Rust-powered SQL transpiler for 32+ dialects. Parse, generate, transpile, format, and validate SQL.
Project description
polyglot-sql (Python)
Rust-powered SQL transpiler for 30+ dialects.
The polyglot-sql Python package exposes an API backed by the Rust polyglot-sql engine for fast parse/transpile/generate/format/validate workflows.
Installation
pip install polyglot-sql
Quick Start
import polyglot_sql
polyglot_sql.transpile(
"SELECT IFNULL(a, b) FROM t",
read="mysql",
write="postgres",
)
# ["SELECT COALESCE(a, b) FROM t"]
ast = polyglot_sql.parse_one("SELECT 1 + 2", dialect="postgres")
polyglot_sql.generate(ast, dialect="mysql")
data_type = polyglot_sql.parse_data_type("DECIMAL(10, 2)", dialect="duckdb")
data_type.sql("postgres")
# "DECIMAL(10, 2)"
# SQLGlot-compatible narrow form for data types only:
polyglot_sql.parse_one("VARCHAR(255)", dialect="duckdb", into=polyglot_sql.DataType)
polyglot_sql.format_sql("SELECT a,b FROM t WHERE x=1", dialect="postgres")
ast = polyglot_sql.parse_one("SELECT id FROM a UNION ALL SELECT id FROM b")
order_expr = polyglot_sql.parse_one("SELECT id").args["expressions"][0]
ast = polyglot_sql.set_limit(ast, 100)
ast = polyglot_sql.set_offset(ast, 10)
ast = polyglot_sql.set_order_by(ast, order_expr)
polyglot_sql.generate(ast)
# ["SELECT id FROM a UNION ALL SELECT id FROM b ORDER BY id LIMIT 100 OFFSET 10"]
Format Guard Behavior
format_sql uses Rust core formatting guards with default limits:
- input bytes:
16 * 1024 * 1024 - tokens:
1_000_000 - AST nodes:
1_000_000 - set-op chain:
256
import polyglot_sql
try:
pretty = polyglot_sql.format_sql("SELECT 1", dialect="generic")
except polyglot_sql.GenerateError as exc:
# Guard failures contain E_GUARD_* codes in the message.
print(str(exc))
Per-call guard overrides:
pretty = polyglot_sql.format_sql(
"SELECT 1 UNION ALL SELECT 2",
dialect="generic",
max_set_op_chain=1024,
max_input_bytes=32 * 1024 * 1024,
)
result = polyglot_sql.validate("SELECT 1", dialect="postgres")
if result:
print("valid")
options = {
"producer": "https://github.com/tobilg/polyglot",
"datasetNamespace": "postgres://warehouse",
"outputDataset": {
"namespace": "postgres://warehouse",
"name": "analytics.revenue",
},
}
payload = polyglot_sql.openlineage_column_lineage(
"SELECT order_id, amount * 100 AS amount_cents FROM raw.orders",
options,
)
print(payload["facet"]["fields"])
OpenLineage helpers only produce compatible payloads. Transport and client emission are intentionally out of scope.
analysis = polyglot_sql.analyze_query(
"WITH base AS (SELECT id, amount FROM orders) SELECT * FROM base",
{
"dialect": "generic",
"schema": {
"tables": [
{
"name": "orders",
"columns": [
{"name": "id", "type": "INT", "nullable": False},
{"name": "amount", "type": "DECIMAL(10,2)", "nullable": True},
],
}
]
},
},
)
print(analysis["cteFacts"][0]["bodySql"]) # "SELECT id, amount FROM orders"
print(analysis["starProjections"][0]["expandedColumns"]) # ["id", "amount"]
print(analysis["projections"][0]["nullability"]) # "non_null"
print(analysis["baseTables"][0]["name"]) # "orders"
print(analysis["baseTables"][0]["table"]) # "orders"
analysis["relations"] reports sources visible in the analyzed scope.
analysis["baseTables"] reports deduplicated physical table dependencies across
nested CTEs, derived tables, subqueries, and set-operation branches. For
physical relation facts, name remains the qualified display name while
catalog, schema, and table expose parsed identifier parts. Validation
uses broad type families, while query analysis preserves parseable detailed
schema type strings for projection typeHint values. analysis["cteFacts"]
reports top-level CTE definitions, analysis["starProjections"] records the
original star projections and schema-expanded columns, and each projection has
conservative nullability: "non_null", "nullable", or "unknown".
Function-like projections may include transformFunction with the function
name, literal arguments, and column arguments, for example for
DATE_TRUNC('month', created_at).
Validation schema dictionaries use:
schema = {
"strict": True,
"tables": [
{
"name": "orders",
"schema": "analytics",
"aliases": ["o"],
"primaryKey": ["id"],
"uniqueKeys": [["external_id"]],
"foreignKeys": [
{
"columns": ["customer_id"],
"references": {"table": "customers", "columns": ["id"]},
}
],
"columns": [
{"name": "id", "type": "INT", "nullable": False, "primaryKey": True},
{"name": "amount", "type": "DECIMAL(10,2)", "nullable": True},
],
}
],
}
Use the type key for column types. dataType / data_type are not accepted
aliases in this payload.
API Reference
All functions are exported from polyglot_sql.
transpile(sql: str, read: str = "generic", write: str = "generic", *, pretty: bool = False) -> list[str]parse(sql: str, dialect: str = "generic") -> list[dict]parse_one(sql: str, dialect: str = "generic") -> dictparse_one(sql: str, dialect: str = "generic", *, into=polyglot_sql.DataType) -> DataType(onlyDataTypeis supported forinto)parse_data_type(sql: str, dialect: str = "generic") -> DataTypegenerate(ast: dict | list[dict], dialect: str = "generic", *, pretty: bool = False) -> list[str]format_sql(sql: str, dialect: str = "generic", *, max_input_bytes: int | None = None, max_tokens: int | None = None, max_ast_nodes: int | None = None, max_set_op_chain: int | None = None) -> strformat(sql: str, dialect: str = "generic", *, max_input_bytes: int | None = None, max_tokens: int | None = None, max_ast_nodes: int | None = None, max_set_op_chain: int | None = None) -> str(alias offormat_sql)validate(sql: str, dialect: str = "generic") -> ValidationResultoptimize(sql: str, dialect: str = "generic") -> strlineage(column: str, sql: str, dialect: str = "generic") -> dictsource_tables(column: str, sql: str, dialect: str = "generic") -> list[str]analyze_query(sql: str, options: dict | None = None, dialect: str = "generic") -> dictopenlineage_column_lineage(sql: str, options: dict) -> dictopenlineage_job_event(sql: str, options: dict) -> dictopenlineage_run_event(sql: str, options: dict) -> dictdiff(sql1: str, sql2: str, dialect: str = "generic") -> list[dict]dialects() -> list[str]__version__: str
Supported Dialects
Current dialect names returned by polyglot_sql.dialects():
athena, bigquery, clickhouse, cockroachdb, datafusion, databricks, doris, dremio, drill, druid, duckdb, dune, exasol, fabric, generic, hive, materialize, mysql, oracle, postgres, presto, redshift, risingwave, singlestore, snowflake, solr, spark, sqlite, starrocks, tableau, teradata, tidb, trino, tsql.
Error Handling
Exception hierarchy:
PolyglotErrorParseErrorGenerateErrorTranspileErrorValidationError
Unknown dialect names raise built-in ValueError.
validate(...) returns ValidationResult:
result.valid: boolresult.errors: list[ValidationErrorInfo]bool(result)works (Truewhen valid)
Each ValidationErrorInfo has:
message: strline: intcol: intcode: strseverity: str
Performance Note
The package uses Rust internals directly via PyO3 and has zero runtime Python dependencies for SQL processing.
Development
cd crates/polyglot-sql-python
uv sync --group dev
uv run maturin develop
uv run pytest
uv run pyright python/polyglot_sql/
uv run maturin build --release
uv run --with mkdocs mkdocs build --strict --clean --config-file mkdocs.yml --site-dir ../../packages/python-docs/dist
Links
- Repository: https://github.com/tobilg/polyglot
- Issues: https://github.com/tobilg/polyglot/issues
- Python API Docs: https://polyglot-sql-python-api.pages.dev
- TypeScript API Docs: https://polyglot.gh.tobilg.com
- Playground: https://polyglot-playground.gh.tobilg.com/
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