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An easily customizable SQL parser and transpiler

Project description


SQLGlot is a no dependency Python 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 the parser is very lenient when it comes to detecting errors, because it aims to consume as much SQL as possible. On one hand, this makes its implementation simpler, and thus more comprehensible, but on the other hand it means that syntax errors may sometimes go unnoticed.

Contributions are very welcome in SQLGlot; read the contribution guide to get started!

Table of Contents


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!


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 (
  FROM `foo`
    `a` = 1
  CAST(`b`.`a` AS FLOAT) AS `d`
FROM `foo` AS `f`
JOIN `bar` AS `b`
  ON `f`.`a` = `b`.`a`
  ON `f`.`a` = `baz`.`a`

Comments are also preserved in a best-effort basis when transpiling SQL code:

sql = """
/* multi
  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
  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):

# 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:

# find all tables (x, y, z)
for table in parse_one("SELECT * FROM x JOIN y JOIN z").find_all(exp.Table):

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
    sqlglot.transpile("SELECT foo( FROM bar")
except sqlglot.errors.ParseError as e:
  '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

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 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 == "a":
        return parse_one("FUN(a)")
    return node

transformed_tree = expression_tree.transform(transformer)

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

            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"}}
    "x"."a" OR "x"."b" OR "x"."c"
  ) AND (
    "x"."a" OR "x"."b" OR "x"."d"
  ) AS "_col_0"
FROM "x" AS "x"
  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 = {
            "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",

<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},

      SUM(s.price) AS price
    FROM orders o
    JOIN order_items i
      ON = i.order_id
    JOIN sushi s
      ON i.sushi_id =
    GROUP BY o.user_id
user_id price
      1   4.0
      2   3.0

Used By


SQLGlot uses pdocs to serve its API documentation:

make docs-serve

Run Tests and Lint

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)

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

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