All news with #cloud sql tag

🔍 Amazon Aurora DSQL now supports partial indexes, enabling indexes over a subset of table rows defined by a WHERE clause. This reduces index storage and improves query performance for common working sets, such as active orders amid large historical data. Aurora DSQL will use a partial index when a query's filter is covered by the index condition. The feature is available in all AWS Regions where Aurora DSQL is offered.

🚀 AlloyDB now offers PostgreSQL for agents (preview), enabling sandboxed, serverless PostgreSQL instances that provide up-to-the-second read-only access to production data while isolating agent workloads. These instances spin up in seconds, leverage Colossus-backed unified storage for sub-millisecond I/O and massive scan throughput, and scale down to zero to control costs. The engine remains fully PostgreSQL-compatible with vector, full-text, and spatial search plus strong Google Cloud security integrations.

📰 This post introduces native BM25 full-text search in AlloyDB and Cloud SQL for PostgreSQL 17+ via the open-source pg_textsearch extension from TigerData. The change eliminates the need for separate full-text backends, reducing data duplication and sync complexity while delivering industry-standard BM25 ranking directly in the database. AlloyDB further offers accelerated vector search (ScaNN, HNSW) and an out-of-the-box hybrid search UDF to merge vector and keyword results; Cloud SQL supports hybrid results via CTEs and coalesced RRF scoring.

🚀 The AlloyDB Omni Red Hat RPM orchestrator is now generally available alongside AlloyDB Omni v18.3.0, bringing production-ready security, resiliency, and low-downtime operations for PostgreSQL workloads on VMs and bare metal. The release supports four deployment modes, including standalone RPM and RPM orchestrator for HA, and targets regulated, edge, and AI-ready environments. The orchestrator simplifies cluster lifecycle, offers read pools, automated backups to GCS/S3, SELinux support, advanced observability, and Low Downtime Maintenance for enterprise-scale operations.

🔒 This post explains why Database Migration Service (DMS) does not migrate SQL Server instance-level logins and how to migrate them safely. It outlines the security, compliance, and identity-modernization reasons behind that design decision and shows a practical three-step approach using Microsoft's sp_help_revlogin script. The guide covers generating hashed-password CREATE LOGIN statements, applying them to Cloud SQL, and resolving orphaned users, while recommending migration to Customer-Managed Active Directory where possible.

🛠️ Google Cloud Spanner now allows transactions to include any number of DML statements provided each individual statement produces fewer than 80,000 mutation mods. This change moves the previous 80,000 cumulative transaction-level cap to a per-statement limit, enabling larger, more natural transactions without code changes. Existing client libraries remain compatible, though longer transactions may increase lock contention and aborts, so developers should monitor CommitStats and optimize large operations.

🔧 Google’s Finance Engineering team automated a complex migration from a legacy datastore to Cloud Spanner using Antigravity CLI in headless mode to perform repeatable, multi-file refactoring. They standardized DAO refactoring around a MutationConverter interface, enabling deterministic code generation, automated unit tests, and CI-driven verification. The headless pipeline ran batch conversions, executed tests, and fed failures back into Antigravity for self-correction, dramatically reducing manual effort and ensuring high data fidelity.

🔁 This article explains how Google Cloud's Database Migration Service (DMS) automates translating SQL Server stored procedures that return multiple result sets into PostgreSQL equivalents. It outlines the decision matrix that maps simple single-result procedures to PROCEDURE objects and complex multi-result or mixed-return routines to FUNCTIONs returning SETOF refcursor, and describes the testing and application integration changes required.

🔍 At Google Cloud Next, Google announced the preview of graph algorithms in Spanner Graph, bringing Google Research’s graph mining capabilities natively to Spanner to deliver faster, cost-effective, large-scale structural analytics. The feature integrates with ISO GQL, runs on dedicated compute to avoid transactional impact, and scales to graphs with tens of billions of edges. Results can be written back to Spanner or exported to Cloud Storage.

🚀 AlloyDB for PostgreSQL introduces a Hot Standby HA architecture that keeps the standby node actively replaying WAL records, reducing failover time and preserving cache warmth. This change eliminates standby database startup delays and minimizes post-failover performance degradation, improving RTO and stabilizing application throughput. Hot Standby is rolling out for PostgreSQL 18 and will reach earlier versions in months, with no extra cost and retention of the 99.99% SLA.

🛠️ At Google I/O 2026, Google announced expanded integration between AI Studio and Google Cloud, allowing new users to deploy up to two full-stack apps on the Starter Tier without a billing account. Developers can now choose between Firestore (non-relational) and Cloud SQL (relational) with Firebase Auth for unified authentication. The AI agent can infer or provision the appropriate database, provision resources, generate schema and code, and deploy apps directly to Cloud Run for rapid prototyping.

🚀Urban Outfitters, Inc. (URBN) recently migrated its IBM Sterling OMS from an 11TB Oracle backend to Google Cloud’s AlloyDB for PostgreSQL to reduce TCO and improve scalability and performance. The migration was executed through close collaboration among URBN, IBM, and Google Cloud, with embedded engineering teams driving planning, testing, and tuning. Outcomes included optimized storage and compute, two read replicas for higher availability, significant performance improvements, and a shift toward open standards to future-proof operations.

🔍 QueryData launches in preview, offering near-100% accuracy translating natural language into database queries across AlloyDB, Cloud SQL (MySQL and PostgreSQL) and Spanner. Built on Google Cloud’s Gemini LLM and augmented by rich database context, it uses schema ontologies, query blueprints and ambiguity detection to generate precise queries. Deterministic security is enforced via Parameterized Secure Views (PSVs), and integration is supported through a unified QueryData API, the MCP Toolbox for Databases, and context-engineering tools including an Evalbench framework.

🔒 ID.me moved its identity platform to Google Cloud to handle massive scale and improve AI-driven fraud detection. The team migrated over 50 TB across 15 database instances and adopted a two-tier architecture using AlloyDB for heavy workloads and Cloud SQL for standard services. This reduced provisioning and maintenance overhead, sped development from weeks to days, and lowered data-team work time by 40% while enabling real-time fraud analysis.

🚀 Manhattan Associates modernized its Manhattan Active SaaS platform by migrating from legacy Oracle and DB2 to Google Cloud databases. Cloud SQL and BigQuery now power core transactions and real-time analytics, enabling over a billion API calls per day with average responses under 150 ms. Containerized microservices on GKE, Pub/Sub streaming, and managed observability deliver automated failover, cross-region recovery, and faster feature delivery. The shift reduced manual scaling and licensing overhead while boosting operational agility and resilience.

🚀 Cloud SQL now supports autoscaling for read pools in MySQL and PostgreSQL, enabling dynamic adjustment of read-replica capacity based on CPU utilization or average database connections. Read pools present a single stable read endpoint for 1–20 nodes, simplifying application configuration during traffic spikes. Autoscaling is generally available with the Enterprise Plus edition and helps reduce operational overhead, optimize costs by scaling in during low demand, and maintain high availability.

🚀 In 2025, Spanner became a first-class database for AI and multi-model workloads, integrating vectors, graphs, and text search to provide richer context for generative applications. Google expanded AI integrations including ML.PREDICT, Vertex RAG Engine support, an Agent Development Kit, and ANN search with ScaNN to speed semantic retrieval. The release also bridged operational and analytical silos with a columnar engine and tighter BigQuery/Iceberg integrations, simplified Cassandra and MySQL migrations, boosted price-performance, and strengthened enterprise safeguards such as drop protection and default backup schedules.

🔌 AlloyDB now offers managed connection pooling for PostgreSQL as a generally available capability, reducing connection overhead and improving scalability. The service-managed pooler listens on port 6432 and reuses backend connections to cut latency and resource churn, while Google Cloud handles setup, configuration, and maintenance. Choose Transaction or Session pooling based on compatibility needs and configure sizes via Console, gcloud, or API. Customers report support for 3x more clients and up to 5x higher transactional throughput.

⚡ Cloud SQL for MySQL Enterprise Plus now includes optimized writes, an automated runtime tuning suite that adjusts MySQL configuration and I/O behavior to reduce write latency and increase throughput. Enabled by default on Enterprise Plus instances, the feature implements adaptive purge, adaptive I/O limits, sharded I/O, faster REDO recovery, and adaptive buffer-pool warmup. Google provides a reproducible sysbench benchmark and reports up to 3x write throughput improvements versus the Enterprise edition, with results varying by machine type and workload.

📍 Oracle Database@AWS is now generally available in US‑East‑2 (Ohio), EU‑Central‑1 (Frankfurt), and AP‑Northeast‑1 (Tokyo). With this expansion, customers can run OCI Exadata Database Service, OCI Autonomous Database on Dedicated Infrastructure, and OCI Autonomous Recovery Service within AWS data centers across five regions. The launch simplifies migration of on‑premises Exadata workloads and helps meet in‑region data residency requirements. To consume the services, customers request a private offer from Oracle via the AWS Marketplace and provision resources through the AWS Management Console.