AI-powered database migration risk analyzer. Know exactly what will break, how long it'll take, and whether you can safely roll back - before you run a single query in production.
Every engineering team that uses a relational database runs migrations. It is one of the highest-risk operations in the entire deployment lifecycle. A single bad migration can:
- 🔒 Lock a table for minutes on a high-traffic database, causing a full outage.
- 💥 Drop a column that 3 microservices are still reading.
- 🚫 Run irreversibly and corrupt data with no clean rollback path.
- ✅ Pass CI/CD checks completely fine and still destroy production.
The current state of the art is: a senior DBA manually reviews it, or you just run it and pray.
MigrationMind takes three inputs:
- Migration file(s) - SQL, Alembic, Flyway, or raw DDL
- Current schema dump - your live schema snapshot
- Query log (optional) - slow query log or query pattern file
It runs a multi-stage analysis pipeline and produces a structured risk report.
migrationmind analyze \
--migration ./migrations/0042_add_user_index.sql \
--schema ./schema_dump.sql \
--queries ./slow_query_log.txtMigrationMind Risk Report - 0042_add_user_index.sql
════════════════════════════════════════════════════
Risk Score: HIGH (74/100)
Operations detected: 3
✗ ALTER TABLE users ADD COLUMN last_active TIMESTAMP NOT NULL DEFAULT NOW()
→ Postgres 10 detected: full table rewrite required
→ Estimated lock duration: 4–12 minutes (table size: ~8M rows)
→ Rollback: safe (DROP COLUMN)
✗ DROP INDEX idx_users_email
→ 2 queries in your log depend on this index for performance
→ Rollback: safe (recreate index) but requires downtime
✓ CREATE TABLE audit_log (...)
→ No risk. New table, no dependencies.
Affected queries: 2
→ SELECT * FROM users WHERE email = ? (will lose index, 40–200x slowdown)
→ UPDATE users SET last_active = ? WHERE id = ? (blocks during table rewrite)
Rollback complexity: MEDIUM
→ All operations reversible but require 15–20 min window
Suggested safe rewrite:
→ Add column nullable first, backfill in batches, then add NOT NULL
→ Full rewrite plan: migration_0042_safe_rewrite.sql
MigrationMind runs DDL analysis sequentially across a series of specialized parsing, simulation, and estimation modules:
graph TD
A[Migration SQL File] --> Stage1[Stage 1: AST Parser<br/>sqlglot DDL Extraction]
B[Current Schema Dump] --> Stage2[Stage 2: Schema Diff<br/>Before/After State Comparison]
C[Query Log File] --> Stage3[Stage 3: Query Impact<br/>Dependency Cross-Match]
Stage1 --> Stage2
Stage2 --> Stage3
Stage3 --> Stage4[Stage 4: Lock Engine<br/>Downtime & Lock Classification]
Stage4 --> Stage5[Stage 5: Rollback Analyzer<br/>Complexity Classification]
Stage5 --> Stage6[Stage 6: LLM Layer<br/>LiteLLM Reasoning & Suggestion]
Stage6 --> Stage7[Stage 7: Report Generator<br/>Terminal / Markdown / JSON]
style Stage1 fill:#1a73e8,stroke:#fff,stroke-width:2px,color:#fff
style Stage2 fill:#1a73e8,stroke:#fff,stroke-width:2px,color:#fff
style Stage3 fill:#1a73e8,stroke:#fff,stroke-width:2px,color:#fff
style Stage4 fill:#f29900,stroke:#fff,stroke-width:2px,color:#fff
style Stage5 fill:#f29900,stroke:#fff,stroke-width:2px,color:#fff
style Stage6 fill:#12b5cb,stroke:#fff,stroke-width:2px,color:#fff
style Stage7 fill:#15803d,stroke:#fff,stroke-width:2px,color:#fff
- Stage 1 - Parse: Extracts Abstract Syntax Trees (ASTs) of all DDL operations.
- Stage 2 - Schema Diff: Creates before/after schema models to detect removed or modified structures.
- Stage 3 - Query Impact: Cross-matches slow query logs against schema diffs to identify missing resources.
- Stage 4 - Lock Engine: Estimates lock severity and maximum downtime durations.
- Stage 5 - Rollback: Classifies the complexity and risk of reversing each change.
- Stage 6 - LLM Layer: Generates human-friendly explanations and suggests safe refactoring structures.
- Stage 7 - Report: Publishes findings to terminals, JSON payloads, or markdown documents.
pip install migrationmindOr from source:
git clone https://github.com/H8rsh100/MigrationMind.git
cd MigrationMind
pip install -e ".[dev]"Copy .env.example to .env and fill in your values:
cp .env.example .envLITELLM_MODEL=gpt-4o
OPENAI_API_KEY=sk-...
MIGRATIONMIND_DIALECT=postgresql
MIGRATIONMIND_DB_VERSION=14| Command | Description |
|---|---|
migrationmind analyze |
Run the full analysis pipeline |
migrationmind init |
Scaffold a config file in the current project |
migrationmind history |
Show past analysis runs |
| Flag | Default | Description |
|---|---|---|
--migration |
required | Path to migration file |
--schema |
optional | Path to schema dump SQL |
--queries |
optional | Path to query log file |
--dialect |
postgresql |
SQL dialect |
--db-version |
14 |
Database major version |
--output |
terminal |
Output format: terminal, json, markdown |
--no-llm |
false | Skip LLM reasoning stage |
Drop the bundled GitHub Actions workflow into your repo:
cp .github/workflows/migrationmind.yml your-repo/.github/workflows/Every PR that touches a migration file automatically gets a risk report posted as a PR comment.
| Tool | What it does | What's missing |
|---|---|---|
| Flyway / Liquibase | Migration runner | Zero risk analysis |
| Squawk | Postgres linter | Rule-based only, no query impact, no AI |
| pganalyze | Query monitoring | Post-deployment, not pre-deployment |
| Datadog / New Relic | Observability | Observability, not intelligence |
MigrationMind is the only tool combining AST parsing + schema context + query analysis + LLM reasoning into a pre-deployment risk report.
make install # pip install -e ".[dev]"
make test # pytest tests/
make lint # ruff check .
make format # ruff format .
make example # run on bundled example migrationMIT - see LICENSE.