🔄 SQL Dialect Master

Enterprise-grade multi-database SQL conversion engine supporting 12 database dialects.

Current release: 1.1.2

✨ Features

Feature	Description
🔄 SQL Conversion	Convert SQL between 12 database dialects with a complete 12 × 12 conversion matrix
✅ Output Validation	Validate converted SQL with target-dialect parsing plus safe generic-parser fallback
🧠 Semantic Regression Coverage	Preserve predicates, joins, grouping, ordering, limits, windows, and round-trip validity
🔍 AST Semantic Diff	Compare dialect-aware SQL ASTs to detect structural changes introduced by conversion
🗄️ Runtime Semantic Tests	Execute representative conversions against PostgreSQL and DuckDB and compare result sets
📚 Function Encyclopedia	298 SQL functions with cross-database comparison
🗂️ Type Mapping	39 data types × 12 databases matrix
💬 NL2SQL	Natural language to SQL (Chinese/English)
🔧 Rule Engine	Deterministic priority ordering, compiled patterns, dialect-aware conflict detection
🛡️ Security Validation	Statement-level stacked-query detection with configurable blocking policy
⚡ Performance	Rule-aware cache versioning, bounded async batch conversion, and reproducible benchmark
🎨 Modern UI	Streamlit developer workspace: dark / light theme system, sidebar navigation, command palette

💾 Supported Databases

Category	Databases
RDBMS	MySQL 🐬, PostgreSQL 🐘, Oracle 🔴, SQL Server 🟦
Big Data	Hive 🐝, Spark ⚡, Trino 🔷, Databricks 🧱
Cloud DW	Snowflake ❄️, Redshift 🔶
OLAP	ClickHouse 🏠, DuckDB 🦆

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/zengxiangdi/sql-dialect-master.git
cd sql-dialect-master

# Install the application package
python -m pip install -e .

For development and testing, install the optional development dependencies:

python -m pip install -e ".[dev]"

For database-backed semantic tests:

python -m pip install -e ".[dev,semantic]"

Run Streamlit UI (Recommended)

streamlit run sdm_local_v2.py

Open http://localhost:8501 in your browser.

Run FastAPI Backend

uvicorn backend.api.main:app --reload

API docs available at http://localhost:8000/docs

📖 Usage Examples

Python SDK

from backend.core import SQLTranspiler

transpiler = SQLTranspiler()
result = transpiler.transpile(
    sql="SELECT DATE_FORMAT(created_at, '%Y-%m-%d') FROM orders LIMIT 10",
    source="mysql",
    target="postgres"
)
print(result.target_sql)
# Output: SELECT TO_CHAR(created_at, 'YYYY-MM-DD') FROM orders LIMIT 10

REST API

curl -X POST http://localhost:8000/api/convert \
  -H "Content-Type: application/json" \
  -d '{
    "sql": "SELECT * FROM users LIMIT 10",
    "source_dialect": "mysql",
    "target_dialect": "hive"
  }'

Successful and failed conversions return structured metadata. Failed conversions expose a stable machine-readable error_code alongside the human-readable error message.

NL2SQL (Natural Language)

from backend.core import NL2SQLGenerator

generator = NL2SQLGenerator()
result = generator.generate("查询最近7天的订单", dialect="mysql")
print(result.sql)
# Output: SELECT * FROM orders WHERE created_at >= DATE_SUB(CURRENT_DATE, 7)

Relational Query Support (D2)

NL2SQL supports existential relationship queries using EXISTS / NOT EXISTS subqueries instead of physical JOINs, preserving row count and avoiding duplicate expansion from 1:N relationships.

Known relationships (explicit mapping):

• users ↔ orders: users.id = orders.user_id
• customers ↔ orders: customers.id = orders.customer_id
• orders ↔ products: orders.product_id = products.id
• employees ↔ departments: employees.dept_id = departments.id

Examples:

# Positive relational query → EXISTS
result = generator.generate("find users who have orders", "postgres")
# Output: SELECT * FROM users WHERE EXISTS (SELECT 1 FROM orders WHERE users.id = orders.user_id)

# Negative relational query → NOT EXISTS
result = generator.generate("find users without orders", "postgres")
# Output: SELECT * FROM users WHERE NOT EXISTS (SELECT 1 FROM orders WHERE users.id = orders.user_id)

Unknown relationships fail safely:

result = generator.generate("find users who have invoices", "postgres")
# Output: success=False, explanation="Unable to safely infer the relationship..."

Relationship inference uses explicit mapping only — no fabricated foreign keys. Use table_hint to specify schema-qualified tables when needed.

Date Arithmetic Semantics (D3)

Date expressions follow a rolling vs calendar-relative distinction:

Expression	Semantic	DSL (postgres)
today	current calendar date	= CURRENT_DATE
yesterday	rolling -1 day	= DATE_SUB(CURRENT_DATE, 1)
tomorrow	rolling +1 day	= DATE_ADD(CURRENT_DATE, 1)
last N days	rolling N days	>= DATE_SUB(CURRENT_DATE, N)
last N weeks	rolling N×7 days	>= DATE_SUB(CURRENT_DATE, N×7)
last week	rolling 7 days	>= DATE_SUB(CURRENT_DATE, 7)
last N months	calendar -N months	>= ADD_MONTHS(CURRENT_DATE, -N)
last month	calendar -1 month	>= ADD_MONTHS(CURRENT_DATE, -1)
last N years	calendar -N years	>= ADD_MONTHS(CURRENT_DATE, -N×12)
last year	calendar -1 year	>= ADD_MONTHS(CURRENT_DATE, -12)

Key distinction: last week uses a rolling 7-day window, while last month/last year use calendar arithmetic (ADD_MONTHS). This means last month from March 31 yields February 28/29 (not 30 days prior), and last year preserves month/day within the calendar.

Date conditions in combined queries (e.g., "users who have orders from last 7 days") are scoped to the relation table's date column inside the EXISTS subquery.

Type Mapping with Precision (D1)

TypeMapper now supports parameterized types and precision-aware warnings:

from backend.core import TypeMapper

mapper = TypeMapper()
result = mapper.map_type("VARCHAR2(2000)", "oracle", "postgres")
# Output: source_type="VARCHAR2(n)", target_type="VARCHAR(n)", precision=2000

result = mapper.map_type("VARCHAR2(8000)", "oracle", "postgres")
# Output: warnings include "exceeds Oracle's default VARCHAR2 limit of 4000"

Supports VARCHAR, VARCHAR2, NVARCHAR, CHAR with numeric precision or MAX. Unknown parameterized types resolve to the canonical base type.

🏗️ Project Structure

sql-dialect-master/
├── backend/
│   ├── api/
│   │   ├── main.py              # FastAPI application
│   │   └── middleware.py        # Rate limiting, logging
│   ├── benchmarks/
│   │   └── transpiler_benchmark.py # Reproducible conversion benchmark
│   ├── core/
│   │   ├── nl2sql_components/   # Modular NL2SQL components
│   │   │   ├── tokenizer.py     # Language tokenization
│   │   │   ├── templates.py     # Query templates
│   │   │   └── mappings.py      # Language mappings
│   │   ├── config.py            # Configuration management
│   │   ├── transpiler.py        # SQL conversion engine
│   │   ├── parser.py            # SQL parser
│   │   ├── semantic_diff.py     # AST-based semantic difference detector
│   │   ├── rules.py             # Transformation rules
│   │   ├── post_processor.py    # Post-processing
│   │   ├── nl2sql.py            # Natural language to SQL
│   │   ├── functions_lookup.py  # Function encyclopedia
│   │   ├── functions_db.json    # Functions database
│   │   ├── type_mapping.py      # Type mapping
│   │   ├── type_mapping.json    # Types database
│   │   ├── cache.py             # TTL cache
│   │   └── exceptions.py        # Custom exceptions + ErrorCode taxonomy
│   ├── utils/
│   │   └── validators.py        # Input validation
│   │
│   └── tests/                   # Test suite and regression coverage
├── frontend/                    # Streamlit UI Components (v2 architecture)
│   ├── core/                    # Cross-cutting concerns
│   │   ├── design_tokens.py     # Theme model (dark/light) + semantic status
│   │   ├── navigation.py        # NavigationIntent contract
│   │   ├── state.py             # SessionState adapter
│   │   ├── themes.py            # Theme switching (dark/light only)
│   │   ├── styles.py            # CSS generation from design tokens
│   │   ├── escaping.py          # HTML-safe rendering helpers
│   │   └── viewmodels.py        # Typed view models
│   ├── pages/                   # One module per workspace page
│   │   ├── convert.py
│   │   ├── nl2sql.py
│   │   ├── diff.py
│   │   ├── lineage.py
│   │   ├── runtime.py
│   │   ├── functions.py
│   │   ├── types.py
│   │   ├── templates.py
│   │   ├── history.py
│   │   ├── settings.py
│   │   └── query_analysis.py
│   ├── ui/                      # Reusable UI widgets
│   │   ├── command_palette.py
│   │   ├── diff.py
│   │   ├── editor.py
│   │   └── status.py
│   ├── app_context_v2.py        # Shared application context (v2)
│   └── templates_v2.py          # SQL templates (v2)
├── examples/
│   ├── api_client.py             # API usage examples
│   └── basic_usage.py            # SDK usage examples
├── scripts/
│   └── verify_readme_consistency.py # README source-of-truth checks
├── sdm_local_v2.py             # Streamlit Application Entry (v2)
├── pyproject.toml               # Project configuration
├── requirements.txt              # Legacy/runtime dependency list
├── .env.example                 # Environment template
├── LICENSE                      # MIT License
└── README.md

🔧 API Endpoints

Endpoint	Method	Description
/api/convert	POST	Convert SQL between dialects; response includes error_code on failure
/api/parse	POST	Parse SQL and extract structural elements
/api/nl2sql	POST	Generate SQL from natural language
/api/functions	GET	Search SQL functions
/api/functions/{name}	GET	Get function details
/api/types	GET	Get type mapping matrix
/api/types/map	POST	Map type between dialects
/api/dialects	GET	List supported dialects
/health	GET	Health check
/health/deep	GET	Deep health check
/api/stats	GET	Service and dataset statistics

🧪 Quality, Validation & Performance

The project maintains a 12 × 12 source-to-target dialect regression matrix, semantic regression tests, post-processing safety regressions, security boundary checks, deterministic rule-engine tests, cache-versioning tests, async concurrency tests, and stable error-code tests.

Converted SQL is validated after post-processing. Target-dialect parsing is preferred; when a target parser limitation prevents validation but the generic parser accepts the SQL, the result is retained with a compatibility warning. Only when both validations fail is the conversion reported as unsuccessful.

The AST semantic diff utility canonicalizes source and target SQL through SQLGlot and reports structural changes such as different predicates or AST node shapes. It is a regression detector rather than a proof of runtime equivalence.

The semantic CI gate executes representative conversions against PostgreSQL and DuckDB and compares the source and converted result sets. This provides runtime evidence for supported SQL subsets without claiming universal cross-database semantic equivalence.

Security validation structurally detects stacked SQL statements using the parser when available, with configurable policy controlling whether dangerous input is blocked or only warned about.

The async batch API bounds concurrent work and preserves input order. A reproducible transpiler benchmark is available under backend/benchmarks/ and is also executed in CI for performance observability. The benchmark intentionally reports measurements without failing the build on a fixed latency threshold because hosted CI timing is variable.

README source-of-truth checks run in CI and verify the release version, Python requirement, supported dialect list, and benchmark references against repository sources.

To run the benchmark locally:

python backend/benchmarks/transpiler_benchmark.py --iterations 100 --repeats 3

To validate README consistency locally:

python scripts/verify_readme_consistency.py

⚠️ Limitations

SQL dialect conversion is not universally semantics-preserving. The engine validates generated SQL against the target parser, but parser acceptance does not prove equivalent runtime behavior. Database-specific differences in NULL handling, implicit casts, date/time semantics, collation, timezone behavior, JSON/array operations, and other vendor features may require manual review or execution against the target database.

NL2SQL is heuristic/template-driven rather than a calibrated probabilistic model. The returned confidence score should be treated as a generation-quality signal, not as a statistical probability that the SQL is correct.

⚙️ Configuration

The project uses pydantic-settings for robust configuration.

Copy .env.example to .env and customize:

cp .env.example .env

Key settings (prefixed with SDM_):

• SDM_CACHE_ENABLED - Enable/disable caching

Rate limiting deployment

Rate limiting is on by default. The two backends are:

• SDM_RATE_LIMIT_BACKEND=memory (default) — per-process, thread-safe. Limits are per instance, not shared. Run this only when the API is served by a single process; in a multi-worker or multi-instance deployment each worker enforces its own limit, so the effective ceiling is workers × SDM_RATE_LIMIT_REQUESTS.
• SDM_RATE_LIMIT_BACKEND=redis — shared, atomic limits across all workers. Set SDM_REDIS_URL when using this backend. The store connects and pings Redis at startup and fails fast if it is unreachable; the redis package is required (install with pip install redis).

SDM_RATE_LIMIT_ENABLED=false disables enforcement entirely. Use the in-memory backend for local development and the Redis backend for any deployment with more than one worker or instance that must enforce a single global limit.