📊 HiddenMerit Daily · Issue 47

Focus on Database Frontiers, Practical Insights for DBAs June 22, 2026 ｜ 5 Selected Global Breaking News

01｜Alibaba Cloud PolarDB X Cluster Vector Database Officially Released, Accelerating DB4AI

On June 20, Alibaba Cloud officially released PolarDB X Cluster Vector Database, providing a vector retrieval foundation for enterprise‑grade AI applications with three core advantages: extreme performance, high cost‑effectiveness, and ease of use. Built on PolarDB’s high‑availability architecture, the product supports vector retrieval of up to 16,384 dimensions and can manage billions of vector records in a single table.

In terms of performance, PolarDB X Cluster achieves double the queries per second (QPS) compared to open‑source solutions, while also supporting PVL2 (product quantisation) auxiliary indexing and inverted indexing to further optimise query efficiency for large‑scale vector retrieval. In terms of stability, the product provides financial‑grade high availability assurance, ensuring the continuity and data security of AI applications.

Core Application Scenarios: PolarDB X Cluster is primarily designed for AI scenarios such as RAG (retrieval‑augmented generation), multi‑modal search, and personalised recommendations. By natively integrating vector retrieval capabilities, developers can perform hybrid queries on structured and unstructured data within a single platform, without the need for complex data synchronisation and architecture maintenance between business databases and vector databases.

• DBA Perspective: The release of PolarDB X Cluster marks another important milestone for Alibaba Cloud in the “DB4AI” direction. Previously, AI capabilities in cloud databases were mostly provided through kernel extensions (such as pgvector), but PolarDB X Cluster, as a standalone vector database product, is more targeted in performance optimisation and large‑scale deployment. The support for 16,384‑dimension vectors – far exceeding the limits of most vector database products on the market – means DBAs can support larger‑scale AI applications within a single product. For DBAs planning AI data architectures, PolarDB X Cluster provides a new option for integrating “business database + vector database” into one.

• CTO Perspective: Alibaba Cloud’s productisation of a standalone vector database reflects the explosive growth of demand for vector retrieval in AI applications. The “financial‑grade high availability” positioning of PolarDB X Cluster synergises with Alibaba Cloud’s existing advantages in the financial industry. For CTOs already deeply embedded in the Alibaba Cloud ecosystem, this product can reduce the integration complexity of AI application data architectures.

• Investor Perspective: The vector database track is rapidly differentiating. Public cloud vendors, by embedding vector capabilities into their existing database ecosystems, are squeezing the market space of standalone vector database vendors. The release of PolarDB X Cluster further strengthens Alibaba Cloud’s product matrix in the AI infrastructure layer, and its commercialisation progress in AI scenarios is worth watching.

02｜YashanDB’s Multi‑Modal Converged Architecture Fully Unveiled, Driving Data Management Upgrades in the AI Era

On June 19, YashanDB publicly unveiled its multi‑modal converged architecture for the AI data deluge. The architecture manages structured data, vector data, and JSON data uniformly within a single database, supporting vector storage of up to 16,384 dimensions, and enables cross‑modal hybrid queries through a unified SQL interface.

According to disclosures, YashanDB’s kernel code is fully self‑developed, and both its centralised and distributed forms have passed the joint security and reliability assessment of the China Information Security Evaluation Center and the National Secrecy Science and Technology Evaluation Center. The China Electronics Society’s technical appraisal concluded that its core technologies – shared clusters, high availability, and multi‑modal convergence – have reached world‑leading levels. In terms of Oracle and PostgreSQL compatibility, users’ existing applications require only minimal code modifications to complete a smooth migration.

In terms of security, YashanDB has passed MLPS Level 4 certification and EAL4+ security certification, with dynamic fine‑grained permission control capabilities down to the row, column, and field level. Its innovatively introduced data sandbox technology supports cloning fully isolated experimental environments from production baselines, providing a secure “trial‑and‑error space” for AI applications. YashanDB has now been deployed across 24 provinces, municipalities, and autonomous regions, covering 11 key industries, with large‑scale implementations in finance, government, transportation, energy, and telecommunications.

• DBA Perspective: YashanDB’s multi‑modal converged architecture echoes the technical approaches of OceanBase’s “lakehouse integration” and Kingware’s “relational + vector integrated engine” reported in previous issues. YashanDB’s highlights are its “fully self‑developed” kernel and “data sandbox” capability – the latter addresses the pain point of AI applications being “afraid to touch production data” during data exploration. For DBAs, sandbox technology means they can prepare independent data experimentation environments for AI applications without impacting production.

• CTO Perspective: YashanDB receiving “world‑leading” appraisal from the China Electronics Society is a sign that domestic databases are gaining authoritative recognition for their core technology capabilities. The innovation of data sandbox technology in security governance provides CTOs with new ideas for planning AI data governance systems – letting AI “trial and error” in isolated environments rather than “taking risks” on production data.

• Investor Perspective: YashanDB’s fully self‑developed kernel and dual forms (centralised + distributed) having passed security and reliability assessments qualify it for participation in national‑level Xinchuang projects. The data sandbox technology is a differentiated highlight in the AI security direction.

03｜Databricks and Snowflake Continue to Lead DB-Engines Growth Rankings, PostgreSQL Remains #1

According to the latest DB-Engines H1 2026 database technology growth ranking, PostgreSQL topped the list with a score increase of +21.97, becoming the fastest‑growing database technology in the first half of the year. Databricks (+16.04), MongoDB (+11.24), Microsoft Fabric (+10.94), and Snowflake (+6.78) ranked second through fifth, respectively.

The DB-Engines ranking is updated monthly and tracks over 400 database technologies, based on multiple indicators including search engine popularity, professional profiles, job postings, social media activity, and technical discussions.

Redgate advocate Kellyn Gorman commented: “PostgreSQL’s continued growth is not surprising; it’s a testament to the progress of open‑source technology. The performance of Databricks, Fabric, and Snowflake tells us an important story: organisations are no longer just thinking about databases – they are thinking about the entire data and analytics ecosystem. As AI projects mature, platforms that can integrate data management, analytics, and governance will become the defining force for the remainder of the year.”

• DBA Perspective: PostgreSQL’s leadership in the H1 growth ranking confirms the strategic value of PostgreSQL skills in DBAs’ skill reserves. The rise of cloud data platforms such as Databricks and Snowflake indicates that DBAs need to include “cloud‑native data platforms” in their skill tree expansion. The presence of five different technology paths (open‑source relational, lakehouse, document, unified analytics platform, cloud data warehouse) in the top five indicates that the market is in a diversified development stage.

• CTO Perspective: PostgreSQL’s continued growth and the strong performance of Databricks and Snowflake reflect growing enterprise demand for unified data platforms. Gorman’s assessment – “as AI projects mature, platforms that can integrate data management, analytics, and governance will win” – provides CTOs with directional guidance for data platform selection.

• Investor Perspective: The presence of Databricks and Snowflake in the ranking echoes their high valuations in the capital market. PostgreSQL’s continued growth provides a broad market space for commercial service companies in the PG ecosystem.

04｜IDC Analysis: 2026 Chinese Database Market Enters the “Second Half,” AI Innovation Becomes the Main Competitive Battlefield

According to IDC data, China’s financial industry distributed transaction database market reached $370 million in 2025, up 32.1% year‑on‑year. The on‑premises deployment sub‑market was $280 million, with growth of 37.6%. OceanBase, GoldenDB, Tencent Cloud, Huawei Cloud, and Alibaba Cloud ranked in the top five, together holding 90.9% of the market share.

IDC China Research Manager Wang Nan stated during the 2026 China International Financial Expo: “The market is no longer about simple domestic replacement dividends, but has entered a compound competition of capability, ecosystem, and scenarios. In 2026, the financial database market, especially the domestic database replacement track, has essentially entered the ‘second half.’ Future database competition will be based on AI innovation.”

Wang Nan further explained that databases are evolving from passive storage to active understanding, with semantic understanding, similarity reasoning, and cross‑modal correlation becoming core capabilities. AI capabilities are deeply integrated into the kernel, forming a dual‑drive of “AI for DB” and “DB for AI.” Wang Nan emphasised: “Database agents will permeate every aspect of database development, data governance, and operations, enabling databases to shift from semi‑autonomous decision‑making to proactively solving problems.”

• DBA Perspective: IDC’s “second half” judgement means DBAs’ competitiveness in the financial industry will upgrade from “domestic replacement knowledge” to “AI innovation implementation capability.” The top five vendors holding 90.9% of the market share means the window for technology stack selection is narrowing. Database agents will enable databases to shift from semi‑autonomous decision‑making to proactively solving problems, and the DBA role is evolving from “manual operations” to “agent policy manager.”

• CTO Perspective: The top five vendors hold 90.9% of the market share, and market concentration is increasing. The domestic database replacement track entering the “second half” in 2026 means CTOs should focus more on database vendors’ AI innovation capabilities rather than simple feature benchmarking when making selections.

• Investor Perspective: Market elimination is accelerating, and the survival space for tail‑end vendors is shrinking rapidly. Investment should focus on leading vendors and mid‑tier vendors in niche scenarios. The shift from “domestic replacement dividends” to “AI innovation competition” means valuation logic must be upgraded accordingly.

05｜Industry Trend: Data Moves from “Back‑Office Warehouse” to “Core AI Infrastructure”

In mid‑June, multiple industry media outlets reported on the profound transformation of the database industry. Reports noted that when enterprises no longer just ask “can we store the data,” but “can large models directly use my data to answer questions,” databases – seemingly mundane foundational software – are once again riding the wind.

Core Judgements:

• Agents are the New Users: Wang Yicheng, Vice President of Tencent Cloud, stated that the industry is redesigning database product capability systems with agents as the new users, and the database industry is entering the AI 3.0 era.
• Profound Change in the Database Mission: In the past, databases served programmers, BI systems, and deterministic business processes. In the future, a more important task is to enable data to be understood and used by hundreds or thousands of agents.
• Future Software is “Agent + Database”: Zhou Aoying, Fellow of the CCF and Director of the Database Committee, pointed out that future software will be “agent + database,” and databases should become a reliable, accessible, and efficient infrastructure like the power grid.

Market Data: According to the “AI‑Native Database Development Trends White Paper” jointly released by IDC and Mobile Cloud, the Chinese database market is projected to reach $10.6 billion in 2026, with the domestic replacement rate exceeding 70%, shifting from “Xinchuang replacement” to “AI‑driven incremental support.”

• DBA Perspective: Industry reports provide DBAs with a macro framework for understanding change. The judgement that “agents are the new users” is highly consistent with our observations across multiple issues. The primary access subjects of databases in the future will shift from “humans” to “AI agents,” requiring DBAs to redesign permission governance systems, audit tracking mechanisms, and resource isolation strategies. “Databases should become infrastructure like the power grid” means the DBA role will evolve from “administrator” to “infrastructure architect.”

• CTO Perspective: The IDC forecast of a $10.6 billion market and a domestic replacement rate exceeding 70% provides CTOs with a macro‑quantitative basis for formulating medium‑ to long‑term Xinchuang plans. CCF Fellow Zhou Aoying’s judgement that “future software is agent + database” is worth deep consideration by technology decision‑makers.

• Investor Perspective: The shift of databases from “Xinchuang replacement” to “AI‑driven incremental support” means that valuation logic is upgrading from “policy‑driven” to “technology‑driven.” The technology path choices of leading vendors in this AI transformation will determine their valuation differentiation in the capital market.

📚 SQL Little Knowledge Point

This Issue’s Knowledge Point: What is the “Dual‑Drive” of Database Agents?

The “AI for DB” and “DB for AI” dual‑drive proposed by IDC is a core framework for understanding database AI‑ification.

AI for DB (AI Empowers Databases) : Applying AI large model capabilities to database operations, development, and optimisation.

• Anomaly Detection: AI automatically identifies database performance anomalies and potential faults.
• Auto‑Tuning: AI automatically recommends indexes, parameter configurations, and execution plan optimisation.
• Intelligent Diagnosis: AI analyses root causes of slow SQL and lock waits and provides repair recommendations.
• Intelligent Operations: Database agents permeate the entire development, governance, and operations lifecycle.

DB for AI (Databases Support AI) : Databases provide data storage, management, and retrieval capabilities for AI applications.

• Vector Retrieval: Databases natively support vector data storage and similarity search.
• Multi‑Modal Management: Databases uniformly manage structured, semi‑structured, and unstructured data.
• Real‑Time Data Services: Provide low‑latency, high‑concurrency real‑time data access for AI applications.

Value Cycle: AI for DB makes databases smarter and easier to operate, lowering the barrier to database usage. DB for AI enables AI applications to access and manage data more efficiently, accelerating AI implementation. The two form a positive feedback loop – the smarter the database, the better it supports AI applications; the more widespread AI applications become, the more they drive database intelligence upgrades.

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