📊 HiddenMerit Daily · Issue 39 Focus on Database Frontiers, Practical Insights for DBAs June 9, 2026 ｜ 5 Selected Global Breaking News 01｜MongoDB 8.3 “AI‑Native” Launches Domestically, Alibaba Cloud Fires First Shot in AI Database Race In early June, Alibaba Cloud became the first in China to launch MongoDB 8.3. MongoDB 8.3 introduces an “AI‑Native” design philosophy – not “add‑on” AI support, but deep integration of three major capabilities – vector search, auto‑embedding, and intelligent O&M – directly into the database engine, achieving a trinity of “native search, native vectorisation, native O&M.” The three native AI capabilities in detail: Native Search: Vector and full‑text search are built into the engine layer; a single pipeline completes hybrid search combining “vector + full‑text + scalar” (with $rankFusion stage using the RRF algorithm for score fusion), eliminating the need for applications to switch between multiple systems. Native Vectorisation: Write‑time auto‑embedding, transparent to applications, zero sync overhead; the engine layer automatically listens for data changes via Change Stream, calls models to generate vectors, writes back to documents, and triggers index updates. Native O&M: Natural language management, AI‑assisted slow query analysis, index recommendations, and parameter tuning, covering all versions; this capability covers all versions […]

📊 HiddenMerit Daily · Issue 38 Focus on Database Frontiers, Practical Insights for DBAs June 8, 2026 ｜ 5 Selected Global Breaking News 01｜Alibaba Cloud Launches MongoDB 8.3 Domestically: Three “Native” Capabilities for Vector Search, Auto‑Embedding, and Intelligent O&M On June 1, Alibaba Cloud became the first in China to launch MongoDB 8.3. This version deeply integrates three major AI capabilities – vector search, auto‑embedding, and intelligent O&M – directly into the database engine, moving away from “add‑on” AI solutions and achieving an AI‑Native design philosophy of “no data movement, no capability assembly, simplified architecture.” Three Native AI Capabilities: Native Search: Vector and full‑text search are built into the engine layer; a single pipeline completes hybrid search combining “vector + full‑text + scalar,” eliminating the need for applications to switch between multiple systems. Native Vectorisation: Write‑time auto‑embedding, transparent to applications, zero sync overhead; the entire flow from data write to vector generation is completed within the same database. Native O&M: Natural language management; AI‑assisted slow query analysis, index recommendations, and parameter tuning, covering all versions. MongoDB 8.3 also delivers impressive OLTP performance: compared to version 8.0, write throughput increases by 35%, read throughput by 45%, and ACID transaction throughput by […]

📊 HiddenMerit Daily · Issue 37 Focus on Database Frontiers, Practical Insights for DBAs June 5, 2026 ｜ 5 Selected Global Breaking News 01｜Microsoft Azure HorizonDB Launches Public Preview: A PostgreSQL Rebuilt for AI Agents, Storage Engine Rewritten in Rust On June 2, at the Build 2026 conference keynote, Microsoft officially announced that Azure HorizonDB has entered public preview. This is a PostgreSQL‑compatible cloud database rebuilt from the ground up for agentic AI workloads, now available in 5 Azure regions globally. HorizonDB is not a simple upgrade to Azure Database for PostgreSQL, but a completely new architectural design. Key technical points: “Database‑as‑a‑log” architecture: Transactions are committed directly to a shared WAL (Write‑Ahead Log) storage, achieving sub‑millisecond multi‑region commit latency and eliminating the multi‑step coordination overhead of traditional PostgreSQL. Rust storage engine: Microsoft explicitly chose Rust over C/C++ to eliminate memory safety vulnerabilities such as buffer overflows at the language level. This design is highly significant for unattended AI agent high‑frequency query scenarios. Storage‑compute separation: Storage automatically scales to 128TB, compute can scale to 3072 vCores, and supports up to 15 read replicas. DiskANN vector search: Natively embedded vector search capability, supporting vectors of up to 16,000 dimensions (far exceeding pgvector’s […]

📊 HiddenMerit Daily · Issue 36 Focus on Database Frontiers, Practical Insights for DBAs June 4, 2026 ｜ 5 Selected Global Breaking News 01｜Microsoft Azure HorizonDB for PostgreSQL Launches Public Preview: Cloud Database Designed for AI Workloads On June 2, Microsoft officially announced the public preview of Azure HorizonDB for PostgreSQL, a cloud‑native PostgreSQL database built specifically for AI applications, deeply integrating native AI capabilities such as vector search, large model inference, and intelligent index optimisation. HorizonDB is built on the PostgreSQL kernel and is specially optimised for emerging workloads such as generative AI, retrieval‑augmented generation (RAG), and AI agents. Core capabilities include: Native Vector Search: Kernel‑level support for vector indexing, eliminating the need for additional vector database extensions, with significantly better vector query performance than the community pgvector extension. AI Inference Integration: Built‑in AI functions allow direct invocation of Azure OpenAI services within SQL for embedding generation and text inference. Intelligent Auto‑Tuning: Workload‑aware optimisation based on machine learning, automatically adjusting index strategies and query execution plans. Microsoft stated that HorizonDB aims to solve the “data fragmentation” pain point in AI application development – developers no longer need to move data between multiple systems (relational database + vector database + […]