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
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 on a best-effort basis when transpiling SQL code:
sql = """
/* multi
   comment
SELECT
  tbl.cola /* comment 1 */ + tbl.colb /* comment 2 */,
  CAST(x AS INT), # comment 3
  y               -- comment 4
  bar /* comment 5 */,
  tbl #          comment 6
print(sqlglot.transpile(sql, read='mysql', pretty=True)[0])
/*




    
 multi
   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
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': 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
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(
        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
SQLMesh
Fugue
mysql-mimic
Querybook
Quokka
Splink
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
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