polyglot-sql (Python)

Rust-powered SQL transpiler for more than 30 SQL 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 * FROM users LIMIT 10",
    dialect="postgres",
    strict_syntax=True,
    semantic=True,
)
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).

Each analysis["setOperations"][...]["branches"] entry includes a role of "value" or "filter". Lineage results attach optional set_branch metadata to immediate set-operation branch roots with the operator, original zero-based ordinal, and all flag; omitted branches do not renumber the surviving nodes. In OpenLineage output, EXCEPT and INTERSECT right-hand inputs are emitted as indirect FILTER dependencies.

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") -> dict
parse_one(sql: str, dialect: str = "generic", *, into=polyglot_sql.DataType) -> DataType (only DataType is supported for into)
parse_data_type(sql: str, dialect: str = "generic") -> DataType
generate(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) -> str
format(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 of format_sql)
validate(sql: str, dialect: str = "generic", *, strict_syntax: bool = False, semantic: bool = False) -> ValidationResult
optimize(sql: str, dialect: str = "generic") -> str
lineage(column: str, sql: str, dialect: str = "generic") -> dict
lineage_at(ordinal: int, sql: str, dialect: str = "generic") -> dict
lineage_at_with_schema(ordinal: int, sql: str, schema: dict, dialect: str = "generic") -> dict
lineage_with_schema(column: str, sql: str, schema: dict, dialect: str = "generic") -> dict
output_columns(sql: str, dialect: str = "generic") -> dict
output_columns_with_schema(sql: str, schema: dict, dialect: str = "generic") -> dict
source_tables(column: str, sql: str, dialect: str = "generic") -> list[str]
analyze_query(sql: str, options: dict | None = None, dialect: str = "generic") -> dict
openlineage_column_lineage(sql: str, options: dict) -> dict
openlineage_job_event(sql: str, options: dict) -> dict
openlineage_run_event(sql: str, options: dict) -> dict
diff(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:

PolyglotError
ParseError
GenerateError
TranspileError
ValidationError
ColumnResolutionError (reason, column, and ordinal attributes)

Unknown dialect names raise built-in ValueError.

validate(...) returns ValidationResult:

result.valid: bool
result.errors: list[ValidationErrorInfo]
bool(result) works (True when valid)

strict_syntax=True rejects compatibility forms such as trailing commas before clause boundaries. semantic=True adds warning diagnostics W001-W004 for SELECT *, mixed aggregate projections, DISTINCT with ORDER BY, and LIMIT without ORDER BY; warnings do not make the result invalid.

Each ValidationErrorInfo has:

message: str
line: int
col: int
code: str
severity: str

Performance Note

The package uses Rust internals directly via PyO3 and has zero runtime Python dependencies for SQL processing. Published wheels use the dedicated Cargo python_release profile with opt-level=2 and thin LTO. This favors Python query throughput without changing the size-oriented release profile used by WASM. FFI/Go artifacts use their own native throughput profile. Editable development installs continue to use Cargo's dev profile.

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 --profile python_release
uv run --with mkdocs mkdocs build --strict --clean --config-file mkdocs.yml --site-dir ../../packages/python-docs/dist

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MD5	`46c44d662ffccaa7d0162180db36a040`
BLAKE2b-256	`12837daee32b843e4deab0f92991a694e3d48b2f725e1fb2aba7ac2b805ad11b`

polyglot-sql 0.8.1

polyglot-sql (Python)

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Format Guard Behavior

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Algorithm	Hash digest
SHA256	`93c36021a11745523fd7db6542a2c30e9359bd0e00286e5aa5f58d10ac1a25b6`
MD5	`6b1e284b7511ebc9914997acd5d6fb71`
BLAKE2b-256	`7944b9b12a4e68956a626a11e6fb4189d9ee14dae348a6d4fffe0eecb5bf6722`

Algorithm	Hash digest
SHA256	`cfcb23c7cb4640d6cd2cdb601b44fc383d8e73b9f71a434f3e070eb924d857f3`
MD5	`60beca82fc24658599cc7aa3977661ee`
BLAKE2b-256	`9783cb51af541969666d9d892d890f759352f2cd17dc29de3867cad81312e81f`

Algorithm	Hash digest
SHA256	`e730c5430a67d9c81195750b0916717910deba98e697e71df1b0088bc18c1a54`
MD5	`830c02c4e6a277475cc39a774a7c51c2`
BLAKE2b-256	`eee632963a44d07e256b575eac9f1b84d058cf415c98c3b0f797d8ce35a69fa3`

Algorithm	Hash digest
SHA256	`e03f49fe6fed33f51d1d5a932194b3b1eac2d622be98a941db838982f5acdfad`
MD5	`850690ba18bd4d4bbdd79bbfb960be05`
BLAKE2b-256	`e3d976b308cd1605209f23da609adbed3aef7046cc5dd574c6e14894825c1481`

Algorithm	Hash digest
SHA256	`28c0e95b4d5bfdfbe949714371aec0dcc8099cdcd31221f2f7ffbf99ef92f18e`
MD5	`1cb4dce3055c90ca731f5c10628805d6`
BLAKE2b-256	`87229770f1860180d7469d071e9ce66104b347d77ecfa06e4c3e18750134bdae`

Algorithm	Hash digest
SHA256	`94ebe790c58135337047dcfa20e64baa92e1ea0166e212957102a4dbe52448e4`
MD5	`244d19a0d879a163a70c6c87d62f46f0`
BLAKE2b-256	`6d0aad0c502f8c4976991fc67d3e354fd73d0c1540768d0dd2d8381f111ac2d1`

Algorithm	Hash digest
SHA256	`758e3f2b70dfd9ec0b5c89602445d4e7bf869b34c5066917d38f7aa3068d2593`
MD5	`bd3729e0d897dbd366f9f3fdce7a1b4d`
BLAKE2b-256	`ef16e70e59e7808b72cea32ec0a416191475da81cee19da5229f444e7bc98541`

Algorithm	Hash digest
SHA256	`35df320374728c6d9f00aae7e72c43eb6e49033b0e1a28f96113cdbce6d79a46`
MD5	`902325490b30ff2e4531b16d3b4c6db4`
BLAKE2b-256	`87f4d7e0ea5ca8c02b4de50037537917e502783a9136493d85d819c16e3ed02c`

Algorithm	Hash digest
SHA256	`70014a8fc44bc585f2ddd7bd164e63f3f81a818e3f4ed96a9e14bef4dde02b90`
MD5	`2a1901b53026973a3afbdcde7b7fa1bc`
BLAKE2b-256	`fdd2719adbbdcddf8db29e5ee2a106358230104e1a1a21e04d5ad670831b9dbc`

Algorithm	Hash digest
SHA256	`80e3f5ff3efdf698d3cd5de1947c5e8e01703c712b165d5ecac39f97d1c9037a`
MD5	`e5d3f8704a601fc75fb4c086e9259d53`
BLAKE2b-256	`197767ed20806870b14dc0ea89cd36d9fabeb5dc6477b8ccf7500ac0a0838c89`