SQLGlot is a no-dependency SQL parser, transpiler, optimizer, and engine. It can be used to format SQL or translate between 21 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 to get started!

Table of Contents

• 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.

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:

importsqlglotsqlglot.transpile("SELECT EPOCH_MS(1618088028295)", read="duckdb", write="hive")[0]

'SELECT FROM_UNIXTIME(1618088028295 / POW(10, 3))'

SQLGlot can even translate custom time formats:

importsqlglotsqlglot.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:

importsqlglot# 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 identifiersprint(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 4FROM bar /* comment 5 */, tbl # comment 6"""# Note: MySQL-specific comments (`#`) are converted into standard syntaxprint(sqlglot.transpile(sql, read='mysql', pretty=True)[0])

/* multi line comment*/SELECTtbl.cola/* comment 1 */+tbl.colb/* comment 2 */,
CAST(x ASINT), /* 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:

fromsqlglotimportparse_one, exp# print all column references (a and b)forcolumninparse_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)forselectinparse_one("SELECT a, b + 1 AS c FROM d").find_all(exp.Select):
forprojectioninselect.expressions:
print(projection.alias_or_name)
# find all tables (x, y, z)fortableinparse_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:

importsqlglotsqlglot.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:

importsqlglottry:
sqlglot.transpile("SELECT foo FROM (SELECT baz FROM t")
exceptsqlglot.errors.ParseErrorase:
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 proceeds to do a best-effort translation by default. Transpilation is difficult and not all permutations are supported. If transpilation does not work, it may not be implemented yet. Well documented / tested PRs / issues are appreciated. Some transpilation cases that require db schemas are made possible through the optimizer but are not included in base transpilation:

importsqlglotsqlglot.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:

importsqlglotsqlglot.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

Build and Modify SQL

SQLGlot supports incrementally building SQL expressions:

fromsqlglotimportselect, conditionwhere=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:

fromsqlglotimportparse_oneparse_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:

fromsqlglotimportexp, parse_oneexpression_tree=parse_one("SELECT a FROM x")
deftransformer(node):
ifisinstance(node, exp.Column) andnode.name=="a":
returnparse_one("FUN(a)")
returnnodetransformed_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:

importsqlglotfromsqlglot.optimizerimportoptimizeprint(
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"<>0OR"x"."b"<>0OR"x"."c"<>0
)
AND (
"x"."a"<>0OR"x"."b"<>0OR"x"."d"<>0
) AS"_col_0"FROM"x"AS"x"WHERE
CAST("x"."z"ASDATE) = CAST('2021-02-01'ASDATE)

AST Introspection

You can see the AST version of the parsed SQL by calling repr:

fromsqlglotimportparse_oneprint(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:

fromsqlglotimportdiff, parse_onediff(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))),
...
]

See also: Semantic Diff for SQL.

Custom Dialects

Dialects can be added by subclassing Dialect:

fromsqlglotimportexpfromsqlglot.dialects.dialectimportDialectfromsqlglot.generatorimportGeneratorfromsqlglot.tokensimportTokenizer, TokenTypeclassCustom(Dialect):
classTokenizer(Tokenizer):
QUOTES= ["'", '"']
IDENTIFIERS= ["`"]
KEYWORDS= {
**Tokenizer.KEYWORDS,
"INT64": TokenType.BIGINT,
"FLOAT64": TokenType.DOUBLE,
}
classGenerator(Generator):
TRANSFORMS= {exp.Array: lambdaself, 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:

fromsqlglot.executorimportexecutetables= {
"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_idprice14.023.0

See also: Writing a Python SQL engine from scratch.

Used By

• SQLMesh
• Apache Superset
• Dagster
• Fugue
• ibis
• 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 (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