![SQLGlot logo](sqlglot.png)

SQLGlot is a no-dependency SQL parser, transpiler, optimizer, and engine. It can be used to format SQL or translate between [27 different dialects](https://github.com/tobymao/sqlglot/blob/main/sqlglot/dialects/__init__.py) like [DuckDB](https://duckdb.org/), [Presto](https://prestodb.io/) / [Trino](https://trino.io/), [Spark](https://spark.apache.org/) / [Databricks](https://www.databricks.com/), [Snowflake](https://www.snowflake.com/en/), and [BigQuery](https://cloud.google.com/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](https://github.com/tobymao/sqlglot/blob/main/tests/). It is also quite [performant](#benchmarks), while being written purely in Python.

You can easily [customize](#custom-dialects) the parser, [analyze](#metadata) queries, traverse expression trees, and programmatically [build](#build-and-modify-sql) SQL.

SQLGlot can detect a variety of [syntax errors](#parser-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 API [documentation](https://sqlglot.com/) and the expression tree [primer](https://github.com/tobymao/sqlglot/blob/main/posts/ast_primer.md).

Contributions are very welcome in SQLGlot; read the [contribution guide](https://github.com/tobymao/sqlglot/blob/main/CONTRIBUTING.md) and the [onboarding document](https://github.com/tobymao/sqlglot/blob/main/posts/onboarding.md) to get started!

## Table of Contents

* [Install](#install)
* [Versioning](#versioning)
* [Get in Touch](#get-in-touch)
* [FAQ](#faq)
* [Examples](#examples)
   * [Formatting and Transpiling](#formatting-and-transpiling)
   * [Metadata](#metadata)
   * [Parser Errors](#parser-errors)
   * [Unsupported Errors](#unsupported-errors)
   * [Build and Modify SQL](#build-and-modify-sql)
   * [SQL Optimizer](#sql-optimizer)
   * [AST Introspection](#ast-introspection)
   * [AST Diff](#ast-diff)
   * [Custom Dialects](#custom-dialects)
   * [SQL Execution](#sql-execution)
* [Used By](#used-by)
* [Documentation](#documentation)
* [Run Tests and Lint](#run-tests-and-lint)
* [Benchmarks](#benchmarks)
* [Optional Dependencies](#optional-dependencies)

## Install

From PyPI:

```bash
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](https://tobikodata.com/slack)!

## 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")`).

What happened to sqlglot.dataframe?

* The PySpark dataframe api was moved to a standalone library called [SQLFrame](https://github.com/eakmanrq/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:

```python
import sqlglot
sqlglot.transpile("SELECT EPOCH_MS(1618088028295)", read="duckdb", write="hive")[0]
```

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

SQLGlot can even translate custom time formats:

```python
import sqlglot
sqlglot.transpile("SELECT STRFTIME(x, '%y-%-m-%S')", read="duckdb", write="hive")[0]
```

```sql
"SELECT DATE_FORMAT(x, 'yy-M-ss')"
```

Identifier delimiters and data types can be translated as well:

```python
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])
```

```sql
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:

```python
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])
```

```sql
/* 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:

```python
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](https://github.com/tobymao/sqlglot/blob/main/posts/ast_primer.md) to learn more about SQLGlot's internals.

### Parser Errors

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

```python
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:

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

```python
[{
  '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:

```python
import sqlglot
sqlglot.transpile("SELECT APPROX_DISTINCT(a, 0.1) FROM foo", read="presto", write="hive")
```

```sql
APPROX_COUNT_DISTINCT does not support accuracy
'SELECT APPROX_COUNT_DISTINCT(a) FROM foo'
```

This behavior can be changed by setting the [`unsupported_level`](https://github.com/tobymao/sqlglot/blob/b0e8dc96ba179edb1776647b5bde4e704238b44d/sqlglot/errors.py#L9) attribute. For example, we can set it to either `RAISE` or `IMMEDIATE` to ensure an exception is raised instead:

```python
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](https://github.com/tobymao/sqlglot/tree/main/sqlglot/optimizer) 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](#get-in-touch) if you need guidance!

### Build and Modify SQL

SQLGlot supports incrementally building SQL expressions:

```python
from sqlglot import select, condition

where = condition("x=1").and_("y=1")
select("*").from_("y").where(where).sql()
```

```sql
'SELECT * FROM y WHERE x = 1 AND y = 1'
```

It's possible to modify a parsed tree:

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

```sql
'SELECT x FROM z'
```

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

```python
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()
```

```sql
'SELECT FUN(a) FROM x'
```

### SQL Optimizer

SQLGlot can rewrite queries into an "optimized" form. It performs a variety of [techniques](https://github.com/tobymao/sqlglot/blob/main/sqlglot/optimizer/optimizer.py) 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:

```python
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)
)
```

```sql
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`:

```python
from sqlglot import parse_one
print(repr(parse_one("SELECT a + 1 AS z")))
```

```python
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:

```python
from sqlglot import diff, parse_one
diff(parse_one("SELECT a + b, c, d"), parse_one("SELECT c, a - b, d"))
```

```python
[
  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](https://github.com/tobymao/sqlglot/blob/main/posts/sql_diff.md).

### Custom Dialects

[Dialects](https://github.com/tobymao/sqlglot/tree/main/sqlglot/dialects) can be added by subclassing `Dialect`:

```python
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](https://arrow.apache.org/docs/index.html) and [Pandas](https://pandas.pydata.org/).

The example below showcases the execution of a query that involves aggregations and joins:

```python
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
)
```

```python
user_id price
      1   4.0
      2   3.0
```

See also: [Writing a Python SQL engine from scratch](https://github.com/tobymao/sqlglot/blob/main/posts/python_sql_engine.md).

## Used By

* [SQLMesh](https://github.com/TobikoData/sqlmesh)
* [Apache Superset](https://github.com/apache/superset)
* [Dagster](https://github.com/dagster-io/dagster)
* [Fugue](https://github.com/fugue-project/fugue)
* [Ibis](https://github.com/ibis-project/ibis)
* [mysql-mimic](https://github.com/kelsin/mysql-mimic)
* [Querybook](https://github.com/pinterest/querybook)
* [Quokka](https://github.com/marsupialtail/quokka)
* [Splink](https://github.com/moj-analytical-services/splink)
* [SQLFrame](https://github.com/eakmanrq/sqlframe)

## Documentation

SQLGlot uses [pdoc](https://pdoc.dev/) to serve its API documentation.

A hosted version is on the [SQLGlot website](https://sqlglot.com/), 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](https://github.com/tobymao/sqlglot/blob/main/benchmarks/bench.py) 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 benchmark
make bench-optimize   # Run optimization benchmark
```

## Optional Dependencies

SQLGlot uses [dateutil](https://github.com/dateutil/dateutil) to simplify literal timedelta expressions. The optimizer will not simplify expressions like the following if the module cannot be found:

```sql
x + interval '1' month
```
