GCP Release Notes: September 04, 2025

AlloyDB for PostgreSQL

Feature

Parameterized secure views in AlloyDB for PostgreSQL enhance data security and row access control while using SQL, providing a new secure interface for application developers. Access to this Preview feature no longer requires a signup.

Feature

AlloyDB AI natural language delivers secure and accurate responses for application end user natural language questions. Natural language offers fragment-based templates, autogenerated concept types, and SQL summaries. Access to this Preview feature no longer requires a signup.

Gemini Code Assist

Feature

Monitor Gemini Code Assist usage

You can now monitor your organization’s usage of Gemini Code Assist with a dashboard that is automatically available when you enable and use Gemini Code Assist. The dashboard includes some of the most important metrics, giving you a quick way to view aggregated usage data. For more information, see Monitor Gemini Code Assist usage.

Google Kubernetes Engine

Changed

CNI spec version for GKE Dataplane V2 updated to v1.1.0

Starting with GKE patch version 1.34, clusters using GKE Dataplane V2 are being updated from CNI spec v0.3.1 to v1.1.0.

Action required: If you use your own CNI plugins in your GKE cluster (such as self-managed open-source Istio), you must upgrade them to a version compatible with CNI spec v1.1.0 to prevent errors.

Announcement

Kubernetes 1.34 is now available in the Rapid channel

Kubernetes 1.34 is now available in the Rapid channel. For more information about the content of Kubernetes 1.34, read the Kubernetes 1.34 Release Notes.

Changed

Other changes in 1.34

• containerd 2.1: GKE nodes are now upgraded to containerd 2.1. This release includes performance improvements such as faster image downloads. For a complete list of changes, see the official containerd 2.1 release notes.
• VPA InPlaceOrRecreate: This version introduces a new InPlaceOrRecreate mode in Vertical Pod Autoscaler (VPA) (Public Preview) powered by In-Place Pod Resize (IPPR/IPPU) that allows automatically rightsizing workloads often without recreating the Pod. This mode ensures seamless service continuity while minimizing costs during idle periods. If you haven’t used VPA with your workloads before, enable Vertical Pod Autoscaler on your cluster and then create a VPA Object for a workload.

Deprecated

Deprecated in 1.34

The v1beta1 gRPC API between the Kubelet and DRA drivers is deprecated in this release in favor of the v1 API. This API will continue to function but we recommend that all drivers move to the v1 API to prepare for the eventual removal of the v1beta1 API.

Feature

New features in Kubernetes 1.34

• The Kubernetes Dynamic Resource Allocation (DRA) APIs are now generally available. For more information about using DRA in GKE, see About dynamic resource allocation in GKE. The Prioritized list and Admin access features have been promoted to beta and will be enabled by default. The kubelet API has been updated to report status on resources allocated through DRA.
• The Sleep Action for Pod prestop lifecycle hook is now GA. This can be used to delay Pod termination for graceful shutdown.
• Streaming List Response Encoding is now GA. It enables efficient handling of requests for large object collections, improving API server reliability and performance.
• In-Place Pod Resize, which was in beta, is now improved by adding support for decreasing memory limits with best-effort OOM protection. Improved deferred resize retries are also added, which are now prioritized and more responsive to resources becoming available. A new ResizeCompleted event records when a resize is completed.

Feature

On clusters with GKE Dataplane V2 that are on GKE version 1.34 and later, the ptp plugin is removed from the Container Network Interface (CNI) path. Pods that are created on new nodes have interfaces named lxc[INTERFACE_HASH] instead of gke[INTERFACE_HASH]. Additionally, the CNI configuration is moving from the netd DaemonSet to the cni-writer container in the anetd DaemonSet. For more information, see Overview of GKE Dataplane V2.

Feature

GKE alpha clusters enable all alpha and the default beta feature gates, which help you to test and validate upcoming Kubernetes capabilities. You can now modify the feature gates to enable or disable differently from the default values, which provides more granular control when leveraging these experimental features. Note that alpha clusters shouldn’t be used for production workloads to ensure that your workloads remain stable and performant. For more information, see Alpha clusters.

Source: Google Cloud Platform