SQLGlot is a no-dependency SQL parser, transpiler, optimizer, and engine. It can be used to format SQL or translate between 20 different dialects like DuckDB, Presto / Trino, Spark / Databricks, Snowflake, and BigQuery. It aims to read a wide variety of SQL inputs and output syntactically and semantically correct SQL in the targeted dialects.
It is a very comprehensive generic SQL parser with a robust test suite. It is also quite performant, while being written purely in Python.
You can easily customize the parser, analyze queries, traverse expression trees, and programmatically build SQL.
Syntax errors are highlighted and dialect incompatibilities can warn or raise depending on configurations. However, it should be noted that SQL validation is not SQLGlot’s goal, so some syntax errors may go unnoticed.
Learn more about the SQLGlot API in the documentation.
Contributions are very welcome in SQLGlot; read the contribution guide to get started!
Table of Contents
- Install
- Versioning
- Get in Touch
- Examples
- Used By
- Documentation
- Run Tests and Lint
- Benchmarks
- Optional Dependencies
Install
From PyPI:
pip3 install sqlglot
Or with a local checkout:
make install
Requirements for development (optional):
make install-dev
Versioning
Given a version number MAJOR
.MINOR
.PATCH
, SQLGlot uses the following versioning strategy:
- The
PATCH
version is incremented when there are backwards-compatible fixes or feature additions. - The
MINOR
version is incremented when there are backwards-incompatible fixes or feature additions. - The
MAJOR
version is incremented when there are significant backwards-incompatible fixes or feature additions.
Get in Touch
We'd love to hear from you. Join our community Slack channel!
Examples
Formatting and Transpiling
Easily translate from one dialect to another. For example, date/time functions vary between dialects and can be hard to deal with:
import sqlglot
transpile("SELECT EPOCH_MS(1618088028295)", read="duckdb", write="hive")[0]
'SELECT FROM_UNIXTIME(1618088028295 / 1000)'
SQLGlot can even translate custom time formats:
import 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(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 on 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(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 ParseError:
import 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:
transpile("SELECT foo( FROM bar")
except sqlglot.errors.ParseError as e:
print(e.errors)
[{
'description': 'Expecting )',
'line': 1,
'col': 16,
'start_context': 'SELECT foo( ',
'highlight': 'FROM',
'end_context': ' bar',
'into_expression': None,
}]
Unsupported Errors
Presto APPROX_DISTINCT
supports the accuracy argument which is not supported in Hive:
import sqlglot
transpile("SELECT APPROX_DISTINCT(a, 0.1) FROM foo", read="presto", write="hive")
APPROX_COUNT_DISTINCT does not support accuracy
'SELECT APPROX_COUNT_DISTINCT(a) FROM foo'
Build and Modify SQL
SQLGlot supports incrementally building sql expressions:
from sqlglot import select, condition
where = condition("x=1").and_("y=1")
select("*").from_("y").where(where).sql()
'SELECT * FROM y WHERE x = 1 AND y = 1'
You can also modify a parsed tree:
from sqlglot import parse_one
parse_one("SELECT x FROM y").from_("z").sql()
'SELECT x FROM z'
There is also a way to recursively transform the parsed tree by applying a mapping function to each tree node:
from sqlglot import exp, parse_one
expression_tree = parse_one("SELECT a FROM x")
def transformer(node):
if isinstance(node, exp.Column) and node.name == "a":
return parse_one("FUN(a)")
return node
transformed_tree = expression_tree.transform(transformer)
transformed_tree.sql()
'SELECT FUN(a) FROM x'
SQL Optimizer
SQLGlot can rewrite queries into an "optimized" form. It performs a variety of techniques to create a new canonical AST. This AST can be used to standardize queries or provide the foundations for implementing an actual engine. For example:
import sqlglot
from sqlglot.optimizer import optimize
print(
optimize(
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.
A hosted version is on the SQLGlot website, or you can build locally with:
make docs-serve
Run Tests and Lint
make style # Only linter checks
make unit # Only unit tests
make check # Full test suite & linter checks
Benchmarks
Benchmarks run on Python 3.10.5 in seconds.
Query | sqlglot | sqlfluff | sqltree | sqlparse | moz_sql_parser | sqloxide |
---|---|---|---|---|---|---|
tpch | 0.01308 (1.0) | 1.60626 (122.7) | 0.01168 (0.893) | 0.04958 (3.791) | 0.08543 (6.531) | 0.00136 (0.104) |
short | 0.00109 (1.0) | 0.14134 (129.2) | 0.00099 (0.906) | 0.00342 (3.131) | 0.00652 (5.970) | 8.76E-5 (0.080) |
long | 0.01399 (1.0) | 2.12632 (151.9) | 0.01126 (0.805) | 0.04410 (3.151) | 0.06671 (4.767) | 0.00107 (0.076) |
crazy | 0.03969 (1.0) | 24.3777 (614.1) | 0.03917 (0.987) | 11.7043 (294.8) | 1.03280 (26.02) | 0.00625 (0.157) |
Optional Dependencies
SQLGlot uses dateutil to simplify literal timedelta expressions. The optimizer will not simplify expressions like the following if the module cannot be found:
x + interval '1' month
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( 71 sql: str, read: DialectType = None, dialect: DialectType = None, **opts 72) -> t.List[t.Optional[Expression]]: 73 """ 74 Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement. 75 76 Args: 77 sql: the SQL code string to parse. 78 read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql"). 79 dialect: the SQL dialect (alias for read). 80 **opts: other `sqlglot.parser.Parser` options. 81 82 Returns: 83 The resulting syntax tree collection. 84 """ 85 dialect = Dialect.get_or_raise(read or dialect)() 86 return dialect.parse(sql, **opts) 87 88 89@t.overload 90def parse_one(sql: str, *, into: t.Type[E], **opts) -> E: 91 ... 92 93 94@t.overload 95def parse_one(sql: str, **opts) -> Expression: 96 ... 97 98 99def parse_one( 100 sql: str, 101 read: DialectType = None, 102 dialect: DialectType = None, 103 into: t.Optional[exp.IntoType] = None, 104 **opts, 105) -> Expression: 106 """ 107 Parses the given SQL string and returns a syntax tree for the first parsed SQL statement. 108 109 Args: 110 sql: the SQL code string to parse. 111 read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql"). 112 dialect: the SQL dialect (alias for read) 113 into: the SQLGlot Expression to parse into. 114 **opts: other `sqlglot.parser.Parser` options. 115 116 Returns: 117 The syntax tree for the first parsed statement. 118 """ 119 120 dialect = Dialect.get_or_raise(read or dialect)() 121 122 if into: 123 result = dialect.parse_into(into, sql, **opts) 124 else: 125 result = dialect.parse(sql, **opts) 126 127 for expression in result: 128 if not expression: 129 raise ParseError(f"No expression was parsed from '{sql}'") 130 return expression 131 else: 132 raise ParseError(f"No expression was parsed from '{sql}'") 133 134 135def transpile( 136 sql: str, 137 read: DialectType = None, 138 write: DialectType = None, 139 identity: bool = True, 140 error_level: t.Optional[ErrorLevel] = None, 141 **opts, 142) -> t.List[str]: 143 """ 144 Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed 145 to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement. 146 147 Args: 148 sql: the SQL code string to transpile. 149 read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql"). 150 write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql"). 151 identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both: 152 the source and the target dialect. 153 error_level: the desired error level of the parser. 154 **opts: other `sqlglot.generator.Generator` options. 155 156 Returns: 157 The list of transpiled SQL statements. 158 """ 159 write = (read if write is None else write) if identity else write 160 return [ 161 Dialect.get_or_raise(write)().generate(expression, copy=False, **opts) if expression else "" 162 for expression in parse(sql, read, error_level=error_level) 163 ]
Whether to format generated SQL by default.
The default schema used by SQLGlot (e.g. in the optimizer).
71def parse( 72 sql: str, read: DialectType = None, dialect: DialectType = None, **opts 73) -> t.List[t.Optional[Expression]]: 74 """ 75 Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement. 76 77 Args: 78 sql: the SQL code string to parse. 79 read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql"). 80 dialect: the SQL dialect (alias for read). 81 **opts: other `sqlglot.parser.Parser` options. 82 83 Returns: 84 The resulting syntax tree collection. 85 """ 86 dialect = Dialect.get_or_raise(read or dialect)() 87 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").
- dialect: the SQL dialect (alias for read).
- **opts: other
sqlglot.parser.Parser
options.
Returns:
The resulting syntax tree collection.
100def parse_one( 101 sql: str, 102 read: DialectType = None, 103 dialect: DialectType = None, 104 into: t.Optional[exp.IntoType] = None, 105 **opts, 106) -> Expression: 107 """ 108 Parses the given SQL string and returns a syntax tree for the first parsed SQL statement. 109 110 Args: 111 sql: the SQL code string to parse. 112 read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql"). 113 dialect: the SQL dialect (alias for read) 114 into: the SQLGlot Expression to parse into. 115 **opts: other `sqlglot.parser.Parser` options. 116 117 Returns: 118 The syntax tree for the first parsed statement. 119 """ 120 121 dialect = Dialect.get_or_raise(read or dialect)() 122 123 if into: 124 result = dialect.parse_into(into, sql, **opts) 125 else: 126 result = dialect.parse(sql, **opts) 127 128 for expression in result: 129 if not expression: 130 raise ParseError(f"No expression was parsed from '{sql}'") 131 return expression 132 else: 133 raise ParseError(f"No expression was parsed from '{sql}'")
Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
Arguments:
- sql: the SQL code string to parse.
- read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
- dialect: the SQL dialect (alias for read)
- into: the SQLGlot Expression to parse into.
- **opts: other
sqlglot.parser.Parser
options.
Returns:
The syntax tree for the first parsed statement.
136def transpile( 137 sql: str, 138 read: DialectType = None, 139 write: DialectType = None, 140 identity: bool = True, 141 error_level: t.Optional[ErrorLevel] = None, 142 **opts, 143) -> t.List[str]: 144 """ 145 Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed 146 to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement. 147 148 Args: 149 sql: the SQL code string to transpile. 150 read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql"). 151 write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql"). 152 identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both: 153 the source and the target dialect. 154 error_level: the desired error level of the parser. 155 **opts: other `sqlglot.generator.Generator` options. 156 157 Returns: 158 The list of transpiled SQL statements. 159 """ 160 write = (read if write is None else write) if identity else write 161 return [ 162 Dialect.get_or_raise(write)().generate(expression, copy=False, **opts) if expression else "" 163 for expression in parse(sql, read, error_level=error_level) 164 ]
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.