Istio Proxy DNS Explained: How DNS Capture Improves Service Mesh Traffic Control

Istio Proxy DNS Explained: How DNS Capture Improves Service Mesh Traffic Control

Istio is a popular open-source service mesh that provides a range of powerful features for managing and securing microservices-based architectures. We have talked a lot about its capabilities and components, but today we will talk about how we can use Istio to help with the DNS resolution mechanism.

As you already know, In a typical Istio deployment, each service is accompanied by a sidecar proxy, Envoy, which intercepts and manages the traffic between services. The Proxy DNS capability of Istio leverages this proxy to handle DNS resolution requests more intelligently and efficiently.

Traditionally, when a service within a microservices architecture needs to communicate with another service, it relies on DNS resolution to discover the IP address of the target service. However, traditional DNS resolution can be challenging to manage in complex and dynamic environments, such as those found in Kubernetes clusters. This is where the Proxy DNS capability of Istio comes into play.

 Istio Proxy DNS Capabilities

With Proxy DNS, Istio intercepts and controls DNS resolution requests from services and performs the resolution on their behalf. Instead of relying on external DNS servers, the sidecar proxies handle the DNS resolution within the service mesh. This enables Istio to provide several valuable benefits:

  • Service discovery and load balancing: Istio’s Proxy DNS allows for more advanced service discovery mechanisms. It can dynamically discover services and their corresponding IP addresses within the mesh and perform load balancing across instances of a particular service. This eliminates the need for individual services to manage DNS resolution and load balancing.
  • Security and observability: Istio gains visibility into the traffic between services by handling DNS resolution within the mesh. It can apply security policies, such as access control and traffic encryption, at the DNS level. Additionally, Istio can collect DNS-related telemetry data for monitoring and observability, providing insights into service-to-service communication patterns.
  • Traffic management and control: Proxy DNS enables Istio to implement advanced traffic management features, such as routing rules and fault injection, at the DNS resolution level. This allows for sophisticated traffic control mechanisms within the service mesh, enabling A/B testing, canary deployments, circuit breaking, and other traffic management strategies.

 Istio Proxy DNS Use-Cases

There are some moments when you cannot or don’t want to rely on the normal DNS resolution. Why is that? Starting because DNS is a great protocol but lacks some capabilities, such as location discovery. If you have the same DNS assigned to three IPs, it will provide each of them in a round-robin fashion and cannot rely on the location.

Or you have several IPs, and you want to block some of them for some specific service; these are great things you can do with Istio Proxy DNS.

Istio Proxy DNS Enablement

You need to know that Istio Proxy DNS capabilities are not enabled by default, so you must help if you want to use it. The good thing is that you can allow that at different levels, from the full mesh level to just a single pod level, so you can choose what is best for you in each case.

For example, if we want to enable it at the pod level, we need to inject the following configuration in the Istio proxy:

    proxy.istio.io/config: |
		proxyMetadata:   
         # Enable basic DNS proxying
         ISTIO_META_DNS_CAPTURE: "true" 
         # Enable automatic address allocation, optional
         ISTIO_META_DNS_AUTO_ALLOCATE: "true"

The same configuration can be part of the Mesh level as part of the operator installation, as you can find the documentation here on the Istio official page.

Conclusion

In summary, the Proxy DNS capability of Istio enhances the DNS resolution mechanism within the service mesh environment, providing advanced service discovery, load balancing, security, observability, and traffic management features. Istio centralizes and controls DNS resolution by leveraging the sidecar proxies, simplifying the management and optimization of service-to-service communication in complex microservices architectures.

📚 Want to dive deeper into Kubernetes? This article is part of our comprehensive Kubernetes Architecture Patterns guide, where you’ll find all fundamental and advanced concepts explained step by step.

Frequently Asked Questions

What is ISTIO_META_DNS_CAPTURE?

A proxy setting that makes the Istio sidecar intercept DNS queries from the workload and answer them from its own local cache, instead of forwarding everything to kube-dns. It reduces DNS load, gives consistent answers for ServiceEntry hosts, and enables resolution for VMs in the mesh.

When should I enable DNS proxying in Istio?

Three main triggers: heavy east-west traffic overwhelming kube-dns/CoreDNS; ServiceEntries with resolution: DNS whose hosts must resolve identically in every pod; and mesh-expansion scenarios with VMs that have no cluster DNS. If none apply, the default path is fine.

How do I verify the sidecar is actually answering DNS?

Check the proxy config: istioctl proxy-config dns <pod> lists the names the sidecar serves. At runtime, nslookup from the workload container answered with the sidecar’s address (typically the localhost DNS listener) confirms capture is active.

Does Istio DNS capture affect external (non-mesh) domains?

Captured queries for unknown names are forwarded upstream to the original resolvers, so external resolution keeps working. The cache only authoritatively answers for services and ServiceEntry hosts the proxy knows about.

Helm Subcharts Explained: Values, Globals, import-values and Multiple Instances with Alias

Helm Subcharts Explained: Values, Globals, import-values and Multiple Instances with Alias

A Helm subchart is simply a chart that lives inside another chart. That one sentence hides most of the questions people actually have when they start using them: where the subchart comes from, how its values get set, what it can and cannot see from the parent, how to turn it off, and how to deploy the same subchart twice with different configuration.

This guide covers all of it, starting from the basics and ending with the pattern this post was originally written about: running multiple instances of the same subchart with alias. Everything here applies to charts with apiVersion: v2, which is what Helm 3 and Helm 4 use. For the wider picture of how charts, repositories and releases fit together, start with the complete Helm charts guide.

What Is a Helm Subchart?

A subchart is a complete, standalone chart that another chart (the parent, sometimes called an umbrella chart) includes and renders as part of the same release. When you run helm install on the parent, Helm renders the parent’s templates and every enabled subchart’s templates together, and all resulting objects belong to a single release with a single revision history.

There are two ways a chart can become a subchart.

Declared in Chart.yaml (the normal way)

You list it in the dependencies section of the parent’s Chart.yaml, and Helm downloads it into the charts/ directory for you:

# Chart.yaml
apiVersion: v2
name: shop
version: 1.4.0
dependencies:
  - name: postgresql
    version: "~16.2.0"
    repository: "https://charts.bitnami.com/bitnami"
  - name: redis
    version: "~20.1.0"
    repository: "oci://registry-1.docker.io/bitnamicharts"

Dropped manually into charts/

Any chart placed in the parent’s charts/ directory, either unpacked as a folder or as a .tgz archive, is treated as a dependency even if Chart.yaml does not mention it. Directory and file names starting with _ or . are ignored by the loader. This is handy for vendoring a chart you forked, but you lose version constraints and the lock file, so it is best reserved for special cases.

Subchart vs dependency: is there a difference?

In practice, no. “Dependency” is the declaration in Chart.yaml, “subchart” is the chart that ends up in charts/ and gets rendered. Every dependency becomes a subchart once it is fetched, and every subchart is a dependency of its parent. The distinction people usually mean when they search for “helm subchart vs dependency” is actually declared dependency vs manually vendored chart, which is the comparison above. If you want the full reference of every field in the dependencies block and how version ranges are resolved, see the Helm dependencies guide and the post on Helm version constraints.

helm dependency update, build and Chart.lock

Declaring a dependency does nothing on its own. You have to fetch it:

helm dependency update ./shop

update resolves each version range against the repository, downloads the matching archives into charts/, and writes Chart.lock with the exact versions and a digest of the dependency list. Run it whenever you change dependencies in Chart.yaml.

helm dependency build ./shop

build does the reproducible thing: it rebuilds charts/ from the versions pinned in Chart.lock without re-resolving ranges. This is what you want in CI.

Two errors you will see sooner or later:

  • found in Chart.yaml, but missing in charts/ directory means you declared a dependency and never fetched it. Run helm dependency build (or update).
  • the lock file (Chart.lock) is out of sync with the dependencies file (Chart.yaml) means someone edited Chart.yaml without updating the lock. Run helm dependency update and commit the new Chart.lock.

Commit Chart.lock. Whether to commit the charts/*.tgz files is a team choice: committing them makes builds hermetic, not committing them keeps the repository small and relies on helm dependency build in the pipeline.

Passing Values to a Subchart

This is the part that confuses people the most, and it follows one rule: values for a subchart go under a top-level key named after the subchart (or after its alias, as we will see later).

# shop/values.yaml
replicaCount: 3            # parent value

postgresql:                # everything under this key goes to the postgresql subchart
  auth:
    database: shop
    username: shop
  primary:
    persistence:
      size: 20Gi

redis:
  architecture: standalone

Inside the postgresql subchart, templates see .Values.auth.database, not .Values.postgresql.auth.database. Helm strips the prefix and hands the subchart only its own slice. Values you set in the parent under that key are merged on top of the subchart’s own values.yaml defaults, so you only need to write what you want to change.

The same works on the command line:

helm install shop ./shop \
  --set postgresql.primary.persistence.size=50Gi \
  --set redis.architecture=replication

To remove a default the subchart ships with, rather than overriding it, set it to null in the parent. The details and the edge cases of that are covered in Helm null values.

The parent can read subchart values, not the other way around

The parent’s templates can reference .Values.postgresql.auth.database because that key exists in the parent’s value tree. The subchart cannot reference replicaCount from the parent: it only receives its own section plus global. This is intentional. A subchart must render the same way whether it is installed alone or embedded, so it cannot depend on anything its parent happens to define.

Global Values

global is the one channel that reaches every chart in the tree:

# shop/values.yaml
global:
  imageRegistry: registry.example.com
  storageClass: fast-ssd

Every chart, parent and subcharts, can read .Values.global.imageRegistry. Globals flow downwards and parent globals take precedence over any global a subchart defines in its own values.yaml.

Two practical warnings. First, a global only does something if the subchart’s templates actually read it. Bitnami charts, for example, honour global.imageRegistry and global.storageClass because their templates were written to; a random chart may ignore both. Check the subchart’s templates or README before relying on a global. Second, do not use global as a general-purpose way to share configuration between your own charts. It works, but it creates an invisible contract between charts that is hard to trace months later. Explicit per-subchart values are easier to review.

Enabling and Disabling Subcharts: condition and tags

Every declared dependency is rendered by default. To make one optional you add a condition, a tags list, or both.

# Chart.yaml
dependencies:
  - name: postgresql
    version: "~16.2.0"
    repository: "https://charts.bitnami.com/bitnami"
    condition: postgresql.enabled
  - name: prometheus-exporter
    version: "1.x.x"
    repository: "https://example.com/charts"
    tags:
      - monitoring
  - name: grafana-dashboards
    version: "1.x.x"
    repository: "https://example.com/charts"
    tags:
      - monitoring
# values.yaml
postgresql:
  enabled: true

tags:
  monitoring: false

The resolution rules, from the Helm documentation:

  • A condition is a YAML path into the parent’s values that must resolve to a boolean. You can list several comma-separated paths; the first one that exists wins and the rest are ignored.
  • tags enable a chart if any of its tags is true.
  • When both are present and the condition path is set in values, the condition always overrides tags.
  • If neither resolves, the chart is enabled.

The usual pattern is a condition per subchart for fine control and a shared tag for groups that should switch on and off together, such as everything related to monitoring:

helm install shop ./shop --set tags.monitoring=true --set postgresql.enabled=false

A common mistake is to write condition: enabled instead of condition: postgresql.enabled. The path is resolved from the parent’s root, not from inside the subchart’s section.

import-values: Pulling Subchart Values Up to the Parent

Values normally flow down. import-values lets the parent pull specific values up from a subchart, which is useful when the subchart knows something the parent’s templates need, such as a port or a service name.

The exports format

The subchart publishes values under a top-level exports key:

# charts/api/values.yaml
exports:
  data:
    servicePort: 8080

The parent imports them by key:

# Chart.yaml (parent)
dependencies:
  - name: api
    version: "2.x.x"
    repository: "https://example.com/charts"
    import-values:
      - data

After rendering, the parent sees .Values.servicePort at its root. The contents of exports.data are merged into the parent’s root values, not under a data key.

The child-parent format

When the subchart does not export anything, you can map any path explicitly:

dependencies:
  - name: api
    version: "2.x.x"
    repository: "https://example.com/charts"
    import-values:
      - child: service
        parent: apiService

Now .Values.apiService.port in the parent holds whatever the subchart had in service.port. Imported values override defaults in the parent’s values.yaml, but values passed at install time with -f or --set still win.

Use import-values sparingly. If the parent needs a value that the subchart also needs, it is often clearer to define it once in the parent and pass it down, or put it in global.

What a Subchart Cannot Do

Knowing the limits saves a lot of debugging time:

  • It cannot see parent values. Only its own section and global. There is no .Parent.Values.
  • It cannot override parent values. Values flow down; import-values is the only way up, and the parent opts into it.
  • Named templates are global. Everything defined with {{ define }} across the parent and all subcharts shares one namespace. If two charts define mychart.labels, one silently wins. That is why well-written charts prefix every template with the chart name, and why helm create generates names like {{ include "shop.fullname" . }}.
  • It is part of the same release. You cannot upgrade, roll back or uninstall a subchart independently. A rollback of the parent rolls back every subchart.
  • Hooks run in the same release. Subchart hooks execute alongside the parent’s, ordered by weight, which can surprise you if two charts both ship a pre-install migration job. See the Helm hooks guide for ordering rules.

Multiple Instances of the Same Subchart with alias

Now to the less common but very useful case: you want the same subchart twice, each copy with its own configuration.

When you need it

The scenario that led me to this pattern was a set of microservices that belong to one application and share the same technology base. All of them could use the same generic chart (in my case a TIBCO BusinessWorks Container Edition chart; it could just as well be a generic go-microservice or spring-boot chart), but each one needs:

  • its own image,
  • its own configuration and environment variables,
  • its own endpoints, often talking to different databases or external systems.

Without subcharts you end up copying the chart once per service, and every fix to the base chart has to be applied N times. With one subchart instantiated N times, the base chart is versioned once and each service is just a block of values.

Other real cases: two Redis instances (one cache, one queue) with different persistence settings, or a primary and a reporting database from the same PostgreSQL chart.

Declaring the instances

Start from a parent that includes the generic chart once:

# Chart.yaml
apiVersion: v2
name: orders-platform
description: Orders application deployed from a shared service chart
version: 0.2.0
appVersion: "2.7.2"
dependencies:
  - name: service
    version: "~0.2.0"
    repository: "https://charts.example.com"

To have two instances, declare the same chart twice and give each an alias:

# Chart.yaml
apiVersion: v2
name: orders-platform
description: Orders application deployed from a shared service chart
version: 0.3.0
appVersion: "2.7.2"
dependencies:
  - name: service
    alias: order-api
    version: "~0.2.0"
    repository: "https://charts.example.com"
    condition: order-api.enabled
  - name: service
    alias: order-worker
    version: "~0.2.0"
    repository: "https://charts.example.com"
    condition: order-worker.enabled

name stays the same, so helm dependency update downloads the chart once. The alias is what makes each copy distinct: it becomes the values key for that instance and it becomes .Chart.Name inside the instance’s templates.

Configuring each instance

Each alias gets its own section in values.yaml:

# values.yaml
order-api:
  enabled: true
  image:
    repository: registry.example.com/orders/order-api
    tag: "2.5.2"
    pullPolicy: IfNotPresent
  service:
    port: 8080
  env:
    DB_HOST: orders-db.internal

order-worker:
  enabled: true
  replicaCount: 2
  image:
    repository: registry.example.com/orders/order-worker
    tag: "2.5.2"
  env:
    QUEUE_URL: amqp://queue.internal

Anything you do not set falls back to the shared chart’s defaults, so each block contains only what makes that service different.

Three things that bite with aliases

Use lowercase, DNS-safe aliases. Because the alias replaces .Chart.Name, the standard fullname helper builds resource names from it. An alias like serviceA (which the first version of this post used) produces names such as myrelease-serviceA, which Kubernetes rejects because object names must be lowercase. Stick to order-api style names.

Hyphenated aliases need index in parent templates. If the parent’s own templates need to read an instance’s values, .Values.order-api.service.port is a template parse error. Use {{ index .Values "order-api" "service" "port" }} instead. Templates inside the subchart are unaffected, because they see their own slice without the prefix.

The shared chart must use its chart name in resource names. If the base chart hard-codes name: service in its Deployment, both instances render an object with the same name and the install fails. A chart designed for reuse builds names from include "service.fullname", which derives from .Chart.Name and therefore from the alias.

Library Charts vs Subcharts

A library chart (type: library in Chart.yaml) is also declared as a dependency, but it renders nothing by itself. It only provides named templates the parent can include.

Subchart (application)Library chart
Renders Kubernetes objectsYesNo
Has its own values.yaml sectionYesNo, uses the caller’s context
Can be installed aloneYesNo
Typical useBundling a database, cache or serviceShared labels, Deployment skeletons, helpers
Multiple instancesWith aliasNot needed: call the template N times

For the multi-microservice case above there is a real alternative: a library chart that defines a full Deployment/Service template, and a parent that calls it once per service from a range over values. That keeps every service in the parent’s own templates and avoids alias quirks, at the cost of writing a bit more template code. Aliased subcharts are simpler when the shared chart already exists as an installable chart; library charts are cleaner when you design the abstraction from scratch.

helm upgrade with Subcharts

Upgrading a release that contains subcharts is the same helm upgrade, with two details worth knowing.

If you bump a dependency version, you must refresh charts/ before upgrading, either explicitly or with the flag:

helm dependency update ./shop
helm upgrade shop ./shop -f values-prod.yaml

# or in one step: fetches dependencies if they are missing
helm upgrade shop ./shop -f values-prod.yaml --dependency-update

Note that --dependency-update only fetches dependencies that are missing; it does not replace an older archive that is already sitting in charts/. After changing a version range, run helm dependency update explicitly.

Values behave as in any upgrade. Without --reuse-values, Helm starts from the chart’s defaults plus whatever you pass on this command; that is usually what you want, because a new subchart version may have renamed or added keys. --reuse-values merges your overrides on top of the previous release’s values and can hide new subchart defaults. --reset-then-reuse-values is the middle ground: chart defaults first, then the last release’s values, then your overrides. Before a subchart major version bump, render both versions with helm template and diff them. The advanced Helm commands post has more on inspecting a release before touching it.

Umbrella Charts: When to Use Them and When to Avoid Them

An umbrella chart is a parent whose main job is to bundle several subcharts into one deployable unit. Subcharts make them easy to build, which is exactly why they get overused.

They work well when:

  • the components share a lifecycle and are always released together, such as an application and its dedicated cache,
  • you want one versioned artifact per environment that says exactly what is deployed,
  • the same generic chart is instantiated several times, as in the alias example.

They hurt when:

  • components have different release cadences, because every change to one service creates a new revision of all of them and a rollback reverts everything,
  • different teams own different components and step on each other’s values,
  • the release grows so large that a single failed hook or a single invalid object blocks the whole upgrade.

If you are deciding how to organise charts across many services, the trade-offs between one central chart and per-service charts are covered in detail in Helm chart management: centralized vs per-service. Whatever structure you choose, test the charts with the subcharts enabled, and validate the values each instance receives with a values schema.

Frequently Asked Questions

What is a subchart in Helm?

A subchart is a complete Helm chart included inside another chart, either declared in the parent’s Chart.yaml under dependencies and fetched into charts/, or placed there manually. It is rendered as part of the parent’s release, receives its values from a key named after it in the parent’s values, and cannot see the parent’s own values except for global.

What is the difference between a Helm subchart and a dependency?

They are two views of the same thing. A dependency is the entry in Chart.yaml that says which chart and version to use; the subchart is that chart once it has been downloaded into charts/ and rendered. The only practical distinction is between declared dependencies, which have version ranges and a Chart.lock, and charts copied manually into charts/, which have neither.

How do I pass values to a Helm subchart?

Put them under a top-level key with the subchart’s name, or its alias if it has one, in the parent’s values.yaml, or use --set subchartname.key=value. Helm strips that prefix and merges the section over the subchart’s own defaults, so inside the subchart the values appear at .Values.key. Values that every chart should see go under global.

Can a subchart access the parent chart’s values?

No. A subchart only receives its own section of values and the global section. This keeps subcharts self-contained so they render identically whether installed alone or embedded. If a subchart needs something from the parent, pass it explicitly under the subchart’s key or place it in global.

How do I deploy the same Helm chart multiple times as subcharts?

Declare the same chart several times in dependencies with the same name and a different alias for each entry. Each alias becomes its own values key and its own .Chart.Name, so every instance gets separate configuration and separate resource names. Use lowercase, DNS-safe aliases, and make sure the shared chart builds resource names from its fullname helper rather than hard-coding them.

How do I disable a subchart?

Add condition: <subchart>.enabled to the dependency in Chart.yaml and set <subchart>.enabled: false in values or with --set. Alternatively assign tags and set tags.<tag>: false. If both are defined, a condition that resolves in values always overrides tags.

Do I need to run helm dependency update before helm upgrade?

Yes, whenever you change a dependency version in Chart.yaml. helm upgrade --dependency-update fetches dependencies that are missing from charts/, but it does not replace an archive that is already there, so after bumping a version run helm dependency update and commit the new Chart.lock. In CI, helm dependency build recreates charts/ from the lock file.

What is the difference between a library chart and a subchart?

A subchart is an application chart that renders its own Kubernetes objects and has its own values section. A library chart, marked with type: library, renders nothing and only provides named templates for the parent to include. Use subcharts to bundle components, and library charts to share template code across many charts.

Conclusion

Subcharts are Helm’s composition mechanism, and most of their behaviour follows from two ideas: values flow down under a key named after the subchart, and everything renders into one release. Once that is clear, global, condition, tags and import-values are just controlled exceptions to the flow.

The multi-instance pattern with alias builds on the same rules. It lets you keep one well-tested generic chart and instantiate it for every service that shares the same base, each with its own values, instead of maintaining a copy per service. Use it when the instances genuinely share a lifecycle, keep aliases lowercase, and reach for per-service releases or a library chart when they do not. That keeps the benefits of reuse without turning your umbrella chart into the monolith containers were supposed to get rid of.

Kubeconform Explained: Validate Kubernetes Manifests and Prevent API Errors

Kubeconform Explained: Validate Kubernetes Manifests and Prevent API Errors

Kubernetes API changes quite a lot, and we know that in every new version, they are adding new capabilities at the same time that they are deprecating the old ones, so it is a constant evolution, as we already stated in previous articles, as you can see, here regarding Autoscaling v2 and Vertical Autoscaling.

Some of these changes are related to the shift in the apiVersion of some objects, and you have probably already suffered from that v1/alpha going to v1/beta or just moving to a final v1 and deprecating the previous one. So, in the end, it is crucial to ensure that your manifest is in sync with the target version you’re deploying, and some tools can help us with that, including Kubeconform.

What is Kubeconform?

Kubeconform is a powerful utility designed to assist in Kubernetes configuration management and validation. As Kubernetes continues to gain popularity as the go-to container orchestration platform, ensuring the correctness and consistency of configuration files becomes crucial. Kubeconform addresses this need by providing a comprehensive toolset to validate Kubernetes configuration files against predefined standards or custom rules.

Kubeconform supports multiple versions of Kubernetes, allowing you to validate configuration files against different API versions. This flexibility is beneficial when working with clusters running different Kubernetes versions or migrating applications across sets with varying configurations.

Another great feature of Kubeconform is its ability to enforce best practices and standards across Kubernetes configurations. It allows you to define rules, such as enforcing proper labels, resource limits, or security policies, and then validates your configuration files against these rules. This helps catch potential issues early on and ensures that your deployments comply with established guidelines.

How to install Kubeconform?

Kubeconform can be installed from different sources, the most usual ones the standard for your environment using package managers such as brew, apt or similar ones or just getting the binaries from its GitHub page: https://github.com/yannh/kubeconform/releases.

Kubeconform Explained: Validate Kubernetes Manifests and Prevent API Errors

How to launch Kubeconform from the Command Line?

Kubeconform is shipped as a small binary targeted to be executed in the CLI interface and tries to keep its interface minimal to ensure compatibility. Hence, it receives an argument with the file or folder with the manifest files that you want to check, as you can see here:

Kubeconform Explained: Validate Kubernetes Manifests and Prevent API Errors

Then you have several options to do other things, such as the ones shown below:

-ignore-filename-pattern value

regular expression specifying paths to ignore (can be specified multiple times)

-ignore-missing-schemas

skip files with missing schemas instead of failing

-Kubernetes-version string

version of Kubernetes to validate against, e.g.: 1.18.0 (default “master”)

-output string

output format – json, junit, pretty, tap, text (default “text”)

-reject string

comma-separated list of kinds or GVKs to reject

-skip string

comma-separated list of kinds or GVKs to ignore

-strict

disallow additional properties not in schema or duplicated keys

-summary

print a summary at the end (ignored for junit output)

Use-cases of Kuberconform

There are different use cases where Kubeconfrom can play a good role. One is regarding Kubernetes upgrades, sometimes you need to ensure that your current manifest is still going to work in the new release that the cluster will be upgraded to, and with this tool, we can ensure that our YAML is still compatible with the latest version directly getting it from the environment and validate it properly.

Another notable aspect of Kubeconform is its seamless integration into existing CI/CD pipelines. You can easily incorporate kubeconform as a step in your pipeline to automatically validate Kubernetes configuration files before deploying them. By doing so, you can catch configuration errors early in the development process, reduce the risk of deployment failures, and maintain high configuration consistency.

In addition to its validation capabilities, kubeconform provides helpful feedback and suggestions for improving your Kubernetes configuration files. It highlights specific issues or deviations from the defined rules and offers guidance on addressing them. This simplifies the troubleshooting process and helps developers and administrators become more familiar with best practices and Kubernetes configuration standards.

Conclusion

Kubeconform is an invaluable utility for Kubernetes users who strive for reliable and consistent deployments. It empowers teams to maintain a high standard of configuration quality, reduces the likelihood of misconfigurations, and improves the overall stability and security of Kubernetes-based applications.

📚 Want to dive deeper into Kubernetes? This article is part of our comprehensive Kubernetes Architecture Patterns guide, where you’ll find all fundamental and advanced concepts explained step by step.

Kubeconform in GitHub Actions

The pattern that catches broken manifests before they reach a cluster — render, then validate:

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Install kubeconform
        run: |
          curl -sL https://github.com/yannh/kubeconform/releases/latest/download/kubeconform-linux-amd64.tar.gz             | tar xz && sudo mv kubeconform /usr/local/bin/
      - name: Validate rendered chart
        run: |
          helm template ./chart -f values-prod.yaml             | kubeconform -strict -summary                 -kubernetes-version 1.33.0                 -schema-location default                 -schema-location 'https://raw.githubusercontent.com/datreeio/CRDs-catalog/main/{{.Group}}/{{.ResourceKind}}_{{.ResourceAPIVersion}}.json'

The second -schema-location points at the community CRDs catalog, which covers the popular operators (cert-manager, Prometheus Operator, Istio, Argo). For in-house CRDs, generate schemas once with openapi2jsonschema from your CRD YAML and commit them to the repo — validation then works fully offline. Pin -kubernetes-version to what you actually run: it is the difference between “valid YAML” and “valid on your cluster”.

Frequently Asked Questions

What is the difference between kubeconform and kubeval?

Kubeconform is the maintained successor to kubeval: same idea (validate manifests against the Kubernetes OpenAPI schemas) but with up-to-date schemas, CRD support via -schema-location, parallel validation and active development. Kubeval has been archived; new pipelines should use kubeconform.

Can kubeconform validate CRDs (Custom Resource Definitions)?

Yes, with an extra step: custom resources need their schemas available. Point -schema-location at the CRDs-catalog project or generate JSON schemas from your CRDs with openapi2jsonschema, and kubeconform validates custom resources like any core object. Unknown kinds can be skipped with -ignore-missing-schemas while you build the catalog.

How do I run kubeconform against Helm charts?

Render first, validate after: helm template mychart | kubeconform -strict -summary. In CI this is the standard pattern — template with production-like values, pipe to kubeconform, and fail the pipeline on error before anything reaches a cluster.

Does kubeconform check that my manifests match my cluster version?

Yes — -kubernetes-version 1.33.0 validates against that version’s schemas, which catches fields added in newer APIs or removed in the version you actually run. Run it once per supported cluster version if you maintain a range.

Istio Security Policies Explained: PeerAuthentication, RequestAuthentication, and AuthorizationPolicy

Istio Security Policies Explained: PeerAuthentication, RequestAuthentication, and AuthorizationPolicy

Istio Security Policies: The Short Answer

Istio secures traffic with three resources that answer three different questions. PeerAuthentication decides whether workloads must present mTLS identity to each other (“which workload is calling?”). RequestAuthentication validates end-user JWTs on incoming requests (“which user is behind the call?”). AuthorizationPolicy decides who is allowed to do what once identity is established. Most production meshes use all three together: mesh-wide STRICT mTLS, JWT validation at the gateway, and allow-list authorization per namespace.

Related reading: Kubernetes security hardening guide.

PeerAuthenticationRequestAuthenticationAuthorizationPolicy
Question it answersWhich workload is calling?Which user is behind the call?Is this call allowed?
CredentialmTLS certificate (SPIFFE identity)JWT (end-user token)— consumes both
Typical scopeMesh-wide or namespaceGateway or specific workloadNamespace or workload
On failureConnection rejected (STRICT)Invalid token stripped — request still passes unless authz denies403 denied
Default with no policyPERMISSIVE (plaintext accepted)No JWT validationEverything allowed

The three policies in YAML

Mesh-wide strict mTLS — the baseline every hardened mesh starts from:

apiVersion: security.istio.io/v1
kind: PeerAuthentication
metadata:
  name: default
  namespace: istio-system   # root namespace = mesh-wide
spec:
  mtls:
    mode: STRICT

JWT validation for a workload (validates tokens issued by your IdP; on its own it does not require a token — see the AuthorizationPolicy below):

apiVersion: security.istio.io/v1
kind: RequestAuthentication
metadata:
  name: jwt-validation
  namespace: payments
spec:
  selector:
    matchLabels:
      app: payments-api
  jwtRules:
  - issuer: "https://auth.example.com"
    jwksUri: "https://auth.example.com/.well-known/jwks.json"

And the pair that actually enforces it — require a valid principal, allowing only requests that carried a valid JWT:

apiVersion: security.istio.io/v1
kind: AuthorizationPolicy
metadata:
  name: require-jwt
  namespace: payments
spec:
  selector:
    matchLabels:
      app: payments-api
  action: ALLOW
  rules:
  - from:
    - source:
        requestPrincipals: ["https://auth.example.com/*"]

The rest of this article goes through each resource in detail — modes, selectors, rule semantics and the operational gotchas.

Istio Security Policies are crucial in securing microservices within a service mesh environment. We have discussed Istio and the capabilities that it can introduce to your Kubernetes workloads. Still, today we’re going to be more detailed regarding the different objects and resources that would help us make our workloads much more secure and enforce the communication between them. These objects include PeerAuthentication, RequestAuthentication, and AuthorizationPolicy objects.

PeerAuthentication: Enforcing security on pod-to-pod communication

PeerAuthentication focuses on securing communication between services by enforcing mutual TLS (Transport Layer Security) authentication and authorization. It enables administrators to define authentication policies for workloads based on the source of the requests, such as specific namespaces or service accounts. Configuring PeerAuthentication ensures that only authenticated and authorized services can communicate, preventing unauthorized access and man-in-the-middle attacks. This can be achieved depending on the value of the mode where defining this object being STRICT for only allowed mTLS communication, PERMISSIVE to allow both kinds of communication, DISABLE to forbid the mTLS connection and keep the traffic insecure, and UNSET to use the inherit option. This is a sample of the definition of the object:

apiVersion: security.istio.io/v1beta1
kind: PeerAuthentication
metadata:
  name: default
  namespace: foo
spec:
  mtls:
    mode: PERMISSIVE

RequestAuthentication: Defining authentication methods for Istio Workloads

RequestAuthentication, on the other hand, provides fine-grained control over the authentication of inbound requests. It allows administrators to specify rules and requirements for validating and authenticating incoming requests based on factors like JWT (JSON Web Tokens) validation, API keys, or custom authentication methods. With RequestAuthentication, service owners can enforce specific authentication mechanisms for different endpoints or routes, ensuring that only authenticated clients can access protected resources. Here you can see a sample of a RequestAuthentication object:

apiVersion: security.istio.io/v1beta1
kind: RequestAuthentication
metadata:
  name: jwt-auth-policy
  namespace: my-namespace
spec:
  selector:
    matchLabels:
      app: my-app
  jwtRules:
    - issuer: "issuer.example.com"
      jwksUri: "https://example.com/.well-known/jwks.json"

As commented, JWT validation is the most used approach as JWT tokens are becoming the de-facto industry standard for incoming validations and the OAuth V2 authorization protocol. Here you can define the rules the JWT needs to meet to be considered a valid request. But the RequestAuthentication only describes the “authentication methods” supported by the workloads but doesn’t enforce it or provide any details regarding the Authorization.

That means that if you define a workload to need to use JWT authentication, sending the request with the token will validate that token and ensure it is not expired. It meets all the rules you have specified in the object definition, but it will also allow bypassing requests with no token at all, as you’re just defining what the workloads support but not enforcing it. To do that, we need to introduce the last object of this set, the AuthorizationPolicy object.

AuthorizationPolicy: Fine-grained Authorization Policy Definition for Istio Policies

AuthorizationPolicy offers powerful access control capabilities to regulate traffic flow within the service mesh. It allows administrators to define rules and conditions based on attributes like source, destination, headers, and even request payload to determine whether a request should be allowed or denied. AuthorizationPolicy helps enforce fine-grained authorization rules, granting or denying access to specific resources or actions based on the defined policies. Only authorized clients with appropriate permissions can access specific endpoints or perform particular operations within the service mesh. Here you can see a sample of an Authorization Policy object:

apiVersion: security.istio.io/v1beta1
kind: AuthorizationPolicy
metadata:
  name: rbac-policy
  namespace: my-namespace
spec:
  selector:
    matchLabels:
      app: my-app
  rules:
    - from:
        - source:
            principals: ["user:user@example.com"]
      to:
        - operation:
            methods: ["GET"]

Here you can go as detailed as you need; you can apply rules on the source of the request to ensure that only some recommendations can go through (for example, requests that are from a JWT token to use in combination with the RequestAuthenitcation object), but also rules on the target, if this is going to a specific host or path or method or a combination of both. Also, you can apply to ALLOW rules or DENY rules (or even CUSTOM) and define a set of them, and all of them will be enforced as a whole. The evaluation is determined by the following rules as stated in the Istio Official Documentation:

  • If there are any CUSTOM policies that match the request, evaluate and deny the request if the evaluation result is denied.
  • If there are any DENY policies that match the request, deny the request.
  • If there are no ALLOW policies for the workload, allow the request.
  • If any of the ALLOW policies match the request, allow the request. Deny the request.
Authorization Policy validation flow from: https://istio.io/latest/docs/concepts/security/

This will provide all the requirements you could need to be able to do a full definition of all the security policies needed.

 Conclusion

In conclusion, Istio’s Security Policies provide robust mechanisms for enhancing the security of microservices within a service mesh environment. The PeerAuthentication, RequestAuthentication, and AuthorizationPolicy objects offer a comprehensive toolkit to enforce authentication and authorization controls, ensuring secure communication and access control within the service mesh. By leveraging these Istio Security Policies, organizations can strengthen the security posture of their microservices, safeguarding sensitive data and preventing unauthorized access or malicious activities within their service mesh environment.

Frequently Asked Questions

What is the difference between PeerAuthentication and RequestAuthentication in Istio?

PeerAuthentication controls service-to-service identity: whether workloads require mTLS from their peers. RequestAuthentication validates end-user credentials — JWTs on incoming requests. They answer different questions (“which workload is calling?” vs “which user is behind the call?”) and are usually combined.

Does RequestAuthentication reject requests without a token?

No — by itself it only validates tokens that are present and strips invalid ones. To actually require a token you pair it with an AuthorizationPolicy that denies requests without a requestPrincipal. Forgetting this second half is the most common Istio auth misconfiguration.

How does AuthorizationPolicy decide allow or deny?

Evaluation order: CUSTOM policies first, then DENY, then ALLOW. If any ALLOW policy exists for a workload, everything not explicitly allowed is denied. With no policies at all, everything is allowed — so the first ALLOW policy you add flips the default for that workload.

Can I enforce mTLS for the whole mesh at once?

Yes — a mesh-wide PeerAuthentication in the root namespace with mtls.mode: STRICT. Roll it out with PERMISSIVE first and watch telemetry for plaintext connections, then switch to STRICT once nothing legitimate is left.

Kubernetes Security Best Practices: A Shared Responsibility Between Developers and Operators

Kubernetes Security Best Practices: A Shared Responsibility Between Developers and Operators

Kubernetes Security is one of the most critical aspects today in IT world. Kubernetes has become the backbone of modern infrastructure management, allowing organizations to scale and deploy containerized applications with ease. However, the power of Kubernetes also brings forth the responsibility of ensuring robust security measures are in place. This responsibility cannot rest solely on the shoulders of developers or operators alone. It demands a collaborative effort where both parties work together to mitigate potential risks and vulnerabilities.

Even though DevOps and Platform Engineering approaches are pretty standard, there are still tasks responsible for different teams, even though nowadays you have platform and project teams.

Here you will see three easy ways to improve your Kubernetes security from both dev and ops perspectives:

No Vulnerabilities in Container Images

Vulnerability Scan on Container Images is something crucial in nowadays developments because the number of components deployed on the system has grown exponentially, and also the opacity of them as well. Vulnerabilities Scan using tools such as Trivy or the integrated options in our local docker environments such as Docker Desktop or Rancher Desktop is mandatory, but how can you use it to make your application more secure?

  • Developer’s responsibility:
    • Use only allowed standard base images, well-known
    • Reduce, at minimum, the number of components and packages to be installed with your application (better Alpine than Debian)
    • Use a Multi-Stage approach to only include what you will need in your images.
    • Run a vulnerability scan locally before pushing
  • Operator’s responsibility:
    • Force to download all base images for the corporate container registry
    • Enforce vulnerability scan on push, generating alerts and avoiding deployment if the quality criteria are unmet.
    • Perform regular vulnerability scans for runtime images and generate incidents for the development teams based on the issues discovered.

No Additional Privileges in Container Images

Now that our application doesn’t include any vulnerability, we need to ensure the image is not allowed to do what it should, such as elevating privileges. See what you can do depending on your role:

  • Developer responsibility:
    • Never create images with root user and use security context options in your Kubernetes Manifest files
    • Test your images with all the possible capabilities dropped unless needed for some specific reason
    • Make your filesystem read-only and use volumes for the required folders on your application.
  • Operator’s responsibility:

Restrict visibility between components

When we design applications nowadays, it is expected that they require to connect to other applications and components, and the service discovery capabilities in Kubernetes are excellent in how we can interact. Still, also this allows other apps to connect to services that maybe they shouldn’t. See what you can do to help on that aspect depending on your role and responsibility:

  • Developer responsibility:
    • Ensure your application has proper authentication and authorization policies in place to avoid any unauthorized use of your application.
  • Operation responsibility:
    • Manage at the platform level the network visibility of the components but deny all traffic by default and allow the connections required by design by using Network Policies.
    • Use Service Mesh tools to have a central approach for authentication and authorization.
    • Use tools like Kiali to monitor the network traffic and detect unreasonable traffic patterns.

Conclusion

In conclusion, the importance of Kubernetes security cannot be overstated. It requires collaboration and shared responsibility between developers and operators. By focusing on practices such as vulnerability scanning, restricting additional privileges, and restricting visibility between components, organizations can create a more secure Kubernetes environment. By working together, developers and operators can fortify the container ecosystem, safeguarding applications, data, and critical business assets from potential security breaches. With a collaborative approach to Kubernetes security, organizations can confidently leverage the full potential of this powerful orchestration platform while maintaining the highest standards of security. By adopting these practices, organizations can create a more secure Kubernetes environment, protecting their applications and data from potential threats.

📚 Want to dive deeper into Kubernetes? This article is part of our comprehensive Kubernetes Architecture Patterns guide, where you’ll find all fundamental and advanced concepts explained step by step.

For a complete reference of production-ready security controls and a practical hardening checklist, see our Kubernetes security best practices guide.

Kubernetes Vertical Pod Autoscaling Explained: When and Why to Scale Vertically

Kubernetes Vertical Pod Autoscaling Explained: When and Why to Scale Vertically

Kubernetes has introduced as its alpha version in its Kubernetes 1.27 release the Vertical Pod Autoscaling capability to provide the option for the Kubernetes workload to be able to scale using the “vertical” approach by adding more resources to an existing pod. This increases the autoscaling capabilities of your Kubernetes workloads that you have at your disposal such as KEDA or Horizontal Pod Autoscaling.

Related reading: Kubernetes in-place pod resize: scale vertically without restarts.

Vertical Scaling vs Horizontal Scaling

Vertical and horizontal scaling are two approaches used in scaling up the performance and capacity of computer systems, particularly in distributed systems and cloud computing. Vertical scaling, also known as scaling up or scaling vertically, involves adding more resources, such as processing power, memory, or storage, to a single instance or server. This means upgrading the existing compute components or migrating to a more powerful infrastructure. Vertical scaling is often straightforward to implement and requires minimal changes to the software architecture. It is commonly used when the system demands can be met by a single, more powerful infrastructure.

On the other hand, horizontal scaling, also called scaling out or scaling horizontally, involves adding more instances or servers to distribute the workload. Instead of upgrading a single instance, multiple instances are employed, each handling a portion of the workload. Horizontal scaling offers the advantage of increased redundancy and fault tolerance since multiple instances can share the load. Additionally, it provides the ability to handle larger workloads by simply adding more machines to the cluster. However, horizontal scaling often requires more complex software architectures, such as load balancing and distributed file systems, to efficiently distribute and manage the workload across the machines.

In summary, vertical scaling involves enhancing the capabilities of a single object, while horizontal scaling involves distributing the workload across multiple instances. Vertical scaling is easier to implement but may have limitations in terms of the maximum resources available on a single machine. Horizontal scaling provides better scalability and fault tolerance but requires more complex software infrastructure. The choice between vertical and horizontal scaling depends on factors such as the specific requirements of the system, the expected workload, and the available resources.

Why Kubernetes Vertical AutoScaling?

This is an interesting topic because we have been living in a world where the state was that was always better to scale out (using Horizontal Scaling) rather than scaling up (using Vertical Scaling) and especially this was one of the mantras you heard in cloud-native developments. And, that hasn’t changed because horizontal scaling provides much more benefits than vertical scaling and it is well covered with the Autoscaling capabilities or side-projects such as KEDA. So, in that case, why is Kubernetes including this feature and why are we using this site to discuss it?

Because with the transformation of Kubernetes to be the de-facto alternative to any deployment you do nowadays, the characteristic and capabilities of the workloads that you need to handle have extended and that’s why you need to use different techniques to provide the best experience to each of the workloads types

How Kubernetes Vertical Autoscaling?

Here you will find all the documentation about this new feature that as commented is still in the “alpha” stage to is something to try as an experimental mode rather than using it at the production level HPA Documentation

Vertical Scaling works in the way that you will be able to change the resources assigned to the pod, CPU, and memory without needing to restart the pod and change the manifest declaration and that’s a clear benefit of this approach. As you know, until now if you want to change the resources applied to a workload you need to update the manifest document and restart the pod to apply the new changes.

To define this you need to specify the resizePolicy by adding a new section to the manifest pod as you can see here:

apiVersion: v1
kind: Pod
metadata:
  name: qos-demo-5
  namespace: qos-example
spec:
  containers:
  - name: qos-demo-ctr-5
    image: nginx
    resizePolicy:
    - resourceName: cpu
      restartPolicy: NotRequired
    - resourceName: memory
      restartPolicy: RestartContainer
    resources:
      limits:
        memory: "200Mi"
        cpu: "700m"
      requests:
        memory: "200Mi"
        cpu: "700m"

For example in this case we define for the different resource names the policy that we want to apply, if we’re going to change the cpu assigned it won’t require a restart but in case we’re changing the memory it would require a restart.

That implied that if would like to change the CPU assigned you can directly patch the manifest as you can see in the snippet below and that provides an update of the assigned resources:

 kubectl -n qos-example patch pod qos-demo-5 --patch '{"spec":{"containers":[{"name":"qos-demo-ctr-5", "resources":{"requests":{"cpu":"800m"}, "limits":{"cpu":"800m"}}}]}}'

When to use Vertical Scaling are the target scenarios?

It will depend on a lot of different scenarios from the use-case but also from the technology stack that your workload is using to know what of these capabilities can apply. As a normal thing, the CPU change will be easy to adapt to any technology but the memory one would be more difficult depending on the technology used as in most of the technologies the memory assigned is defined at the startup time.

This will help to update components that have changed their requirements as an average scenario or when you’re testing new workloads with live load and you don’t want to disrupt the current processing of the application or simply workloads that don’t support horizontal scaling because are designed on a single-replica mode

 Conclusion

In conclusion, Kubernetes has introduced Vertical Pod Autoscaling, enabling Kubernetes vertical autoscaling of workloads by adding resources to existing pods. Kubernetes Vertical autoscaling allows for resource changes without restarting pods, providing flexibility in managing CPU and memory allocations.

Kubernetes Vertical autoscaling offers a valuable option for adapting to evolving workload needs. It complements horizontal scaling by providing flexibility without the need for complex software architectures. By combining vertical and horizontal scaling approaches, Kubernetes users can optimize their deployments based on specific workload characteristics and available resources.

📚 Want to dive deeper into Kubernetes? This article is part of our comprehensive Kubernetes Architecture Patterns guide, where you’ll find all fundamental and advanced concepts explained step by step.

Enable Sticky Sessions in Kubernetes Using Istio (Session Affinity Explained)

Enable Sticky Sessions in Kubernetes Using Istio (Session Affinity Explained)

Istio allows you to configure Sticky Session, among other network features, for your Kubernetes workloads. As we have commented in several posts regarding Istio, istio deploys a service mesh that provides a central control plane to have all the configuration regarding the network aspects of your Kubernetes workloads. This covers many different aspects of the communication inside the container platform, such as security covering security transport, authentication or authorization, and, at the same time, network features, such as routing and traffic distribution, which is the main topic for today’s article.

These routing capabilities are similar to what a traditional Load Balancer of Level 7 can provide. When we talk about Level 7, we’re referring to the conventional levels that compound the OSI stack, where level 7 is related to the Application Level.

A Sticky Session or Session Affinity configuration is one of the most common features you can need to implement in this scenario. The use-case is the following one:

How To Enable Sticky Session on Your Kubernetes Workloads using Istio?

You have several instances of your workloads, so different pod replicas in a Kubernetes situation. All of these pods behind the same service. By default, it will redirect the requests in a round-robin fashion among the pod replicas in a Ready state, so Kubernetes understand that they’re ready to get the request unless you define it differently.

But in some cases, mainly when you are dealing with a web application or any stateful application that handles the concept of a session, you could want the replica that processes the first request and also handles the rest of the request during the lifetime of the session.

Of course, you could do that easily just by routing all traffic to one request, but in that case, we lose other features such as traffic load balancing and HA. So, this is usually implemented using Session Affinity or Sticky Session policies that provides best of both worlds: same replica handling all the request from an user, but traffic distribution between different users.

How Sticky Session Works?

The behavior behind this is relatively easy. Let’s see how it works.

First, the important thing is that you need “something” as part of your network requests that identify all the requests that belong to the same session, so the routing component (in this case, this role is played by istio) can determine which part needs to handle these requests.

This is “something” that we use to do that, it can be different depending on your configuration, but usually, this is a Cookie or an HTTP Header that we send in each request. Hence, we know that the replica handles all requests of that specific type.

How does Istio implement Sticky Session support?

In the case of using Istio to do this role, we can implement that by using a specific Destination Rule that allows us, among other capabilities, to define the traffic policy to define how we want the traffic to be split and to implement the Sticky Session we need to use the “consistentHash” feature, that allows that all the requests that compute to the same hash will be sent to the replica.

When we define the consistentHash features, we can say how this hash will be created and, in other words, which components will be used to generate this hash, and this can be one of the following options:

  • httpHeaderName: Uses an HTTP Header to do the traffic distribution
  • httpCookie: Uses an HTTP Cookie to do the traffic distribution
  • httpQueryParameterName: Uses a Query String to do the traffic Distribution.
  • maglev: Uses Google’s Maglev Load Balancer to do the determination. You can read more about Maglev in the article from Google.
  • ringHash: Uses a ring-based hashed approach to load balancing between the available pods.

So, as you can see, you will have a lot of different options. Still, just the first three would be the most used to implement a sticky session, and usually, the HTTP Cookie (httpCookie) option will be the preferred one, as it would rely on the HTTP approach to manage the session between clients and servers.

Sticky Session Implementation Sample using TIBCO BW

We will define a very simple TIBCO BW workload to implement a REST service, serving a GET reply with a hardcoded value. To simplify the validation process, the application will log the hostname of the pod so quickly we can see who is handling each of the requests:

How To Enable Sticky Session on Your Kubernetes Workloads using Istio?

We deploy this in our Kubernetes cluster and expose it using a Kubernetes service; in our case, the name of this service will be test2-bwce-srv

On top of that, we apply the istio configuration, which will require three (3) istio objects: gateway, virtual service, and the destination rule. As our focus is on the destination rule, we will try to keep it as simple as possible in the other two objects:

 apiVersion: networking.istio.io/v1beta1
kind: Gateway
metadata:
  name: default-gw
spec:
  selector:
    istio: ingressgateway
  servers:
  - hosts:
    - '*'
    port:
      name: http
      number: 80
      protocol: HTTP

Virtual Service:

apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: test-vs
spec:
  gateways:
  - default-gw
  hosts:
  - test.com
  http:
  - match:
    - uri:
        prefix: /
    route:
    - destination:
        host: test2-bwce-srv
        port:
          number: 8080

And finally, the DestinationRule will use a httpCookie that we will name ISTIOD, as you can see in the snippet below:

apiVersion: networking.istio.io/v1beta1
kind: DestinationRule
metadata:
    name: default-sticky-dr
    namespace: default
spec:
    host: test2-bwce-srv.default.svc.cluster.local
    trafficPolicy:
      loadBalancer:
        consistentHash:
          httpCookie: 
            name: ISTIOID
            ttl: 60s

Now, that we have already started our test, and after launching the first request, we get a new Cookie that is generated by istio itself that is shown in the Postman response window:

Enable Sticky Sessions in Kubernetes Using Istio (Session Affinity Explained)

This request has been handled for one of the replicas available of the service, as you can see here:

Enable Sticky Sessions in Kubernetes Using Istio (Session Affinity Explained)

All subsequent request from Postman already includes the cookie, and all of them are handled from the same pod:

Enable Sticky Sessions in Kubernetes Using Istio (Session Affinity Explained)

While the other replica’ log is empty, as all the requests have been routed to that specific pod.

Enable Sticky Sessions in Kubernetes Using Istio (Session Affinity Explained)

Summary

We covered in this article the reason behind the need for a sticky session in Kubernetes workload and how we can achieve that using the capabilities of the Istio Service Mesh. So, I hope this can help implement this configuration on your workloads that you can need today or in the future

📚 Want to dive deeper into Kubernetes? This article is part of our comprehensive Kubernetes Architecture Patterns guide, where you’ll find all fundamental and advanced concepts explained step by step.

Kubernetes Autoscaling 1.26 Explained: HPA v2 Changes and Impact on KEDA

Kubernetes Autoscaling 1.26 Explained: HPA v2 Changes and Impact on KEDA

Introduction

Kubernetes Autoscaling has suffered a dramatic change. Since the Kubernetes 1.26 release, all components should migrate their HorizontalPodAutoscaler objects from the v1 to the new release v2that has been available since Kubernetes 1.23.

HorizontalPodAutoscaler is a crucial component in any workload deployed on a Kubernetes cluster, as the scalability of this solution is one of the great benefits and key features of this kind of environment.

A little bit of History

Kubernetes has introduced a solution for the autoscaling capability since the version Kubernetes 1.3 a long time ago, in 2016. And the solution was based on a control loop that runs at a specific interval that you can configure with the property --horizontal-pod-autoscaler-sync-period parameters that belong to the kube-controller-manager.

So, once during this period, it will get the metrics and evaluate through the condition defined on the HorizontalPodAutoscaler component. Initially, it was based on the compute resources used by the pod, main memory, and CPU.

Kubernetes Autoscaling 1.26: A Game-Changer for KEDA Users?

This provided an excellent feature, but with the past of time and adoption of the Kubernetes environment, it has been shown as a little narrow to handle all the scenarios that we should have, and here is where other awesome projects we have discussed here, such as KEDA brings into the picture to provide a much more flexible set of features.

Kubernetes AutoScaling Capabilities Introduced v2

With the release of the v2 of the Autoscaling API objects, we have included a range of capabilities to upgrade the flexibility and options available now. There most relevant ones are the following:

  • Scaling on custom metrics: With the new release, you can configure an HorizontalPodAutoscaler object to scale using custom metrics. When we talk about custom metrics, we talk about any metric generated from Kubernetes. You can see a detailed walkthrough about using Custom metrics in the official documentation
  • Scaling on multiple metrics: With the new release, you also have the option to scale based on more than one metric. So now the HorizontalPodAutoscalerwill evaluate each scaling rule condition, propose a new scale value for each of them, and take the maximum value as the final one.
  • Support for Metrics API: With the new release, the controller from the HoriztalPodAutoscaler components retrieves metrics from a series of registered APIs, such as metrics.k8s.io, custom.metrics.k8s.io ,external.metrics.k8s.io. For more information on the different metrics available, you can take a look at the design proposal
  • Configurable Scaling Behavior: With the new release, you have a new field, behavior, that allows configuring how the component will behave in terms of scaling up or scaling down activity. So, you can define different policies for the scaling up and others for the scaling down, limit the max number of replicas that can be added or removed in a specific period, to handle the issues with the spikes of some components as Java workloads, among others. Also, you can define a stabilization window to avoid stress when the metric is still fluctuating.

Kubernetes Autoscaling v2 vs KEDA

We have seen all the new benefits that Autoscaling v2 provides, so I’m sure that most of you are asking the same question: Is Kubernetes Autoscaling v2 killing KEDA?

Since the latest releases of KEDA, KEDA already includes the new objects under the autoscaling/v2 group as part of their development, as KEDA relies on the native objects from Kubernetes, and simplify part of the process you need to do when you want to use custom metric or external ones as they have scalers available for pretty much everything you could need now or even in the future.

But, even with that, there are still features that KEDA provides that are not covered here, such as the scaling “from zero” and “to zero” capabilities that are very relevant for specific kinds of workloads and to get a very optimized use of resources. Still, it’s safe to say that with the new features included in the autoscaling/v2 release, the gap is now smaller. Depending on your needs, you can go with the out-of-the-box capabilities without including a new component in your architecture.

📚 Want to dive deeper into Kubernetes? This article is part of our comprehensive Kubernetes Architecture Patterns guide, where you’ll find all fundamental and advanced concepts explained step by step.

Mastering Istio ServiceEntry: Connect Your Service Mesh to APIs

Mastering Istio ServiceEntry: Connect Your Service Mesh to APIs

What Is An Istio ServiceEntry?

Istio ServiceEntry is the way to define an endpoint that doesn’t belong to the Istio Service Registry. Once the ServiceEntry is part of the registry, it can define rules and enforce policies as if they belong to the mesh.

Istio Service Entry answers the question you probably have done several times when using a Service Mesh. How can I do the same magic with external endpoints that I can do when everything is under my service mesh scope? And Istio Service Entry objects provide precisely that:

A way to have an extended mesh managing another kind of workload or, even better, in Istio’s own words:

ServiceEntry enables adding additional entries into Istio’s internal service registry so that auto-discovered services in the mesh can access/route to these manually specified services.

These services could be external to the mesh (e.g., web APIs) or mesh-internal services that are not part of the platform’s service registry (e.g., a set of VMs talking to services in Kubernetes).

What are the main capabilities of Istio ServiceEntry?

Here you can see a sample of the YAML definition of a Service Entry:

apiVersion: networking.istio.io/v1beta1
kind: ServiceEntry
metadata:
  name: external-svc-redirect
spec:
  hosts:
  - wikipedia.org
  - "*.wikipedia.org"
  location: MESH_EXTERNAL
  ports:
  - number: 443
    name: https
    protocol: TLS
  resolution: NONE

In this case, we have an external-svc-redirectServiceEntry object that is handling all calls going to the wikipedia.org, and we define the port and protocol to be used (TLS – 443) and classify this service as external to the mesh (MESH_EXTERNAL) as this is an external Web page.

You can also specify more details inside the ServiceEntry configuration, so you can, for example, define a hostname or IP and translate that to a different hostname and port because you can also specify the resolution mode you want to use for this specific Service Entry. If you see the snippet above, you will find a resolution field with NONE value that says it will not make any particular resolution. But other values valid are the following ones:

  • NONE: Assume that incoming connections have already been resolved (to a specific destination IP address).
  • STATIC: Use the static IP addresses specified in endpoints as the backing instances associated with the service.
  • DNS: Attempt to resolve the IP address by querying the ambient DNS asynchronously.
  • DNSROUNDROBIN: Attempt to resolve the IP address by querying the ambient DNS asynchronously. Unlike DNS, DNSROUNDROBIN only uses the first IP address returned when a new connection needs to be initiated without relying on complete results of DNS resolution, and references made to hosts will be retained even if DNS records change frequently eliminating draining connection pools and connection cycling.

To define the target of the ServiceEntry, you need to specify its endpoints by using a WorkloadEntry object. To do that, you need to provide the following data:

  • address: Address associated with the network endpoint without the port.
  • ports: Set of ports associated with the endpoint
  • weight: The load balancing weight associated with the endpoint.
  • locality: The locality associated with the endpoint. A locality corresponds to a failure domain (e.g., country/region/zone).
  • network: Network enables Istio to group endpoints resident in the same L3 domain/network.

What Can You Do With Istio ServiceEntry?

The number of use cases is enormous. Once a ServiceEntry is similar to what you have a Virtual Service defined, you can apply any destination rule to them to do a load balancer, a protocol switch, or any logic that can be done with the DestinationRule object. The same applies to the rest of the Istio CRD, such as RequestAuthentication, and PeerAuthorization, among others.

You can also have a graphical representation of the ServiceEntry inside Kiali, a visual representation for the Istio Service Mesh, as you can see in the picture below:

Understanding Istio ServiceEntry: How to Extend Your Service Mesh to External Endpoints

As you can define, an extended mesh with endpoints outside the Kubernetes cluster is something that is becoming more usual with the explosion of clusters available and the hybrid environments when you need to manage clusters of different topologies and not lose the centralized policy-based network management that the Istio Service Mesh provides to your platform.

Secure Your Services with Istio: A Step-by-Step Guide to Setting up Istio TLS Connections

Secure Your Services with Istio: A Step-by-Step Guide to Setting up Istio TLS Connections

Introduction

Istio TLS configuration is one of the essential features when we enable a Service Mesh. Istio Service Mesh provides so many features to define in a centralized, policy way how transport security, among other characteristics, is handled in the different workloads you have deployed on your Kubernetes cluster.

One of the main advantages of this approach is that you can have your application focus on the business logic they need to implement. These security aspects can be externalized and centralized without necessarily including an additional effort in each application you have deployed. This is especially relevant if you are following a polyglot approach (as you should) across your Kubernetes cluster workloads.

So, this time we’re going to have our applications just handling HTTP traffic for both internal and external, and depending on where we are reaching, we will force that connection to be TLS without the workload needed to be aware of it. So, let’s see how we can enable this Istio TLS configuration

Scenario View

We will use this picture you can see below to keep in mind the concepts and components that will interact as part of the different configurations we will apply to this.

  • We will use the ingress gateway to handle all incoming traffic to the Kubernetes cluster and the egress gateway to handle all outcoming traffic from the cluster.
  • We will have a sidecar container deployed in each application to handle the communication from the gateways or the pod-to-pod communication.

To simplify the testing applications, we will use the default sample applications Istio provides, which you can find here.

How to Expose TLS in Istio?

This is the easiest part, as all the incoming communication you will receive from the outside will enter the cluster through the Istio Ingress Gateway, so it is this component the one that needs to handle the TLS connection and then use the usual security approach to talk to the pod exposing the logic.

By default, the Istio Ingress Gateway already exposes a TLS port, as you can see in the picture below:

Secure Your Services with Istio: A Step-by-Step Guide to Setting up Istio TLS Connections

So we will need to define a Gateway that receives all this traffic through the HTTPS and redirect that to the pods, and we will do it as you can see here:

apiVersion: networking.istio.io/v1alpha3
kind: Gateway
metadata:
  name: bookinfo-gateway-https
  namespace: default
spec:
  selector:
    istio: ingressgateway
  servers:
    - hosts:
        - '*'
      port:
        name: https
        number: 443
        protocol: HTTPS
      tls:
        mode: SIMPLE # enables HTTPS on this port
        credentialName: httpbin-credential 

As we can see, it is a straightforward configuration, just adding the port HTTPS on the 443 and providing the TLS configuration:

And with that, we can already reach using SSL the same pages:

Secure Your Services with Istio: A Step-by-Step Guide to Setting up Istio TLS Connections

How To Consume SSL from Istio?

Now that we have generated a TLS incoming request without the application knowing anything, we will go one step beyond that and do the most challenging configuration. We will set up TLS/SSL connection to any outgoing communication outside the Kubernetes cluster without the application knowing anything about it.

To do so, we will use one of the Istio concepts we have already covered in a specific article. That concept is the Istio Service Entry that allows us to define an endpoint to manage it inside the MESH.

Here we can see the Wikipedia endpoint added to the Service Mesh registry:

 apiVersion: networking.istio.io/v1beta1
kind: ServiceEntry
metadata:
  name: se-app
  namespace: default
spec:
  hosts:
  - wikipedia.org
  ports:
  - name: https
    number: 443
    protocol: HTTPS
  resolution: DNS

Once we have configured the ServiceEntry, we can define a DestinationRule to force all connections to wikipedia.org will use the TLS configuration:

apiVersion: networking.istio.io/v1beta1
kind: DestinationRule
metadata:
  name: tls-app
  namespace: default
spec:
  host: wikipedia.org
  trafficPolicy:
    tls:
      mode: SIMPLE

📚 Want to dive deeper into Kubernetes? This article is part of our comprehensive Kubernetes Architecture Patterns guide, where you’ll find all fundamental and advanced concepts explained step by step.