📊 HiddenMerit Daily · Issue 1

Focus on Database Frontiers, Practical Insights for DBAs April 27, 2026 ｜ 5 Selected Global Hot Topics

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01｜Oracle 23ai Introduces “Vector+Relational” Hybrid Query, AI Apps No Longer Need ETL

At its London user conference this month, Oracle released a major update to AI Database, embedding vector search, no-code Agent Factory, unified memory inference engine and other capabilities directly into the database kernel.

Key Updates：

• Vector Database Capabilities：Vector indexing for unstructured data (text, audio), can be queried jointly with transactional data (limited preview)

• Private Agent Factory：No-code AI Agent builder, with built-in knowledge base Agent and data analysis Agent

• Unified Memory Core：Low-latency inference engine, unifying vector/JSON/graph/relational data storage

• Deep Data Security：Row/column + Agent level data access control

DBA Perspective Previously, AI applications required a separate vector database + ETL pipeline. Now Oracle sinks AI capabilities into the kernel, DBAs only need to manage one database to support transactions + analytics + RAG. The Private Agent Factory deserves special attention — DBAs need to relearn “database + Agent” permission models to prevent Agents from accessing sensitive data beyond their authority. Impact on Teams：If upgrading to 23ai, start with a “knowledge base Agent” pilot to evaluate the real cost of vector indexing on storage and query latency.

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02｜Provincial Government Cloud Refactors 12 Core Systems: QPS ↑170%, Storage Compression 1:4.5

A provincial government cloud platform completed a full domestic-technology refactoring of 12 livelihood systems (social security, healthcare, taxation). The legacy system suffered from “data silos” and 24-hour latency.

Solution & Outcomes：

Quantitative Results：

• Core API QPS：20k → 54k (↑170%)

• Storage compression ratio 1:4.5, saved ¥12M in hardware costs

• Annual savings on license fees and maintenance approx. ¥35M

DBA Perspective This is a benchmark case of HTAP integration in government core scenarios. The traditional “offline data warehouse” paradigm is broken, and real-time materialized views become key. DBAs need to focus on two points：

1. Maintenance cost of materialized views：How to balance real-time requirements and write performance in high-frequency update scenarios?

2. Dual-track migration approach：Highly valuable for similar domestic-tech refactoring projects. DBAs should verify the data validation tools and rollback plans for “shadow read + dual-write”. Takeaway：Domestic databases can now support 50k-level QPS + 99.999% availability. Small and medium teams can learn from this architectural mindset (HTAP + containerization) without waiting for large-scale refactoring.

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03｜MySQL 9.0 (Innovation Release)： Supports Vector Data Type + JavaScript Stored Programs

MySQL 9.0, released as an Innovation release (non-LTS), brings several new features for AI and developer experience.

Key New Features：

• Vector Data Type：Supports multi-dimensional vector storage and approximate search. Example：

  sql

  CREATE TABLE vectors (id INT
  , feature_vector VECTOR(
  256
  )
  )
  ;
  INSERT INTO vectors VALUES (
  1
  , VECTOR(
  1.0
  , 0.0
  , 0.5
  )
  )
  ;

• JavaScript Stored Programs (Enterprise Edition)：Write stored procedures/functions in JavaScript, which can call SQL internally.

• Security Removal：Deprecates SHA-1 and mysql_native_password, enforcing upgrade to caching_sha2_password.

DBA Perspective

• The Vector type is MySQL’s direct response to AI scenarios, but the current version only supports storage and basic operations — no efficient similarity search index (e.g., HNSW) yet. For production environments requiring high-performance vector search, you still need specialized vector databases or Elasticsearch.

• JavaScript stored programs：Provide a more flexible programming paradigm for complex business logic, but note this is an Enterprise Edition feature. DBAs need to assess its impact on database CPU and execution time, and define timeout and resource control policies.

• Security upgrade：Must check compatibility of old clients/drivers with caching_sha2_password. Recommendation：Continue using MySQL 8.4 LTS for production, and explore 9.0’s new features in development/test environments.

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04｜Google Cloud Launches AlloyDB AI： Built-in pgvector, 10x Performance Boost

In mid-April, Google Cloud officially released AlloyDB AI, an extension of AlloyDB for PostgreSQL that embeds pgvector natively and optimizes vector query performance.

Key Highlights：

• Vertical integration：AlloyDB’s columnar engine can be directly used for vector similarity search, claiming 10x speedup over standard PostgreSQL.

• Unified SQL interface：Simultaneously retrieve business data (e.g., product info) and vector embeddings (e.g., image features).

• Automatic index management：Automatically selects and maintains IVF, HNSW indexes.

DBA Perspective AlloyDB AI is an important step forward for cloud-native database vectorization. Compared to self-managed PostgreSQL + pgvector, AlloyDB solves two pain points：

1. Performance：Accelerates vector search via columnar engine, avoiding full table scans.

2. Operations：Automatic index management reduces DBA manual tuning burden. Note：Currently only available in AlloyDB’s PostgreSQL-compatible version, not standard RDS PostgreSQL. If your team is heavily invested in Google Cloud, consider migrating image search, recommendation system workloads to AlloyDB. Comparison：Oracle takes the “in-database + Agent factory” approach, while AlloyDB strengthens the open-source ecosystem (pgvector) — two different strategies.

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05｜Neon Open-Sources Serverless PostgreSQL： Introduces Branching, Instant Read-Write Separation

Neon, a cloud-native PostgreSQL built with Rust + storage-compute separation, recently open-sourced and introduced database branching, allowing users to create database branches like Git — zero-copy, copy-on-write.

Core Capabilities：

• Instant branches：Create a branch of any data state in seconds for development, testing, analysis.

• Read/write branches：Each branch can independently write without affecting the main branch.

• Auto-suspend：Branches with no active connections automatically sleep, reducing compute costs.

DBA Perspective Neon’s branching feature disrupts the traditional “clone-restore” workflow. Previously, to give developers a “writable copy”, DBAs often had to：clone the entire instance → restore from backup → reconfigure permissions — taking hours. Now Neon branches can be created in seconds, and copy-on-write greatly saves storage. Use Cases：

• Quickly reproduce production issues：Create a writable branch alongside the production branch, reproduce a bug, then discard it.

• CI/CD integration：Automatically create a database branch for each PR to run tests. Note：Neon is currently more suitable for stateless scenarios or serverless applications; traditional enterprise features (e.g., fine-grained auditing, backup policies) still need validation. Takeaway for DBAs：Serverless + branching capabilities are reshaping database development experience and may become standard features of cloud databases in the future.

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Summary of This Issue

📌 Editor’s Note AI is moving from “external service” to “database kernel”. HTAP has proven its value in government core systems. The open-source ecosystem is also evolving rapidly. As DBAs, we should both focus on production stability and proactively embrace new capabilities — such as Agent permission management, serverless branching workflows.

Welcome to leave comments：Which of the above topics inspires you the most in your daily work? See you tomorrow!