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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.

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 from 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': 13,
      'start_context': 'SELECT foo( ',
      'highlight': 'FROM',
      'end_context': ' bar'
    }]

### 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 y, 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:

    make docs-serve

## Run Tests and Lint

    make check  # Set SKIP_INTEGRATION=1 to skip integration tests

## 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(
     88    sql: str,
     89    read: None = None,
     90    into: t.Type[E] = ...,
     91    **opts,
     92) -> E:
     93    ...
     94
     95
     96@t.overload
     97def parse_one(
     98    sql: str,
     99    read: DialectType,
    100    into: t.Type[E],
    101    **opts,
    102) -> E:
    103    ...
    104
    105
    106@t.overload
    107def parse_one(
    108    sql: str,
    109    read: None = None,
    110    into: t.Union[str, t.Collection[t.Union[str, t.Type[Expression]]]] = ...,
    111    **opts,
    112) -> Expression:
    113    ...
    114
    115
    116@t.overload
    117def parse_one(
    118    sql: str,
    119    read: DialectType,
    120    into: t.Union[str, t.Collection[t.Union[str, t.Type[Expression]]]],
    121    **opts,
    122) -> Expression:
    123    ...
    124
    125
    126@t.overload
    127def parse_one(
    128    sql: str,
    129    **opts,
    130) -> Expression:
    131    ...
    132
    133
    134def parse_one(
    135    sql: str,
    136    read: DialectType = None,
    137    into: t.Optional[exp.IntoType] = None,
    138    **opts,
    139) -> Expression:
    140    """
    141    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
    142
    143    Args:
    144        sql: the SQL code string to parse.
    145        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    146        into: the SQLGlot Expression to parse into.
    147        **opts: other `sqlglot.parser.Parser` options.
    148
    149    Returns:
    150        The syntax tree for the first parsed statement.
    151    """
    152
    153    dialect = Dialect.get_or_raise(read)()
    154
    155    if into:
    156        result = dialect.parse_into(into, sql, **opts)
    157    else:
    158        result = dialect.parse(sql, **opts)
    159
    160    for expression in result:
    161        if not expression:
    162            raise ParseError(f"No expression was parsed from '{sql}'")
    163        return expression
    164    else:
    165        raise ParseError(f"No expression was parsed from '{sql}'")
    166
    167
    168def transpile(
    169    sql: str,
    170    read: DialectType = None,
    171    write: DialectType = None,
    172    identity: bool = True,
    173    error_level: t.Optional[ErrorLevel] = None,
    174    **opts,
    175) -> t.List[str]:
    176    """
    177    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
    178    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
    179
    180    Args:
    181        sql: the SQL code string to transpile.
    182        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
    183        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
    184        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
    185            the source and the target dialect.
    186        error_level: the desired error level of the parser.
    187        **opts: other `sqlglot.generator.Generator` options.
    188
    189    Returns:
    190        The list of transpiled SQL statements.
    191    """
    192    write = (read if write is None else write) if identity else write
    193    return [
    194        Dialect.get_or_raise(write)().generate(expression, **opts)
    195        for expression in parse(sql, read, error_level=error_level)
    196    ]

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, into: Union[str, Type[sqlglot.expressions.Expression], Collection[Union[str, Type[sqlglot.expressions.Expression]]], NoneType] = None, **opts) -> sqlglot.expressions.Expression: View Source

    135def parse_one(
    136    sql: str,
    137    read: DialectType = None,
    138    into: t.Optional[exp.IntoType] = None,
    139    **opts,
    140) -> Expression:
    141    """
    142    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
    143
    144    Args:
    145        sql: the SQL code string to parse.
    146        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    147        into: the SQLGlot Expression to parse into.
    148        **opts: other `sqlglot.parser.Parser` options.
    149
    150    Returns:
    151        The syntax tree for the first parsed statement.
    152    """
    153
    154    dialect = Dialect.get_or_raise(read)()
    155
    156    if into:
    157        result = dialect.parse_into(into, sql, **opts)
    158    else:
    159        result = dialect.parse(sql, **opts)
    160
    161    for expression in result:
    162        if not expression:
    163            raise ParseError(f"No expression was parsed from '{sql}'")
    164        return expression
    165    else:
    166        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").
- 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

    169def transpile(
    170    sql: str,
    171    read: DialectType = None,
    172    write: DialectType = None,
    173    identity: bool = True,
    174    error_level: t.Optional[ErrorLevel] = None,
    175    **opts,
    176) -> t.List[str]:
    177    """
    178    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
    179    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
    180
    181    Args:
    182        sql: the SQL code string to transpile.
    183        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
    184        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
    185        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
    186            the source and the target dialect.
    187        error_level: the desired error level of the parser.
    188        **opts: other `sqlglot.generator.Generator` options.
    189
    190    Returns:
    191        The list of transpiled SQL statements.
    192    """
    193    write = (read if write is None else write) if identity else write
    194    return [
    195        Dialect.get_or_raise(write)().generate(expression, **opts)
    196        for expression in parse(sql, read, error_level=error_level)
    197    ]

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.