HiddenMerit Daily · Issue 46

# 📊 HiddenMerit Daily · Issue 46

> Focus on Database Frontiers, Practical Insights for DBAs

> June 18, 2026 ｜ 5 Selected Global Breaking News

## 01｜OceanBase Launches AI Integrated Solution at Financial Expo: Nearly 70% of Trillion‑Yuan Banks Have Adopted It, Ranked #1 in Distributed Market for Three Consecutive Years

On June 16, the 2026 China International Financial Expo opened in Shanghai. Ant Group’s OceanBase launched its “Financial AI Integrated Data Solution,” processing transactions, analytics, and multi‑modal data within a single engine, supporting hybrid search across vectors, text, and more. It also includes built‑in data branching capability, enabling safe AI exploration and iteration in isolated environments.

Key Data:

- Market Position: According to IDC data, OceanBase has been ranked #1 in market share in China’s financial industry distributed database on‑premises deployment market for three consecutive years.

- Bank Coverage: Nearly 70% of banks with trillion‑yuan assets have deployed their core systems on OceanBase.

- Financial Institution Coverage: Has served over 400 financial institutions, covering state‑owned major banks, joint‑stock banks, city commercial banks, and others; 75% of leading insurance institutions, 80% of leading securities firms, and 60% of leading fund institutions have chosen OceanBase for their core business systems.

OceanBase CEO Yang Bing identified two forces reshaping the data foundation: first, the data consumer has changed – Gartner predicts that by 2028, one‑third of enterprise software interactions will be completed by agents; second, the shape of data has changed – over 80% of global data is unstructured. Yang Bing noted: “The first priority for financial institutions to implement AI is to build a multi‑modal converged unified data foundation, preparing for the shift of data from structured to multi‑modal, and customers from humans to agents.”

Benchmark Case: Bank of Beijing has completed the migration of more than 200 systems over three years, covering core business and comprehensive office domains, and has developed a replicable and scalable distributed database migration methodology.

- DBA Perspective: The choice of nearly 70% of trillion‑yuan banks means that large‑scale replacement by domestic distributed databases in financial core scenarios has entered the “batch delivery” phase. Bank of Beijing’s migration of over 200 systems in three years is among the fastest in the financial industry, providing DBAs with a reference cadence and scale benchmark. The AI solution has already been implemented in internal projects such as Alipay AI Pay, confirming that AI databases are no longer just concepts. DBAs should focus on mixed‑load tuning under the “lakehouse‑integrated” architecture and security isolation strategies for data branching capabilities.

- CTO Perspective: OceanBase’s three consecutive years as #1 in the financial distributed market, combined with Bank of Beijing’s full‑business‑domain validation, confirms that the reliability of domestic distributed databases in financial core systems has been fully validated by leading institutions. Gartner’s prediction that one‑third of enterprise software interactions will be completed by agents by 2028 means financial CTOs must proactively build AI‑native data architectures.

## 02｜IDC Analysis: 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 noted that the Chinese database market landscape is further converging, with leading vendors’ market share gradually expanding, mid‑tier vendors focusing on niche scenarios, and a large number of tail‑end vendors being eliminated. “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.” The former relies on large models and generative AI to improve database anomaly detection, auto‑tuning, and other capabilities, while the latter relies on databases to support agent applications and development. Wang Nan emphasised: “We have talked about database autonomy for many years, but 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 that DBAs’ competitiveness in the financial industry will upgrade from “domestic replacement knowledge” to “AI innovation implementation capability.” 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.” The top five vendors holding 90.9% of the market share means the window for technology stack selection is narrowing.

- Investor Perspective: The top five vendors hold 90.9% of the market share, and market elimination is accelerating. 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.

## 03｜MongoDB Enterprise Advanced Exposes High‑Risk Deserialisation Vulnerability: c3p0 Connection Pool Flaw Could Lead to Remote Code Execution

On June 17, security advisory SB2026061728 disclosed a high‑risk vulnerability in MongoDB Enterprise Advanced with IBM Ops‑Manager. The vulnerability stems from insecure deserialisation of maliciously crafted Java serialised objects in the userOverridesAsString property of the c3p0 connection pool, allowing remote attackers to execute arbitrary code.

The vulnerability has a high CVSSv4 rating, with a remote attack vector, requiring low privileges, and a technical impact of full control. The impact is further amplified when embedded JNDI references trigger remote factoryClassLocation dereferencing. An official patch has been released. Users are advised to install updates from the official website.

- DBA Perspective: The attack path of this vulnerability is worth vigilance – the combination of MongoDB Enterprise Edition and IBM Ops‑Manager is common in large financial institution database management scenarios. The c3p0 connection pool vulnerability means attackers could penetrate the database core through a security gap in the management tool layer. DBAs using MongoDB Enterprise Advanced should immediately review c3p0 connection pool configurations and evaluate patch upgrade priorities.

- CTO Perspective: Connection pool components are ubiquitous in the database middleware layer, and the damage radius of their security vulnerabilities is often underestimated. It is recommended to include the database middleware layer (connection pools, drivers, ORM) in regular security scanning scope.

- Investor Perspective: The continued exposure of security vulnerabilities in the database middleware layer will drive sustained growth in enterprise customer demand for database security auditing and middleware scanning.

## 04｜CETC Kingware Releases Three‑Year Evolution Path: From “Steady‑State Foundation” to “Intelligent Computing Engine”

CETC Kingware (formerly Renda Kingware) recently published a technical article titled “Large‑Scale Database Management Systems: 2026 Core Scenario Architecture Evolution Path.” The article judges that over the next three years, the core challenges will shift from “high availability” and “read‑write separation” to “heterogeneous convergence” and “intelligent native.”

Three Major Technology Trends:

1. Deep Coupling of AI Native and Vector Computing: 70% of AI application scenarios will rely on database‑built‑in vector engines rather than external standalone vector databases, eliminating the sync latency between “relational storage” and “vector indexing.”

2. Blurring Boundaries Between Real‑Time Data Warehouses and Transactional Databases: HTAP has become a mainstream requirement; databases must complete complex analyses in real‑time while processing transactions.

3. Cloud‑Native Elasticity Evolving from “Minutes” to “Seconds”: Achieving independent elasticity for compute and storage nodes.

Four Core Scenarios:

- Enterprise‑grade intelligent customer service and knowledge Q&A: Built‑in “knowledge graph + vector search” dual indexing.

- Full‑domain real‑time risk control and anti‑fraud: Millisecond‑level cross‑table joins, real‑time aggregation, and graph algorithm analysis.

- IoT and edge data real‑time analysis: Edge‑side time‑series data processing and cloud‑side global modelling collaboration.

- Dynamic resource scheduling under mixed workloads: Automatically identifies and isolates heavy queries to prevent analytical tasks from affecting transaction processing.

KingbaseES V9 Evolution Solution: Builds a “relational + vector” integrated engine, supporting direct vector similarity search in standard SQL; achieves RPO=0 and RTO<30s high‑availability standards through KES RAC; achieves automatic sharding of massive time‑series data and edge collaboration through KES Sharding.

- DBA Perspective: Kingware’s three‑year evolution path provides DBAs with a clear career coordinate system. The fact that 70% of AI applications will rely on database‑built‑in vector engines means DBAs must master vector index maintenance strategies and mixed‑load tuning. KES RAC’s RPO=0/RTO<30s is a hard indicator of financial‑grade high availability and a core evaluation baseline for DBAs in Xinchuang selection.

- CTO Perspective: Kingware’s definition of the core challenges for the next three years as “heterogeneous convergence” and “intelligent native” is highly consistent with OceanBase’s judgement of “multi‑modal converged unified data foundation.” HTAP becoming a mainstream requirement means CTOs should prioritise databases with “relational + vector” integrated capability in selection.

## 05｜EDB Korea Cases Reveal PostgreSQL Trends: From Oracle Replacement to AI Data Control

According to IT Brief India, the Industrial Bank of Korea (IBK) has migrated 15 core systems to the EDB Postgres AI platform. The bank stated that the previous proprietary database limited customisation under restrictive licensing terms, while the new platform allows SQL, stored procedures, and packages to be converted with limited modifications. Park Cheol‑min, Manager of IBK’s IT Operations Department, said: “Compared to Oracle, we achieved significant licensing cost reductions, which is a core win in communicating IT budgets with leadership. Based on PostgreSQL, EPAS supports AI and vector operation extensions such as pgvector, providing us with a scalable platform that can extend to AI services in the future.”

Another case is South Korea’s Shinhan EZ Insurance, a digital insurer that has migrated its core systems to the EDB platform on the public cloud. The insurer stated that its legacy database licensing model was unsuitable for the elastic scaling required by cloud environments, and maintenance costs were rising.

EDB Chief Product Officer Nancy Hensley noted: “The Agentic era has significantly increased the strategic value of the data layer. When autonomous agents enter production environments, they process enterprise data at extremely high speeds and concurrency, making the data layer the control point of the entire AI strategy.” Research by MIT Technology Review Insights, conducted in partnership with EDB, found that enterprises that place the highest priority on data control and organisational infrastructure achieve 5 times the ROI on AI investments compared to peers.

- DBA Perspective: The EDB cases once again validate PostgreSQL’s core position in the “post‑Oracle era.” IBK’s migration of 15 core systems and substantial licensing cost reduction provide DBAs with a quantitative reference for Oracle migration arguments. Vector extensions such as pgvector make PostgreSQL a natural foundation for AI applications, and DBAs need to accelerate their vector retrieval skills.

- CTO Perspective: The cases of IBK and Shinhan EZ Insurance prove that PostgreSQL has the ability to replace Oracle at scale in financial core systems. EDB’s judgement that “the data layer is the control point of AI strategy in the Agentic era” is highly consistent with the views of OceanBase and Kingware. The data showing 5x ROI on AI investments provides a quantitative basis for CTOs’ decisions on data governance investment.

- Investor Perspective: EDB’s success in the Korean market (IBK’s 15 core system migrations, Shinhan EZ Insurance core on cloud) demonstrates the large‑scale potential of commercial services around the PostgreSQL ecosystem. Vector extensions such as pgvector make PostgreSQL increasingly valuable in the AI era.

## 📚 SQL Little Knowledge Point

This Issue’s Knowledge Point: What is a “Database Agent”?

A database agent is a new paradigm for database operations in the AI era – embedding AI large model capabilities throughout the database development and operations lifecycle, enabling databases to shift from “semi‑autonomous decision‑making” to “proactively solving problems.”

Difference from Traditional Automation:

- Traditional Automation: Based on fixed rules (e.g., “alert if CPU > 90%”), lacking contextual understanding.

- Database Agent: Based on large model reasoning capabilities, it can understand whether “CPU increase is due to a missing index on a certain SQL” or “due to hardware failure,” and thus choose different response strategies.

Application Scenarios for Database Agents:

- Intelligent Diagnosis: Automatically analyses root causes of slow SQL, lock waits, and performance anomalies.

- Intelligent Tuning: Automatically recommends indexes, parameter configurations, and execution plan optimisation.

- Intelligent Operations: Automatically executes fault repair, capacity prediction, and resource scheduling.

- Intelligent Development: Natural language to SQL generation, automatic test data generation.

Significance for DBAs: Database agents will permeate every aspect of development, data governance, and operations. The DBA role will evolve from “manual operations” to “agent policy manager” – defining agent operation boundaries, auditing execution traces, and triggering circuit breakers during anomalies.

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