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SQLGlot is a no-dependency SQL parser, transpiler, optimizer, and engine. It can be used to format SQL or translate between 19 different dialects like DuckDB, Presto, Spark, Snowflake, and BigQuery. It aims to read a wide variety of SQL inputs and output syntactically correct SQL in the targeted dialects.

It is a very comprehensive generic SQL parser with a robust test suite. It is also quite performant, while being written purely in Python.

You can easily customize the parser, analyze queries, traverse expression trees, and programmatically build SQL.

Syntax errors are highlighted and dialect incompatibilities can warn or raise depending on configurations. However, it should be noted that SQL validation is not SQLGlot’s goal, so some syntax errors may go unnoticed.

Learn more about the SQLGlot API in the documentation.

Contributions are very welcome in SQLGlot; read the contribution guide to get started!

## Table of Contents

- Install
- Versioning
- Get in Touch
- Examples
  - Formatting and Transpiling
  - Metadata
  - Parser Errors
  - Unsupported Errors
  - Build and Modify SQL
  - SQL Optimizer
  - AST Introspection
  - AST Diff
  - Custom Dialects
  - SQL Execution
- Used By
- Documentation
- Run Tests and Lint
- Benchmarks
- Optional Dependencies

## Install

From PyPI:

    pip3 install sqlglot

Or with a local checkout:

    make install

Requirements for development (optional):

    make install-dev

## Versioning

Given a version number `MAJOR`.`MINOR`.`PATCH`, SQLGlot uses the following versioning strategy:

- The `PATCH` version is incremented when there are backwards-compatible fixes or feature additions.
- The `MINOR` version is incremented when there are backwards-incompatible fixes or feature additions.
- The `MAJOR` version is incremented when there are significant backwards-incompatible fixes or feature additions.

## Get in Touch

We'd love to hear from you. Join our community Slack channel!

## Examples

### Formatting and Transpiling

Easily translate from one dialect to another. For example, date/time functions vary between dialects and can be hard to deal with:

    import sqlglot
    sqlglot.transpile("SELECT EPOCH_MS(1618088028295)", read="duckdb", write="hive")[0]

    'SELECT FROM_UNIXTIME(1618088028295 / 1000)'

SQLGlot can even translate custom time formats:

    import sqlglot
    sqlglot.transpile("SELECT STRFTIME(x, '%y-%-m-%S')", read="duckdb", write="hive")[0]

    "SELECT DATE_FORMAT(x, 'yy-M-ss')"

As another example, let's suppose that we want to read in a SQL query that contains a CTE and a cast to `REAL`, and then transpile it to Spark, which uses backticks for identifiers and `FLOAT` instead of `REAL`:

    import sqlglot

    sql = """WITH baz AS (SELECT a, c FROM foo WHERE a = 1) SELECT f.a, b.b, baz.c, CAST("b"."a" AS REAL) d FROM foo f JOIN bar b ON f.a = b.a LEFT JOIN baz ON f.a = baz.a"""
    print(sqlglot.transpile(sql, write="spark", identify=True, pretty=True)[0])

    WITH `baz` AS (
      SELECT
        `a`,
        `c`
      FROM `foo`
      WHERE
        `a` = 1
    )
    SELECT
      `f`.`a`,
      `b`.`b`,
      `baz`.`c`,
      CAST(`b`.`a` AS FLOAT) AS `d`
    FROM `foo` AS `f`
    JOIN `bar` AS `b`
      ON `f`.`a` = `b`.`a`
    LEFT JOIN `baz`
      ON `f`.`a` = `baz`.`a`

Comments are also preserved in a best-effort basis when transpiling SQL code:

    sql = """
    /* multi
       line
       comment
    */
    SELECT
      tbl.cola /* comment 1 */ + tbl.colb /* comment 2 */,
      CAST(x AS INT), # comment 3
      y               -- comment 4
    FROM
      bar /* comment 5 */,
      tbl #          comment 6
    """

    print(sqlglot.transpile(sql, read='mysql', pretty=True)[0])

    /* multi
       line
       comment
    */
    SELECT
      tbl.cola /* comment 1 */ + tbl.colb /* comment 2 */,
      CAST(x AS INT), /* comment 3 */
      y /* comment 4 */
    FROM bar /* comment 5 */, tbl /*          comment 6 */

### Metadata

You can explore SQL with expression helpers to do things like find columns and tables:

    from sqlglot import parse_one, exp

    # print all column references (a and b)
    for column in parse_one("SELECT a, b + 1 AS c FROM d").find_all(exp.Column):
        print(column.alias_or_name)

    # find all projections in select statements (a and c)
    for select in parse_one("SELECT a, b + 1 AS c FROM d").find_all(exp.Select):
        for projection in select.expressions:
            print(projection.alias_or_name)

    # find all tables (x, y, z)
    for table in parse_one("SELECT * FROM x JOIN y JOIN z").find_all(exp.Table):
        print(table.name)

### Parser Errors

When the parser detects an error in the syntax, it raises a ParserError:

    import sqlglot
    sqlglot.transpile("SELECT foo( FROM bar")

    sqlglot.errors.ParseError: Expecting ). Line 1, Col: 13.
      select foo( FROM bar
                  ~~~~

Structured syntax errors are accessible for programmatic use:

    import sqlglot
    try:
        sqlglot.transpile("SELECT foo( FROM bar")
    except sqlglot.errors.ParseError as e:
        print(e.errors)

    [{
      'description': 'Expecting )',
      'line': 1,
      'col': 16,
      'start_context': 'SELECT foo( ',
      'highlight': 'FROM',
      'end_context': ' bar',
      'into_expression': None,
    }]

### Unsupported Errors

Presto `APPROX_DISTINCT` supports the accuracy argument which is not supported in Hive:

    import sqlglot
    sqlglot.transpile("SELECT APPROX_DISTINCT(a, 0.1) FROM foo", read="presto", write="hive")

    APPROX_COUNT_DISTINCT does not support accuracy
    'SELECT APPROX_COUNT_DISTINCT(a) FROM foo'

### Build and Modify SQL

SQLGlot supports incrementally building sql expressions:

    from sqlglot import select, condition

    where = condition("x=1").and_("y=1")
    select("*").from_("y").where(where).sql()

    'SELECT * FROM y WHERE x = 1 AND y = 1'

You can also modify a parsed tree:

    from sqlglot import parse_one
    parse_one("SELECT x FROM y").from_("z").sql()

    'SELECT x FROM z'

There is also a way to recursively transform the parsed tree by applying a mapping function to each tree node:

    from sqlglot import exp, parse_one

    expression_tree = parse_one("SELECT a FROM x")

    def transformer(node):
        if isinstance(node, exp.Column) and node.name == "a":
            return parse_one("FUN(a)")
        return node

    transformed_tree = expression_tree.transform(transformer)
    transformed_tree.sql()

    'SELECT FUN(a) FROM x'

### SQL Optimizer

SQLGlot can rewrite queries into an "optimized" form. It performs a variety of techniques to create a new canonical AST. This AST can be used to standardize queries or provide the foundations for implementing an actual engine. For example:

    import sqlglot
    from sqlglot.optimizer import optimize

    print(
        optimize(
            sqlglot.parse_one("""
                SELECT A OR (B OR (C AND D))
                FROM x
                WHERE Z = date '2021-01-01' + INTERVAL '1' month OR 1 = 0
            """),
            schema={"x": {"A": "INT", "B": "INT", "C": "INT", "D": "INT", "Z": "STRING"}}
        ).sql(pretty=True)
    )

    SELECT
      (
        "x"."a" <> 0 OR "x"."b" <> 0 OR "x"."c" <> 0
      )
      AND (
        "x"."a" <> 0 OR "x"."b" <> 0 OR "x"."d" <> 0
      ) AS "_col_0"
    FROM "x" AS "x"
    WHERE
      CAST("x"."z" AS DATE) = CAST('2021-02-01' AS DATE)

### AST Introspection

You can see the AST version of the sql by calling `repr`:

    from sqlglot import parse_one
    print(repr(parse_one("SELECT a + 1 AS z")))

    (SELECT expressions:
      (ALIAS this:
        (ADD this:
          (COLUMN this:
            (IDENTIFIER this: a, quoted: False)), expression:
          (LITERAL this: 1, is_string: False)), alias:
        (IDENTIFIER this: z, quoted: False)))

### AST Diff

SQLGlot can calculate the difference between two expressions and output changes in a form of a sequence of actions needed to transform a source expression into a target one:

    from sqlglot import diff, parse_one
    diff(parse_one("SELECT a + b, c, d"), parse_one("SELECT c, a - b, d"))

    [
      Remove(expression=(ADD this:
        (COLUMN this:
          (IDENTIFIER this: a, quoted: False)), expression:
        (COLUMN this:
          (IDENTIFIER this: b, quoted: False)))),
      Insert(expression=(SUB this:
        (COLUMN this:
          (IDENTIFIER this: a, quoted: False)), expression:
        (COLUMN this:
          (IDENTIFIER this: b, quoted: False)))),
      Move(expression=(COLUMN this:
        (IDENTIFIER this: c, quoted: False))),
      Keep(source=(IDENTIFIER this: b, quoted: False), target=(IDENTIFIER this: b, quoted: False)),
      ...
    ]

See also: Semantic Diff for SQL.

### Custom Dialects

Dialects can be added by subclassing `Dialect`:

    from sqlglot import exp
    from sqlglot.dialects.dialect import Dialect
    from sqlglot.generator import Generator
    from sqlglot.tokens import Tokenizer, TokenType

    class Custom(Dialect):
        class Tokenizer(Tokenizer):
            QUOTES = ["'", '"']
            IDENTIFIERS = ["`"]

            KEYWORDS = {
                **Tokenizer.KEYWORDS,
                "INT64": TokenType.BIGINT,
                "FLOAT64": TokenType.DOUBLE,
            }

        class Generator(Generator):
            TRANSFORMS = {exp.Array: lambda self, e: f"[{self.expressions(e)}]"}

            TYPE_MAPPING = {
                exp.DataType.Type.TINYINT: "INT64",
                exp.DataType.Type.SMALLINT: "INT64",
                exp.DataType.Type.INT: "INT64",
                exp.DataType.Type.BIGINT: "INT64",
                exp.DataType.Type.DECIMAL: "NUMERIC",
                exp.DataType.Type.FLOAT: "FLOAT64",
                exp.DataType.Type.DOUBLE: "FLOAT64",
                exp.DataType.Type.BOOLEAN: "BOOL",
                exp.DataType.Type.TEXT: "STRING",
            }

    print(Dialect["custom"])

    <class '__main__.Custom'>

### SQL Execution

One can even interpret SQL queries using SQLGlot, where the tables are represented as Python dictionaries. Although the engine is not very fast (it's not supposed to be) and is in a relatively early stage of development, it can be useful for unit testing and running SQL natively across Python objects. Additionally, the foundation can be easily integrated with fast compute kernels (arrow, pandas). Below is an example showcasing the execution of a SELECT expression that involves aggregations and JOINs:

    from sqlglot.executor import execute

    tables = {
        "sushi": [
            {"id": 1, "price": 1.0},
            {"id": 2, "price": 2.0},
            {"id": 3, "price": 3.0},
        ],
        "order_items": [
            {"sushi_id": 1, "order_id": 1},
            {"sushi_id": 1, "order_id": 1},
            {"sushi_id": 2, "order_id": 1},
            {"sushi_id": 3, "order_id": 2},
        ],
        "orders": [
            {"id": 1, "user_id": 1},
            {"id": 2, "user_id": 2},
        ],
    }

    execute(
        """
        SELECT
          o.user_id,
          SUM(s.price) AS price
        FROM orders o
        JOIN order_items i
          ON o.id = i.order_id
        JOIN sushi s
          ON i.sushi_id = s.id
        GROUP BY o.user_id
        """,
        tables=tables
    )

    user_id price
          1   4.0
          2   3.0

See also: Writing a Python SQL engine from scratch.

## Used By

- SQLMesh
- Fugue
- ibis
- mysql-mimic
- Querybook
- Quokka
- Splink

## Documentation

SQLGlot uses pdoc to serve its API documentation.

A hosted version is on the SQLGlot website, or you can build locally with:

    make docs-serve

## Run Tests and Lint

    make style  # Only linter checks
    make unit   # Only unit tests
    make check  # Full test suite & linter checks

## Benchmarks

Benchmarks run on Python 3.10.5 in seconds.

Query | sqlglot | sqlfluff | sqltree | sqlparse | moz_sql_parser | sqloxide
--- | --- | --- | --- | --- | --- | ---
tpch | 0.01308 (1.0) | 1.60626 (122.7) | 0.01168 (0.893) | 0.04958 (3.791) | 0.08543 (6.531) | 0.00136 (0.104)
short | 0.00109 (1.0) | 0.14134 (129.2) | 0.00099 (0.906) | 0.00342 (3.131) | 0.00652 (5.970) | 8.76E-5 (0.080)
long | 0.01399 (1.0) | 2.12632 (151.9) | 0.01126 (0.805) | 0.04410 (3.151) | 0.06671 (4.767) | 0.00107 (0.076)
crazy | 0.03969 (1.0) | 24.3777 (614.1) | 0.03917 (0.987) | 11.7043 (294.8) | 1.03280 (26.02) | 0.00625 (0.157)

## Optional Dependencies

SQLGlot uses dateutil to simplify literal timedelta expressions. The optimizer will not simplify expressions like the following if the module cannot be found:

    x + interval '1' month

View Source

      1"""
      2.. include:: ../README.md
      3
      4----
      5"""
      6
      7from __future__ import annotations
      8
      9import logging
     10import typing as t
     11
     12from sqlglot import expressions as exp
     13from sqlglot.dialects.dialect import Dialect as Dialect, Dialects as Dialects
     14from sqlglot.diff import diff as diff
     15from sqlglot.errors import (
     16    ErrorLevel as ErrorLevel,
     17    ParseError as ParseError,
     18    TokenError as TokenError,
     19    UnsupportedError as UnsupportedError,
     20)
     21from sqlglot.expressions import (
     22    Expression as Expression,
     23    alias_ as alias,
     24    and_ as and_,
     25    cast as cast,
     26    column as column,
     27    condition as condition,
     28    except_ as except_,
     29    from_ as from_,
     30    func as func,
     31    intersect as intersect,
     32    maybe_parse as maybe_parse,
     33    not_ as not_,
     34    or_ as or_,
     35    select as select,
     36    subquery as subquery,
     37    table_ as table,
     38    to_column as to_column,
     39    to_identifier as to_identifier,
     40    to_table as to_table,
     41    union as union,
     42)
     43from sqlglot.generator import Generator as Generator
     44from sqlglot.parser import Parser as Parser
     45from sqlglot.schema import MappingSchema as MappingSchema, Schema as Schema
     46from sqlglot.tokens import Tokenizer as Tokenizer, TokenType as TokenType
     47
     48if t.TYPE_CHECKING:
     49    from sqlglot._typing import E
     50    from sqlglot.dialects.dialect import DialectType as DialectType
     51
     52logger = logging.getLogger("sqlglot")
     53
     54
     55try:
     56    from sqlglot._version import __version__, __version_tuple__
     57except ImportError:
     58    logger.error(
     59        "Unable to set __version__, run `pip install -e .` or `python setup.py develop` first."
     60    )
     61
     62
     63pretty = False
     64"""Whether to format generated SQL by default."""
     65
     66schema = MappingSchema()
     67"""The default schema used by SQLGlot (e.g. in the optimizer)."""
     68
     69
     70def parse(sql: str, read: DialectType = None, **opts) -> t.List[t.Optional[Expression]]:
     71    """
     72    Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.
     73
     74    Args:
     75        sql: the SQL code string to parse.
     76        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
     77        **opts: other `sqlglot.parser.Parser` options.
     78
     79    Returns:
     80        The resulting syntax tree collection.
     81    """
     82    dialect = Dialect.get_or_raise(read)()
     83    return dialect.parse(sql, **opts)
     84
     85
     86@t.overload
     87def parse_one(sql: str, *, into: t.Type[E], **opts) -> E:
     88    ...
     89
     90
     91@t.overload
     92def parse_one(sql: str, **opts) -> Expression:
     93    ...
     94
     95
     96def parse_one(
     97    sql: str,
     98    read: DialectType = None,
     99    dialect: DialectType = None,
    100    into: t.Optional[exp.IntoType] = None,
    101    **opts,
    102) -> Expression:
    103    """
    104    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
    105
    106    Args:
    107        sql: the SQL code string to parse.
    108        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    109        dialect: the SQL dialect (alias for read)
    110        into: the SQLGlot Expression to parse into.
    111        **opts: other `sqlglot.parser.Parser` options.
    112
    113    Returns:
    114        The syntax tree for the first parsed statement.
    115    """
    116
    117    dialect = Dialect.get_or_raise(read or dialect)()
    118
    119    if into:
    120        result = dialect.parse_into(into, sql, **opts)
    121    else:
    122        result = dialect.parse(sql, **opts)
    123
    124    for expression in result:
    125        if not expression:
    126            raise ParseError(f"No expression was parsed from '{sql}'")
    127        return expression
    128    else:
    129        raise ParseError(f"No expression was parsed from '{sql}'")
    130
    131
    132def transpile(
    133    sql: str,
    134    read: DialectType = None,
    135    write: DialectType = None,
    136    identity: bool = True,
    137    error_level: t.Optional[ErrorLevel] = None,
    138    **opts,
    139) -> t.List[str]:
    140    """
    141    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
    142    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
    143
    144    Args:
    145        sql: the SQL code string to transpile.
    146        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
    147        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
    148        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
    149            the source and the target dialect.
    150        error_level: the desired error level of the parser.
    151        **opts: other `sqlglot.generator.Generator` options.
    152
    153    Returns:
    154        The list of transpiled SQL statements.
    155    """
    156    write = (read if write is None else write) if identity else write
    157    return [
    158        Dialect.get_or_raise(write)().generate(expression, **opts)
    159        for expression in parse(sql, read, error_level=error_level)
    160    ]

logger = <Logger sqlglot (WARNING)>
pretty = False

Whether to format generated SQL by default.

schema = <sqlglot.schema.MappingSchema object>

The default schema used by SQLGlot (e.g. in the optimizer).

def parse( sql: str, read: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, **opts) -> List[Optional[sqlglot.expressions.Expression]]: View Source

    71def parse(sql: str, read: DialectType = None, **opts) -> t.List[t.Optional[Expression]]:
    72    """
    73    Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.
    74
    75    Args:
    76        sql: the SQL code string to parse.
    77        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    78        **opts: other `sqlglot.parser.Parser` options.
    79
    80    Returns:
    81        The resulting syntax tree collection.
    82    """
    83    dialect = Dialect.get_or_raise(read)()
    84    return dialect.parse(sql, **opts)

Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.

###### Arguments:

- sql: the SQL code string to parse.
- read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
- **opts: other `sqlglot.parser.Parser` options.

###### Returns:

> The resulting syntax tree collection.

def parse_one( sql: str, read: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, into: Union[str, Type[sqlglot.expressions.Expression], Collection[Union[str, Type[sqlglot.expressions.Expression]]], NoneType] = None, **opts) -> sqlglot.expressions.Expression: View Source

     97def parse_one(
     98    sql: str,
     99    read: DialectType = None,
    100    dialect: DialectType = None,
    101    into: t.Optional[exp.IntoType] = None,
    102    **opts,
    103) -> Expression:
    104    """
    105    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
    106
    107    Args:
    108        sql: the SQL code string to parse.
    109        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    110        dialect: the SQL dialect (alias for read)
    111        into: the SQLGlot Expression to parse into.
    112        **opts: other `sqlglot.parser.Parser` options.
    113
    114    Returns:
    115        The syntax tree for the first parsed statement.
    116    """
    117
    118    dialect = Dialect.get_or_raise(read or dialect)()
    119
    120    if into:
    121        result = dialect.parse_into(into, sql, **opts)
    122    else:
    123        result = dialect.parse(sql, **opts)
    124
    125    for expression in result:
    126        if not expression:
    127            raise ParseError(f"No expression was parsed from '{sql}'")
    128        return expression
    129    else:
    130        raise ParseError(f"No expression was parsed from '{sql}'")

Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.

###### Arguments:

- sql: the SQL code string to parse.
- read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
- dialect: the SQL dialect (alias for read)
- into: the SQLGlot Expression to parse into.
- **opts: other `sqlglot.parser.Parser` options.

###### Returns:

> The syntax tree for the first parsed statement.

def transpile( sql: str, read: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, write: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, identity: bool = True, error_level: Optional[sqlglot.errors.ErrorLevel] = None, **opts) -> List[str]: View Source

    133def transpile(
    134    sql: str,
    135    read: DialectType = None,
    136    write: DialectType = None,
    137    identity: bool = True,
    138    error_level: t.Optional[ErrorLevel] = None,
    139    **opts,
    140) -> t.List[str]:
    141    """
    142    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
    143    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
    144
    145    Args:
    146        sql: the SQL code string to transpile.
    147        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
    148        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
    149        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
    150            the source and the target dialect.
    151        error_level: the desired error level of the parser.
    152        **opts: other `sqlglot.generator.Generator` options.
    153
    154    Returns:
    155        The list of transpiled SQL statements.
    156    """
    157    write = (read if write is None else write) if identity else write
    158    return [
    159        Dialect.get_or_raise(write)().generate(expression, **opts)
    160        for expression in parse(sql, read, error_level=error_level)
    161    ]

Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.

###### Arguments:

- sql: the SQL code string to transpile.
- read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
- write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
- identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both: the source and the target dialect.
- error_level: the desired error level of the parser.
- **opts: other `sqlglot.generator.Generator` options.

###### Returns:

> The list of transpiled SQL statements.