sqlglot.dialects API documentation

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## Dialects

While there is a SQL standard, most SQL engines support a variation of that standard. This makes it difficult to write portable SQL code. SQLGlot bridges all the different variations, called "dialects", with an extensible SQL transpilation framework.

The base `sqlglot.dialects.dialect.Dialect` class implements a generic dialect that aims to be as universal as possible.

Each SQL variation has its own `Dialect` subclass, extending the corresponding `Tokenizer`, `Parser` and `Generator` classes as needed.

### Implementing a custom Dialect

Creating a new SQL dialect may seem complicated at first, but it is actually quite simple in SQLGlot:

    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 = ["'", '"']  # Strings can be delimited by either single or double quotes
            IDENTIFIERS = ["`"]  # Identifiers can be delimited by backticks

            # Associates certain meaningful words with tokens that capture their intent
            KEYWORDS = {
                **Tokenizer.KEYWORDS,
                "INT64": TokenType.BIGINT,
                "FLOAT64": TokenType.DOUBLE,
            }

        class Generator(Generator):
            # Specifies how AST nodes, i.e. subclasses of exp.Expression, should be converted into SQL
            TRANSFORMS = {
                exp.Array: lambda self, e: f"[{self.expressions(e)}]",
            }

            # Specifies how AST nodes representing data types should be converted into SQL
            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",
            }

The above example demonstrates how certain parts of the base `Dialect` class can be overridden to match a different specification. Even though it is a fairly realistic starting point, we strongly encourage the reader to study existing dialect implementations in order to understand how their various components can be modified, depending on the use-case.

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      1# ruff: noqa: F401
      2"""
      3## Dialects
      4
      5While there is a SQL standard, most SQL engines support a variation of that standard. This makes it difficult
      6to write portable SQL code. SQLGlot bridges all the different variations, called "dialects", with an extensible
      7SQL transpilation framework.
      8
      9The base `sqlglot.dialects.dialect.Dialect` class implements a generic dialect that aims to be as universal as possible.
     10
     11Each SQL variation has its own `Dialect` subclass, extending the corresponding `Tokenizer`, `Parser` and `Generator`
     12classes as needed.
     13
     14### Implementing a custom Dialect
     15
     16Creating a new SQL dialect may seem complicated at first, but it is actually quite simple in SQLGlot:
     17
     18```python
     19from sqlglot import exp
     20from sqlglot.dialects.dialect import Dialect
     21from sqlglot.generator import Generator
     22from sqlglot.tokens import Tokenizer, TokenType
     23
     24
     25class Custom(Dialect):
     26    class Tokenizer(Tokenizer):
     27        QUOTES = ["'", '"']  # Strings can be delimited by either single or double quotes
     28        IDENTIFIERS = ["`"]  # Identifiers can be delimited by backticks
     29
     30        # Associates certain meaningful words with tokens that capture their intent
     31        KEYWORDS = {
     32            **Tokenizer.KEYWORDS,
     33            "INT64": TokenType.BIGINT,
     34            "FLOAT64": TokenType.DOUBLE,
     35        }
     36
     37    class Generator(Generator):
     38        # Specifies how AST nodes, i.e. subclasses of exp.Expression, should be converted into SQL
     39        TRANSFORMS = {
     40            exp.Array: lambda self, e: f"[{self.expressions(e)}]",
     41        }
     42
     43        # Specifies how AST nodes representing data types should be converted into SQL
     44        TYPE_MAPPING = {
     45            exp.DataType.Type.TINYINT: "INT64",
     46            exp.DataType.Type.SMALLINT: "INT64",
     47            exp.DataType.Type.INT: "INT64",
     48            exp.DataType.Type.BIGINT: "INT64",
     49            exp.DataType.Type.DECIMAL: "NUMERIC",
     50            exp.DataType.Type.FLOAT: "FLOAT64",
     51            exp.DataType.Type.DOUBLE: "FLOAT64",
     52            exp.DataType.Type.BOOLEAN: "BOOL",
     53            exp.DataType.Type.TEXT: "STRING",
     54        }
     55```
     56
     57The above example demonstrates how certain parts of the base `Dialect` class can be overridden to match a different
     58specification. Even though it is a fairly realistic starting point, we strongly encourage the reader to study existing
     59dialect implementations in order to understand how their various components can be modified, depending on the use-case.
     60
     61----
     62"""
     63
     64import importlib
     65
     66DIALECTS = [
     67    "Athena",
     68    "BigQuery",
     69    "ClickHouse",
     70    "Databricks",
     71    "Doris",
     72    "Drill",
     73    "Druid",
     74    "DuckDB",
     75    "Dune",
     76    "Hive",
     77    "Materialize",
     78    "MySQL",
     79    "Oracle",
     80    "Postgres",
     81    "Presto",
     82    "PRQL",
     83    "Redshift",
     84    "RisingWave",
     85    "Snowflake",
     86    "Spark",
     87    "Spark2",
     88    "SQLite",
     89    "StarRocks",
     90    "Tableau",
     91    "Teradata",
     92    "Trino",
     93    "TSQL",
     94]
     95
     96MODULE_BY_DIALECT = {name: name.lower() for name in DIALECTS}
     97DIALECT_MODULE_NAMES = MODULE_BY_DIALECT.values()
     98
     99MODULE_BY_ATTRIBUTE = {
    100    **MODULE_BY_DIALECT,
    101    "Dialect": "dialect",
    102    "Dialects": "dialect",
    103}
    104
    105__all__ = list(MODULE_BY_ATTRIBUTE)
    106
    107
    108def __getattr__(name):
    109    module_name = MODULE_BY_ATTRIBUTE.get(name)
    110    if module_name:
    111        module = importlib.import_module(f"sqlglot.dialects.{module_name}")
    112        return getattr(module, name)
    113
    114    raise AttributeError(f"module {__name__} has no attribute {name}")

Athena = <MagicMock id='140374168101040'>
BigQuery = <MagicMock id='140374175098928'>
ClickHouse = <MagicMock id='140374175096432'>
Databricks = <MagicMock id='140374171297936'>
Doris = <MagicMock id='140374165470464'>
Drill = <MagicMock id='140374165462544'>
Druid = <MagicMock id='140374177410560'>
DuckDB = <MagicMock id='140374178503920'>
Dune = <MagicMock id='140374178278336'>
Hive = <MagicMock id='140374178270944'>
Materialize = <MagicMock id='140374170434768'>
MySQL = <MagicMock id='140374171265744'>
Oracle = <MagicMock id='140374174224432'>
Postgres = <MagicMock id='140374178654640'>
Presto = <MagicMock id='140374178120560'>
PRQL = <MagicMock id='140374178123344'>
Redshift = <MagicMock id='140374174402832'>
RisingWave = <MagicMock id='140374178167248'>
Snowflake = <MagicMock id='140374166107808'>
Spark = <MagicMock id='140374166109728'>
Spark2 = <MagicMock id='140374175510016'>
SQLite = <MagicMock id='140374175508432'>
StarRocks = <MagicMock id='140374167923072'>
Tableau = <MagicMock id='140374167915392'>
Teradata = <MagicMock id='140374163754768'>
Trino = <MagicMock id='140374163762640'>
TSQL = <MagicMock id='140374163787008'>
Dialect = <MagicMock id='140374163794928'>
Dialects = <MagicMock id='140374163802912'>