Why Relying Solely on Large Language Models for Database Migration Is Risky – Insights from SQLShift

1. Database Migration Challenges

When enterprises upgrade IT architecture, move to the cloud, or replace foreign‑made databases, migrating the database becomes an unavoidable core task. In practice, difficulties concentrate on two layers:

Table‑object layer : data‑type mapping, length adjustment, default‑value correction—issues largely solved by existing tools such as OMA, OMS, DTS.

Non‑table‑object layer : stored procedures, functions, triggers, packages, views—these encapsulate real business logic and are the most error‑prone during migration.

Even when a migration path appears compatible, hidden dialect differences often surface, for example:

Oracle → OceanBase (Oracle mode) – SYSDATE() works in Oracle but fails in OceanBase.

Dynamic SQL – the USING clause behaves more strictly on the target side.

Full‑width symbols, implicit type conversion, lax syntax that break on the target.

Domestic databases claim “full compatibility” but omit critical behavioral differences.

These hidden differences require DBAs to read code line‑by‑line, repeatedly debug, and manually rewrite, leading to high cost and risk.

2. Why Directly Using Large Models Is Unreliable

In early research of SQLShift, the team deliberately avoided the naive approach of “just apply AI”. Instead, they posed a concrete question:

Do large models truly possess reliable database syntax and version‑awareness?

Actual Evaluation Results (Multiple Model Tests)

Comparative testing of mainstream large models—including GPT, Claude, Gemini, DeepSeek, Qwen, and Kimi—revealed a consistent conclusion: even top‑ranked models frequently hallucinate on database version details and subtle syntax.

Key takeaways:

Relying solely on the model’s internal knowledge to answer compatibility questions is insufficiently reliable.

Combining web‑search results improves accuracy but still cannot guarantee consistent correctness.

Typical Test Questions

From which version does OceanBase (MySQL mode) support temporary tables?

Does GaussDB v2.0_3.x centralized edition support ON COMMIT DROP?

Does GaussDB v2.0_3.x centralized edition have a built‑in UUID generation function?

DBAs consider these factual and verifiable, yet large models often produce:

Incorrect version numbers

Non‑existent features

Behavior descriptions that conflict with official documentation

If a model freely generates migration SQL without constraints, the risk is uncontrollable.

3. Core Idea of SQLShift

SQLShift is positioned as an intelligent dialect‑conversion tool that focuses on non‑table objects. Its “intelligence” does not rely on a single LLM; instead, it builds an engineering‑level AI migration system.

4. How SQLShift Makes AI Controllable

1️⃣ Domain‑Specific Fine‑Tuning, Not General QA

Leverages official database documentation, syntax specifications, and best‑practice guides.

Incorporates real migration cases—stored procedures and functions—from actual projects.

Builds high‑quality training and validation sets through DBA‑verified manual checks.

Goal: let the model learn the “real world of databases” instead of internet impressions.

2️⃣ Multi‑Stage Verification, Not One‑Shot Generation

Syntax check : validates generated statements with the target database parser.

Semantic check (exploratory) : a second model reviews high‑risk logic.

Historical case comparison : matches against known compatible or incompatible patterns.

This prevents “looks correct but is actually wrong” hidden risks.

3️⃣ Human‑AI Collaboration, Not a Black‑Box

SQLShift explicitly marks risky code sections.

Provides conversion rationale and modification suggestions.

DBAs quickly confirm, and the tool automatically adjusts the migration scripts.

Each user feedback loop feeds back into model and rule optimization.

4️⃣ Modular Decomposition for Large Objects

The model first splits massive stored procedures (often thousands of lines) into logical modules.

Each sub‑module is converted independently.

The converted pieces are re‑assembled on the target side, overcoming context‑length limits and improving accuracy.

5. Value of SQLShift

Exposes hidden dialect differences that would otherwise remain invisible.

Transforms guess‑based migration into an evidence‑based, verifiable engineering process.

Significantly reduces repetitive labor and pitfall costs for DBAs handling non‑table objects.

DBAs can spend time on judgment and decision‑making instead of endless trial‑and‑error.

Conclusion

AI is reshaping database migration, but truly usable AI must be constrained and validated by an engineering system. SQLShift adopts a slower, more stable path: it does not chase “looks smart” but ensures the generated SQL runs, is usable, and can be delivered in real database environments.

References

[1] SQLShift : https://sqlshift.cn/