GCP Release Notes: April 17, 2026

AlloyDB for PostgreSQL

Issue

When querying your Elasticsearch data using standard SQL queries and specifying an OFFSET, if the OFFSET gets pushed down, it gets applied twice. For example, if your SQL query contains OFFSET 5, AlloyDB tries to push the OFFSET down. Then, AlloyDB applies the OFFSET again when the results are returned.

Feature

External search with AlloyDB now supports Elasticsearch in Preview.

With this update, you can use the external_search_fdw extension to connect to Elasticsearch and perform hybrid searches within AlloyDB. This integration allows you to combine the capabilities of AlloyDB with Elasticsearch for advanced search scenarios. For more information, see Access Elasticsearch data from AlloyDB.

Announcement

The following AlloyDB AI functions are available in Preview:

• You can now use AI function acceleration and the new AI Function Apply node to run faster queries with AI functions. This feature optimizes the execution of SQL queries that use the ai.if and ai.rank functions in PostgreSQL 17. For more information, see Accelerate performance for queries with AI functions.
• You can now use optimized AI functions to accelerate your AI queries while reducing operational costs. By training a smaller, faster proxy model on a sample of your data, AlloyDB can process most AI queries locally and only fall back to a remote LLM when necessary. For more information, see Accelerate queries using optimized functions.

• You can now use the sentiment analysis and summarization functions. These functions let you process and analyze unstructured data directly in your database:

  • ai.analyze_sentiment: classifies the emotional tone of text as positive, negative, or neutral, helping you analyze real-time customer feedback from thousands of raw, unstructured product reviews.
  • ai.summarize: condenses lengthy text into its essential information. Use this to extract key decisions and action items from sources like meeting transcripts or technical documentation.
  • ai.agg_summarize: an aggregate function that processes multiple rows in a column to generate a single, unified summary for a group. For instance, you can summarize all reviews for a specific seller using a GROUP BY clause.

  For more information, see Evaluate sentiment and Summarize content.

BigQuery

Feature

Using folders to organize and control access to single file code assets is generally available (GA). In addition, you can perform bulk move and delete operations, refresh folder contents, and view full breadcrumb paths based on resource permissions. For more information, see Create and manage folders.

Cloud SQL for SQL Server

Feature

You can now integrate Cloud SQL for SQL Server with Vertex AI and third-party models (Preview).

By integrating your Cloud SQL for SQL Server instance with Vertex AI, you can generate vector embeddings from models hosted in Vertex AI directly from your Cloud SQL instance.

Cloud SQL for SQL Server supports model endpoints from the following sources:

• Vertex AI
• Hugging Face
• OpenAI

For more information, see Integrate Cloud SQL for SQL Server with Vertex AI.

Dataplex

Feature

Data quality now supports rule reusability. You can now define data quality rules as templates and reuse them across multiple catalog entries to standardize your data quality processes. You can also use a shared library of system rule templates for common data validation scenarios. For more information, see Reuse data quality rules.

Feature

You can now build and run a Knowledge Catalog discovery agent to get more relevant search results for complex natural language queries.

For more information, see Build an agent to discover your data.

Generative AI on Vertex AI

Feature

RAG Cross Corpus Retrieval

RAG Cross Corpus Retrieval is available in public preview. This feature allows you to retrieve relevant contexts or generate answers from multiple RAG corpora simultaneously using the AsyncRetrieveContexts and AskContexts APIs.

For more information, see RAG Cross Corpus Retrieval.

Google Cloud Managed Service for Apache Kafka

Feature

The Managed Service for Apache Kafka remote MCP server is generally available (GA).

Memorystore for Valkey

Feature

You can secure access to your instances by using basic token-based authentication. This feature is available in Preview.

NetApp Volumes

Feature

The ONTAP-mode for the Flex Unified pools is generally available (GA). For more information about this new mode, see About ONTAP-mode.

Feature

Google Cloud NetApp Volumes Flex Unified service level is generally available (GA) for NFS, SMB, and NVMe/TCP protocols. For more information, see Key features.

Feature

The large capacity volumes feature, a file-only solution with NFS and SMB protocols for massive datasets, is generally available (GA) for the Flex Unified service level. For more information, see Large capacity volumes.

Network Intelligence Center

Feature

You can use the Network Management API remote Model Context Protocol (MCP) server to create, view, and delete Connectivity Tests.

Oracle Database@Google Cloud

Feature

You can now use the Oracle Database@Google Cloud remote MCP server. The remote MCP server lets you interact easily with Oracle Database@Google Cloud resources from LLMs, AI applications, and AI-enabled development platforms.

This feature is in Preview.

Pub/Sub

Feature

The Pub/Sub remote MCP server is generally available (GA).

Security Command Center

Feature

Through the Application Design Center, Security Command Center helps you perform proactive security assessments (Preview) throughout your application development lifecycle. This integration shows both design-time and runtime findings in Security Command Center. For more information, see Application lifecycle security assessments.

Service Extensions

Feature

You can use authorization extensions to insert custom services directly into the Secure Web Proxy processing path. This feature is in Preview. For more information, see Callouts for Secure Web Proxy.

Feature

Authorization extensions support authorization policy request and content profiles in Preview.

Spanner

Feature

Repeatable read isolation is generally available. You can use it to reduce latency and transaction failure rates for workloads that have many reads contending with fewer writes. For more information, see Repeatable read isolation.

Feature

Columnar engine for Spanner is now generally available (GA). Columnar engine is a storage technique used with analytical queries to make scans up to 200 times faster on live operational data without affecting transaction workloads. This release enables support for Columnar Engine in databases that use the Postgres interface.

For more information, see the Columnar engine for Spanner overview.

Source: Google Cloud Platform