MinIO in Maintenance Mode: What It Means for the Community Edition, OEM Users, and Open-Source Alternatives

MinIO in Maintenance Mode: What It Means for the Community Edition, OEM Users, and Open-Source Alternatives

Background: MinIO and the Maintenance Mode announcement

MinIO has long been one of the most popular self-hosted S3-compatible object storage solutions, especially in Kubernetes and on‑premise environments. Its simplicity, performance, and API compatibility made it a common default choice for backups, artifacts, logs, and internal object storage.

In late 2025, MinIO marked its upstream repository as Maintenance Mode and clarified that the Community Edition would be distributed source-only, without official pre-built binaries or container images. This move triggered renewed discussion across the industry about sustainability, governance, and the risks of relying on a single-vendor-controlled “open core” storage layer.

A detailed industry analysis of this shift, including its broader ecosystem impact, can be found in this InfoQ article

What exactly changed?

1. Maintenance Mode

Maintenance Mode means:

  • No new features
  • No roadmap-driven improvements
  • Limited fixes, typically only for critical issues
  • No active review of community pull requests

As highlighted by InfoQ, this effectively freezes MinIO Community as a stable but stagnant codebase, pushing innovation and evolution exclusively toward the commercial offerings.

2. Source-only distribution

Official binaries and container images are no longer published for the Community Edition. Users must:

  • Build MinIO from source
  • Maintain their own container images
  • Handle signing, scanning, and provenance themselves

This aligns with a broader industry pattern noted by InfoQ: infrastructure projects increasingly shifting operational burden back to users unless they adopt paid tiers.

Direct implications for Community users

Security and patching

With no active upstream development:

  • Vulnerability response times may increase
  • Users must monitor security advisories independently
  • Regulated environments may find Community harder to justify

InfoQ emphasizes that this does not make MinIO insecure by default, but it changes the shared-responsibility model significantly.

Operational overhead

Teams now need to:

  • Pin commits or tags explicitly
  • Build and test their own releases
  • Maintain CI pipelines for a core storage dependency

This is a non-trivial cost for what was previously perceived as a “drop‑in” component.

Support and roadmap

The strategic message is clear: active development, roadmap influence, and predictable maintenance live behind the commercial subscription.

Impact on OEM and embedded use cases

The InfoQ analysis draws an important distinction between API consumers and technology embedders.

Using MinIO as an external S3 service

If your application simply consumes an S3 endpoint:

  • The impact is moderate
  • Migration is largely operational
  • Application code usually remains unchanged

Embedding or redistributing MinIO

If your product:

  • Ships MinIO internally
  • Builds gateways or features on MinIO internals
  • Depends on MinIO-specific operational tooling

Then the impact is high:

  • You inherit maintenance and security responsibility
  • Long-term internal forking becomes likely
  • Licensing (AGPL) implications must be reassessed carefully

For OEM vendors, this often forces a strategic re-evaluation rather than a tactical upgrade.

Forks and community reactions

At the time of writing:

  • Several community forks focus on preserving the MinIO Console / UI experience
  • No widely adopted, full replacement fork of the MinIO server exists
  • Community discussion, as summarized by InfoQ, reflects caution rather than rapid consolidation

The absence of a strong server-side fork suggests that most organizations are choosing migration over replacement-by-fork.

Fully open-source alternatives to MinIO

InfoQ highlights that the industry response is not about finding a single “new MinIO”, but about selecting storage systems whose governance and maintenance models better match long-term needs.

Ceph RGW

Best for: Enterprise-grade, highly available environments
Strengths: Mature ecosystem, large community, strong governance
Trade-offs: Operational complexity

SeaweedFS

Best for: Teams seeking simplicity and permissive licensing
Strengths: Apache-2.0 license, active development, integrated S3 API
Trade-offs: Partial S3 compatibility for advanced edge cases

Garage

Best for: Self-hosted and geo-distributed systems
Strengths: Resilience-first design, active open-source development
Trade-offs: AGPL license considerations

Zenko / CloudServer

Best for: Multi-cloud and Scality-aligned architectures
Strengths: Open-source S3 API implementation
Trade-offs: Different architectural assumptions than MinIO

Recommended strategies by scenario

If you need to reduce risk immediately

  • Freeze your current MinIO version
  • Build, scan, and sign your own images
  • Define and rehearse a migration path

If you operate Kubernetes on-prem with HA requirements

  • Ceph RGW is often the most future-proof option

If licensing flexibility is critical

  • Start evaluation with SeaweedFS

If operational UX matters

  • Shift toward automation-first workflows
  • Treat UI forks as secondary tooling, not core infrastructure

Conclusion

MinIO’s shift of the Community Edition into Maintenance Mode is less about short-term breakage and more about long-term sustainability and control.

As the InfoQ analysis makes clear, the real risk is not technical incompatibility but governance misalignment. Organizations that treat object storage as critical infrastructure should favor solutions with transparent roadmaps, active communities, and predictable maintenance models.

For many teams, this moment serves as a natural inflection point: either commit to self-maintaining MinIO, move to a commercially supported path, or migrate to a fully open-source alternative designed for the long run.

Helm 4.0 Everything you need to know about the biggest evolution of the Helm ecosystem

Helm 4.0 Everything you need to know about the biggest evolution of the Helm ecosystem

Helm is one of the main utilities within the Kubernetes ecosystem, and therefore the release of a new major version, such as Helm 4.0, is something to consider because it is undoubtedly something that will need to be analyzed, evaluated, and managed in the coming months.

Due to this, we will see many comments and articles around this topic, so we will try to shed some light.

Main New Features of Helm 4.0

According to the project itself in its announcement, Helm 4 introduces three major blocks of changes: new plugin system, better integration with Kubernetes ** and internal modernization of SDK and performance**.

New Plugin System (includes WebAssembly)

The plugin system has been completely redesigned, with a special focus on security through the introduction of a new WebAssembly runtime that, while optional, is recommended as it runs in a “sandbox” mode that offers limits and guarantees from a security perspective.

In any case, there is no need to worry excessively, as the “classic” plugins continue to work, but the message is clear: for security and extensibility, the direction is Wasm.

Server-Side Apply and Better Integration with Other Controllers

From this version, Helm 4 supports Server-Side Apply (SSA) through the --server-side flag, which has already become stable since Kubernetes version v1.22 and allows updates on objects to be handled server-side to avoid conflicts between different controllers managing the same resources.

It also incorporates integration with kstatus to ensure the state of a component in a more reliable way than what currently happens with the use of the --wait parameter.

Other Additional Improvements

Additionally, there is another list of improvements that, while of lesser scope, are important qualitative leaps, such as the following:

  • Installation by digest in OCI registries: (helm install myapp oci://...@sha256:<digest>)
  • Multi-document values: you can pass multiple YAML values in a single multi-doc file, facilitating complex environments/overlays.
  • New --set-json argument that allows for easily passing complex structures compared to the current solution using the --set parameter

Why a Major (v4) and Not Another Minor of 3.x?

As explained in the official release post, there were features that the team could not introduce in v3 without breaking public SDK APIs and internal architecture:

  • Strong change in the plugin system (WebAssembly, new types, deep integration with the core).
  • Restructuring of Go packages and establishment of a stable SDK at helm.sh/helm/v4, code-incompatible with v3.
  • Introduction and future evolution of Charts v3, which require the SDK to support multiple versions of chart APIs.

With all this, continuing in the 3.x branch would have violated SemVer: the major number change is basically “paying” the accumulated technical debt to be able to move forward.

Additionally, a new evolution of the charts is expected in the future, moving from v2 to a future v3 that is not yet fully defined, and currently, v2 charts run correctly in this new version.

Do I Have to Migrate to This New Version?

The short answer is: yes. And possibly the long answer is: yes, and quickly. In the official Helm 4 announcement, they specify the support schedule for Helm 3:

  • Helm 3 bug fixes until July 8, 2026.
  • Helm 3 security fixes until November 11, 2026.
  • No new features will be backported to Helm 3 during this period; only Kubernetes client libraries will be updated to support new K8s versions.

Practical translation:

  • You have ~1 year to plan a smooth migration to Helm 4 with bug support.
  • After November 2026, continuing to use Helm 3 will become increasingly risky from a security and compatibility standpoint.

Best Practices for Migration

To carry out the migration, it is important to remember that it is perfectly possible and feasible to have both versions installed on the same machine or agent, so a “gradual” migration can be done to ensure that the end of support for version v3 is reached with everything migrated correctly, and for that, the following steps are recommended:

  • Conduct an analysis of all Helm commands and usage from the perspective of integration pipelines, upgrade scripts, or even the import of Helm client libraries in Helm-based developments.
  • Especially carefully review all uses of --post-renderer, helm registry login, --atomic, --force.
  • After the analysis, start testing Helm 4 first in non-production environments, reusing the same charts and values, reverting to Helm 3 if a problem is detected until it is resolved.
  • If you have critical plugins, explicitly test them with Helm 4 before making the global change.

Problems with Ingress in Kubernetes: Complexity, Incompatibilities, and the Future with Gateway API

Problems with Ingress in Kubernetes: Complexity, Incompatibilities, and the Future with Gateway API

Introduction

Ingresses have been, since the early versions of Kubernetes, the most common way to expose applications to the outside. Although their initial design was simple and elegant, the success of Kubernetes and the growing complexity of use cases have turned Ingress into a problematic piece: limited, inconsistent between vendors, and difficult to govern in enterprise environments.

In this article, we analyze why Ingresses have become a constant source of friction, how different Ingress Controllers have influenced this situation, and why more and more organizations are considering alternatives like Gateway API.

What Ingresses are and why they were designed this way

The Ingress ecosystem revolves around two main resources:

🏷️ IngressClass

Defines which controller will manage the associated Ingresses. Its scope is cluster-wide, so it is usually managed by the platform team.

🌐 Ingress

It is the resource that developers use to expose a service. It allows defining routes, domains, TLS certificates, and little more.

Its specification is minimal by design, which allowed for rapid adoption, but also laid the foundation for current problems.

The problem: a standard too simple for complex needs

As Kubernetes became an enterprise standard, users wanted to replicate advanced configurations of traditional proxies: rewrites, timeouts, custom headers, CORS, etc.
But Ingress did not provide native support for all this.

Vendors reacted… and chaos was born.

Annotations vs CRDs: two incompatible paths

Different Ingress Controllers have taken very different paths to add advanced capabilities:

📝 Annotations (NGINX, HAProxy…)

Advantages:

  • Flexible and easy to use
  • Directly in the Ingress resource

Disadvantages:

  • Hundreds of proprietary annotations
  • Fragmented documentation
  • Non-portable configurations between vendors

📦 Custom CRDs (Traefik, Kong…)

Advantages:

  • More structured and powerful
  • Better validation and control

Disadvantages:

  • Adds new non-standard objects
  • Requires installation and management
  • Less interoperability

Result?
Infrastructures deeply coupled to a vendor, complicating migrations, audits, and automation.

The complexity for development teams

The design of Ingress implies two very different responsibilities:

  • Platform: defines IngressClass
  • Application: defines Ingress

But the reality is that the developer ends up making decisions that should be the responsibility of the platform area:

  • Certificates
  • Security policies
  • Rewrite rules
  • CORS
  • Timeouts
  • Corporate naming practices

This causes:

  • Inconsistent configurations
  • Bottlenecks in reviews
  • Constant dependency between teams
  • Lack of effective standardization

In large companies, where security and governance are critical, this is especially problematic.

NGINX Ingress: the decommissioning that reignited the debate

The recent decommissioning of the NGINX Ingress Controller has highlighted the fragility of the ecosystem:

  • Thousands of clusters depend on it
  • Multiple projects use its annotations
  • Migrating involves rewriting entire configurations

This has reignited the conversation about the need for a real standard… and there appears Gateway API.

Gateway API: a promising alternative (but not perfect)

Gateway API was born to solve many of the limitations of Ingress:

  • Clear separation of responsibilities (infrastructure vs application)
  • Standardized extensibility
  • More types of routes (HTTPRoute, TCPRoute…)
  • Greater expressiveness without relying on proprietary annotations

But it also brings challenges:

  • Requires gradual adoption
  • Not all vendors implement the same
  • Migration is not trivial

Even so, it is shaping up to be the future of traffic management in Kubernetes.

Conclusion

Ingresses have been fundamental to the success of Kubernetes, but their own simplicity has led them to become a bottleneck. The lack of interoperability, differences between vendors, and complex governance in enterprise environments make it clear that it is time to adopt more mature models.

Gateway API is not perfect, but it moves in the right direction.
Organizations that want future stability should start planning their transition.

Optimizing Kubernetes Scheduling with Node Affinity Rules: Trade-offs and Best Practices

Optimizing Kubernetes Scheduling with Node Affinity Rules: Trade-offs and Best Practices

Understanding Node Affinity and its Benefits

Node affinity rules are an essential feature in Kubernetes that allow you to control the scheduling of pods based on node properties. By using node affinity rules, you can specify constraints on which nodes pods can be scheduled, enabling you to optimize resource allocation and enhance performance.

Node affinity works by allowing you to define rules for pod scheduling based on node labels. When defining node affinity rules, you have two options: required and preferred rules. Required rules ensure that pods are scheduled only on nodes that satisfy the defined criteria. If no suitable node is available, the pod remains unscheduled. On the other hand, preferred rules provide a soft constraint and attempt to schedule pods on nodes that match the specified criteria. However, if no such node is available, the pod can still be scheduled on other nodes.

Node affinity rules are an “expanded” option of the simply way by using node selectors. Node selectors are a simple form of node affinity that allows you to assign labels to nodes and match those labels with selectors defined in the pod specification. By specifying a node selector, you can ensure that pods are scheduled only on nodes with matching labels. Node selectors are useful for basic affinity requirements but lack the flexibility and fine-grained control provided by more advanced affinity options.

Challenges and Trade-offs: Worst Case Scenario with Node Affinity Rules

But this awesome capability has some trade-offs that you need to take in consideration because nothing comes with a price that you need to be aware of, so, let’s go to the important question, what is the worst case scenario of using any of those options?

Let’s imagine that you are deploying a workloads consisting on three replicas that are sharing the load and providing resiliency and fault-tolerance, there are three replicas because they use a consensus protocol that requiren an odd number of replicas. So you decide to define a set of nodes for this workload and use node affinity rules to ensure the pods are scheduled to those nodes. And, you need to think: should I use the preferred mode or the requiredMode?

Let’s say that you go with the required option and you define it like this, what happen if one of your nodes goes down? The pod will be try to be rescheduled again and unless there are another node “with same label” to that, it cannot be deployed? If you additional defined a pod anti-affinity rule to ensure each of the replicas is in a different host to ensure that in case that one node is going down you lose only a single replica, you’re losing the option to rescheudle the workload even if you have another nodes without the label available. So, you’re not in a so reliable option.

Ok, so you go with the preferred to ensure that you workload is for sure scheduled even if it is in another node, and in that case you can end up on the situation that those nodes are scheduled on other nodes keeping those nodes with the proper label without the workload that they should have, making the situation strange and more difficult to administer because you cannot ensure your workloads is on the nodes that you expected to be.

Additional to that, if the nodes has even taints to ensure other workloads cannot be placed there, you can end up in a situation that the “labeled-pods” are scheduled on non-labeled nodes, and the non-labeled pods cannot use the nodes because they’re tainted and can be not be able to use the un-labeled ones if there are not enough resources. So you’re generating an impact on the other workloasd and potentially affecting the schedulling of the other workloads.

 Preparing for Unexpected Outages with Node Affinity

So, as you can see, each decision has some disadvatanges that you need to take in consdieration before defining those rules, because if you don’t, you will figure it out when this happen on an production enviornment probably as a result of some unexpected outage, because we all know that in the meantime that nothing bad happens everything works as expected, but the potential of these solutions and its reason to be used is exactly to provide the tools and the options to be prepared when bad things happens.

So, next time that you need to define a node affinity rule try to think about the disadvantages of each of the option and try to select that one that works best for you and mitigate the problems that it can bring to the table of your production environment.

Integrating Kyverno CLI into CI/CD Pipelines with GitHub Actions

Integrating Kyverno CLI into CI/CD Pipelines with GitHub Actions

Introduction

As Kubernetes clusters become an integral part of infrastructure, maintaining compliance with security and configuration policies is crucial. Kyverno, a policy engine designed for Kubernetes, can be integrated into your CI/CD pipelines to enforce configuration standards and automate policy checks. In this article, we’ll walk through integrating Kyverno CLI with GitHub Actions, providing a seamless workflow for validating Kubernetes manifests before they reach your cluster.

What is Kyverno CLI?

Kyverno is a Kubernetes-native policy management tool, enabling users to enforce best practices, security protocols, and compliance across clusters. Kyverno CLI is a command-line interface that lets you apply, test, and validate policies against YAML manifests locally or in CI/CD pipelines. By integrating Kyverno CLI with GitHub Actions, you can automate these policy checks, ensuring code quality and compliance before deploying resources to Kubernetes.

Benefits of Using Kyverno CLI in CI/CD Pipelines

Integrating Kyverno into your CI/CD workflow provides several advantages:

  1. Automated Policy Validation: Detect policy violations early in the CI/CD pipeline, preventing misconfigured resources from deployment.
  2. Enhanced Security Compliance: Kyverno enables checks for security best practices and compliance frameworks.
  3. Faster Development: Early feedback on policy violations streamlines the process, allowing developers to fix issues promptly.

Setting Up Kyverno CLI in GitHub Actions

Step 1: Install Kyverno CLI

To use Kyverno in your pipeline, you need to install the Kyverno CLI in your GitHub Actions workflow. You can specify the Kyverno version required for your project or use the latest version.

Here’s a sample GitHub Actions YAML configuration to install Kyverno CLI:

name: CI Pipeline with Kyverno Policy Checks

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

jobs:
  kyverno-policy-check:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout Code
        uses: actions/checkout@v2

      - name: Install Kyverno CLI
        run: |
          curl -LO https://github.com/kyverno/kyverno/releases/download/v<version>/kyverno-cli-linux.tar.gz
          tar -xzf kyverno-cli-linux.tar.gz
          sudo mv kyverno /usr/local/bin/

Replace <version> with the version of Kyverno CLI you wish to use. Alternatively, you can replace it with latest to always fetch the latest release.

Step 2: Define Policies for Validation

Create a directory in your repository to store Kyverno policies. These policies define the standards that your Kubernetes resources should comply with. For example, create a directory structure as follows:

.
└── .github
    └── policies
        ├── disallow-latest-tag.yaml
        └── require-requests-limits.yaml

Each policy is defined in YAML format and can be customized to meet specific requirements. Below are examples of policies that might be used:

  • Disallow latest Tag in Images: Prevents the use of the latest tag to ensure version consistency.
  • Enforce CPU/Memory Limits: Ensures resource limits are set for containers, which can prevent resource abuse.

Step 3: Add a GitHub Actions Step to Validate Manifests

In this step, you’ll use Kyverno CLI to validate Kubernetes manifests against the policies defined in the .github/policies directory. If a manifest fails validation, the pipeline will halt, preventing non-compliant resources from being deployed.

Here’s the YAML configuration to validate manifests:

- name: Validate Kubernetes Manifests
  run: |
    kyverno apply .github/policies -r manifests/

Replace manifests/ with the path to your Kubernetes manifests in the repository. This command applies all policies in .github/policies against each YAML file in the manifests directory, stopping the pipeline if any non-compliant configurations are detected.

Step 4: Handle Validation Results

To make the output of Kyverno CLI more readable, you can use additional GitHub Actions steps to format and handle the results. For instance, you might set up a conditional step to notify the team if any manifest is non-compliant:

- name: Check for Policy Violations
  if: failure()
  run: echo "Policy violation detected. Please review the failed validation."

Alternatively, you could configure notifications to alert your team through Slack, email, or other integrations whenever a policy violation is identified.

Example: Validating a Kubernetes Manifest

Suppose you have a manifest defining a Kubernetes deployment as follows:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      app: nginx
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
        - name: nginx
          image: nginx:latest  # Should trigger a violation

The policy disallow-latest-tag.yaml checks if any container image uses the latest tag and rejects it. When this manifest is processed, Kyverno CLI flags the image and halts the CI/CD pipeline with an error, preventing the deployment of this manifest until corrected.

Conclusion

Integrating Kyverno CLI into a GitHub Actions CI/CD pipeline offers a robust, automated solution for enforcing Kubernetes policies. With this setup, you can ensure Kubernetes resources are compliant with best practices and security standards before they reach production, enhancing the stability and security of your deployments.

Using Kubernetes Ingress on OpenShift: How Routes Are Generated and When to Use Each

Using Kubernetes Ingress on OpenShift: How Routes Are Generated and When to Use Each

Introduction
OpenShift, Red Hat’s Kubernetes platform, has its own way of exposing services to external clients. In vanilla Kubernetes, you would typically use an Ingress resource along with an ingress controller to route external traffic to services. OpenShift, however, introduced the concept of a Route and an integrated Router (built on HAProxy) early on, before Kubernetes Ingress even existed. Today, OpenShift supports both Routes and standard Ingress objects, which can sometimes lead to confusion about when to use each and how they relate.

This article explores how OpenShift handles Kubernetes Ingress resources, how they translate to Routes, the limitations of this approach, and guidance on when to use Ingress versus Routes.

OpenShift Routes and the Router: A Quick Overview


OpenShift Routes are OpenShift-specific resources designed to expose services externally. They are served by the OpenShift Router, which is an HAProxy-based proxy running inside the cluster. Routes support advanced features such as:

  • Weighted backends for traffic splitting
  • Sticky sessions (session affinity)
  • Multiple TLS termination modes (edge, passthrough, re-encrypt)
  • Wildcard subdomains
  • Custom certificates and SNI
  • Path-based routing

Because Routes are OpenShift-native, the Router understands these features natively and can be configured accordingly. This tight integration enables powerful and flexible routing capabilities tailored to OpenShift environments.

Using Kubernetes Ingress in OpenShift (Default Behavior)


Starting with OpenShift Container Platform (OCP) 3.10, Kubernetes Ingress resources are supported. When you create an Ingress, OpenShift automatically translates it into an equivalent Route behind the scenes. This means you can use standard Kubernetes Ingress manifests, and OpenShift will handle exposing your services externally by creating Routes accordingly.

Example: Kubernetes Ingress and Resulting Route

Here is a simple Ingress manifest:

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: example-ingress
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /
spec:
  rules:
  - host: www.example.com
    http:
      paths:
      - path: /testpath
        pathType: Prefix
        backend:
          service:
            name: test-service
            port:
              number: 80

OpenShift will create a Route similar to:

apiVersion: route.openshift.io/v1
kind: Route
metadata:
  name: example-route
spec:
  host: www.example.com
  path: /testpath
  to:
    kind: Service
    name: test-service
    weight: 100
  port:
    targetPort: 80
  tls:
    termination: edge

This automatic translation simplifies migration and supports basic use cases without requiring Route-specific manifests.

Tuning Behavior with Annotations (Ingress ➝ Route)

When you use Ingress on OpenShift, only OpenShift-aware annotations are honored during the Ingress ➝ Route translation. Controller-specific annotations for other ingress controllers (e.g., nginx.ingress.kubernetes.io/*) are ignored by the OpenShift Router. The following annotations are commonly used and supported by the OpenShift router to tweak the generated Route:

Purpose Annotation Typical Values Effect on Generated Route
TLS termination route.openshift.io/termination edge · reencrypt · passthrough Sets Route spec.tls.termination to the chosen mode.
HTTP→HTTPS redirect (edge) route.openshift.io/insecureEdgeTerminationPolicy Redirect · Allow · None Controls spec.tls.insecureEdgeTerminationPolicy (commonly Redirect).
Backend load-balancing haproxy.router.openshift.io/balance roundrobin · leastconn · source Sets HAProxy balancing algorithm for the Route.
Per-route timeout haproxy.router.openshift.io/timeout duration like 60s, 5m Configures HAProxy timeout for requests on that Route.
HSTS header haproxy.router.openshift.io/hsts_header e.g. max-age=31536000;includeSubDomains;preload Injects HSTS header on responses (edge/re-encrypt).

Note: Advanced features like weighted backends/canary or wildcard hosts are not expressible via standard Ingress. Use a Route directly for those.

Example: Ingress with OpenShift router annotations

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: example-ingress-https
  annotations:
    route.openshift.io/termination: edge
    route.openshift.io/insecureEdgeTerminationPolicy: Redirect
    haproxy.router.openshift.io/balance: leastconn
    haproxy.router.openshift.io/timeout: 60s
    haproxy.router.openshift.io/hsts_header: max-age=31536000;includeSubDomains;preload
spec:
  rules:
  - host: www.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: test-service
            port:
              number: 80

This Ingress will be realized as a Route with edge TLS and an automatic HTTP→HTTPS redirect, using least connections balancing and a 60s route timeout. The HSTS header will be added by the router on HTTPS responses.

Limitations of Using Ingress to Generate Routes
While convenient, using Ingress to generate Routes has limitations:

  • Missing advanced features: Weighted backends and sticky sessions require Route-specific annotations and are not supported via Ingress.
  • TLS passthrough and re-encrypt modes: These require OpenShift-specific annotations on Routes and are not supported through standard Ingress.
  • Ingress without host: An Ingress without a hostname will not create a Route; Routes require a host.
  • Wildcard hosts: Wildcard hosts (e.g., *.example.com) are only supported via Routes, not Ingress.
  • Annotation compatibility: Some OpenShift Route annotations do not have equivalents in Ingress, leading to configuration gaps.
  • Protocol support: Ingress supports only HTTP/HTTPS protocols, while Routes can handle non-HTTP protocols with passthrough TLS.
  • Config drift risk: Because Routes created from Ingress are managed by OpenShift, manual edits to the generated Route may be overwritten or cause inconsistencies.

These limitations mean that for advanced routing configurations or OpenShift-specific features, using Routes directly is preferable.

When to Use Ingress vs. When to Use Routes
Choosing between Ingress and Routes depends on your requirements:

  • Use Ingress if:
  • You want portability across Kubernetes platforms.
  • You have existing Ingress manifests and want to minimize changes.
  • Your application uses only basic HTTP or HTTPS routing.
  • You prefer platform-neutral manifests for CI/CD pipelines.
  • Use Routes if:
  • You need advanced routing features like weighted backends, sticky sessions, or multiple TLS termination modes.
  • Your deployment is OpenShift-specific and can leverage OpenShift-native features.
  • You require stability and full support for OpenShift routing capabilities.
  • You need to expose non-HTTP protocols or use TLS passthrough/re-encrypt modes.
  • You want to use wildcard hosts or custom annotations not supported by Ingress.

In many cases, teams use a combination: Ingress for portability and Routes for advanced or OpenShift-specific needs.

Conclusion


On OpenShift, Kubernetes Ingress resources are automatically converted into Routes, enabling basic external service exposure with minimal effort. This allows users to leverage existing Kubernetes manifests and maintain portability. However, for advanced routing scenarios and to fully utilize OpenShift’s powerful Router features, using Routes directly is recommended.

Both Ingress and Routes coexist seamlessly on OpenShift, allowing you to choose the right tool for your application’s requirements.

Talos: A Modern Kubernetes-Optimized Linux Distribution

Talos: A Modern Kubernetes-Optimized Linux Distribution

Introduction

Managing a Kubernetes cluster can quickly become overwhelming, particularly when your operating system adds unnecessary complexity. Enter Talos Linux—a groundbreaking, container-optimized, immutable OS explicitly designed for Kubernetes environments. It’s API-driven, completely secure, and strips away traditional management methods, including SSH and package managers.

Talos Linux revolutionizes node management by drastically simplifying operations and enhancing security. In this deep dive, we’ll explore why Talos is capturing attention, its core architecture, and the practical implications for Kubernetes teams.

What is Talos Linux?

Talos Linux is a specialized open-source Linux distribution meticulously crafted to run Kubernetes securely and efficiently. Unlike general-purpose operating systems, Talos discards all irrelevant features and focuses exclusively on Kubernetes, ensuring:

  • Immutable Design: Changes are handled through atomic upgrades rather than manual interventions.
  • API-Driven Management: Administrators use talosctl, a CLI that interacts securely with nodes through a gRPC API.
  • Security by Default: No SSH access, comprehensive kernel hardening, TPM integration, disk encryption, and secure boot features.
  • Minimal and Predictable: Talos minimizes resource usage and reduces operational overhead by eliminating unnecessary services and processes.

Maintainers and Backing

Talos is maintained by Sidero Labs, renowned for their expertise in Kubernetes tooling and bare-metal provisioning. The active, open-source community of cloud-native engineers and SREs continuously contribute to its growth and evolution.

Talos Architecture Deep Dive

Talos Linux employs a radical design that prioritizes security, simplicity, and performance:

  • API-Only Interaction: There is no traditional shell access, eliminating many common vulnerabilities associated with SSH.
  • Atomic Upgrades: System updates are atomic—new versions boot directly into a stable, validated state.
  • Resource Efficiency: Talos’s stripped-down design reduces its footprint significantly, ensuring optimum resource utilization and faster startup times.
  • Enhanced Security Measures: It incorporates kernel-level protections, secure boot, disk encryption, and TPM-based security, aligning with stringent compliance requirements.

Kubernetes Distribution based on Talos Linux

Sidero Labs also offers a complete Kubernetes distribution built directly upon Talos Linux, known as “Talos Kubernetes.” This streamlined distribution combines the benefits of Talos Linux with pre-configured Kubernetes components, making it easier and faster to deploy highly secure, production-ready Kubernetes clusters. This simplifies cluster management further by reducing the overhead and complexity typically associated with installing and maintaining Kubernetes separately.

Real-World Use-Cases

Talos shines particularly well in scenarios demanding heightened security, predictability, and streamlined operations:

  • Security-Conscious Clusters: Zero-trust architectures greatly benefit from Talos’s immutable and restricted-access model.
  • Edge Computing and IoT: Its minimal resource consumption and robust management via API make it ideal for edge deployments, where remote management is essential.
  • CI/CD and GitOps Pipelines: The declarative configuration, compatible with YAML and GitOps methodologies, enables automated and reproducible Kubernetes environments.

How to Download and Try Talos Linux

Talos Linux is easy to test and evaluate. You can download it directly from the official Talos GitHub releases. Sidero Labs provides comprehensive documentation and straightforward quick-start guides for deploying Talos Linux on various platforms, including bare-metal servers, virtual machines, and cloud environments such as AWS, Azure, and GCP. For a quick test-drive, running it within a local virtual machine or container is a convenient option.

Talos Compared to Traditional OS Choices

Talos presents distinct advantages compared to more familiar options like Ubuntu, CoreOS, or Flatcar:

Feature Talos Ubuntu Flatcar
SSH Access
Package Manager ✅ (apt) ✅ (rpm)
Kubernetes Native ✅ Built-in ✅ (via tools)
Security Defaults 🔒 High Moderate High
Immutable OS
Resource Efficiency ✅ High Moderate High
API-driven Management Limited

What You Cannot Do with Talos Linux

Talos Linux’s specialized design intentionally restricts certain traditional operating system functionalities. Notably:

  • No SSH Access: Direct shell access to nodes is disabled. All interactions must occur through talosctl.
  • No Package Managers: Traditional tools like apt, yum, or similar are absent; changes are done through immutable updates.
  • No Additional Applications: It doesn’t support running additional, non-Kubernetes services or workloads directly on Talos nodes.

These restrictions enforce best practices, significantly enhance security, and ensure a predictable, consistent operational environment.

Conclusion

Talos Linux represents a substantial shift in Kubernetes node management—secure, lean, and entirely Kubernetes-focused. For organizations prioritizing security, compliance, operational simplicity, and efficiency, Talos provides a robust and future-ready foundation.

If your Kubernetes strategy values minimalism, security, and simplicity, Talos Linux offers compelling reasons to consider adoption.

References
Talos Documentation
Sidero Labs
Talos GitHub Repository

Helm v3.17 Introduces take-ownership: What It Solves and When To Use It

Helm v3.17 Introduces take-ownership: What It Solves and When To Use It

Helm has long been the standard for managing Kubernetes applications using packaged charts, bringing a level of reproducibility and automation to the deployment process. However, some operational tasks, such as renaming a release or migrating objects between charts, have traditionally required cumbersome workarounds. With the introduction of the --take-ownership flag in Helm v3.17 (released in January 2025), a long-standing pain point is finally addressed—at least partially.

In this post, we will explore:

  • What the --take-ownership flag does
  • Why it was needed
  • The caveats and limitations
  • Real-world use cases where it helps
  • When not to use it

Understanding Helm Object Ownership

When Helm installs or upgrades a chart, it injects metadata—labels and annotations—into every managed Kubernetes object. These include:

app.kubernetes.io/managed-by: Helm
meta.helm.sh/release-name: my-release
meta.helm.sh/release-namespace: default

This metadata serves an important role: Helm uses it to track and manage resources associated with each release. As a safeguard, Helm does not allow another release to modify objects it does not own and when you trying that you will see messages like the one below:

Error: Unable to continue with install: Service "provisioner-agent" in namespace "test-my-ns" exists and cannot be imported into the current release: invalid ownership metadata; annotation validation error: key "meta.helm.sh/release-name" must equal "dp-core-infrastructure11": current value is "dp-core-infrastructure"

While this protects users from accidental overwrites, it creates limitations for advanced use cases.

Why --take-ownership Was Needed

Let’s say you want to:

  • Rename an existing Helm release from api-v1 to api.
  • Move a ConfigMap or Service from one chart to another.
  • Rebuild state during GitOps reconciliation when previous Helm metadata has drifted.

Previously, your only option was to:

  1. Uninstall the existing release.
  2. Reinstall under the new name.

This approach introduces downtime, and in production systems, that’s often not acceptable.

What the Flag Does

helm upgrade my-release ./my-chart --take-ownership

When this flag is passed, Helm will:

  • Skip the ownership validation for existing objects.
  • Override the labels and annotations to associate the object with the current release.

In practice, this allows you to claim ownership of resources that previously belonged to another release, enabling seamless handovers.

⚠️ What It Doesn’t Do

This flag does not:

  • Clean up references from the previous release.
  • Protect you from future uninstalls of the original release (which might still remove shared resources).
  • Allow you to adopt completely unmanaged Kubernetes resources (those not initially created by Helm).

In short, it’s a mechanism for bypassing Helm’s ownership checks, not a full lifecycle manager.

Real-World Use Cases

Let’s go through common scenarios where this feature is useful.

✅ 1. Renaming a Release Without Downtime

Before:

helm uninstall old-name
helm install new-name ./chart

Now:

helm upgrade new-name ./chart --take-ownership

✅ 2. Migrating Objects Between Charts

You’re refactoring a large chart into smaller, modular ones and need to reassign certain Service or Secret objects.

This flag allows the new release to take control of the object without deleting or recreating it.

✅ 3. GitOps Drift Reconciliation

If objects were deployed out-of-band or their metadata changed unintentionally, GitOps tooling using Helm can recover without manual intervention using --take-ownership.

Best Practices and Recommendations

  • Use this flag intentionally, and document where it’s applied.
  • If possible, remove the previous release after migration to avoid confusion.
  • Monitor Helm’s behavior closely when managing shared objects.
  • For non-Helm-managed resources, continue to use kubectl annotate or kubectl label to manually align metadata.

Conclusion

The --take-ownership flag is a welcomed addition to Helm’s CLI arsenal. While not a universal solution, it smooths over many of the rough edges developers and SREs face during release evolution and GitOps adoption.

It brings a subtle but powerful improvement—especially in complex environments where resource ownership isn’t static.

Stay updated with Helm releases, and consider this flag your new ally in advanced release engineering.

Extending Kyverno Policies: Creating Custom Rules for Enhanced Kubernetes Security

Extending Kyverno Policies: Creating Custom Rules for Enhanced Kubernetes Security

Kyverno offers a robust, declarative approach to enforcing security and compliance standards within Kubernetes clusters by allowing users to define and enforce custom policies. For an in-depth look at Kyverno’s functionality, including core concepts and benefits, see my detailed article here. In this guide, we’ll focus on extending Kyverno policies, providing a structured walkthrough of its data model, and illustrating use cases to make the most of Kyverno in a Kubernetes environment.

Understanding the Kyverno Policy Data Model

Kyverno policies consist of several components that define how the policy should behave, which resources it should affect, and the specific rules that apply. Let’s dive into the main parts of the Kyverno policy model:

  1. Policy Definition: This is the root configuration where you define the policy’s metadata, including name, type, and scope. Policies can be created at the namespace level for specific areas or as cluster-wide rules to enforce uniform standards across the entire Kubernetes cluster.
  2. Rules: Policies are made up of rules that dictate what conditions Kyverno should enforce. Each rule can include logic for validation, mutation, or generation based on your needs.
  3. Match and Exclude Blocks: These sections allow fine-grained control over which resources the policy applies to. You can specify resources by their kinds (e.g., Pods, Deployments), namespaces, labels, and even specific names. This flexibility is crucial for creating targeted policies that impact only the resources you want to manage.
    1. Match block: Defines the conditions under which the rule applies to specific resources.
    2. Exclude block: Used to explicitly omit resources that match certain conditions, ensuring that unaffected resources are not inadvertently included.
  4. Validation, Mutation, and Generation Actions: Each rule can take different types of actions:
    1. Validation: Ensures resources meet specific criteria and blocks deployment if they don’t.
    2. Mutation: Adjusts resource configurations to align with predefined standards, which is useful for auto-remediation.
    3. Generation: Creates or manages additional resources based on existing resource configurations.

Example: Restricting Container Image Sources to Docker Hub

A common security requirement is to limit container images to trusted registries. The example below demonstrates a policy that only permits images from Docker Hub.

apiVersion: kyverno.io/v1
kind: ClusterPolicy
metadata:
  name: restrict-dockerhub-images
spec:
  rules:
    - name: only-dockerhub-images
      match:
        resources:
          kinds:
            - Pod
      validate:
        message: "Only Docker Hub images are allowed."
        pattern:
          spec:
            containers:
              - image: "docker.io/*"

This policy targets all Pod resources in the cluster and enforces a validation rule that restricts the image source to docker.io. If a Pod uses an image outside Docker Hub, Kyverno denies its deployment, reinforcing secure sourcing practices.

Practical Use-Cases for Kyverno Policies

Kyverno policies can handle a variety of Kubernetes management tasks through validation, mutation, and generation. Let’s explore examples for each type to illustrate Kyverno’s versatility:

1. Validation Policies

Validation policies in Kyverno ensure that resources comply with specific configurations or security standards, stopping any non-compliant resources from deploying.

Use-Case: Enforcing Resource Limits for Containers

This example prevents deployments that lack resource limits, ensuring all Pods specify CPU and memory constraints.

apiVersion: kyverno.io/v1
kind: ClusterPolicy
metadata:
  name: enforce-resource-limits
spec:
  rules:
    - name: require-resource-limits
      match:
        resources:
          kinds:
            - Pod
      validate:
        message: "Resource limits (CPU and memory) are required for all containers."
        pattern:
          spec:
            containers:
              - resources:
                  limits:
                    cpu: "?*"
                    memory: "?*"

By enforcing resource limits, this policy helps prevent resource contention in the cluster, fostering stable and predictable performance.

2. Mutation Policies

Mutation policies allow Kyverno to automatically adjust configurations in resources to meet compliance requirements. This approach is beneficial for consistent configurations without manual intervention.

Use-Case: Adding Default Labels to Pods

This policy adds a default label, environment: production, to all new Pods that lack this label, ensuring that resources align with organization-wide labeling standards.

apiVersion: kyverno.io/v1
kind: ClusterPolicy
metadata:
  name: add-default-label
spec:
  rules:
    - name: add-environment-label
      match:
        resources:
          kinds:
            - Pod
      mutate:
        patchStrategicMerge:
          metadata:
            labels:
              environment: "production"

This mutation policy is an example of how Kyverno can standardize resource configurations at scale by dynamically adding missing information, reducing human error and ensuring labeling consistency.

3. Generation Policies

Generation policies in Kyverno are used to create or update related resources, enhancing Kubernetes automation by responding to specific configurations or needs in real-time.

Use-Case: Automatically Creating a ConfigMap for Each New Namespace

This example policy generates a ConfigMap in every new namespace, setting default configuration values for all resources in that namespace.

apiVersion: kyverno.io/v1
kind: ClusterPolicy
metadata:
  name: generate-configmap
spec:
  rules:
    - name: add-default-configmap
      match:
        resources:
          kinds:
            - Namespace
      generate:
        kind: ConfigMap
        name: default-config
        namespace: "{{request.object.metadata.name}}"
        data:
          default-key: "default-value"

This generation policy is triggered whenever a new namespace is created, automatically provisioning a ConfigMap with default settings. This approach is especially useful in multi-tenant environments, ensuring new namespaces have essential configurations in place.

Conclusion

Extending Kyverno policies enables Kubernetes administrators to establish and enforce tailored security and operational practices within their clusters. By leveraging Kyverno’s capabilities in validation, mutation, and generation, you can automate compliance, streamline operations, and reinforce security standards seamlessly.

Kyverno: A Detailed Way of Enforcing Standard and Custom Policies

Kyverno: A Detailed Way of Enforcing Standard and Custom Policies

In the Kubernetes ecosystem, security and governance are key aspects that need continuous attention. While Kubernetes offers some out-of-the-box (OOTB) security features such as Pod Security Admission (PSA), these might not be sufficient for complex environments with varying compliance requirements. This is where Kyverno comes into play, providing a powerful yet flexible solution for managing and enforcing policies across your cluster.

In this post, we will explore the key differences between Kyverno and PSA, explain how Kyverno can be used in different use cases, and show you how to install and deploy policies with it. Although custom policy creation will be covered in a separate post, we will reference some pre-built policies you can use right away.

What is Pod Security Admission (PSA)?

Kubernetes introduced Pod Security Admission (PSA) as a replacement for the now deprecated PodSecurityPolicy (PSP). PSA focuses on enforcing three predefined levels of security: Privileged, Baseline, and Restricted. These levels control what pods are allowed to run in a namespace based on their security context configurations.

  • Privileged: Minimal restrictions, allowing privileged containers and host access.
  • Baseline: Applies standard restrictions, disallowing privileged containers and limiting host access.
  • Restricted: The strictest level, ensuring secure defaults and enforcing best practices for running containers.

While PSA is effective for basic security requirements, it lacks flexibility when enforcing fine-grained or custom policies. We have a full article covering this topic that you can read here.

Kyverno vs. PSA: Key Differences

Kyverno extends beyond the capabilities of PSA by offering more granular control and flexibility. Here’s how it compares:

  1. Policy Types: While PSA focuses solely on security, Kyverno allows the creation of policies for validation, mutation, and generation of resources. This means you can modify or generate new resources, not just enforce security rules.
  2. Customizability: Kyverno supports custom policies that can enforce your organization’s compliance requirements. You can write policies that govern specific resource types, such as ensuring that all deployments have certain labels or that container images come from a trusted registry.
  3. Policy as Code: Kyverno policies are written in YAML, allowing for easy integration with CI/CD pipelines and GitOps workflows. This makes policy management declarative and version-controlled, which is not the case with PSA.
  4. Audit and Reporting: With Kyverno, you can generate detailed audit logs and reports on policy violations, giving administrators a clear view of how policies are enforced and where violations occur. PSA lacks this built-in reporting capability.
  5. Enforcement and Mutation: While PSA primarily enforces restrictions on pods, Kyverno allows not only validation of configurations but also modification of resources (mutation) when required. This adds an additional layer of flexibility, such as automatically adding annotations or labels.

When to Use Kyverno Over PSA

While PSA might be sufficient for simpler environments, Kyverno becomes a valuable tool in scenarios requiring:

  • Custom Compliance Rules: For example, enforcing that all containers use a specific base image or restricting specific container capabilities across different environments.
  • CI/CD Integrations: Kyverno can integrate into your CI/CD pipelines, ensuring that resources comply with organizational policies before they are deployed.
  • Complex Governance: When managing large clusters with multiple teams, Kyverno’s policy hierarchy and scope allow for finer control over who can deploy what and how resources are configured.

If your organization needs a more robust and flexible security solution, Kyverno is a better fit compared to PSA’s more generic approach.

Installing Kyverno

To start using Kyverno, you’ll need to install it in your Kubernetes cluster. This is a straightforward process using Helm, which makes it easy to manage and update.

Step-by-Step Installation

Add the Kyverno Helm repository:

    helm repo add kyverno https://kyverno.github.io/kyverno/

    Update Helm repositories:

      helm repo update

      Install Kyverno in your Kubernetes cluster:

        helm install kyverno kyverno/kyverno --namespace kyverno --create-namespace

        Verify the installation:

          kubectl get pods -n kyverno

          After installation, Kyverno will begin enforcing policies across your cluster, but you’ll need to deploy some policies to get started.

          Deploying Policies with Kyverno

          Kyverno policies are written in YAML, just like Kubernetes resources, which makes them easy to read and manage. You can find several ready-to-use policies from the Kyverno Policy Library, or create your own to match your requirements.

          Here is an example of a simple validation policy that ensures all pods use trusted container images from a specific registry:

          apiVersion: kyverno.io/v1
          kind: ClusterPolicy
          metadata:
            name: require-trusted-registry
          spec:
            validationFailureAction: enforce
            rules:
            - name: check-registry
              match:
                resources:
                  kinds:
                  - Pod
              validate:
                message: "Only images from 'myregistry.com' are allowed."
                pattern:
                  spec:
                    containers:
                    - image: "myregistry.com/*"

          This policy will automatically block the deployment of any pod that uses an image from a registry other than myregistry.com.

          Applying the Policy

          To apply the above policy, save it to a YAML file (e.g., trusted-registry-policy.yaml) and run the following command:

          kubectl apply -f trusted-registry-policy.yaml

          Once applied, Kyverno will enforce this policy across your cluster.

          Viewing Kyverno Policy Reports

          Kyverno generates detailed reports on policy violations, which are useful for audits and tracking policy compliance. To check the reports, you can use the following commands:

          List all Kyverno policy reports:

            kubectl get clusterpolicyreport

            Describe a specific policy report to get more details:

              kubectl describe clusterpolicyreport <report-name>

              These reports can be integrated into your monitoring tools to trigger alerts when critical violations occur.

              Conclusion

              Kyverno offers a flexible and powerful way to enforce policies in Kubernetes, making it an essential tool for organizations that need more than the basic capabilities provided by PSA. Whether you need to ensure compliance with internal security standards, automate resource modifications, or integrate policies into CI/CD pipelines, Kyverno’s extensive feature set makes it a go-to choice for Kubernetes governance.

              For now, start with the out-of-the-box policies available in Kyverno’s library. In future posts, we’ll dive deeper into creating custom policies tailored to your specific needs.