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_scannextension now provides a satisfy limit feature that improves query recall for vector searches. If a search returns fewer results than specified in theLIMITclause, the scan continues until theLIMITis met or a configured upper bound is reached. To enable this feature, set thescann.satisfy_limitflag torelaxed order. You can also use thescann.max_pct_leaves_to_searchflag 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_workersflag to control the number of background workers and increase throughput across multiple indexes.
- scann.maintenance_background_naptime_sflag 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
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