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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 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

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:

import 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
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(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(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
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:
    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 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:

import 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
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:

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(
        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))),
  ...
]

See also: Semantic Diff for SQL.

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

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

  1# ruff: noqa: F401
  2"""
  3.. include:: ../README.md
  4
  5----
  6"""
  7
  8from __future__ import annotations
  9
 10import logging
 11import typing as t
 12
 13from sqlglot import expressions as exp
 14from sqlglot.dialects.dialect import Dialect as Dialect, Dialects as Dialects
 15from sqlglot.diff import diff as diff
 16from sqlglot.errors import (
 17    ErrorLevel as ErrorLevel,
 18    ParseError as ParseError,
 19    TokenError as TokenError,
 20    UnsupportedError as UnsupportedError,
 21)
 22from sqlglot.expressions import (
 23    Expression as Expression,
 24    alias_ as alias,
 25    and_ as and_,
 26    case as case,
 27    cast as cast,
 28    column as column,
 29    condition as condition,
 30    except_ as except_,
 31    from_ as from_,
 32    func as func,
 33    intersect as intersect,
 34    maybe_parse as maybe_parse,
 35    not_ as not_,
 36    or_ as or_,
 37    select as select,
 38    subquery as subquery,
 39    table_ as table,
 40    to_column as to_column,
 41    to_identifier as to_identifier,
 42    to_table as to_table,
 43    union as union,
 44)
 45from sqlglot.generator import Generator as Generator
 46from sqlglot.parser import Parser as Parser
 47from sqlglot.schema import MappingSchema as MappingSchema, Schema as Schema
 48from sqlglot.tokens import Token as Token, Tokenizer as Tokenizer, TokenType as TokenType
 49
 50if t.TYPE_CHECKING:
 51    from sqlglot._typing import E
 52    from sqlglot.dialects.dialect import DialectType as DialectType
 53
 54logger = logging.getLogger("sqlglot")
 55
 56
 57try:
 58    from sqlglot._version import __version__, __version_tuple__
 59except ImportError:
 60    logger.error(
 61        "Unable to set __version__, run `pip install -e .` or `python setup.py develop` first."
 62    )
 63
 64
 65pretty = False
 66"""Whether to format generated SQL by default."""
 67
 68schema = MappingSchema()
 69"""The default schema used by SQLGlot (e.g. in the optimizer)."""
 70
 71
 72def tokenize(sql: str, read: DialectType = None, dialect: DialectType = None) -> t.List[Token]:
 73    """
 74    Tokenizes the given SQL string.
 75
 76    Args:
 77        sql: the SQL code string to tokenize.
 78        read: the SQL dialect to apply during tokenizing (eg. "spark", "hive", "presto", "mysql").
 79        dialect: the SQL dialect (alias for read).
 80
 81    Returns:
 82        The resulting list of tokens.
 83    """
 84    return Dialect.get_or_raise(read or dialect).tokenize(sql)
 85
 86
 87def parse(
 88    sql: str, read: DialectType = None, dialect: DialectType = None, **opts
 89) -> t.List[t.Optional[Expression]]:
 90    """
 91    Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.
 92
 93    Args:
 94        sql: the SQL code string to parse.
 95        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
 96        dialect: the SQL dialect (alias for read).
 97        **opts: other `sqlglot.parser.Parser` options.
 98
 99    Returns:
100        The resulting syntax tree collection.
101    """
102    return Dialect.get_or_raise(read or dialect).parse(sql, **opts)
103
104
105@t.overload
106def parse_one(sql: str, *, into: t.Type[E], **opts) -> E: ...
107
108
109@t.overload
110def parse_one(sql: str, **opts) -> Expression: ...
111
112
113def parse_one(
114    sql: str,
115    read: DialectType = None,
116    dialect: DialectType = None,
117    into: t.Optional[exp.IntoType] = None,
118    **opts,
119) -> Expression:
120    """
121    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
122
123    Args:
124        sql: the SQL code string to parse.
125        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
126        dialect: the SQL dialect (alias for read)
127        into: the SQLGlot Expression to parse into.
128        **opts: other `sqlglot.parser.Parser` options.
129
130    Returns:
131        The syntax tree for the first parsed statement.
132    """
133
134    dialect = Dialect.get_or_raise(read or dialect)
135
136    if into:
137        result = dialect.parse_into(into, sql, **opts)
138    else:
139        result = dialect.parse(sql, **opts)
140
141    for expression in result:
142        if not expression:
143            raise ParseError(f"No expression was parsed from '{sql}'")
144        return expression
145    else:
146        raise ParseError(f"No expression was parsed from '{sql}'")
147
148
149def transpile(
150    sql: str,
151    read: DialectType = None,
152    write: DialectType = None,
153    identity: bool = True,
154    error_level: t.Optional[ErrorLevel] = None,
155    **opts,
156) -> t.List[str]:
157    """
158    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
159    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
160
161    Args:
162        sql: the SQL code string to transpile.
163        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
164        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
165        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
166            the source and the target dialect.
167        error_level: the desired error level of the parser.
168        **opts: other `sqlglot.generator.Generator` options.
169
170    Returns:
171        The list of transpiled SQL statements.
172    """
173    write = (read if write is None else write) if identity else write
174    write = Dialect.get_or_raise(write)
175    return [
176        write.generate(expression, copy=False, **opts) if expression else ""
177        for expression in parse(sql, read, error_level=error_level)
178    ]
logger = <Logger sqlglot (WARNING)>
pretty = False

Whether to format generated SQL by default.

schema = <sqlglot.schema.MappingSchema object>

The default schema used by SQLGlot (e.g. in the optimizer).

def tokenize( sql: str, read: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None) -> List[sqlglot.tokens.Token]:
73def tokenize(sql: str, read: DialectType = None, dialect: DialectType = None) -> t.List[Token]:
74    """
75    Tokenizes the given SQL string.
76
77    Args:
78        sql: the SQL code string to tokenize.
79        read: the SQL dialect to apply during tokenizing (eg. "spark", "hive", "presto", "mysql").
80        dialect: the SQL dialect (alias for read).
81
82    Returns:
83        The resulting list of tokens.
84    """
85    return Dialect.get_or_raise(read or dialect).tokenize(sql)

Tokenizes the given SQL string.

Arguments:
  • sql: the SQL code string to tokenize.
  • read: the SQL dialect to apply during tokenizing (eg. "spark", "hive", "presto", "mysql").
  • dialect: the SQL dialect (alias for read).
Returns:

The resulting list of tokens.

def parse( sql: str, read: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, **opts) -> List[Optional[sqlglot.expressions.Expression]]:
 88def parse(
 89    sql: str, read: DialectType = None, dialect: DialectType = None, **opts
 90) -> t.List[t.Optional[Expression]]:
 91    """
 92    Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.
 93
 94    Args:
 95        sql: the SQL code string to parse.
 96        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
 97        dialect: the SQL dialect (alias for read).
 98        **opts: other `sqlglot.parser.Parser` options.
 99
100    Returns:
101        The resulting syntax tree collection.
102    """
103    return Dialect.get_or_raise(read or dialect).parse(sql, **opts)

Parses the given SQL string into a collection of syntax trees, one per parsed SQL statement.

Arguments:
  • sql: the SQL code string to parse.
  • read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
  • dialect: the SQL dialect (alias for read).
  • **opts: other sqlglot.parser.Parser options.
Returns:

The resulting syntax tree collection.

def parse_one( sql: str, read: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, into: Union[str, Type[sqlglot.expressions.Expression], Collection[Union[str, Type[sqlglot.expressions.Expression]]], NoneType] = None, **opts) -> sqlglot.expressions.Expression:
114def parse_one(
115    sql: str,
116    read: DialectType = None,
117    dialect: DialectType = None,
118    into: t.Optional[exp.IntoType] = None,
119    **opts,
120) -> Expression:
121    """
122    Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.
123
124    Args:
125        sql: the SQL code string to parse.
126        read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
127        dialect: the SQL dialect (alias for read)
128        into: the SQLGlot Expression to parse into.
129        **opts: other `sqlglot.parser.Parser` options.
130
131    Returns:
132        The syntax tree for the first parsed statement.
133    """
134
135    dialect = Dialect.get_or_raise(read or dialect)
136
137    if into:
138        result = dialect.parse_into(into, sql, **opts)
139    else:
140        result = dialect.parse(sql, **opts)
141
142    for expression in result:
143        if not expression:
144            raise ParseError(f"No expression was parsed from '{sql}'")
145        return expression
146    else:
147        raise ParseError(f"No expression was parsed from '{sql}'")

Parses the given SQL string and returns a syntax tree for the first parsed SQL statement.

Arguments:
  • sql: the SQL code string to parse.
  • read: the SQL dialect to apply during parsing (eg. "spark", "hive", "presto", "mysql").
  • dialect: the SQL dialect (alias for read)
  • into: the SQLGlot Expression to parse into.
  • **opts: other sqlglot.parser.Parser options.
Returns:

The syntax tree for the first parsed statement.

def transpile( sql: str, read: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, write: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, identity: bool = True, error_level: Optional[sqlglot.errors.ErrorLevel] = None, **opts) -> List[str]:
150def transpile(
151    sql: str,
152    read: DialectType = None,
153    write: DialectType = None,
154    identity: bool = True,
155    error_level: t.Optional[ErrorLevel] = None,
156    **opts,
157) -> t.List[str]:
158    """
159    Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed
160    to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.
161
162    Args:
163        sql: the SQL code string to transpile.
164        read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
165        write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
166        identity: if set to `True` and if the target dialect is not specified the source dialect will be used as both:
167            the source and the target dialect.
168        error_level: the desired error level of the parser.
169        **opts: other `sqlglot.generator.Generator` options.
170
171    Returns:
172        The list of transpiled SQL statements.
173    """
174    write = (read if write is None else write) if identity else write
175    write = Dialect.get_or_raise(write)
176    return [
177        write.generate(expression, copy=False, **opts) if expression else ""
178        for expression in parse(sql, read, error_level=error_level)
179    ]

Parses the given SQL string in accordance with the source dialect and returns a list of SQL strings transformed to conform to the target dialect. Each string in the returned list represents a single transformed SQL statement.

Arguments:
  • sql: the SQL code string to transpile.
  • read: the source dialect used to parse the input string (eg. "spark", "hive", "presto", "mysql").
  • write: the target dialect into which the input should be transformed (eg. "spark", "hive", "presto", "mysql").
  • identity: if set to True and if the target dialect is not specified the source dialect will be used as both: the source and the target dialect.
  • error_level: the desired error level of the parser.
  • **opts: other sqlglot.generator.Generator options.
Returns:

The list of transpiled SQL statements.