In the dynamic realm of container orchestration and secrets management, the integration of Kubernetes with HashiCorp Vault stands as a pivotal undertaking, offering enhanced security and streamlined operational workflows. However, this collaboration is not without its complexities, presenting a set of formidable challenges that organizations must navigate. From intricacies in configuration to ensuring seamless communication between these powerful tools, the journey to successfully integrate Kubernetes with HashiCorp Vault demands a strategic approach.

In this exploration, we delve into the top 10 challenges faced in this integration process, shedding light on the key hurdles that organizations encounter and providing insights into overcoming these obstacles for a robust and secure deployment. Here are the top 10 challenges you might face:

Authentication and Authorization: Configuring proper authentication and authorization mechanisms to control access to Vault secrets for both Kubernetes and traditional applications can be challenging.

Secrets Management: Managing secrets across different platforms, ensuring their security, and automating their lifecycle is a fundamental challenge.

Secret Rotation: Implementing automated secret rotation policies and procedures for secrets stored in Vault can be complex, especially for legacy applications that may not support dynamic secret retrieval.

Networking and Security: Establishing secure communication between Kubernetes pods, traditional applications, and Vault while maintaining network segmentation and firewall rules can be tricky.

Integration Complexity: Integrating Vault with a variety of application types, databases, and cloud services, especially when dealing with legacy systems, can lead to integration complexities.

Compliance and Auditing: Meeting compliance requirements and tracking access and usage of secrets for auditing purposes can be challenging, especially in regulated industries.

Secrets Versioning: Managing different versions of secrets, ensuring backward compatibility, and handling secrets rotation gracefully can be complex.

Backup and Disaster Recovery: Developing and testing robust backup and disaster recovery plans for Vault’s data and configurations is crucial to ensure business continuity.

Monitoring and Alerting: Setting up monitoring and alerting solutions to detect and respond to any issues or breaches in real-time is a significant challenge.

Documentation and Training: Ensuring that your team has the necessary skills and knowledge to manage and troubleshoot the integrated environment is an ongoing challenge, as technologies evolve.

Certainly, the real challenges linked to the widespread use of DevOps tools are undeniable. This is why numerous organizations caught in the predicament of managing multiple DevOps platforms are choosing to streamline by consolidating into a central platform. Yet, what does this consolidation involve, and how do you determine the optimal single DevOps platform for migration?  Read this Case Study for more answers.

curl -H "Authorization: Bearer ${TOKEN}" \
  -H "Content-Type: application/vnd.api+json" \
 --request POST \
  -d @- \
"https://app.terraform.io/api/v2/organizations/${ORG_NAME}/registry-providers/private/${ORG_NAME}/${PROVIDER_NAME}/versions" <<EOT
​
{
  "data": {
    "type": "registry-provider-versions",
    "attributes": {
      "version": "${VERSION}",
      "key-id": "${KEY_ID}",
      "protocols": ["6.0"]
    }
  }
}
EOT 

Overview

HashiCorp’s Terraform Cloud provides a centralized platform for managing infrastructure as code. It’s a leading provider in remote Terraform management with remote state management, automated VCS integrations, and integrations. One of its features, a private registry, can be used to develop internal Terraform providers where control, security, and customizations are paramount. An organization will want to deploy a private provider if a public is disqualified for direct upstream consumption for a specific reason. Some more specific use cases include: ​

  1. Customization and Control: A private registry allows organizations to maintain control over their proprietary or custom-built Terraform providers. It enables them to manage, version, and distribute these providers securely within the organization.
  2. Security and Compliance: A private registry ensures that only authorized users within the organization can access and utilize specific Terraform providers for sensitive or proprietary infrastructure configurations. This control aids in compliance with internal policies and regulatory requirements.
  3. Versioning and Stability: With a private registry, teams can maintain a stable versioning system for their Terraform providers. This helps ensure project infrastructure configurations remain consistent and compatible with the specified provider versions. ​ Let’s explore an example using the Terraform Provider Scaffolding Framework to build a custom Terraform provider and publish it to a private registry. Scaffold provides a framework starter kit that you can use out of the box to replace your APIs. ​

Signing Your Provider

​ Code signing guarantees that the generated artifacts originate from your source, allowing users to verify this authenticity by comparing the produced signature with your publicly available signing key. It will require you to generate a key pair through the GNU PGP utility. To develop, you can use the following command. Ensure you replace GPG_PASSWORD and your name with values that make sense.

gpg --default-new-key-algo rsa4096 --batch --passphrase "${GPG_PASSWORD}" --quick-gen-key 'Your Name <name@example.com>' default default

Export Public Key

​ With your newly generated key securely stored, the next step involves exporting and uploading it to Terraform Cloud. This action facilitates verification while deploying your signed artifacts, ensuring their authenticity within the platform’s environment. The GPG Key API requires the public key to validate the signature. To access the list of key IDs, you can execute: gpg --list-secret-keys --keyid-format LONG. The key is denoted in the output.

[keyboxd]
---------
sec   rsa4096/<KEY ID> 2023-11-22 [SC] [expires: 2026-11-21]

​ You can then get your public key as a single string. KEY=$(gpg --armor --export ${KEY_ID} | awk '{printf "%s\\n", $0}'). You’ll then need to build a payload with the output of that file and POST that to https://app.terraform.io/api/registry/private/v2/gpg-keys. The ORG_NAME is your Terraform cloud organization.

{
  "data": {
        "type": "gpg-keys",
        "attributes": {
            "namespace": "${ORG_NAME}",
            "ascii-armor": "${KEY}"
        }  
    }
}

Export Private Key For CI (Optional)

​ If you plan to use this key in a CI Platform, you can also export the key and upload it gpg --export-secret-keys --armor ${KEY_ID} > /tmp/gpg.pgp to a secure Vault. ​

Packaging Terraform Providers with GoReleaser

Goreleaser simplifies the process of building and releasing Go binaries. Using GoReleaser, we can bundle different architectures, operating systems, etc.

  1. Create a terraform registry manifest. The protocol version is essential. If using Plugin Framework, you want 6.0. If using Plugin SDKv2, you’ll wish to 5.0.
{
    "version": 1,
    "metadata": {
      "protocol_versions": ["6.0"]
    }
}

​ 2. Configuring Goreleaser Ensure your goreleaser.yml configuration includes settings for multi-architecture support and signing. This file should live at the provider’s root, next to your main codebase. ​

before:
  hooks:
    - go mod tidy
builds:
- env:
    - CGO_ENABLED=0
  mod_timestamp: '{{ .CommitTimestamp }}'
  flags:
    - -trimpath
  ldflags:
    - '-s -w -X main.version={{ .Version }} -X main.commit={{ .Commit }}'
  goos:
    - freebsd
    - windows
    - linux
    - darwin
  goarch:
    - amd64
    - '386'
    - arm
    - arm64
  ignore:
    - goos: darwin
      goarch: '386'
  binary: '{{ .ProjectName }}_v{{ .Version }}'
archives:
- format: zip
  name_template: '{{ .ProjectName }}_{{ .Version }}_{{ .Os }}_{{ .Arch }}'
checksum:
  extra_files:
    - glob: 'terraform-registry-manifest.json'
      name_template: '{{ .ProjectName }}_{{ .Version }}_manifest.json'
  name_template: '{{ .ProjectName }}_{{ .Version }}_SHA256SUMS'
  algorithm: sha256
signs:
  - artifacts: checksum
    args:
      - "--batch"
      - "--local-user"
      - "{{ .Env.GPG_FINGERPRINT }}"
      - "--output"
      - "${signature}"
      - "--detach-sign"
      - "${artifact}"
    stdin: '{{ .Env.GPG_PASSWORD }}'
release:
  extra_files:
    - glob: 'terraform-registry-manifest.json'
      name_template: '{{ .ProjectName }}_{{ .Version }}_manifest.json'
changelog:
  skip: true
  1. Tag your branch Your git strategy may differ, but GoReleaser uses branch tags to determine versions.
git tag 0.0.1
git checkout 0.0.1

​ 4. Execute GoReleaser to bundle the binaries locally without publishing. We skipped publishing as we will manually upload them to Terraform Cloud.

export GPG_TTY=$(tty)
export GPG_FINGERPRINT=${KEY_ID}
goreleaser release --skip=publish

Go Releaser ​ Now we have our artifacts.

Publishing to Terraform Cloud Private Registry

Release Process ​ ​ Once you have the signed binaries, you can publish them to the Terraform Cloud private registry. Hashicorp provides a guide, which we will follow.

Register the provider (first time only)

  1. Create a provider config file and POST that body utilizing your terraform cloud API token. A provider name is usually a singular descriptor representing a business unit, such as google or aws.
curl --header "Authorization: Bearer ${TERRAFORM_CLOUD_API_TOKEN}" \
--header "Content-Type: application/vnd.api+json" \
--request POST \
-d @- \
"https://app.terraform.io/api/v2/organizations/${ORG_NAME}/registry-providers" <<EOT
​
{
 "data": {
     "type": "registry-providers",
     "attributes": {
         "name": "${PROVIDER_NAME}",
         "namespace": "${ORG_NAME}",
         "registry-name": "private"
     }
 }
}
EOT

Uploading your versions

  1. Create Version Shell within Private Registry Providers
curl -H "Authorization: Bearer ${TOKEN}" \
  -H "Content-Type: application/vnd.api+json" \
 --request POST \
  -d @- \
"https://app.terraform.io/api/v2/organizations/${ORG_NAME}/registry-providers/private/${ORG_NAME}/${PROVIDER_NAME}/versions" <<EOT
​
{
  "data": {
    "type": "registry-provider-versions",
    "attributes": {
      "version": "${VERSION}",
      "key-id": "${KEY_ID}",
      "protocols": ["6.0"]
    }
  }
}
EOT 

The response will contain upload links that you will use to upload the SHA256SUMS and SHA256.sig files.

"links": {
    "shasums-upload": "https://archivist.terraform.io/v1/object/dmF1b64hd73ghd63",
    "shasums-sig-upload": "https://archivist.terraform.io/v1/object/dmF1b37dj37dh33d"
 }
  1. Upload Signatures
# Replace ${VERSION} and ${PROVIDER_NAME} with actual values
curl -sS -T "dist/terraform-provider-${PROVIDER_NAME}_${VERSION}_SHA256SUMS" "${SHASUM_UPLOAD}"
curl -sS -T "dist/terraform-provider-${PROVIDER_NAME}_${VERSION}_SHA256SUMS.sig" "${SHASUM_SIG_UPLOAD}"
  1. Register platform for every arch and operating system.
FILENAME="terraform-provider-${PROVIDER_NAME}_${VERSION}_${OS}_${ARCH}.zip"
SHA=$(shasum -a 256 "dist/${FILENAME}" | awk '{print $1}' )
# OS ex. darwin/linux/windows
# ARCH ex. arm/amd64
# FILENAME. terraform-provider-<PROVIDER_NAME>_<VERSION>_<OS>_<ARCH>.zip. Define through name_template
​
curl -H "Authorization: Bearer ${TOKEN}" \
 -H "Content-Type: application/vnd.api+json" \
 --request POST \
 -d @- \
  "https://app.terraform.io/api/v2/organizations/${ORG_NAME}/registry-providers/private/${ORG_NAME}/${PROVIDER_NAME}/versions/${VERSION}/platforms" << EOT
  {
    "data": {
        "type": "registry-provider-version-platforms",
        "attributes": {
            "shasum": "${SHA}",
            "os": "${OS}",
            "arch": "${ARCH}",
            "filename": "${FILENAME}"
        }
    }
}
EOT

The response will contain upload the provider binary to:

"links": {
  "provider-binary-upload": "https://archivist.terraform.io/v1/object/dmF1b45c367djh45nj78"
}
  1. Upload archvied binaries
curl -sS -T "dist/${FILENAME}" "${PROVIDER_BINARY_URL}"
  1. Repeat steps 3 and 4 for every architecture and operating system.

Using the provider

​ ​ Registry ​ ​ ​ Private providers hosted within terraform cloud are only available to users within the organization. When developing locally, ensure you set up credentials through the terraform login, creating a credentials.tfrc.json file.

credentials "app.terraform.io" { 
  # valid user API token: 
  token = "xxxxxx.atlasv1.zzzzzzzzzzzzz"
}

With the authentication bits setup, you can utilize the new provider by defining the provider block substituting in those existing variables.

terraform {
  required_providers {
    ${PROVIDER_NAME} = {
      source = "app.terraform.io/${ORG_NAME}/${PROVIDER_NAME}"
      version = "${VERSION}"
    }
  }
}
​
provider "${PROVIDER_NAME}" { 
  # Configuration options 
}

Document Provider

For user consumption, a common practice is to provide provider documentation for your resources utilizing Terraform plugin docs. This plugin generator allows you to generate markdowns from examples and schema definitions, which users can then consume. At the time of publication, this feature is currently not supported within the terraform cloud. ​

Cleanup

​ To remove the provider from the registry.

Delete version

curl -H "Authorization: Bearer ${TOKEN}" \
 --request DELETE \
  "https://app.terraform.io/api/v2/organizations/${ORG_NAME}/registry-providers/private/${ORG_NAME}/${PROVIDER_NAME}/versions/${VERSION}"

Delete provider

curl -H "Authorization: Bearer ${TOKEN}" \
 --request DELETE \
  "https://app.terraform.io/api/v2/organizations/${ORG_NAME}/registry-providers/private/${ORG_NAME}/${PROVIDER_NAME}"

Deregister GPG Key

curl -H "Authorization: Bearer ${TOKEN}" \
 --request DELETE \
https://app.terraform.io/api/registry/private/v2/gpg-keys/${ORG_NAME}/${KEY_ID}

Conclusion

​ Publishing custom Terraform providers to the Terraform Cloud private registry involves bundling, signing, and uploading binaries and metadata through the API. Following these steps, you can effectively manage and distribute your Terraform provider to support various architectures and operating systems. ​

https://app.terraform.io/api/registry/private/v2/gpg-keys/${ORG_NAME}/${KEY_ID}

By, Samuel Cadavid, Senior Solutions Consultant

 

In the dynamic world of cloud computing, Kubernetes has emerged as a frontrunner in orchestrating containerized applications. However, as the complexity and scale of deployments grow, managing resources efficiently becomes a daunting task. This is where Artificial Intelligence (AI) steps in, revolutionizing how we approach Kubernetes management, specifically in in-place pod resizing, vertical and horizontal scaling, and power-aware batch scheduling. During my recent trip to KubeCon, I was able to attend a session hosted by Vinay Kulkarni (eBay) and Haoran Qiu (UIUC) that delved into this topic, including how cluster autoscaler currently handles pods pending due to insufficient resources, changes to the autoscaling workflow that right-sizes over-provisioned pods, and the latest research that leverages machine learning to achieve multi-dimensional autoscaling. The session got me thinking about how AI plays a role in pod-resizing, and it inspired me to keep digging into it afterward.  

AI-Driven In-Place Pod Resizing 

Traditionally, resizing pods in Kubernetes meant recreating them with the new size specifications. This process, while effective, leads to downtime and potential service disruptions. AI-driven in-place pod resizing changes this narrative.

How it Works 

AI algorithms continuously monitor the resource usage patterns of each pod. When a pod requires more resources, AI predicts this need and dynamically adjusts CPU and memory allocations without restarting the pod. This approach minimizes downtime and ensures that applications scale seamlessly with fluctuating demands. 

Benefits

 

 

Vertical and Horizontal Scaling: AI at the Helm 

Vertical Scaling with AI 

AI-driven vertical scaling involves adjusting the CPU and memory limits of a pod. Using predictive analytics, AI determines the optimal size for a pod based on historical data and current trends. This proactive resizing prevents resource exhaustion and improves performance. 

Horizontal Scaling with AI 

In horizontal scaling, AI plays a pivotal role in deciding when to add or remove pod instances. By analyzing traffic patterns, workload demands, and system health, AI can automate the scaling process, ensuring that the cluster meets the demand without manual intervention. 

Advantages 

Power-Aware Batch Scheduling with AI 

Energy efficiency is becoming increasingly important in data center operations. AI-driven power-aware batch scheduling in Kubernetes is a game-changer in this realm.

The Concept

This approach involves scheduling batch jobs in a manner that optimizes power usage. AI algorithms analyze the power consumption patterns of nodes and schedule jobs on those consuming less power or during off-peak hours, significantly reducing the overall energy footprint. 

Impact 

The integration of AI into Kubernetes management is not just a trend; it’s a necessity for efficient, cost-effective, and sustainable operations. AI’s role in in-place pod resizing, vertical and horizontal scaling, and power-aware batch scheduling marks a significant leap towards smarter, more autonomous cloud infrastructures. As we continue to embrace these AI-driven strategies, we pave the way for more resilient, responsive, and responsible computing environments. Here, I’ve only scratched the surface of what AI can do for Kubernetes management. As technology evolves, we can expect even more innovative solutions to emerge, further simplifying and enhancing the way we manage cloud resources. 

 

Watch the full lecture here!