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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 import Dialect, Dialects
     13from sqlglot.diff import diff
     14from sqlglot.errors import ErrorLevel, ParseError, TokenError, UnsupportedError
     15from sqlglot.expressions import Expression
     16from sqlglot.expressions import alias_ as alias
     17from sqlglot.expressions import (
     18    and_,
     19    column,
     20    condition,
     21    except_,
     22    from_,
     23    intersect,
     24    maybe_parse,
     25    not_,
     26    or_,
     27    select,
     28    subquery,
     29)
     30from sqlglot.expressions import table_ as table
     31from sqlglot.expressions import to_column, to_table, union
     32from sqlglot.generator import Generator
     33from sqlglot.parser import Parser
     34from sqlglot.schema import MappingSchema, Schema
     35from sqlglot.tokens import Tokenizer, TokenType
     36
     37if t.TYPE_CHECKING:
     38    from sqlglot.dialects.dialect import DialectType
     39
     40    T = t.TypeVar("T", bound=Expression)
     41
     42
     43__version__ = "11.1.2"
     44
     45pretty = False
     46"""Whether to format generated SQL by default."""
     47
     48schema = MappingSchema()
     49"""The default schema used by SQLGlot (e.g. in the optimizer)."""
     50
     51
     52def parse(sql: str, read: DialectType = None, **opts) -> t.List[t.Optional[Expression]]:
     53    """
     54    Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.
     55
     56    Args:
     57        sql: the SQL code string to parse.
     58        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
     59        **opts: other `sqlglot.parser.Parser` options.
     60
     61    Returns:
     62        The resulting syntax tree collection.
     63    """
     64    dialect = Dialect.get_or_raise(read)()
     65    return dialect.parse(sql, **opts)
     66
     67
     68@t.overload
     69def parse_one(
     70    sql: str,
     71    read: None = None,
     72    into: t.Type[T] = ...,
     73    **opts,
     74) -> T:
     75    ...
     76
     77
     78@t.overload
     79def parse_one(
     80    sql: str,
     81    read: DialectType,
     82    into: t.Type[T],
     83    **opts,
     84) -> T:
     85    ...
     86
     87
     88@t.overload
     89def parse_one(
     90    sql: str,
     91    read: None = None,
     92    into: t.Union[str, t.Collection[t.Union[str, t.Type[Expression]]]] = ...,
     93    **opts,
     94) -> Expression:
     95    ...
     96
     97
     98@t.overload
     99def parse_one(
    100    sql: str,
    101    read: DialectType,
    102    into: t.Union[str, t.Collection[t.Union[str, t.Type[Expression]]]],
    103    **opts,
    104) -> Expression:
    105    ...
    106
    107
    108@t.overload
    109def parse_one(
    110    sql: str,
    111    **opts,
    112) -> Expression:
    113    ...
    114
    115
    116def parse_one(
    117    sql: str,
    118    read: DialectType = None,
    119    into: t.Optional[exp.IntoType] = None,
    120    **opts,
    121) -> Expression:
    122    """
    123    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
    124
    125    Args:
    126        sql: the SQL code string to parse.
    127        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    128        into: the SQLGlot Expression to parse into.
    129        **opts: other `sqlglot.parser.Parser` options.
    130
    131    Returns:
    132        The syntax tree for the first parsed statement.
    133    """
    134
    135    dialect = Dialect.get_or_raise(read)()
    136
    137    if into:
    138        result = dialect.parse_into(into, sql, **opts)
    139    else:
    140        result = dialect.parse(sql, **opts)
    141
    142    for expression in result:
    143        if not expression:
    144            raise ParseError(f"No expression was parsed from '{sql}'")
    145        return expression
    146    else:
    147        raise ParseError(f"No expression was parsed from '{sql}'")
    148
    149
    150def transpile(
    151    sql: str,
    152    read: DialectType = None,
    153    write: DialectType = None,
    154    identity: bool = True,
    155    error_level: t.Optional[ErrorLevel] = None,
    156    **opts,
    157) -> t.List[str]:
    158    """
    159    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
    160    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
    161
    162    Args:
    163        sql: the SQL code string to transpile.
    164        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
    165        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
    166        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
    167            the source and the target dialect.
    168        error_level: the desired error level of the parser.
    169        **opts: other `sqlglot.generator.Generator` options.
    170
    171    Returns:
    172        The list of transpiled SQL statements.
    173    """
    174    write = write or read if identity else write
    175    return [
    176        Dialect.get_or_raise(write)().generate(expression, **opts)
    177        for expression in parse(sql, read, error_level=error_level)
    178    ]

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

    53def parse(sql: str, read: DialectType = None, **opts) -> t.List[t.Optional[Expression]]:
    54    """
    55    Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.
    56
    57    Args:
    58        sql: the SQL code string to parse.
    59        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    60        **opts: other `sqlglot.parser.Parser` options.
    61
    62    Returns:
    63        The resulting syntax tree collection.
    64    """
    65    dialect = Dialect.get_or_raise(read)()
    66    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

    117def parse_one(
    118    sql: str,
    119    read: DialectType = None,
    120    into: t.Optional[exp.IntoType] = None,
    121    **opts,
    122) -> Expression:
    123    """
    124    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
    125
    126    Args:
    127        sql: the SQL code string to parse.
    128        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
    129        into: the SQLGlot Expression to parse into.
    130        **opts: other `sqlglot.parser.Parser` options.
    131
    132    Returns:
    133        The syntax tree for the first parsed statement.
    134    """
    135
    136    dialect = Dialect.get_or_raise(read)()
    137
    138    if into:
    139        result = dialect.parse_into(into, sql, **opts)
    140    else:
    141        result = dialect.parse(sql, **opts)
    142
    143    for expression in result:
    144        if not expression:
    145            raise ParseError(f"No expression was parsed from '{sql}'")
    146        return expression
    147    else:
    148        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

    151def transpile(
    152    sql: str,
    153    read: DialectType = None,
    154    write: DialectType = None,
    155    identity: bool = True,
    156    error_level: t.Optional[ErrorLevel] = None,
    157    **opts,
    158) -> t.List[str]:
    159    """
    160    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
    161    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
    162
    163    Args:
    164        sql: the SQL code string to transpile.
    165        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
    166        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
    167        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
    168            the source and the target dialect.
    169        error_level: the desired error level of the parser.
    170        **opts: other `sqlglot.generator.Generator` options.
    171
    172    Returns:
    173        The list of transpiled SQL statements.
    174    """
    175    write = write or read if identity else write
    176    return [
    177        Dialect.get_or_raise(write)().generate(expression, **opts)
    178        for expression in parse(sql, read, error_level=error_level)
    179    ]

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.