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# SQLGlot

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
- 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

## 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" OR "x"."b" OR "x"."c"
      ) AND (
        "x"."a" OR "x"."b" OR "x"."d"
      ) 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

- 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 typing as t
     10
     11from sqlglot import expressions as exp
     12from sqlglot.dialects.dialect import Dialect as Dialect, Dialects as Dialects
     13from sqlglot.diff import diff as diff
     14from sqlglot.errors import (
     15    ErrorLevel as ErrorLevel,
     16    ParseError as ParseError,
     17    TokenError as TokenError,
     18    UnsupportedError as UnsupportedError,
     19)
     20from sqlglot.expressions import (
     21    Expression as Expression,
     22    alias_ as alias,
     23    and_ as and_,
     24    column as column,
     25    condition as condition,
     26    except_ as except_,
     27    from_ as from_,
     28    intersect as intersect,
     29    maybe_parse as maybe_parse,
     30    not_ as not_,
     31    or_ as or_,
     32    select as select,
     33    subquery as subquery,
     34    table_ as table,
     35    to_column as to_column,
     36    to_table as to_table,
     37    union as union,
     38)
     39from sqlglot.generator import Generator as Generator
     40from sqlglot.parser import Parser as Parser
     41from sqlglot.schema import MappingSchema as MappingSchema, Schema as Schema
     42from sqlglot.tokens import Tokenizer as Tokenizer, TokenType as TokenType
     43
     44if t.TYPE_CHECKING:
     45    from sqlglot.dialects.dialect import DialectType as DialectType
     46
     47    T = t.TypeVar("T", bound=Expression)
     48
     49
     50__version__ = "11.2.2"
     51
     52pretty = False
     53"""Whether to format generated SQL by default."""
     54
     55schema = MappingSchema()
     56"""The default schema used by SQLGlot (e.g. in the optimizer)."""
     57
     58
     59def parse(sql: str, read: DialectType = None, **opts) -> t.List[t.Optional[Expression]]:
     60    """
     61    Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.
     62
     63    Args:
     64        sql: the SQL code string to parse.
     65        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
     66        **opts: other `sqlglot.parser.Parser` options.
     67
     68    Returns:
     69        The resulting syntax tree collection.
     70    """
     71    dialect = Dialect.get_or_raise(read)()
     72    return dialect.parse(sql, **opts)
     73
     74
     75@t.overload
     76def parse_one(
     77    sql: str,
     78    read: None = None,
     79    into: t.Type[T] = ...,
     80    **opts,
     81) -> T:
     82    ...
     83
     84
     85@t.overload
     86def parse_one(
     87    sql: str,
     88    read: DialectType,
     89    into: t.Type[T],
     90    **opts,
     91) -> T:
     92    ...
     93
     94
     95@t.overload
     96def parse_one(
     97    sql: str,
     98    read: None = None,
     99    into: t.Union[str, t.Collection[t.Union[str, t.Type[Expression]]]] = ...,
    100    **opts,
    101) -> Expression:
    102    ...
    103
    104
    105@t.overload
    106def parse_one(
    107    sql: str,
    108    read: DialectType,
    109    into: t.Union[str, t.Collection[t.Union[str, t.Type[Expression]]]],
    110    **opts,
    111) -> Expression:
    112    ...
    113
    114
    115@t.overload
    116def parse_one(
    117    sql: str,
    118    **opts,
    119) -> Expression:
    120    ...
    121
    122
    123def parse_one(
    124    sql: str,
    125    read: DialectType = None,
    126    into: t.Optional[exp.IntoType] = None,
    127    **opts,
    128) -> Expression:
    129    """
    130    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
    131
    132    Args:
    133        sql: the SQL code string to parse.
    134        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    135        into: the SQLGlot Expression to parse into.
    136        **opts: other `sqlglot.parser.Parser` options.
    137
    138    Returns:
    139        The syntax tree for the first parsed statement.
    140    """
    141
    142    dialect = Dialect.get_or_raise(read)()
    143
    144    if into:
    145        result = dialect.parse_into(into, sql, **opts)
    146    else:
    147        result = dialect.parse(sql, **opts)
    148
    149    for expression in result:
    150        if not expression:
    151            raise ParseError(f"No expression was parsed from '{sql}'")
    152        return expression
    153    else:
    154        raise ParseError(f"No expression was parsed from '{sql}'")
    155
    156
    157def transpile(
    158    sql: str,
    159    read: DialectType = None,
    160    write: DialectType = None,
    161    identity: bool = True,
    162    error_level: t.Optional[ErrorLevel] = None,
    163    **opts,
    164) -> t.List[str]:
    165    """
    166    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
    167    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
    168
    169    Args:
    170        sql: the SQL code string to transpile.
    171        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
    172        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
    173        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
    174            the source and the target dialect.
    175        error_level: the desired error level of the parser.
    176        **opts: other `sqlglot.generator.Generator` options.
    177
    178    Returns:
    179        The list of transpiled SQL statements.
    180    """
    181    write = write or read if identity else write
    182    return [
    183        Dialect.get_or_raise(write)().generate(expression, **opts)
    184        for expression in parse(sql, read, error_level=error_level)
    185    ]

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

    60def parse(sql: str, read: DialectType = None, **opts) -> t.List[t.Optional[Expression]]:
    61    """
    62    Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.
    63
    64    Args:
    65        sql: the SQL code string to parse.
    66        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    67        **opts: other `sqlglot.parser.Parser` options.
    68
    69    Returns:
    70        The resulting syntax tree collection.
    71    """
    72    dialect = Dialect.get_or_raise(read)()
    73    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

    124def parse_one(
    125    sql: str,
    126    read: DialectType = None,
    127    into: t.Optional[exp.IntoType] = None,
    128    **opts,
    129) -> Expression:
    130    """
    131    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
    132
    133    Args:
    134        sql: the SQL code string to parse.
    135        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    136        into: the SQLGlot Expression to parse into.
    137        **opts: other `sqlglot.parser.Parser` options.
    138
    139    Returns:
    140        The syntax tree for the first parsed statement.
    141    """
    142
    143    dialect = Dialect.get_or_raise(read)()
    144
    145    if into:
    146        result = dialect.parse_into(into, sql, **opts)
    147    else:
    148        result = dialect.parse(sql, **opts)
    149
    150    for expression in result:
    151        if not expression:
    152            raise ParseError(f"No expression was parsed from '{sql}'")
    153        return expression
    154    else:
    155        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

    158def transpile(
    159    sql: str,
    160    read: DialectType = None,
    161    write: DialectType = None,
    162    identity: bool = True,
    163    error_level: t.Optional[ErrorLevel] = None,
    164    **opts,
    165) -> t.List[str]:
    166    """
    167    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
    168    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
    169
    170    Args:
    171        sql: the SQL code string to transpile.
    172        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
    173        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
    174        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
    175            the source and the target dialect.
    176        error_level: the desired error level of the parser.
    177        **opts: other `sqlglot.generator.Generator` options.
    178
    179    Returns:
    180        The list of transpiled SQL statements.
    181    """
    182    write = write or read if identity else write
    183    return [
    184        Dialect.get_or_raise(write)().generate(expression, **opts)
    185        for expression in parse(sql, read, error_level=error_level)
    186    ]

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.