on nodes with specific labels. This can be useful for spreading pods across different availability zones or instance types for high availability.

5.3. Taints and Tolerations

Karpenter automatically adds taints to nodes in certain situations (e.g., during spot instance interruption). You can also configure Karpenter to add custom taints to nodes through the Provisioner. Pods must have matching tolerations to be scheduled on tainted nodes. This is a powerful mechanism for controlling pod placement and isolating workloads.

Example of adding a custom taint in the Provisioner:

yaml
apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
name: tainted-provisioner
spec:
# ... other settings ...
taints:
- key: my-custom-taint
value: "special-workload"
effect: NoSchedule

Pods that should run on nodes provisioned by this Provisioner would need a corresponding toleration:

“`yaml
apiVersion: v1
kind: Pod

…

spec:
tolerations:
– key: my-custom-taint
value: “special-workload”
operator: Equal
effect: NoSchedule
containers:
# …
“`
5.4 Startup Taints

Startup taints are a special kind of taint that is present on a node when it initially joins the cluster. These taints are automatically removed by Karpenter only after all pods currently scheduled to the node report readiness.

yaml
apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
name: startup-taint-provisioner
spec:
# ... other settings ...
startupTaints:
- key: initialization-required
effect: NoSchedule

This is extremely useful for cases where a node needs to perform some initialization before it can accept general workloads. A common example is initializing specialized hardware (like a GPU or FPGA) or loading large datasets into memory. A daemonset could be deployed to perform the initialization, and the startup taint would prevent any other pods from being scheduled until the initialization is complete and the daemonset’s pods are ready.

5.5. Custom AMIs (AWS Example)

By default, Karpenter uses the latest recommended EKS-optimized AMI for the selected instance type and Kubernetes version. However, you can specify a custom AMI in the Provisioner using the amiSelector field:

“`yaml
apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
name: custom-ami-provisioner
spec:
# … other settings …
provider:
# … other provider settings …
amiSelector:
name: my-custom-ami-* # Select AMIs with names matching this pattern.

``
You can also use theamiFamilyto choose amongAL2,Bottlerocket,Ubuntu, andWindows` AMIs.
5.6. Block Device Mappings (AWS Example)

You can customize the block device mappings for the instances launched by Karpenter. This allows you to control the size and type of the root volume and add additional EBS volumes. This is done within the provider section of the Provisioner:

“`yaml
apiVersion: karpenter.sh/v1alpha5
kind: Provisioner

…

spec:
provider:
# …
blockDeviceMappings:
– deviceName: /dev/xvda # The root volume.
ebs:
volumeSize: 50Gi # Set the root volume size to 50 GB.
volumeType: gp3 # Use the gp3 volume type.
deleteOnTermination: true # Delete on instance termination
– deviceName: /dev/xvdb # An additional volume.
ebs:
volumeSize: 100Gi
volumeType: io1
iops: 10000
deleteOnTermination: true
“`

5.7. User Data (AWS Example)

You can provide custom user data to the instances launched by Karpenter. User data is a script that is executed when the instance starts. This can be used to perform additional configuration tasks, such as installing software, configuring services, or joining a cluster.
The user data is specified in the provider section, and must be base64-encoded:

“`yaml
apiVersion: karpenter.sh/v1alpha5
kind: Provisioner

…

spec:
provider:
# …
userData: |
MIME-Version: 1.0
Content-Type: multipart/mixed; boundary=”BOUNDARY”

    --BOUNDARY
    Content-Type: text/x-shellscript; charset="us-ascii"

    #!/bin/bash
    echo "Hello from Karpenter user data!"
    # Add your custom configuration commands here.

    --BOUNDARY--

“`
5.8. Consolidation Policies

Karpenter’s consolidation feature can be fine-tuned to control how aggressively it consolidates workloads. The consolidationPolicy field in the Provisioner allows you to choose between:

  • WhenEmpty (default): Karpenter will only consider a node for termination if it is completely empty (no pods running). This is the most conservative approach.
  • WhenUnderutilized: Karpenter will actively try to consolidate workloads even if nodes are not completely empty. It uses an internal algorithm to determine when a node is sufficiently underutilized to justify consolidation. This is a more aggressive approach that can lead to greater cost savings.

“`yaml
apiVersion: karpenter.sh/v1alpha5
kind: Provisioner

…

spec:
consolidationPolicy: WhenUnderutilized
# …
“`

5.9. Weighting Provisioners

When multiple Provisioners could potentially satisfy the requirements of a pending pod, Karpenter uses a weighting system to choose the best Provisioner. By default, all Provisioners have a weight of 0, meaning they are equally likely to be chosen. You can assign a higher weight to a Provisioner to make it more likely to be selected:
yaml
apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
name: preferred-provisioner
spec:
weight: 10
# ... other settings ...
yaml
apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
name: less-preferred-provisioner
spec:
weight: 1 # Optional, defaults to 0
# ... other settings ...

In this example, preferred-provisioner will be chosen much more often than less-preferred-provisioner when both are viable options. This is useful for scenarios where you have a preferred configuration (e.g., using a specific instance type or availability zone) but want to fall back to another Provisioner if the preferred option is not available.

5.10. Disruption Budgets

While Karpenter’s consolidation and TTL features can significantly improve efficiency, it’s important to ensure that these actions don’t disrupt your workloads. Kubernetes Pod Disruption Budgets (PDBs) can be used to limit the number of pods that can be voluntarily disrupted at any given time. Karpenter respects PDBs when performing consolidation and node expiration.

yaml
apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
name: my-app-pdb
spec:
minAvailable: 2 # Ensure at least 2 pods are always available.
selector:
matchLabels:
app: my-app

If Karpenter needs to evict pods from a node for consolidation or expiration, it will check the PDB to ensure that the eviction won’t violate the budget. If the budget would be violated, Karpenter will wait until it’s safe to proceed.

6. Karpenter vs. Cluster Autoscaler: A Detailed Comparison

Feature Karpenter Cluster Autoscaler (CA)
Provisioning Model Just-in-Time (JIT), direct instance creation Indirect, via Auto Scaling Groups (or similar)
Provisioning Speed Very Fast (seconds) Slower (minutes)
Bin Packing Highly optimized, precise instance selection Less optimized, limited by ASG configurations
Flexibility Highly configurable (Provisioners) Less flexible, relies on predefined ASGs
Management Simplified, no ASG management Requires managing ASGs
Cloud Agnostic Designed for multi-cloud Primarily cloud-provider specific (though adaptable)
Spot Instances Built-in, robust interruption handling Supported, but can be more complex to configure
Consolidation Active, configurable (WhenEmpty, WhenUnderutilized) Passive, based on utilization thresholds
Node Expiration Supported (ttlSecondsUntilExpired) Supported (via ASG lifecycle hooks, less direct)
Extensibility Highly extensible (CRDs, modular design) Less extensible

Key Differences Summarized:

  • Direct vs. Indirect Provisioning: Karpenter’s direct provisioning is the fundamental difference, leading to speed and efficiency gains.
  • Configuration: Karpenter’s Provisioner CRD offers more granular control than the CA’s configuration, which is often tied to cloud provider-specific constructs.
  • Bin Packing: Karpenter’s bin packing algorithm is significantly more sophisticated, leading to better resource utilization.
  • Active vs. Passive Consolidation: Karpenter can actively consolidate workloads, while the CA relies on passive utilization thresholds.

7. Best Practices for Using Karpenter

  • Start Small: Begin with a single Provisioner and a small set of instance types to gain familiarity with Karpenter’s behavior.
  • Monitor Closely: Use Kubernetes events, logs, and Prometheus metrics to monitor Karpenter’s actions and resource usage.
  • Use Multiple Provisioners: Create separate Provisioners for different workload types, environments, or teams to optimize resource allocation and cost.
  • Leverage Spot Instances: Use spot instances for cost savings, but ensure you have appropriate interruption handling in place.
  • Configure TTLs: Use ttlSecondsAfterEmpty and ttlSecondsUntilExpired to manage node lifecycle and optimize resource utilization.
  • Use Node Affinity/Anti-Affinity: Control pod placement using Kubernetes scheduling features.
  • Use Taints and Tolerations: Isolate workloads and manage node initialization with taints.
  • Set Resource Limits: Prevent runaway provisioning by

Leave a Comment

Your email address will not be published. Required fields are marked *

Scroll to Top