Sqlglot

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What is sqlglot?

sqlglot is a versatile tool developed by tobymao that serves as a no-dependency SQL parser, transpiler, optimizer, and engine. It is specifically designed to handle SQL operations across a wide range of dialects—24 to be precise—including popular ones like DuckDB, Presto, Spark, Snowflake, and BigQuery. sqlglot's primary function is to read SQL inputs and produce syntactically and semantically accurate SQL outputs in the desired dialect, making it an invaluable tool for developers who work with multiple databases or need to ensure SQL compatibility across different systems.

How to Use sqlglot

Using sqlglot is straightforward and intuitive. It can be installed via PyPI, and once set up, you can start parsing, translating, and optimizing SQL queries. To parse a SQL query in a specific dialect, you specify the dialect during parsing. For instance, if you have a SQL query written in Spark SQL, you can parse it by indicating the dialect as "spark". Transpiling between dialects is just as easy; you define both the source and target dialects to convert your SQL from one format to another.

Moreover, sqlglot allows for programmatic SQL building and modification. You can construct SQL expressions incrementally or transform existing SQL trees to suit your needs. It also provides features to handle syntax errors gracefully and warn users of dialect incompatibilities. While sqlglot is forgiving with syntax errors, it does not serve as a strict SQL validator, which provides flexibility for users to manage their SQL scripts seamlessly.

Key Features of sqlglot

Multi-Dialect Support: sqlglot can handle SQL translations across 24 different dialects, making it a versatile tool for database management.
SQL Parsing and Transpiling: It reads SQL inputs and outputs them in the targeted dialect, ensuring compatibility and correctness.
SQL Optimization: sqlglot optimizes SQL queries by rewriting them into a more efficient form, aiding in performance improvements.
Syntax Error Handling: Provides detailed error messages and highlights syntax errors, with the ability to warn about dialect incompatibilities.
Customizability: Users can customize dialects, analyze queries, and traverse expression trees to suit specific needs.
Programmatic SQL Construction: Allows for incremental building and modification of SQL expressions, making it easy to create and adjust queries dynamically.
Execution Engine: sqlglot can interpret SQL queries using Python dictionaries as tables, useful for testing and simple execution scenarios.

Overall, sqlglot is a powerful tool for developers and database administrators looking to manage SQL queries across various platforms efficiently and effectively. It provides robust features for parsing, translating, and optimizing SQL, all while being highly customizable and user-friendly.

SQLGlot logo SQLGlot is a no-dependency SQL parser, transpiler, optimizer, and engine. It can be used to format SQL or translate between 24 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, SQLGlot does not aim to be a SQL validator, so it may fail to detect certain syntax errors. Learn more about SQLGlot in the API documentation and the expression tree primer. Contributions are very welcome in SQLGlot; read the contribution guide and the onboarding document to get started!

Install
Versioning
Get in Touch
FAQ
Examples
Formatting and Transpiling
Metadata
Parser Errors
Unsupported Errors
Build and Modify SQL
SQL Optimizer
AST Introspection
AST Diff
Custom Dialects
SQL Execution
Used By
Documentation
Run Tests and Lint
Benchmarks
Optional Dependencies

Install

From PyPI:

pip3 install "sqlglot[rs]"
# Without Rust tokenizer (slower):
# 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!

FAQ

I tried to parse SQL that should be valid but it failed, why did that happen?

Most of the time, issues like this occur because the "source" dialect is omitted during parsing. For example, this is how to correctly parse a SQL query written in Spark SQL: parse_one(sql, dialect="spark") (alternatively: read="spark"). If no dialect is specified, parse_one will attempt to parse the query according to the "SQLGlot dialect", which is designed to be a superset of all supported dialects. If you tried specifying the dialect and it still doesn't work, please file an issue. I tried to output SQL but it's not in the correct dialect!
Like parsing, generating SQL also requires the target dialect to be specified, otherwise the SQLGlot dialect will be used by default. For example, to transpile a query from Spark SQL to DuckDB, do parse_one(sql, dialect="spark").sql(dialect="duckdb") (alternatively: transpile(sql, read="spark", write="duckdb")). I tried to parse invalid SQL and it worked, even though it should raise an error! Why didn't it validate my SQL?
SQLGlot does not aim to be a SQL validator - it is designed to be very forgiving. This makes the codebase more comprehensive and also gives more flexibility to its users, e.g. by allowing them to include trailing commas in their projection lists. What happened to sqlglot.dataframe?
The PySpark dataframe api was moved to a standalone library called SQLFrame in v24. It now allows you to run queries as opposed to just generate SQL.

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 / POW(10, 3))'

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')"

Identifier delimiters and data types can be translated as well:

import sqlglot
# Spark SQL requires backticks (`) for delimited identifiers and uses `FLOAT` over `REAL`
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"""
# Translates the query into Spark SQL, formats it, and delimits all of its identifiers
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:

sql = """
/* multi
 line
 comment
*/
SELECT
 tbl.cola /* comment 1 */ + tbl.colb /* comment 2 */,
 CAST(x AS SIGNED), # comment 3
 y -- comment 4
FROM
 bar /* comment 5 */,
 tbl # comment 6
# Note: MySQL-specific comments (`#`) are converted into standard syntax
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 */

Metadata

You can explore SQL with expression helpers to do things like find columns and tables in a query:

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)

Read the ast primer to learn more about SQLGlot's internals.

Parser Errors

When the parser detects an error in the syntax, it raises a ParseError:

import sqlglot
sqlglot.transpile("SELECT foo FROM (SELECT baz FROM t")

sqlglot.errors.ParseError: Expecting ). Line 1, Col: 34.
 SELECT foo FROM (SELECT baz FROM t
 ~

Structured syntax errors are accessible for programmatic use:

import sqlglot
try:
 sqlglot.transpile("SELECT foo FROM (SELECT baz FROM t")
except sqlglot.errors.ParseError as e:
 print(e.errors)

 'description': 'Expecting )',
 'line': 1,
 'col': 34,
 'start_context': 'SELECT foo FROM (SELECT baz FROM ',
 'highlight': 't',
 'end_context': '',
 'into_expression': None

Unsupported Errors

It may not be possible to translate some queries between certain dialects. For these cases, SQLGlot may emit a warning and will proceed to do a best-effort translation by default:

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'

This behavior can be changed by setting the unsupported_level attribute. For example, we can set it to either RAISE or IMMEDIATE to ensure an exception is raised instead:

import sqlglot
sqlglot.transpile("SELECT APPROX_DISTINCT(a, 0.1) FROM foo", read="presto", write="hive", unsupported_level=sqlglot.ErrorLevel.RAISE)

sqlglot.errors.UnsupportedError: APPROX_COUNT_DISTINCT does not support accuracy

There are queries that require additional information to be accurately transpiled, such as the schemas of the tables referenced in them. This is because certain transformations are type-sensitive, meaning that type inference is needed in order to understand their semantics. Even though the qualify and annotate_types optimizer rules can help with this, they are not used by default because they add significant overhead and complexity. Transpilation is generally a hard problem, so SQLGlot employs an "incremental" approach to solving it. This means that there may be dialect pairs that currently lack support for some inputs, but this is expected to improve over time. We highly appreciate well-documented and tested issues or PRs, so feel free to reach out if you need guidance!

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'

It's possible to modify a parsed tree:

from sqlglot import parse_one
parse_one("SELECT x FROM y").from_("z").sql()

'SELECT x FROM z'

Parsed expressions can also be transformed recursively by applying a mapping function to each node in the tree:

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 parsed 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 semantic 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)))),
 Keep(
 source=Column(this=Identifier(this=a, quoted=False)),
 target=Column(this=Identifier(this=a, quoted=False))),

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

SQLGlot is able to interpret SQL queries, where the tables are represented as Python dictionaries. The engine is not supposed to be fast, but 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, such as Arrow and Pandas. The example below showcases the execution of a query 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
Apache Superset
Dagster
Fugue
ibis
mysql-mimic
Querybook
Quokka
Splink
SQLFrame

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 (or unit-rs, to use the Rust tokenizer)
make test # Unit and integration tests (or test-rs, to use the Rust tokenizer)
make check # Full test suite & linter checks

Benchmarks

Benchmarks run on Python 3.10.12 in seconds.

Query	sqlglot	sqlglotrs	sqlfluff	sqltree	sqlparse	moz_sql_parser	sqloxide
tpch	0.00944 (1.0)	0.00590 (0.625)	0.32116 (33.98)	0.00693 (0.734)	0.02858 (3.025)	0.03337 (3.532)	0.00073 (0.077)
short	0.00065 (1.0)	0.00044 (0.687)	0.03511 (53.82)	0.00049 (0.759)	0.00163 (2.506)	0.00234 (3.601)	0.00005 (0.073)
long	0.00889 (1.0)	0.00572 (0.643)	0.36982 (41.56)	0.00614 (0.690)	0.02530 (2.844)	0.02931 (3.294)	0.00059 (0.066)
crazy	0.02918 (1.0)	0.01991 (0.682)	1.88695 (64.66)	0.02003 (0.686)	7.46894 (255.9)	0.64994 (22.27)	0.00327 (0.112)

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

How to Use

To use the sqlglot, follow these steps:

Visit https://github.com/tobymao/sqlglot
Follow the setup instructions to create an account (if required)
Connect the MCP server to your Claude Desktop application
Start using sqlglot capabilities within your Claude conversations

Additional Information

Created

March 12, 2025

Company

tobymao

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