o 8Di@s`dZddlZddlZgdZddeDZeZiedddZeeZ e Z dd Z dS) a} ## 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: ```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 = ["'", '"'] # 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. ---- N)AthenaBigQuery ClickHouse DatabricksDorisDremioDrillDruidDuckDBDuneFabricHive MaterializeMySQLOraclePostgresPrestoPRQLRedshift RisingWave SnowflakeSparkSpark2SQLite StarRocksTableauTeradataTrinoTSQLExasolcCsi|]}||qS)lower).0namer r R/var/www/Datamplify/venv/lib/python3.10/site-packages/sqlglot/dialects/__init__.py dsr%dialect)DialectDialectscCs`t|}|r&ttd|}Wdn1swYt||Stdtd|)Nzsqlglot.dialects.zmodule z has no attribute )MODULE_BY_ATTRIBUTEget _import_lock importlib import_modulegetattrAttributeError__name__)r# module_namemoduler r r$ __getattr__us  r3) __doc__r, threadingDIALECTSMODULE_BY_DIALECTvaluesDIALECT_MODULE_NAMESr)list__all__RLockr+r3r r r r$s>!