Release notes

Introduction link

The release notes include important installation changes and known issues. They also highlight ways that you can take advantage of new features or enhancements to improve your product usage.

For more information, see the following related resources:

• Upgrade guide: Steps to upgrade from the previous minor version to the current version.
• Version reference: Information about Solo’s version support.

🔥 Breaking changes link

Review details about the following breaking changes. The severity is intended as a guide to help you assess how much attention to pay to this area during the upgrade, but can vary depending on your environment.

🚨 High link

Review severe changes that can impact production and require manual intervention.

• Removal of memory_ballast extension from OTel collector: The memory_ballast extension is deprecated and no longer effective for OTel collector agents. If you use this extension to manage garbage collection, you must update your collector configuration.

🔔 Medium link

Review changes that might have impact to production and require manual intervention, but possibly not until the next version is released.

• New Helm defaults for Solo image repositories: The default Helm values for the Solo images changed from docker.io to gcr.io. Make sure to update any internal tooling, such as in airgapped environments.

ℹ️ Low link

Review informational updates that you might want to implement but that are unlikely to materially impact production.

• Upstream Prometheus Helm chart upgrade: The Prometheus Helm chart version is upgraded to a newer version, which requires the Prometheus deployment to be re-created. Gloo Mesh uses a Helm pre-upgrade hooks to re-create the deployment which can cause issues in automated environments, such as Argo CD.

• OTel image name change: The name of the OpenTelemetry image that is used in Gloo Mesh is changed as follows:

  • Old name: gloo-otel-collector
  • New name: otel-collector

  Make sure to update any CI/CD pipelines to pull the new image (gcr.io/gloo-mesh/otel-collector:0.1.4), such as in an airgapped environment.

Removal of memory_ballast extension from the OTel collector link

The memory_ballast extension is deprecated and no longer effective for the OTel collector agents. To ensure that your OTel collector instances continue to perform garbage collection correctly:

• If you do not currently customize the memory_ballast extension, you can safely remove it from your OTel collector configuration. No further steps are necessary.
• If you do customize the memory_ballast extension, you can instead control garbage collection with a soft memory limit by setting the GOMEMLIMIT environment variable to the recommended value of 80-90% of the total memory.

  1. Get your current installation Helm values, and save them in a file.

       helm get values gloo-platform -n gloo-mesh -o yaml > gloo-single.yaml
     open gloo-single.yaml
       

  2. If you added any additional memory_ballast extension configuration, remove it from your OTel collector.

       
     telemetryCollectorCustomization:
       extraExtensions:
         # Remove this extension
         memory_ballast:
           ... 
       

  3. Add the GOMEMLIMIT environment variable to your OTel collector configuration to set a custom soft memory limit. For more information, see the Go environment variables documentation. Note that for GOMEMLIMIT to take effect, you must also provide memory limits for the OTel collectors. You can also enable the useGOMEMLIMIT environment variable to ensure that the GOMEMLIMIT environment variable is used instead of the memory_ballast extension. * To increase frequency of garbage collection, set GOMEMLIMIT to a higher value than the default 90% of total memory. For example, if your total memory is 3000 MiB, you could set a 95% soft limit of 2850MiB. * To decrease frequency of garbage collection, set GOMEMLIMIT to a lower value than the default 90% of total memory. For example, if your total memory is 3000 MiB, you could set an 85% soft limit of 2550MiB.

       
     telemetryCollectorCustomization:
       extraEnvs:
       - name: GOMEMLIMIT
         value: <memory_limit>
       

  4. Upgrade your installation by using your updated values file.

       helm upgrade gloo-platform gloo-platform/gloo-platform \
        --namespace gloo-mesh \
        --version $GLOO_VERSION \
        --values gloo-single.yaml
       

  5. Verify that your memory limit settings were added to the Gloo telemetry collector configmap.

       kubectl get configmap gloo-telemetry-collector-config -n gloo-mesh -o yaml
       

  6. Perform a rollout restart of the telemetry collector daemon set to force your configmap changes to be applied to the telemetry collector agent pod.

       kubectl rollout restart -n gloo-mesh daemonset/gloo-telemetry-collector-agent
       

  1. Get the Helm values files for your current version.

     1. Get your current values for the management cluster.

          helm get values gloo-platform -n gloo-mesh -o yaml --kube-context $MGMT_CONTEXT > mgmt-plane.yaml
        open mgmt-plane.yaml
          

     2. Get your current values for the workload clusters.

          helm get values gloo-platform -n gloo-mesh -o yaml --kube-context $REMOTE_CONTEXT > data-plane.yaml
        open data-plane.yaml
          

  2. If you added any additional memory_ballast extension configuration, remove it from your OTel collector configuration in both Helm values files.

       
     telemetryCollectorCustomization:
       extraExtensions:
         # Remove this extension
         memory_ballast:
           ... 
       

  3. In both Helm values files, add the GOMEMLIMIT environment variable to your OTel collector configuration to set a custom soft memory limit. For more information, see the Go environment variables documentation. Note that for GOMEMLIMIT to take effect, you must also provide memory limits for the OTel collectors. You can also enable the useGOMEMLIMIT environment variable to ensure that the GOMEMLIMIT environment variable is used instead of the memory_ballast extension. * To increase frequency of garbage collection, set GOMEMLIMIT to a higher value than the default 90% of total memory. For example, if your total memory is 3000 MiB, you could set a 95% soft limit of 2850MiB. * To decrease frequency of garbage collection, set GOMEMLIMIT to a lower value than the default 90% of total memory. For example, if your total memory is 3000 MiB, you could set an 85% soft limit of 2550MiB.

       
     telemetryCollectorCustomization:
       extraEnvs:
       - name: GOMEMLIMIT
         value: <memory_limit>
       

  4. Apply the updates to your management cluster.

     1. Upgrade the management cluster.

          helm upgrade gloo-platform gloo-platform/gloo-platform \
          --kube-context $MGMT_CONTEXT \
          --namespace gloo-mesh \
          -f mgmt-plane.yaml \
          --version $GLOO_VERSION
          

     2. Verify that the memory limit settings were added to the Gloo telemetry collector configmap.

          kubectl get configmap gloo-telemetry-collector-config -n gloo-mesh -o yaml --context $MGMT_CONTEXT
          

     3. Perform a rollout restart of the telemetry collector daemon set to force your configmap changes to be applied to the telemetry collector agent pods.

          kubectl rollout restart -n gloo-mesh daemonset/gloo-telemetry-collector-agent --context $MGMT_CONTEXT
          

  5. Apply the updates to your workload cluster.

     1. Upgrade the workload cluster.

          helm upgrade gloo-platform gloo-platform/gloo-platform \
          --kube-context $REMOTE_CONTEXT \
          --namespace gloo-mesh \
          -f data-plane.yaml \
          --version $GLOO_VERSION
          

     2. Verify that the memory limit settings were added to the Gloo telemetry collector configmap.

          kubectl get configmap gloo-telemetry-collector-config -n gloo-mesh -o yaml --context $REMOTE_CONTEXT
          

     3. Perform a rollout restart of the telemetry collector daemon set to force your configmap changes to be applied to the telemetry collector agent pods.

          kubectl rollout restart -n gloo-mesh daemonset/gloo-telemetry-collector-agent --context $REMOTE_CONTEXT
          

New Helm defaults for Solo image repositories link

The default Helm values for the Solo images changed from docker.io to gcr.io. For example, the Redis image changed from docker.io/redis:7.2.4-alpine to gcr.io/gloo-mesh/redis:7.2.4-alpine. If you internal tooling is set up to pull the images from docker.io, such as in air-gapped environments, you must update that tooling to pull the images from the Google Cloud Registry repository.

For an overview of the images that are used in an air-gapped environment, see the Install in air-gapped environments docs in the Gloo Mesh Enterprise documentation.

Upstream Prometheus upgrade link

Gloo Mesh includes a built-in Prometheus server to help monitor the health of your Gloo components. This release of Gloo upgrades the Prometheus community Helm chart from version 19.7.2 to 25.11.0. As part of this upgrade, upstream Prometheus changed the selector labels for the deployment, which requires recreating the deployment. To help with this process, the Gloo Helm chart includes a pre-upgrade hook that automatically recreates the Prometheus deployment during a Helm upgrade. This breaking change impacts upgrades from previous versions to version 2.6.0 and later.

If you do not want the redeployment to happen automatically, you can disable this process by setting the prometheus.skipAutoMigration Helm value to true. For example, you might use Argo CD, which converts Helm pre-upgrade hooks to Argo PreSync hooks and causes issues. To ensure that the Prometheus server is deployed with the right version, follow these steps:

1. Confirm that you have an existing deployment of Prometheus at the old Helm chart version of chart: prometheus-19.7.2.

     kubectl get deploy -n gloo-mesh prometheus-server -o yaml | grep chart
     

2. Delete the Prometheus deployment. Note that while Prometheus is deleted, you cannot observe Gloo performance metrics.

     kubectl delete deploy -n gloo-mesh prometheus-server
     

3. In your Helm values file, set the prometheus.skipAutoMigration field to true.
4. Continue with the Helm upgrade of Gloo Mesh. The upgrade recreates the Prometheus server deployment at the new version.

⚒️ Installation changes link

In addition to comparing differences across versions in the changelog, review the following installation changes from the previous minor version to version 2.6.

Safe mode enabled by default link

Starting in version 2.6.0, safe mode is enabled on the Gloo management server by default to ensure that the server translates input snapshots only if all input snapshots are present in Redis or its local memory. This way, translation only occurs based on a complete translation context that includes all workload clusters.

Enabling safe mode resolves a race condition that was identified in version 2.5.3, 2.4.11, and earlier that could be triggered during simultaneous restarts of the management plane and Redis, including an upgrade to a newer Gloo Mesh Enterprise version. If hit, this failure mode could lead to partial translations on the Gloo management server which could result in Istio resources being temporarily deleted from the output snapshots that are sent to the Gloo agents.

To learn more about safe mode, see Safe mode.

New default values for Gloo UI auth sessions link

Some of the default Helm values changed for configuring the Gloo UI auth session storage:

• glooUi.auth.oidc.session.backend: The default value changed from "" (empty) to cookie to ensure auth sessions are stored in browser cookies by default.
• glooUi.auth.oidc.session.redis.host: The default value changed from "" (empty) to gloo-mesh-redis.gloo-mesh:6379 to ensure a valid Redis host is set when glooUi.auth.oidc.session.backend is changed to redis.

To learn how to set up Gloo UI auth session storage, see Store UI sessions.

  
glooUi: 
  auth: 
    oidc:
      # Session storage configuration. If omitted, a cookie is used.
      session:
        # Backend to use for auth session storage. 'cookie' and 'redis' are supported.
        backend: "cookie"
        # Redis instance configuration.
        redis:
          # Host at which the Redis instance is accessible. To use the default Redis deployment, specify 'gloo-mesh-redis.gloo-mesh:6379'.
          host: "gloo-mesh-redis.gloo-mesh:6379"
  

  
glooUi: 
  auth: 
    oidc:
      # Session storage configuration. If omitted, a cookie is used.
      session:
        # Backend to use for auth session storage. 'cookie' and 'redis' are supported.
        backend: ""
        # Redis instance configuration.
        redis:
          # Host at which the Redis instance is accessible. To use the default Redis deployment, specify 'gloo-mesh-redis.gloo-mesh:6379'.
          host: ""
  

New container settings for Redis link

In 2.6, the default container settings for the built-in Redis changed. To apply these settings, Redis must be restarted during the upgrade. Make sure that safe mode is enabled before you proceed with the upgrade so that translation of input snapshots halts until the input snapshots of all connected Gloo agents are re-populated in Redis. In 2.6.0, safe mode is enabled by default.

To learn more about safe mode, see Safe mode.

Redis installation for snapshots and insights link

In version 2.5 and earlier, you configured the details for the backing Redis instance for different Gloo Mesh components in the Helm chart section for each component. When components had to share Redis instances, this approach could lead to misconfiguration. For example, the Gloo external auth service and portal server must share the same configuration details to use the same Redis instance, but these Redis details were configured separately.

Now, you can configure the details for the backing Redis instance consistently across components by using the new redisStore section of the Helm chart. The redisStore section also organizes Redis into four main use cases, which you can configure to use separate Redis instances. All the components that read or write data for this particular use case automatically get the same Redis details set up for them. This way, you can consistently configure Redis.

The previous way of configuring Redis is still supported, so you do not have to update your configuration right away. However, you might want to update to redisStore for simplicity, as well as to use the new use case-driven approach that lets you configure Redis for snapshot and insights.

Optional steps to migrate to the redisStore configuration:

The following example shows how your configuration might look when you use the built-in Redis options for the control plane (redis), external auth service, developer portal, and rate limiter. For other configuration options, such as using shared instances or bringing your own external Redis instance like Amazon ElastiCache, see the updated Redis documentation.

1. Review your existing Helm values file to find your current Redis values.

     
   redis:
     deployment:
       # Enable the creation of the local gloo-mesh-redis deployment and service.
       enabled: true
     

2. Remove your existing Redis values, and enable the redisStore options instead. The following example removes the default redis option and enables two built-in instances for the gloo-redis-snapshot and gloo-redis-insights deployments.

     redis:
     deployment:
       # Disable the creation of the legacy Redis deployment and service,
       # so that you can use the redisStore configuration path instead.
       enabled: false
   redisStore:
     insights:
       # Enable the creation of the local Redis deployment and service.
       deployment:
         enabled: true
     snapshot:
       # Enable the creation of the local Redis deployment and service.
       deployment:
         enabled: true
     

3. Continue with the upgrade.

🌟 New features link

Review the following new features that are introduced in version 2.6 and that you can enable in your environment.

Go version bump link

In 2.6.11, the Go version that is used in Gloo Mesh was upgraded to 1.24. This upgrade introduced the following changes:

• RSA key generation: The minimum size for the RSA key that is used in the generated RootTrustPolicy was increased to 1024 bytes. If an existing RSA key is used with a size below 1024 bytes, the key size is increased and a warning is logged.

Ambient service mesh support with the Istio lifecycle manager link

Ambient mesh is now considered generally available in Gloo Mesh version 2.6. To try it out, see Install Istio in ambient mode.

Delimiters in JWT token claims link

As of version 2.6.6, you can configure custom delimiters when you extract claims from JWT tokens. This way, you can append the claim information in a header in a different format than the default comma-delimited format. For example steps, see Extract claims to headers.

Istio 1.21, 1.22, and 1.23 support link

You can now use Istio 1.21 and 1.22 in version 2.6.0 and later, and Istio 1.23 in version 2.6.3 and later. Note that you must upgrade the Gloo management server to version 2.6 first before you start upgrading your Istio version. To find the image reposititories for the Solo distribution of Istio, see the Istio images built by Solo support article. Istio versions 1.16 and 1.17 are no longer supported in 2.6.

For more information about supported Istio versions, see the Supported Solo distributions of Istio.

Kubernetes 1.29 and 1.30 support link

Starting in version 2.6.0, Gloo Mesh can now run on Kubernetes 1.29 and 1.30. Kubernetes version 1.22 and 1.23 are no longer supported. For more information about supported Kubernetes and Istio versions, see the version support matrix.

I/O threads for Redis link

A new Helm value redis.deployment.ioThreads was introduced to specify the number of I/O threads to use for the built-in Redis instance. Redis is mostly single threaded, however some operations, such as UNLINK or slow I/O accesses can be performed on side threads. Increasing the number of side threads can help improve and maximize the performance of Redis as these operations can run in parallel.

If you set I/O threads, the Redis pod must be restarted during the upgrade so that the changes can be applied. During the restart, the input snapshots from all connected Gloo agents are removed from the Redis cache. If you also update settings in the Gloo management server that require the management server pod to restart, the management server’s local memory is cleared and all Gloo agents are disconnected. Although the Gloo agents attempt to reconnect to send their input snapshots and re-populate the Redis cache, some agents might take longer to connect or fail to connect at all. To ensure that the Gloo management server halts translation until the input snapshots of all workload cluster agents are present in Redis, it is recommended to enable safe mode on the management server alongside updating the I/O threads for the Redis pod. For more information, see Safe mode. Note that in version 2.6.0 and later, safe mode is enabled by default.

To update I/O side threads in Redis as part of your Gloo Mesh Enterprise upgrade:

1. Scale down the number of Gloo management server pods to 0.

     kubectl scale deployment gloo-mesh-mgmt-server --replicas=0 -n gloo-mesh
     

2. Upgrade Gloo Mesh Enterprise and use the following settings in your Helm values file for the management server. Make sure to also increase the number of CPU cores to one core per thread, and add an additional CPU core for the main Redis thread. The following example also enables safe mode on the Gloo management server to ensure translation is done with the complete context of all workload clusters.

     
   glooMgmtServer:
     safeMode: true
   redis: 
     deployment: 
       ioThreads: 2
       resources: 
         requests: 
           cpu: 3
         limits: 
           cpu: 3
     

3. Scale the Gloo management server back up to the number of desired replicas. The following example uses 1 replica.

     kubectl scale deployment gloo-mesh-mgmt-server --replicas=1 -n gloo-mesh
     

Persistence mode for built-in Redis link

To back up data for the Gloo Mesh control plane components like the management server, you can use a Solo-provided built-in Redis instance that is installed in the cluster. By default, this built-in Redis instance does not persist data, which gets lost during pod restarts. Now, you can also configure the built-in Redis to use persistent storage. This way, data persists across Redis restarts, such as after an upgrade. By persisting the data, you can reduce delays in the relay process that otherwise would happen after a restart. For more information, see Persistent storage.

🔄 Feature changes link

Review the following changes that might impact how you use certain features in your Gloo environment.

Improved error logging link

The Gloo management server translates your Gloo custom resources into many underlying Istio resources. When the management server cannot translate a resource, it returns debug logs that vary in severity from errors to warnings or info.

In this release, the management server logs are improved in the following ways:

• All translation errors are now logged at the debug level. This way, the management server logs are not cluttered by errors that do not impact the management server’s health.
• Fixed a bug that caused many duplicate error logs. Now, you have fewer logs to sift through.

For example, you might have a service that does not select any existing workloads. This scenario might be intentional, such as if you use a CI/CD tool like ArgoCD to deploy your environment in phases. Translation does not complete until you update the service’s selector or create the workload. Previously, the translation error would show up many times in the management server logs, even though the situation is intentional and the management server is healthy and can translate other objects. Now, the translation error is logged less verbosely at the debug level.

You can still review translation errors in the following ways:

• Translation errors and warnings are shown in the statuses of Gloo custom resources. For example, if a policy fails to apply to a route, you can review the warning in the policy and the route table statuses.
• In the management server, enable debug logging by enabling the --verbose=true setting. Example command:

    kubectl patch deploy -n gloo-mesh gloo-mesh-mgmt-server --type "json" -p '[{"op":"add","path":"/spec/template/spec/containers/0/args/-","value":"--verbose=true"}]'
    

Istio resources not generated on non-mesh clusters link

Added a fix that now prevents Istio resources from being written to the management cluster when the management cluster is not registered as a workload cluster at the same time. Previously, if you had an Istio installation in your management cluster that was not managed by Gloo Mesh and a Gloo agent was deployed to that cluster at the same time, the agent discovered these Istio resources and included them in the snapshot that was sent to the management server. The agent also wrote Istio resources to that cluster, which might have interfered with existing Istio resources in that cluster.

Previously generated resources are not automatically removed during the upgrade. You must manually clean up these resources in the management cluster. You can use the following script to remove these resources.

  #!/bin/bash

namespace=$1

if [ -z "$namespace" ]; then
  echo "Usage: cleanup.sh <namespace>"
  exit 1
fi

kubectl delete authorizationpolicies.security.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete certificaterequests.internal.gloo.solo.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete clusteristioinstallations.internal.gloo.solo.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete destinationrules.networking.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete discoveredcnis.internal.gloo.solo.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete discoveredgateways.internal.gloo.solo.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete envoyfilters.networking.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete gateways.networking.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete issuedcertificates.internal.gloo.solo.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete istiooperators.install.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete meshes.internal.gloo.solo.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete peerauthentications.security.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete podbouncedirectives.internal.gloo.solo.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete portalconfigs.internal.gloo.solo.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete proxyconfigs.networking.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete requestauthentications.security.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete serviceentries.networking.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete sidecars.networking.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete spireregistrationentries.internal.gloo.solo.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete telemetries.telemetry.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete virtualservices.networking.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete wasmplugins.extensions.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete workloadentries.networking.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete workloadgroups.networking.istio.io -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
kubectl delete xdsconfigs.internal.gloo.solo.io  -n $namespace -l "reconciler.mesh.gloo.solo.io/name=translator"
  

🚧 Known issues link

The Solo team fixes bugs, delivers new features, and makes changes on a regular basis as described in the changelog. Some issues, however, might impact many users for common use cases. These known issues are as follows:

• Cluster names: Do not use underscores (_) in the names of your clusters or in the kubeconfig context for your clusters.
• Istio:
  • Due to a lack of support for the Istio CNI and iptables for the Istio proxy, you cannot run Istio (and therefore Gloo Mesh) on AWS Fargate. For more information, see the Amazon EKS issue.
  • In Gloo Mesh version 2.6 and later, ambient mode requires the Solo distribution of Istio version 1.22.3 or later (1.22.3-solo).
  • In Istio 1.22.0-1.22.3, the ISTIO_DELTA_XDS environment variable must be set to false. For more information, see this upstream Istio issue. Note that this issue is resolved in Istio 1.22.4.
  • Istio 1.20 is supported only as patch version 1.20.1-patch1 and later. Do not use patch versions 1.20.0 and 1.20.1, which contain bugs that impact several Gloo Mesh features that rely on Istio ServiceEntries.
• meshctl: The meshctl CLI version 2.6.0 has a known issue with the install command that prevents you from using --set options. This issue was resolved in version 2.6.1 and the 2.6.0 meshctl binaries were updated to point to the 2.6.1 meshctl binaries instead. Because of this change, you might see version 2.6.1 when you run meshctl version, even if you installed the 2.6.0 meshctl binaries.
• OTel pipeline: FIPS-compliant builds are not currently supported for the OTel collector agent image.