GCP Release Notes: September 29, 2025

AlloyDB for PostgreSQL

Announcement

The alloydb_scann extension version 0.1.3 is updated to include the following vector search improvements in (Preview):

• You can now automatically create ScaNN indexes that are optimized for search performance or for a balance between index build times and search performance with the auto index feature.
• AlloyDB AI’s adaptive filtering for filtered vector searches now dynamically switches between pre-filtering and inline filtering. This enhancement further optimizes query performance by allowing the query optimizer to dynamically choose the most efficient filtering strategy. For more information, see Activate adaptive filtering in AlloyDB AI.
• AlloyDB AI now integrates ScaNN indexes with the columnar engine. You can now accelerate your vector similarity search by loading ScaNN indexes into the columnar engine.
• The alloydb_scann extension now provides a satisfy limit feature that improves query recall for vector searches. If a search returns fewer results than specified in the LIMIT clause, the scan continues until the LIMIT is met or a configured upper bound is reached. To enable this feature, set the scann.satisfy_limit flag to relaxed order. You can also use the scann.max_pct_leaves_to_search flag to configure the upper bound for the search.
• You can enable vector search index recommendations for Scalable Nearest Neighbors (ScaNN) indexes using the AlloyDB index advisor. For more information, see Use the AlloyDB index advisor with query insights or View the index advisor’s index recommendations.

• You can configure automatic index maintenance using the following flags:

  • scann.max_background_workers flag to control the number of background workers and increase throughput across multiple indexes.
  • scann.maintenance_background_naptime_s flag to control the minimum delay between maintenance runs.

BigQuery

Feature

To simplify access management for your Iceberg tables, you can use credential vending mode with the Apache Iceberg REST catalog in BigLake metastore. Credential vending removes the need for catalog users to have direct access to Cloud Storage buckets. This feature is in Preview.

Feature

You can now create BigQuery non-incremental materialized views over Spanner data to improve query performance by periodically caching results. This feature is in Preview.

Feature

BigQuery data preparation supports unnesting arrays, which expands each array element into its own row for easier analysis. For more information, see Unnest arrays. This feature is generally available (GA).

Announcement

History-based query optimizations are now enabled by default. If history-based optimizations have been previously disabled, you can re-enable history-based optimizations for your project or organization.

Cloud Service Mesh

Announcement

CNI/managed data plane controller version 1.23.6-asm.15 is rolling out to all release channels.

Fixed

CVE	CNI	MDP Controller
CVE-2025-4802	Yes	Yes
CVE-2023-29383	Yes	Yes
CVE-2024-56406	Yes	Yes
CVE-2023-7008	Yes	Yes
CVE-2025-1377	Yes	Yes
CVE-2023-4039	Yes	Yes
CVE-2025-46836	Yes	Yes
CVE-2023-50495	Yes	Yes
CVE-2025-4598	Yes	Yes
CVE-2025-3576	Yes	Yes
CVE-2025-30258	Yes	Yes
CVE-2017-11164	Yes	Yes
CVE-2022-41409	Yes	Yes
CVE-2025-1372	Yes	Yes
CVE-2022-27943	Yes	Yes
CVE-2022-4899	Yes	Yes
CVE-2023-34969	Yes	Yes
CVE-2023-45918	Yes	Yes

Gemini Code Assist

Feature

Add code snippets to the chat context

You can now select, attach, and direct Gemini to focus on code snippets with IntelliJ Gemini Code Assist. Code snippet selection enables discrete analysis of smaller code blocks instead of entire files.

Source: Google Cloud Platform