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")

SQLGlot-Compatible Builders

The common SQLGlot builder surface is available directly from polyglot_sql. Builders return normal Polyglot expression objects and are immutable: each chained call returns a new expression.

query = (
    polyglot_sql.select("customer_id", "COUNT(*) AS orders")
    .from_("orders")
    .where("status = 'complete'")
    .group_by("customer_id")
    .order_by("orders DESC")
    .limit(10)
)

query.sql("postgres")
# "SELECT customer_id, COUNT(*) AS orders FROM orders WHERE status = 'complete' GROUP BY customer_id ORDER BY orders DESC LIMIT 10"

active = polyglot_sql.column("status").eq("active")
active.sql()
# "status = 'active'"

The shared builder feature set also includes named aggregate/string/math/date helpers, all join and set-operation variants, named windows, lateral views, hints, row locks, CTAS, CASE, INSERT, UPDATE, DELETE, and conditional MERGE actions. Repeated clauses append by default; pass append=False to replace one. Advanced parser options, mutable copy=False behavior, and the complete SQLGlot expression catalog are not included. Polyglot expressions remain the native AST type; SQLGlot is not a runtime dependency.

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"]

Complexity Guard Options

parse, parse_one (including into=DataType), parse_data_type, validate, validate_with_schema, analyze_query, and transpile accept the keyword-only complexity_guard, a ComplexityGuardOptions typed dictionary using the shared camelCase keys: maxParserDepth, maxInputBytes, maxTokens, maxAstNodes, maxAstDepth, maxParenthesisDepth, and maxFunctionCallDepth. Omit the argument, pass None, or omit a key to use its default. A field-level None disables that check; a nonnegative integer overrides it. For example:

polyglot_sql.transpile(sql, complexity_guard={"maxParserDepth": 128})
polyglot_sql.validate(sql, dialect="snowflake", complexity_guard={"maxFunctionCallDepth": 128})
polyglot_sql.parse_one(sql, dialect="snowflake", complexity_guard={"maxFunctionCallDepth": None})

analyze_query also accepts options={"complexityGuard": {...}}; do not supply both forms in one call. Limits must be nonnegative integers or None; booleans, floats, out-of-range integers, and unknown guard keys are rejected. Omitting the entire guard preserves dialect-specific defaults (including ClickHouse's higher function-nesting limit). A supplied dictionary uses the shared Rust defaults for omitted fields. Validation reports guard exhaustion as diagnostics; parsing and analysis raise ParseError.

Parser depth defaults to 1024 logical levels on native targets (32 on WASM) and is checked during parsing, before an AST exists. Zero rejects parsing descents. Other checks remain independent. Raising or disabling limits can permit stack exhaustion and process termination, even for trusted generated SQL. Increasing a limit does not increase stack space; these limits are not general time/memory budgets. Application owners should control overrides. Other SQL-consuming helpers, including lineage and optimization, continue to inherit default protection.

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")

Schema-aware validation uses the same Rust validator as the TypeScript SDK:

sql = "SELECT o.order_id FROM orders o WHERE o.missing_column = TRUE"
schema = {"tables": [{"name": "orders", "columns": [{"name": "order_id", "type": "INT"}]}]}
result = polyglot_sql.validate_with_schema(
    sql, schema, dialect="snowflake", check_types=True, check_references=True,
)
for error in result.errors:
    print(error.code, error.message)
    if error.start is not None and error.end is not None:
        print(sql[error.start:error.end])

Unknown tables, columns and aliases are checked by default. check_references also checks ambiguous columns and foreign-key metadata; check_types enables type checks. strict overrides the schema's strict value, which defaults to True; strict=False reports reference/type findings as warnings. An empty column list or a * column denotes an open schema, so unknown columns are not rejected solely because their names are absent. Nonempty lists without * are treated as complete. Options use snake_case keyword arguments, not an options dictionary. Invalid schemas and unknown dialects raise ValueError.

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"

Non-projection uses are available through the same shared Rust analysis:

analysis = polyglot_sql.analyze_query(
    "SELECT o.id FROM orders o WHERE o.amount > 0", dialect="duckdb"
)
use = analysis["columnUses"][0]
print(use["context"])                          # "filter"
print(use["references"][0]["column"])          # "amount"
print(use["scopePath"])                        # "root"

columnUses groups references by clause expression without changing projection lineage. It covers joins, filters, grouping, HAVING/QUALIFY, window keys/frames, ordering and set-operation filter inputs. scopePath/expressionPath identify the scope and expression; expressionSql is dialect-rendered SQL. Optional span objects use half-open Unicode-character offsets in the original input. Reference spans locate uses, not upstream definitions. Unknown or ambiguous sources remain conservative; whole-expression spans are omitted when unavailable.

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
• validate_with_schema(sql: str, schema: dict, dialect: str = "generic", *, check_types: bool = False, check_references: bool = False, strict: bool | None = None, semantic: bool = False, strict_syntax: 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(...) and validate_with_schema(...) return 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 checks every query scope and reports errors for invalid grouping (E230), aggregate placement/nesting (E231), and window placement/nesting (E232). These errors make the result invalid, including with strict=False. Quality hints remain warnings: SELECT * (W001), uncertain grouping (W002), DISTINCT with ORDER BY (W003), and unordered LIMIT (W004). Default validation remains syntax-only.

Schema validation always checks DML targets and references, independently of check_types. Type checks use lexical query scopes and name-aligned set-operation outputs. An empty column list or * denotes an open schema; validation is not a database execution check and cannot prove runtime/session-dependent behavior.

Analysis options and schemas reject unknown keys, including nested metadata. Public TypedDict models such as AnalyzeQueryOptions, ValidationSchema, QueryAnalysis, and FunctionCatalogSpec describe their dictionary payloads. Analysis retains best-effort references for missing columns but marks them unknown, not resolved. Lambda-local parameters are not physical dependencies.

Python also accepts a declarative function catalog (Rust offers FunctionCatalogSpec::build and the existing FunctionCatalog trait):

catalog: polyglot_sql.FunctionCatalogSpec = {
    "functions": [{
        "name": "my_udf",
        "signatures": [{"minArity": 1, "maxArity": 2}],
    }],
}
result = polyglot_sql.validate_with_schema(
    "SELECT my_udf(1)", {"tables": []}, check_types=True,
    function_catalog=catalog,
)

The catalog replaces the embedded function name/arity catalog; it does not activate check_types automatically. Overloads are supported; omitted/null maxArity means variadic. nameCase is insensitive by default, or sensitive, and can be overridden per function. Native typed-function checks remain active. Blank names, empty signature lists, negative/noninteger arities, reversed bounds, conflicting case overrides, and unknown fields are rejected. Other SDKs do not expose this catalog option.

Each ValidationErrorInfo has:

• message: str
• line: int
• col: int
• code: str
• severity: str
• start: int | None (zero-based Unicode character offset)
• end: int | None (exclusive Unicode character offset)

Source ranges refer to the original SQL and support Python string slicing. Reference diagnostics point to the offending identifier when available; synthetic or schema-only findings have no source range. Existing line and col fields remain integers and use 0 when unavailable.

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

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/