478 lines
13 KiB
Markdown
478 lines
13 KiB
Markdown
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SQLGlot is a no-dependency SQL parser, transpiler, optimizer, and engine. It can be used to format SQL or translate between [19 different dialects](https://github.com/tobymao/sqlglot/blob/main/sqlglot/dialects/__init__.py) like [DuckDB](https://duckdb.org/), [Presto](https://prestodb.io/), [Spark](https://spark.apache.org/), [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 correct SQL in the targeted dialects.
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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.
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You can easily [customize](#custom-dialects) the parser, [analyze](#metadata) queries, traverse expression trees, and programmatically [build](#build-and-modify-sql) SQL.
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Syntax [errors](#parser-errors) are highlighted and dialect incompatibilities can warn or raise depending on configurations. However, it should be noted that SQL validation is not SQLGlot’s goal, so some syntax errors may go unnoticed.
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Contributions are very welcome in SQLGlot; read the [contribution guide](https://github.com/tobymao/sqlglot/blob/main/CONTRIBUTING.md) to get started!
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## Table of Contents
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* [Install](#install)
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* [Get in Touch](#get-in-touch)
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* [Examples](#examples)
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* [Formatting and Transpiling](#formatting-and-transpiling)
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* [Metadata](#metadata)
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* [Parser Errors](#parser-errors)
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* [Unsupported Errors](#unsupported-errors)
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* [Build and Modify SQL](#build-and-modify-sql)
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* [SQL Optimizer](#sql-optimizer)
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* [AST Introspection](#ast-introspection)
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* [AST Diff](#ast-diff)
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* [Custom Dialects](#custom-dialects)
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* [SQL Execution](#sql-execution)
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* [Used By](#used-by)
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* [Documentation](#documentation)
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* [Run Tests and Lint](#run-tests-and-lint)
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* [Benchmarks](#benchmarks)
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* [Optional Dependencies](#optional-dependencies)
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## Install
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From PyPI:
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```
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pip3 install sqlglot
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```
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Or with a local checkout:
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```
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make install
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```
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Requirements for development (optional):
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```
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make install-dev
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```
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## Get in Touch
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We'd love to hear from you. Join our community [Slack channel](https://tobikodata.com/slack)!
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## Examples
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### Formatting and Transpiling
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Easily translate from one dialect to another. For example, date/time functions vary from dialects and can be hard to deal with:
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```python
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import sqlglot
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sqlglot.transpile("SELECT EPOCH_MS(1618088028295)", read="duckdb", write="hive")[0]
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```
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```sql
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'SELECT FROM_UNIXTIME(1618088028295 / 1000)'
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```
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SQLGlot can even translate custom time formats:
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```python
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import sqlglot
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sqlglot.transpile("SELECT STRFTIME(x, '%y-%-m-%S')", read="duckdb", write="hive")[0]
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```
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```sql
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"SELECT DATE_FORMAT(x, 'yy-M-ss')"
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```
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As another example, let's suppose that we want to read in a SQL query that contains a CTE and a cast to `REAL`, and then transpile it to Spark, which uses backticks for identifiers and `FLOAT` instead of `REAL`:
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```python
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import sqlglot
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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"""
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print(sqlglot.transpile(sql, write="spark", identify=True, pretty=True)[0])
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```
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```sql
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WITH `baz` AS (
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SELECT
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`a`,
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`c`
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FROM `foo`
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WHERE
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`a` = 1
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)
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SELECT
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`f`.`a`,
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`b`.`b`,
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`baz`.`c`,
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CAST(`b`.`a` AS FLOAT) AS `d`
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FROM `foo` AS `f`
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JOIN `bar` AS `b`
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ON `f`.`a` = `b`.`a`
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LEFT JOIN `baz`
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ON `f`.`a` = `baz`.`a`
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```
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Comments are also preserved in a best-effort basis when transpiling SQL code:
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```python
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sql = """
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/* multi
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line
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comment
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*/
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SELECT
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tbl.cola /* comment 1 */ + tbl.colb /* comment 2 */,
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CAST(x AS INT), # comment 3
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y -- comment 4
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FROM
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bar /* comment 5 */,
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tbl # comment 6
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"""
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print(sqlglot.transpile(sql, read='mysql', pretty=True)[0])
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```
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```sql
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/* multi
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line
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comment
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*/
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SELECT
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tbl.cola /* comment 1 */ + tbl.colb /* comment 2 */,
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CAST(x AS INT), /* comment 3 */
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y /* comment 4 */
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FROM bar /* comment 5 */, tbl /* comment 6 */
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```
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### Metadata
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You can explore SQL with expression helpers to do things like find columns and tables:
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```python
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from sqlglot import parse_one, exp
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# print all column references (a and b)
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for column in parse_one("SELECT a, b + 1 AS c FROM d").find_all(exp.Column):
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print(column.alias_or_name)
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# find all projections in select statements (a and c)
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for select in parse_one("SELECT a, b + 1 AS c FROM d").find_all(exp.Select):
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for projection in select.expressions:
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print(projection.alias_or_name)
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# find all tables (x, y, z)
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for table in parse_one("SELECT * FROM x JOIN y JOIN z").find_all(exp.Table):
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print(table.name)
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```
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### Parser Errors
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When the parser detects an error in the syntax, it raises a ParserError:
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```python
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import sqlglot
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sqlglot.transpile("SELECT foo( FROM bar")
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```
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```
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sqlglot.errors.ParseError: Expecting ). Line 1, Col: 13.
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select foo( FROM bar
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~~~~
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```
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Structured syntax errors are accessible for programmatic use:
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```python
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import sqlglot
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try:
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sqlglot.transpile("SELECT foo( FROM bar")
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except sqlglot.errors.ParseError as e:
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print(e.errors)
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```
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```python
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[{
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'description': 'Expecting )',
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'line': 1,
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'col': 13,
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'start_context': 'SELECT foo( ',
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'highlight': 'FROM',
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'end_context': ' bar'
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}]
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```
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### Unsupported Errors
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Presto `APPROX_DISTINCT` supports the accuracy argument which is not supported in Hive:
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```python
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import sqlglot
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sqlglot.transpile("SELECT APPROX_DISTINCT(a, 0.1) FROM foo", read="presto", write="hive")
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```
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```sql
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APPROX_COUNT_DISTINCT does not support accuracy
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'SELECT APPROX_COUNT_DISTINCT(a) FROM foo'
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```
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### Build and Modify SQL
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SQLGlot supports incrementally building sql expressions:
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```python
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from sqlglot import select, condition
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where = condition("x=1").and_("y=1")
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select("*").from_("y").where(where).sql()
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```
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```sql
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'SELECT * FROM y WHERE x = 1 AND y = 1'
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```
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You can also modify a parsed tree:
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```python
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from sqlglot import parse_one
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parse_one("SELECT x FROM y").from_("z").sql()
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```
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```sql
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'SELECT x FROM y, z'
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```
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There is also a way to recursively transform the parsed tree by applying a mapping function to each tree node:
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```python
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from sqlglot import exp, parse_one
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expression_tree = parse_one("SELECT a FROM x")
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def transformer(node):
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if isinstance(node, exp.Column) and node.name == "a":
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return parse_one("FUN(a)")
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return node
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transformed_tree = expression_tree.transform(transformer)
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transformed_tree.sql()
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```
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```sql
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'SELECT FUN(a) FROM x'
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```
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### SQL Optimizer
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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:
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```python
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import sqlglot
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from sqlglot.optimizer import optimize
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print(
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optimize(
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sqlglot.parse_one("""
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SELECT A OR (B OR (C AND D))
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FROM x
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WHERE Z = date '2021-01-01' + INTERVAL '1' month OR 1 = 0
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"""),
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schema={"x": {"A": "INT", "B": "INT", "C": "INT", "D": "INT", "Z": "STRING"}}
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).sql(pretty=True)
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)
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```
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```sql
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SELECT
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(
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"x"."a" OR "x"."b" OR "x"."c"
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) AND (
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"x"."a" OR "x"."b" OR "x"."d"
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) AS "_col_0"
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FROM "x" AS "x"
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WHERE
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CAST("x"."z" AS DATE) = CAST('2021-02-01' AS DATE)
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```
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### AST Introspection
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You can see the AST version of the sql by calling `repr`:
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```python
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from sqlglot import parse_one
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print(repr(parse_one("SELECT a + 1 AS z")))
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```
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```python
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(SELECT expressions:
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(ALIAS this:
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(ADD this:
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(COLUMN this:
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(IDENTIFIER this: a, quoted: False)), expression:
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(LITERAL this: 1, is_string: False)), alias:
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(IDENTIFIER this: z, quoted: False)))
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```
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### AST Diff
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SQLGlot can calculate the 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:
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```python
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from sqlglot import diff, parse_one
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diff(parse_one("SELECT a + b, c, d"), parse_one("SELECT c, a - b, d"))
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```
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```python
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[
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Remove(expression=(ADD this:
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(COLUMN this:
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(IDENTIFIER this: a, quoted: False)), expression:
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(COLUMN this:
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(IDENTIFIER this: b, quoted: False)))),
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Insert(expression=(SUB this:
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(COLUMN this:
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(IDENTIFIER this: a, quoted: False)), expression:
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(COLUMN this:
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(IDENTIFIER this: b, quoted: False)))),
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Move(expression=(COLUMN this:
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(IDENTIFIER this: c, quoted: False))),
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Keep(source=(IDENTIFIER this: b, quoted: False), target=(IDENTIFIER this: b, quoted: False)),
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...
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]
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```
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See also: [Semantic Diff for SQL](https://github.com/tobymao/sqlglot/blob/main/posts/sql_diff.md).
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### Custom Dialects
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[Dialects](https://github.com/tobymao/sqlglot/tree/main/sqlglot/dialects) can be added by subclassing `Dialect`:
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```python
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from sqlglot import exp
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from sqlglot.dialects.dialect import Dialect
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from sqlglot.generator import Generator
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from sqlglot.tokens import Tokenizer, TokenType
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class Custom(Dialect):
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class Tokenizer(Tokenizer):
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QUOTES = ["'", '"']
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IDENTIFIERS = ["`"]
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KEYWORDS = {
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**Tokenizer.KEYWORDS,
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"INT64": TokenType.BIGINT,
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"FLOAT64": TokenType.DOUBLE,
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}
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class Generator(Generator):
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TRANSFORMS = {exp.Array: lambda self, e: f"[{self.expressions(e)}]"}
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TYPE_MAPPING = {
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exp.DataType.Type.TINYINT: "INT64",
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exp.DataType.Type.SMALLINT: "INT64",
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exp.DataType.Type.INT: "INT64",
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exp.DataType.Type.BIGINT: "INT64",
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exp.DataType.Type.DECIMAL: "NUMERIC",
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exp.DataType.Type.FLOAT: "FLOAT64",
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exp.DataType.Type.DOUBLE: "FLOAT64",
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exp.DataType.Type.BOOLEAN: "BOOL",
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exp.DataType.Type.TEXT: "STRING",
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}
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print(Dialect["custom"])
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```
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```
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<class '__main__.Custom'>
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```
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### SQL Execution
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One can even interpret SQL queries using SQLGlot, where the tables are represented as Python dictionaries. Although the engine is not very fast (it's not supposed to be) and is in a relatively early stage of development, 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 (arrow, pandas). Below is an example showcasing the execution of a SELECT expression that involves aggregations and JOINs:
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```python
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from sqlglot.executor import execute
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tables = {
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"sushi": [
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{"id": 1, "price": 1.0},
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{"id": 2, "price": 2.0},
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{"id": 3, "price": 3.0},
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],
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"order_items": [
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{"sushi_id": 1, "order_id": 1},
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{"sushi_id": 1, "order_id": 1},
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{"sushi_id": 2, "order_id": 1},
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{"sushi_id": 3, "order_id": 2},
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],
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"orders": [
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{"id": 1, "user_id": 1},
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{"id": 2, "user_id": 2},
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],
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}
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execute(
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"""
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SELECT
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o.user_id,
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SUM(s.price) AS price
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FROM orders o
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JOIN order_items i
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ON o.id = i.order_id
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JOIN sushi s
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ON i.sushi_id = s.id
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GROUP BY o.user_id
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""",
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tables=tables
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)
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```
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```python
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user_id price
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1 4.0
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2 3.0
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```
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See also: [Writing a Python SQL engine from scratch](https://github.com/tobymao/sqlglot/blob/main/posts/python_sql_engine.md).
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## Used By
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* [SQLMesh](https://github.com/TobikoData/sqlmesh)
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* [Fugue](https://github.com/fugue-project/fugue)
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* [ibis](https://github.com/ibis-project/ibis)
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* [mysql-mimic](https://github.com/kelsin/mysql-mimic)
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* [Querybook](https://github.com/pinterest/querybook)
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* [Quokka](https://github.com/marsupialtail/quokka)
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* [Splink](https://github.com/moj-analytical-services/splink)
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## Documentation
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SQLGlot uses [pdoc](https://pdoc.dev/) to serve its API documentation:
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```
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make docs-serve
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```
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## Run Tests and Lint
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```
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make check # Set SKIP_INTEGRATION=1 to skip integration tests
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```
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## Benchmarks
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[Benchmarks](https://github.com/tobymao/sqlglot/blob/main/benchmarks/bench.py) run on Python 3.10.5 in seconds.
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| Query | sqlglot | sqlfluff | sqltree | sqlparse | moz_sql_parser | sqloxide |
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| --------------- | --------------- | --------------- | --------------- | --------------- | --------------- | --------------- |
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| tpch | 0.01308 (1.0) | 1.60626 (122.7) | 0.01168 (0.893) | 0.04958 (3.791) | 0.08543 (6.531) | 0.00136 (0.104) |
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| short | 0.00109 (1.0) | 0.14134 (129.2) | 0.00099 (0.906) | 0.00342 (3.131) | 0.00652 (5.970) | 8.76E-5 (0.080) |
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| long | 0.01399 (1.0) | 2.12632 (151.9) | 0.01126 (0.805) | 0.04410 (3.151) | 0.06671 (4.767) | 0.00107 (0.076) |
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| crazy | 0.03969 (1.0) | 24.3777 (614.1) | 0.03917 (0.987) | 11.7043 (294.8) | 1.03280 (26.02) | 0.00625 (0.157) |
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## Optional Dependencies
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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:
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```sql
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x + interval '1' month
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```
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