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 Applynode to run faster queries with AI functions. This feature optimizes the execution of SQL queries that use theai.ifandai.rankfunctions 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 aGROUP BYclause.
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
