sqlglot 1.7.1

An easily customizable SQL parser and transpiler

SQLGlot

SQLGlot is a no dependency Python SQL parser and transpiler. It can be used to format SQL or translate between different dialects like Presto, Spark, and Hive. It aims to read a wide variety of SQL inputs and output syntatically correct SQL in the targeted dialects.

It is currently the fastest pure-Python SQL parser.

You can easily customize the parser to support UDF's across dialects as well through the transform API.

Syntax errors are highlighted and dialect incompatibilities can warn or raise depending on configurations.

Install

From PyPI

pip3 install sqlglot

Or with a local checkout

pip3 install -e .

Examples

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')

SELECT TO_UTC_TIMESTAMP(FROM_UNIXTIME(1618088028295 / 1000, 'yyyy-MM-dd HH:mm:ss'), 'UTC')

Formatting and Transpiling

Read in a SQL statement with a CTE and CASTING to a REAL and then transpiling to Spark.

Spark uses backticks as identifiers and the REAL type is transpiled to FLOAT.

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"""
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`

Custom Transforms

A simple transform on types can be accomplished by providing a dict of Expression/TokenType => lambda/string

from sqlglot import *

transpile("SELECT CAST(a AS INT) FROM x", transforms={TokenType.INT: 'SPECIAL INT'})[0]

SELECT CAST(a AS SPECIAL INT) FROM x

More complicated transforms can be accomplished by using the Tokenizer, Parser, and Generator directly.

In this example, we want to parse a UDF SPECIAL_UDF and then output another version called SPECIAL_UDF_INVERSE with the arguments switched.

from sqlglot import *
from sqlglot.expressions import Func

class SpecialUDF(Func):
    arg_types = {'a': True, 'b': True}

tokens = Tokenizer().tokenize("SELECT SPECIAL_UDF(a, b) FROM x")

Here is the output of the tokenizer.

[
    <Token token_type: TokenType.SELECT, text: SELECT, line: 0, col: 0>,
    <Token token_type: TokenType.VAR, text: SPECIAL_UDF, line: 0, col: 7>,
    <Token token_type: TokenType.L_PAREN, text: (, line: 0, col: 18>,
    <Token token_type: TokenType.VAR, text: a, line: 0, col: 19>,
    <Token token_type: TokenType.COMMA, text: ,, line: 0, col: 20>,
    <Token token_type: TokenType.VAR, text: b, line: 0, col: 22>,
    <Token token_type: TokenType.R_PAREN, text: ), line: 0, col: 23>,
    <Token token_type: TokenType.FROM, text: FROM, line: 0, col: 25>,
    <Token token_type: TokenType.VAR, text: x, line: 0, col: 30>,
]

expression = Parser(functions={
    'SPECIAL_UDF': SpecialUDF.from_arg_list,
}).parse(tokens)[0]

The expression tree produced by the parser.

(FROM this:
 (TABLE this: x, db: ), expression:
 (SELECT expressions:
  (COLUMN this:
   (FUNC a:
    (COLUMN this: a, db: , table: ), b:
    (COLUMN this: b, db: , table: )), db: , table: )))

Finally generating the new SQL.

Generator(transforms={
    SpecialUDF: lambda self, e: f"SPECIAL_UDF_INVERSE({self.sql(e, 'b')}, {self.sql(e, 'a')})"
}).generate(expression)

SELECT SPECIAL_UDF_INVERSE(b, a) FROM x

Transform Trees

There is also a way to transform the parsed tree directly by applying a mapping function to each tree node recursively.

import sqlglot
import sqlglot.expressions as exp

expression_tree = sqlglot.parse_one("SELECT a FROM x")

def transformer(node):
    if isinstance(node, exp.Column) and node.args["this"].text == "a":
        return sqlglot.parse_one("FUN(a)")
    return node

transformed_tree = expression_tree.transform(transformer)
transformed_tree.sql()

The snippet above produces the following transformed expression:

SELECT FUN(a) FROM x

Parse Errors

A syntax error will result in an parse error.

transpile("SELECT foo( FROM bar")

sqlglot.errors.ParseError: Expected )
  SELECT foo( __FROM__ bar

Unsupported Errors

Presto APPROX_DISTINCT supports the accuracy argument which is not supported in Spark.

transpile(
    'SELECT APPROX_DISTINCT(a, 0.1) FROM foo',
    read='presto',
    write='spark',
)

WARNING:root:APPROX_COUNT_DISTINCT does not support accuracy

SELECT APPROX_COUNT_DISTINCT(a) FROM foo

Rewrite Sql

Modify sql expressions like adding a CTAS

from sqlglot import Generator, parse_one
from sqlglot.rewriter import Rewriter

expression = parse_one("SELECT * FROM y")
Rewriter(expression).ctas('x').expression.sql()

CREATE TABLE x AS SELECT * FROM y

Benchmarks

Benchmarks run on Python 3.9.6 in seconds.

Query	sqlglot	sqlparse	moz_sql_parser	sqloxide
short	0.00042	0.00079	0.00129	2.701e-05
long	0.00425	0.01149	0.01577	0.0002045
crazy	0.01231	2.12605	0.26796	0.0010257

Run Tests and Lint

python -m unittest && python -m pylint sqlglot/ tests/