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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

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" <> 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

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

  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    cast as cast,
 25    column as column,
 26    condition as condition,
 27    except_ as except_,
 28    from_ as from_,
 29    func as func,
 30    intersect as intersect,
 31    maybe_parse as maybe_parse,
 32    not_ as not_,
 33    or_ as or_,
 34    select as select,
 35    subquery as subquery,
 36    table_ as table,
 37    to_column as to_column,
 38    to_identifier as to_identifier,
 39    to_table as to_table,
 40    union as union,
 41)
 42from sqlglot.generator import Generator as Generator
 43from sqlglot.parser import Parser as Parser
 44from sqlglot.schema import MappingSchema as MappingSchema, Schema as Schema
 45from sqlglot.tokens import Tokenizer as Tokenizer, TokenType as TokenType
 46
 47if t.TYPE_CHECKING:
 48    from sqlglot.dialects.dialect import DialectType as DialectType
 49
 50    T = t.TypeVar("T", bound=Expression)
 51
 52
 53try:
 54    from sqlglot._version import __version__, __version_tuple__  # type: ignore
 55except ImportError:
 56    pass
 57
 58
 59pretty = False
 60"""Whether to format generated SQL by default."""
 61
 62schema = MappingSchema()
 63"""The default schema used by SQLGlot (e.g. in the optimizer)."""
 64
 65
 66def parse(sql: str, read: DialectType = None, **opts) -> t.List[t.Optional[Expression]]:
 67    """
 68    Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.
 69
 70    Args:
 71        sql: the SQL code string to parse.
 72        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
 73        **opts: other `sqlglot.parser.Parser` options.
 74
 75    Returns:
 76        The resulting syntax tree collection.
 77    """
 78    dialect = Dialect.get_or_raise(read)()
 79    return dialect.parse(sql, **opts)
 80
 81
 82@t.overload
 83def parse_one(
 84    sql: str,
 85    read: None = None,
 86    into: t.Type[T] = ...,
 87    **opts,
 88) -> T:
 89    ...
 90
 91
 92@t.overload
 93def parse_one(
 94    sql: str,
 95    read: DialectType,
 96    into: t.Type[T],
 97    **opts,
 98) -> T:
 99    ...
100
101
102@t.overload
103def parse_one(
104    sql: str,
105    read: None = None,
106    into: t.Union[str, t.Collection[t.Union[str, t.Type[Expression]]]] = ...,
107    **opts,
108) -> Expression:
109    ...
110
111
112@t.overload
113def parse_one(
114    sql: str,
115    read: DialectType,
116    into: t.Union[str, t.Collection[t.Union[str, t.Type[Expression]]]],
117    **opts,
118) -> Expression:
119    ...
120
121
122@t.overload
123def parse_one(
124    sql: str,
125    **opts,
126) -> Expression:
127    ...
128
129
130def parse_one(
131    sql: str,
132    read: DialectType = None,
133    into: t.Optional[exp.IntoType] = None,
134    **opts,
135) -> Expression:
136    """
137    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
138
139    Args:
140        sql: the SQL code string to parse.
141        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
142        into: the SQLGlot Expression to parse into.
143        **opts: other `sqlglot.parser.Parser` options.
144
145    Returns:
146        The syntax tree for the first parsed statement.
147    """
148
149    dialect = Dialect.get_or_raise(read)()
150
151    if into:
152        result = dialect.parse_into(into, sql, **opts)
153    else:
154        result = dialect.parse(sql, **opts)
155
156    for expression in result:
157        if not expression:
158            raise ParseError(f"No expression was parsed from '{sql}'")
159        return expression
160    else:
161        raise ParseError(f"No expression was parsed from '{sql}'")
162
163
164def transpile(
165    sql: str,
166    read: DialectType = None,
167    write: DialectType = None,
168    identity: bool = True,
169    error_level: t.Optional[ErrorLevel] = None,
170    **opts,
171) -> t.List[str]:
172    """
173    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
174    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
175
176    Args:
177        sql: the SQL code string to transpile.
178        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
179        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
180        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
181            the source and the target dialect.
182        error_level: the desired error level of the parser.
183        **opts: other `sqlglot.generator.Generator` options.
184
185    Returns:
186        The list of transpiled SQL statements.
187    """
188    write = (read if write is None else write) if identity else write
189    return [
190        Dialect.get_or_raise(write)().generate(expression, **opts)
191        for expression in parse(sql, read, error_level=error_level)
192    ]
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]]:
67def parse(sql: str, read: DialectType = None, **opts) -> t.List[t.Optional[Expression]]:
68    """
69    Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.
70
71    Args:
72        sql: the SQL code string to parse.
73        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
74        **opts: other `sqlglot.parser.Parser` options.
75
76    Returns:
77        The resulting syntax tree collection.
78    """
79    dialect = Dialect.get_or_raise(read)()
80    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:
131def parse_one(
132    sql: str,
133    read: DialectType = None,
134    into: t.Optional[exp.IntoType] = None,
135    **opts,
136) -> Expression:
137    """
138    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
139
140    Args:
141        sql: the SQL code string to parse.
142        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
143        into: the SQLGlot Expression to parse into.
144        **opts: other `sqlglot.parser.Parser` options.
145
146    Returns:
147        The syntax tree for the first parsed statement.
148    """
149
150    dialect = Dialect.get_or_raise(read)()
151
152    if into:
153        result = dialect.parse_into(into, sql, **opts)
154    else:
155        result = dialect.parse(sql, **opts)
156
157    for expression in result:
158        if not expression:
159            raise ParseError(f"No expression was parsed from '{sql}'")
160        return expression
161    else:
162        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]:
165def transpile(
166    sql: str,
167    read: DialectType = None,
168    write: DialectType = None,
169    identity: bool = True,
170    error_level: t.Optional[ErrorLevel] = None,
171    **opts,
172) -> t.List[str]:
173    """
174    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
175    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
176
177    Args:
178        sql: the SQL code string to transpile.
179        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
180        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
181        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
182            the source and the target dialect.
183        error_level: the desired error level of the parser.
184        **opts: other `sqlglot.generator.Generator` options.
185
186    Returns:
187        The list of transpiled SQL statements.
188    """
189    write = (read if write is None else write) if identity else write
190    return [
191        Dialect.get_or_raise(write)().generate(expression, **opts)
192        for expression in parse(sql, read, error_level=error_level)
193    ]

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.