- Notifications
You must be signed in to change notification settings - Fork911
Python SQL Parser and Transpiler
License
tobymao/sqlglot
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
SQLGlot is a no-dependency SQL parser, transpiler, optimizer, and engine. It can be used to format SQL or translate between30 different dialects likeDuckDB,Presto /Trino,Spark /Databricks,Snowflake, andBigQuery. 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 robusttest suite. It is also quiteperformant, while being written purely in Python.
You can easilycustomize the parser,analyze queries, traverse expression trees, and programmaticallybuild SQL.
SQLGlot can detect a variety ofsyntax errors, such as unbalanced parentheses, incorrect usage of reserved keywords, and so on. These errors are highlighted and dialect incompatibilities can warn or raise depending on configurations.
Learn more about SQLGlot in the APIdocumentation and the expression treeprimer.
Contributions are very welcome in SQLGlot; read thecontribution guide and theonboarding document to get started!
- Install
- Versioning
- Get in Touch
- FAQ
- Examples
- Used By
- Documentation
- Run Tests and Lint
- Benchmarks
- Optional Dependencies
From PyPI:
pip3 install"sqlglot[rs]"# Without Rust tokenizer (slower):# pip3 install sqlglot
Or with a local checkout:
make installRequirements for development (optional):
make install-devGiven a version numberMAJOR.MINOR.PATCH, SQLGlot uses the following versioning strategy:
- The
PATCHversion is incremented when there are backwards-compatible fixes or feature additions. - The
MINORversion is incremented when there are backwards-incompatible fixes or feature additions. - The
MAJORversion is incremented when there are significant backwards-incompatible fixes or feature additions.
We'd love to hear from you. Join our communitySlack channel!
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_onewill 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")).
What happened to sqlglot.dataframe?
- The PySpark dataframe api was moved to a standalone library calledSQLFrame in v24. It now allows you to run queries as opposed to just generate SQL.
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(xASINT),/* 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 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 theast primer to learn more about SQLGlot's internals.
When the parser detects an error in the syntax, it raises aParseError:
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}]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:
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 theunsupported_level attribute. For example, we can set it to eitherRAISE orIMMEDIATE 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 accuracyThere 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 thequalify andannotate_types optimizerrules 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 toreach out if you need guidance!
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'SQLGlot can rewrite queries into an "optimized" form. It performs a variety oftechniques 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)
You can see the AST version of the parsed SQL by callingrepr:
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))])
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.
Dialects can be added by subclassingDialect:
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'>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 asArrow andPandas.
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.
SQLGlot usespdoc to serve its API documentation.
A hosted version is on theSQLGlot website, or you can build locally with:
make docs-servemake style # Only linter checksmake 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 checksBenchmarks 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) |
make bench # Run parsing benchmarkmake bench-optimize # Run optimization benchmarkSQLGlot usesdateutil to simplify literal timedelta expressions. The optimizer will not simplify expressions like the following if the module cannot be found:
x+ interval'1' month
About
Python SQL Parser and Transpiler
Topics
Resources
License
Uh oh!
There was an error while loading.Please reload this page.