📊 HiddenMerit Morning Post · Issue 75

Focus on Database Frontiers, Practical Insights for DBAs August 13, 2026 ｜ 5 Selected Global Breaking News

01｜SQLite Exposes “WAL-Reset” 16‑Year Race Condition Vulnerability: Data Corruption Risk in Billions of Embedded Devices

On August 12, the Tailscale team disclosed a race condition vulnerability that had lain dormant in SQLite’s WAL (Write‑Ahead Logging) mode for 16 years, dubbed “WAL-Reset.” SQLite is the world’s most widely deployed embedded database, officially stated to run on billions of devices (WeChat, Alipay, browsers – virtually every app uses it).

Vulnerability Details: In WAL mode, SQLite improves write performance through a “write to scratchpad (WAL file), then periodically reconcile to the master copy (checkpoint)” approach. The vulnerability is triggered when a checkpoint operation coincides with a WAL reset – the checkpoint process calculates pending WAL pages based on old page numbers, but concurrently new pages are appended and the WAL reset changes the page numbering baseline. The checkpoint process believes it has written pages that in fact never made it to the master copy, resulting in permanent data loss. This vulnerability has existed since SQLite introduced WAL mode in 2009 – over 16 years.

Real‑World Impact: Tailscale experienced 19 SQLite database corruption events over six months, each requiring over an hour to recover, with no common pattern found across the 19 incidents. The vulnerability has been fixed in SQLite 3.51.4.

• DBA Perspective: SQLite’s 16‑year vulnerability once again confirms the principle that “old code does not mean bug‑free.” Although embedded databases are typically not directly under a DBA’s purview, SQLite is widely used for local storage in mobile apps, IoT devices, and desktop software. DBAs working with SQLite‑based applications should work with development teams to check the SQLite version used by applications, upgrade to 3.51.4 or higher, and monitor WAL mode stability configurations.

02｜Huawei Cloud GaussDB Releases MySQL‑Compatible Edition, Accelerating the “Last Mile” of Xinchuang Migration

In mid‑August, Huawei Cloud officially released GaussDB MySQL Compatible Edition, designed to help existing MySQL users complete a smooth migration to domestic databases at minimal cost. The new version achieves deep compatibility with the MySQL ecosystem in SQL syntax, data types, system functions, views, and stored procedures.

Previously, the open‑source community MySQL 8.0 officially reached EOL on April 30, 2026, leaving numerous MySQL users facing technology stack decisions. Huawei Cloud’s release precisely targets this window – GaussDB MySQL Compatible Edition offers a “near‑zero‑modification” migration experience, lowering the risk and barrier for enterprises replacing existing MySQL deployments. Kingware has previously proposed new selection standards moving “from scale ranking to value restructuring” – technical foundation solidity, ecosystem synergy depth, and industry value thickness are core dimensions for measuring the long‑term competitiveness of domestic databases. Huawei Cloud concurrently released the “GaussDB Financial Core System Migration White Paper,” disclosing migration validation data from GaussDB deployments in multiple large banks’ core systems.

• DBA Perspective: Huawei Cloud GaussDB MySQL Compatible Edition targets the market window opened by MySQL 8.0’s EOL. For DBAs still deliberating over “MySQL replacement paths,” this solution provides a “MySQL‑like” experience for domestic database replacement without changing development habits. DBAs should pay attention to its real‑world compatibility performance in complex stored procedures, triggers, and distributed transactions.

03｜Ant Lingguang’s 30 Million Flash Application Data Architecture Revealed: OceanBase Logical Table Solution Solves AI‑Generated Application Data Management Challenges

Recently, OceanBase publicly disclosed the data architecture practices supporting Ant Group’s Lingguang AI‑generated applications for the first time. Within just months of launch, the Lingguang platform has hosted approximately 30 million “flash applications” – users can generate a ledger, registration page, check‑in tool, or other applications with a single sentence, completed in 30 seconds.

Core Challenge: Each continuously running application requires independent database capabilities – defining table structures, reading/writing data, and executing SQL queries. Traditional approaches fail on both paths: sharing a single JSON large table across all applications renders SQL aggregation capabilities ineffective and extremely inefficient; creating separate physical tables for each application places enormous pressure on the control plane and storage burden.

OceanBase Solution: A “logical table” approach for massive AI applications – each application has an independent table structure at the operational level, supporting table creation, data insertion, and standard SQL queries, with an experience identical to a standalone database. Under the hood, 30 million logical tables share the same physical storage pool, with the SQL engine mapping JSON data to relational tables based on each application’s independent schema definition to complete precise calculations.

Ant Group Chief Architect Huang Ting likened it to: “It’s like 30 million people living in one building – you can’t build a separate house for each person, there’s not enough land; but you also can’t put everyone in a single large common room. Each company has its own office, sharing utilities and property management.”

• DBA Perspective: Lingguang’s practice confirms a larger trend – AI‑generated applications are changing the service objects of databases. Databases are no longer facing “a single increasingly large database,” but tens of millions of independent, dynamically generated data spaces. OceanBase’s logical table solution provides DBAs with an architectural paradigm for handling “massive small data spaces” in AI application scenarios. DBAs should pay attention to the operational practices of logical table solutions in multi‑tenant isolation, resource scheduling, and migration from single‑application to independent physical tables.

04｜MongoDB, Budibase, and Other Database Products Expose High‑Risk Vulnerabilities: CVSS Scores Up to 9.6

Multiple database‑related products exposed high‑risk vulnerabilities this week:

MongoDB Multiple Vulnerabilities: Hong Kong HKCERT issued a security advisory indicating that MongoDB contains multiple vulnerabilities. Remote attackers could exploit some vulnerabilities to trigger security restriction bypass, remote code execution, data manipulation, denial of service, privilege escalation, and sensitive information disclosure. Affected products include MongoDB Server and others.

Budibase SQL Injection (CVE-2026-73300, CVSS 9.6) : The open‑source low‑code platform Budibase has multipleStatements: true enabled in its MySQL integration component configuration, allowing attackers to inject malicious SQL commands through user input fields, leading to full database compromise. Fixed in version 3.40.0.

IBM Db2 Improper Authorisation (CVE-2026-16480) : Affects Db2 versions 11.5.0 through 11.5.9 and 12.1.0 through 12.1.5. Unprivileged users can bypass permission checks to modify database catalog data. Upgrading to the latest version is recommended.

• DBA Perspective: Budibase’s SQL injection vulnerability (CVSS 9.6) reminds DBAs that low‑code platform data connector configurations often become security blind spots – the seemingly convenient multipleStatements: true configuration effectively opens the door to SQL injection. DBAs using Budibase should immediately upgrade to 3.40.0 and review all MySQL data source connection configurations.

05｜Kingware Redefines Domestic Database Ranking Standards: From “Scale Ranking” to “Value Restructuring”

In August, CETC Kingware published an in‑depth article titled “How Kingware Defines New Standards for Domestic Database Ranking: From ‘Scale Ranking’ to ‘Value Restructuring’,” systematically阐述 the shift in domestic database industry ranking logic from “scale competition” to “value restructuring.”

Core Judgment: As the domestic database industry moves from “existence” to “excellence” in the deep water zone, the traditional linear thinking of defining “rankings” by installation volume, number of covered industries, or funding scale is no longer applicable. The true differentiator is no longer “who has the longer list,” but “whose technical foundation is harder, whose ecosystem moat is deeper, and who can bear the ‘weight of national critical information infrastructure.’”

Three New Dimensions:

1. Technical Foundation Solidity: Full self‑development of the core engine. KingbaseES’s core engine is completely self‑developed, not based on open‑source secondary development, enabling independent control of technology evolution direction and rapid response to national security and industry development needs.
2. Ecosystem Synergy Depth: Breadth and depth of “full‑stack mutual certification.” Kingware has completed comprehensive mutual certification with mainstream domestic chips, operating systems, and middleware, building an open and collaborative domestic application environment.
3. Industry Value Thickness: “Deep adaptation” to critical scenarios and full‑lifecycle service capability.

• DBA Perspective: Kingware’s “three‑dimensional evaluation framework” provides DBAs with a selection assessment framework that goes beyond “benchmarks” and “installation volume” in Xinchuang selection. Technical foundation solidity, ecosystem synergy depth, and industry value thickness – these three dimensions better reflect a domestic database’s long‑term competitiveness than simply focusing on market share. DBAs can systematically evaluate different products based on these three dimensions during selection.

📚 SQL Little Knowledge Point

This Issue’s Knowledge Point: What is SQLite’s WAL Mode and the “WAL-Reset” Race Condition Vulnerability?

SQLite’s WAL (Write‑Ahead Logging) mode is a mechanism for improving write performance – data is first written to the WAL file (scratchpad), then through a checkpoint operation, it is unified into the main database file (master copy).

Normal WAL Mode Workflow:

1. New data is appended to the end of the WAL file
2. The checkpoint process copies WAL pages back to the main database file
3. After completion, the WAL file is reset

“WAL-Reset” Race Condition Trigger Conditions:

• A checkpoint process is executing
• Another process simultaneously appends new pages to the WAL
• The WAL reset operation changes the page numbering baseline
• The checkpoint process continues writing based on old page numbers, causing some pages to be missed

Impact: Permanent data loss (rather than corruption that renders data unreadable). Over 16 years, any application using SQLite in WAL mode could have been affected – the trigger conditions are extremely rare but exist. Fixed in version 3.51.4.

HiddenMerit Team Production Slogan: 绩优隐于内，金石启新程 | Hidden deep. Merit bold. Forge ahead.