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!
- Install
- Versioning
- Get in Touch
- Examples
- Used By
- Documentation
- Run Tests and Lint
- Benchmarks
- Optional Dependencies
From PyPI:
pip3 install sqlglot
Or with a local checkout:
make install
Requirements for development (optional):
make install-dev
Given a version number MAJOR.MINOR.PATCH, SQLGlot uses the following versioning strategy:
- The
PATCHversion is incremented when there are backwards-compatible fixes or feature additions. - The
MINORversion is incremented when there are backwards-incompatible fixes or feature additions. - The
MAJORversion is incremented when there are significant backwards-incompatible fixes or feature additions.
We'd love to hear from you. Join our community Slack channel!
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 */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)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'
}]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'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'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)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)))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.
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'>
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 3.0See also: Writing a Python SQL engine from scratch.
SQLGlot uses pdoc to serve its API documentation:
make docs-serve
make check # Set SKIP_INTEGRATION=1 to skip integration tests
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) |
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