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sqlglot.dialects.fabric

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 1from __future__ import annotations
 2
 3from sqlglot import exp
 4from sqlglot.dialects.dialect import NormalizationStrategy
 5from sqlglot.dialects.tsql import TSQL
 6
 7
 8class Fabric(TSQL):
 9    """
10    Microsoft Fabric Data Warehouse dialect that inherits from T-SQL.
11
12    Microsoft Fabric is a cloud-based analytics platform that provides a unified
13    data warehouse experience. While it shares much of T-SQL's syntax, it has
14    specific differences and limitations that this dialect addresses.
15
16    Key differences from T-SQL:
17    - Case-sensitive identifiers (unlike T-SQL which is case-insensitive)
18    - Limited data type support with mappings to supported alternatives
19    - Temporal types (DATETIME2, DATETIMEOFFSET, TIME) limited to 6 digits precision
20    - Certain legacy types (MONEY, SMALLMONEY, etc.) are not supported
21    - Unicode types (NCHAR, NVARCHAR) are mapped to non-unicode equivalents
22
23    References:
24    - Data Types: https://learn.microsoft.com/en-us/fabric/data-warehouse/data-types
25    - T-SQL Surface Area: https://learn.microsoft.com/en-us/fabric/data-warehouse/tsql-surface-area
26    """
27
28    # Fabric is case-sensitive unlike T-SQL which is case-insensitive
29    NORMALIZATION_STRATEGY = NormalizationStrategy.CASE_SENSITIVE
30
31    class Generator(TSQL.Generator):
32        # Fabric-specific type mappings - override T-SQL types that aren't supported
33        # Reference: https://learn.microsoft.com/en-us/fabric/data-warehouse/data-types
34        TYPE_MAPPING = {
35            **TSQL.Generator.TYPE_MAPPING,
36            # Fabric doesn't support these types, map to alternatives
37            exp.DataType.Type.MONEY: "DECIMAL",
38            exp.DataType.Type.SMALLMONEY: "DECIMAL",
39            exp.DataType.Type.DATETIME: "DATETIME2(6)",
40            exp.DataType.Type.SMALLDATETIME: "DATETIME2(6)",
41            exp.DataType.Type.NCHAR: "CHAR",
42            exp.DataType.Type.NVARCHAR: "VARCHAR",
43            exp.DataType.Type.TEXT: "VARCHAR(MAX)",
44            exp.DataType.Type.IMAGE: "VARBINARY",
45            exp.DataType.Type.TINYINT: "SMALLINT",
46            exp.DataType.Type.UTINYINT: "SMALLINT",  # T-SQL parses TINYINT as UTINYINT
47            exp.DataType.Type.JSON: "VARCHAR",
48            exp.DataType.Type.XML: "VARCHAR",
49            exp.DataType.Type.UUID: "VARBINARY(MAX)",  # UNIQUEIDENTIFIER has limitations in Fabric
50            # Override T-SQL mappings that use different names in Fabric
51            exp.DataType.Type.DECIMAL: "DECIMAL",  # T-SQL uses NUMERIC
52            exp.DataType.Type.DOUBLE: "FLOAT",
53            exp.DataType.Type.INT: "INT",  # T-SQL uses INTEGER
54        }
55
56        def datatype_sql(self, expression: exp.DataType) -> str:
57            """
58            Override datatype generation to handle Fabric-specific precision limitations.
59
60            Fabric limits temporal types (TIME, DATETIME2, DATETIMEOFFSET) to max 6 digits precision.
61            When no precision is specified, we default to 6 digits.
62            """
63            if expression.is_type(
64                exp.DataType.Type.TIME,
65                exp.DataType.Type.DATETIME2,
66                exp.DataType.Type.TIMESTAMPTZ,  # DATETIMEOFFSET in Fabric
67            ):
68                # Get the current precision (first expression if it exists)
69                precision = expression.find(exp.DataTypeParam)
70
71                # Determine the target precision
72                if precision is None:
73                    # No precision specified, default to 6
74                    target_precision = 6
75                elif precision.this.is_int:
76                    # Cap precision at 6
77                    current_precision = precision.this.to_py()
78                    target_precision = min(current_precision, 6)
79
80                # Create a new expression with the target precision
81                new_expression = exp.DataType(
82                    this=expression.this,
83                    expressions=[exp.DataTypeParam(this=exp.Literal.number(target_precision))],
84                )
85
86                return super().datatype_sql(new_expression)
87
88            return super().datatype_sql(expression)

class Fabric(sqlglot.dialects.tsql.TSQL): View Source

 9class Fabric(TSQL):
10    """
11    Microsoft Fabric Data Warehouse dialect that inherits from T-SQL.
12
13    Microsoft Fabric is a cloud-based analytics platform that provides a unified
14    data warehouse experience. While it shares much of T-SQL's syntax, it has
15    specific differences and limitations that this dialect addresses.
16
17    Key differences from T-SQL:
18    - Case-sensitive identifiers (unlike T-SQL which is case-insensitive)
19    - Limited data type support with mappings to supported alternatives
20    - Temporal types (DATETIME2, DATETIMEOFFSET, TIME) limited to 6 digits precision
21    - Certain legacy types (MONEY, SMALLMONEY, etc.) are not supported
22    - Unicode types (NCHAR, NVARCHAR) are mapped to non-unicode equivalents
23
24    References:
25    - Data Types: https://learn.microsoft.com/en-us/fabric/data-warehouse/data-types
26    - T-SQL Surface Area: https://learn.microsoft.com/en-us/fabric/data-warehouse/tsql-surface-area
27    """
28
29    # Fabric is case-sensitive unlike T-SQL which is case-insensitive
30    NORMALIZATION_STRATEGY = NormalizationStrategy.CASE_SENSITIVE
31
32    class Generator(TSQL.Generator):
33        # Fabric-specific type mappings - override T-SQL types that aren't supported
34        # Reference: https://learn.microsoft.com/en-us/fabric/data-warehouse/data-types
35        TYPE_MAPPING = {
36            **TSQL.Generator.TYPE_MAPPING,
37            # Fabric doesn't support these types, map to alternatives
38            exp.DataType.Type.MONEY: "DECIMAL",
39            exp.DataType.Type.SMALLMONEY: "DECIMAL",
40            exp.DataType.Type.DATETIME: "DATETIME2(6)",
41            exp.DataType.Type.SMALLDATETIME: "DATETIME2(6)",
42            exp.DataType.Type.NCHAR: "CHAR",
43            exp.DataType.Type.NVARCHAR: "VARCHAR",
44            exp.DataType.Type.TEXT: "VARCHAR(MAX)",
45            exp.DataType.Type.IMAGE: "VARBINARY",
46            exp.DataType.Type.TINYINT: "SMALLINT",
47            exp.DataType.Type.UTINYINT: "SMALLINT",  # T-SQL parses TINYINT as UTINYINT
48            exp.DataType.Type.JSON: "VARCHAR",
49            exp.DataType.Type.XML: "VARCHAR",
50            exp.DataType.Type.UUID: "VARBINARY(MAX)",  # UNIQUEIDENTIFIER has limitations in Fabric
51            # Override T-SQL mappings that use different names in Fabric
52            exp.DataType.Type.DECIMAL: "DECIMAL",  # T-SQL uses NUMERIC
53            exp.DataType.Type.DOUBLE: "FLOAT",
54            exp.DataType.Type.INT: "INT",  # T-SQL uses INTEGER
55        }
56
57        def datatype_sql(self, expression: exp.DataType) -> str:
58            """
59            Override datatype generation to handle Fabric-specific precision limitations.
60
61            Fabric limits temporal types (TIME, DATETIME2, DATETIMEOFFSET) to max 6 digits precision.
62            When no precision is specified, we default to 6 digits.
63            """
64            if expression.is_type(
65                exp.DataType.Type.TIME,
66                exp.DataType.Type.DATETIME2,
67                exp.DataType.Type.TIMESTAMPTZ,  # DATETIMEOFFSET in Fabric
68            ):
69                # Get the current precision (first expression if it exists)
70                precision = expression.find(exp.DataTypeParam)
71
72                # Determine the target precision
73                if precision is None:
74                    # No precision specified, default to 6
75                    target_precision = 6
76                elif precision.this.is_int:
77                    # Cap precision at 6
78                    current_precision = precision.this.to_py()
79                    target_precision = min(current_precision, 6)
80
81                # Create a new expression with the target precision
82                new_expression = exp.DataType(
83                    this=expression.this,
84                    expressions=[exp.DataTypeParam(this=exp.Literal.number(target_precision))],
85                )
86
87                return super().datatype_sql(new_expression)
88
89            return super().datatype_sql(expression)

Microsoft Fabric Data Warehouse dialect that inherits from T-SQL.

Microsoft Fabric is a cloud-based analytics platform that provides a unified data warehouse experience. While it shares much of T-SQL's syntax, it has specific differences and limitations that this dialect addresses.

Key differences from T-SQL:

Case-sensitive identifiers (unlike T-SQL which is case-insensitive)
Limited data type support with mappings to supported alternatives
Temporal types (DATETIME2, DATETIMEOFFSET, TIME) limited to 6 digits precision
Certain legacy types (MONEY, SMALLMONEY, etc.) are not supported
Unicode types (NCHAR, NVARCHAR) are mapped to non-unicode equivalents

References:

Data Types: https://learn.microsoft.com/en-us/fabric/data-warehouse/data-types
T-SQL Surface Area: https://learn.microsoft.com/en-us/fabric/data-warehouse/tsql-surface-area

NORMALIZATION_STRATEGY = <NormalizationStrategy.CASE_SENSITIVE: 'CASE_SENSITIVE'>

Specifies the strategy according to which identifiers should be normalized.

SUPPORTS_COLUMN_JOIN_MARKS = False

Whether the old-style outer join (+) syntax is supported.

tokenizer_class = <class 'sqlglot.tokens.Tokenizer'>

jsonpath_tokenizer_class = <class 'sqlglot.tokens.JSONPathTokenizer'>

parser_class = <class 'sqlglot.parser.Parser'>

generator_class = <class 'Fabric.Generator'>

TIME_TRIE: Dict = {'y': {'e': {'a': {'r': {0: True}}}, 0: True, 'y': {'y': {'y': {0: True}}, 0: True}}, 'd': {'a': {'y': {'o': {'f': {'y': {'e': {'a': {'r': {0: True}}}}}}, 0: True}}, 'y': {0: True}, 'w': {0: True}, 'd': {'d': {'d': {0: True}}, 0: True}, 0: True}, 'w': {'e': {'e': {'k': {0: True, 'd': {'a': {'y': {0: True}}}}}}, 'w': {0: True}, 'k': {0: True}}, 'h': {'o': {'u': {'r': {0: True}}}, 'h': {0: True}, 0: True}, 'm': {'i': {'n': {'u': {'t': {'e': {0: True}}}}, 0: True, 'l': {'l': {'i': {'s': {'e': {'c': {'o': {'n': {'d': {0: True}}}}}}}}}}, 's': {0: True}, 'o': {'n': {'t': {'h': {0: True}}}}, 'm': {0: True}, 0: True}, 'n': {0: True}, 's': {'e': {'c': {'o': {'n': {'d': {0: True}}}}}, 's': {0: True}, 0: True}, 'Y': {0: True, 'Y': {'Y': {'Y': {0: True}}, 0: True}}, 'M': {'M': {'M': {'M': {0: True}, 0: True}, 0: True}, 0: True}, 'H': {'H': {0: True}, 0: True}, 'f': {'f': {'f': {'f': {'f': {'f': {0: True}}}}}}}

FORMAT_TRIE: Dict = {'y': {'e': {'a': {'r': {0: True}}}, 0: True, 'y': {'y': {'y': {0: True}}, 0: True}}, 'd': {'a': {'y': {'o': {'f': {'y': {'e': {'a': {'r': {0: True}}}}}}, 0: True}}, 'y': {0: True}, 'w': {0: True}, 'd': {'d': {'d': {0: True}}, 0: True}, 0: True}, 'w': {'e': {'e': {'k': {0: True, 'd': {'a': {'y': {0: True}}}}}}, 'w': {0: True}, 'k': {0: True}}, 'h': {'o': {'u': {'r': {0: True}}}, 'h': {0: True}, 0: True}, 'm': {'i': {'n': {'u': {'t': {'e': {0: True}}}}, 0: True, 'l': {'l': {'i': {'s': {'e': {'c': {'o': {'n': {'d': {0: True}}}}}}}}}}, 's': {0: True}, 'o': {'n': {'t': {'h': {0: True}}}}, 'm': {0: True}, 0: True}, 'n': {0: True}, 's': {'e': {'c': {'o': {'n': {'d': {0: True}}}}}, 's': {0: True}, 0: True}, 'Y': {0: True, 'Y': {'Y': {'Y': {0: True}}, 0: True}}, 'M': {'M': {'M': {'M': {0: True}, 0: True}, 0: True}, 0: True}, 'H': {'H': {0: True}, 0: True}, 'f': {'f': {'f': {'f': {'f': {'f': {0: True}}}}}}}

INVERSE_TIME_MAPPING: Dict[str, str] = {'%Y': 'yyyy', '%j': 'dayofyear', '%d': 'dd', '%W': 'wk', '%h': 'hour', '%I': 'hh', '%M': 'mm', '%S': 'ss', '%-S': 's', '%f': 'ffffff', '%w': 'dw', '%m': 'MM', '%-M': 'm', '%y': 'yy', '%B': 'MMMM', '%b': 'MMM', '%-m': 'M', '%A': 'dddd', '%-d': 'd', '%H': 'HH', '%-H': 'H', '%-I': 'h'}

INVERSE_TIME_TRIE: Dict = {'%': {'Y': {0: True}, 'j': {0: True}, 'd': {0: True}, 'W': {0: True}, 'h': {0: True}, 'I': {0: True}, 'M': {0: True}, 'S': {0: True}, '-': {'S': {0: True}, 'M': {0: True}, 'm': {0: True}, 'd': {0: True}, 'H': {0: True}, 'I': {0: True}}, 'f': {0: True}, 'w': {0: True}, 'm': {0: True}, 'y': {0: True}, 'B': {0: True}, 'b': {0: True}, 'A': {0: True}, 'H': {0: True}}}

INVERSE_FORMAT_MAPPING: Dict[str, str] = {}

INVERSE_FORMAT_TRIE: Dict = {}

INVERSE_CREATABLE_KIND_MAPPING: dict[str, str] = {}

ESCAPED_SEQUENCES: Dict[str, str] = {}

QUOTE_START = "'"

QUOTE_END = "'"

IDENTIFIER_START = '['

IDENTIFIER_END = ']'

BIT_START: Optional[str] = None

BIT_END: Optional[str] = None

HEX_START: Optional[str] = '0x'

HEX_END: Optional[str] = ''

BYTE_START: Optional[str] = None

BYTE_END: Optional[str] = None

UNICODE_START: Optional[str] = None

UNICODE_END: Optional[str] = None

Inherited Members

sqlglot.dialects.tsql.TSQL: SUPPORTS_SEMI_ANTI_JOIN; LOG_BASE_FIRST; TYPED_DIVISION; CONCAT_COALESCE; ALTER_TABLE_ADD_REQUIRED_FOR_EACH_COLUMN; TIME_FORMAT; TIME_MAPPING; CONVERT_FORMAT_MAPPING; FORMAT_TIME_MAPPING; Tokenizer; Parser

class Fabric.Generator(sqlglot.dialects.tsql.TSQL.Generator): View Source

32    class Generator(TSQL.Generator):
33        # Fabric-specific type mappings - override T-SQL types that aren't supported
34        # Reference: https://learn.microsoft.com/en-us/fabric/data-warehouse/data-types
35        TYPE_MAPPING = {
36            **TSQL.Generator.TYPE_MAPPING,
37            # Fabric doesn't support these types, map to alternatives
38            exp.DataType.Type.MONEY: "DECIMAL",
39            exp.DataType.Type.SMALLMONEY: "DECIMAL",
40            exp.DataType.Type.DATETIME: "DATETIME2(6)",
41            exp.DataType.Type.SMALLDATETIME: "DATETIME2(6)",
42            exp.DataType.Type.NCHAR: "CHAR",
43            exp.DataType.Type.NVARCHAR: "VARCHAR",
44            exp.DataType.Type.TEXT: "VARCHAR(MAX)",
45            exp.DataType.Type.IMAGE: "VARBINARY",
46            exp.DataType.Type.TINYINT: "SMALLINT",
47            exp.DataType.Type.UTINYINT: "SMALLINT",  # T-SQL parses TINYINT as UTINYINT
48            exp.DataType.Type.JSON: "VARCHAR",
49            exp.DataType.Type.XML: "VARCHAR",
50            exp.DataType.Type.UUID: "VARBINARY(MAX)",  # UNIQUEIDENTIFIER has limitations in Fabric
51            # Override T-SQL mappings that use different names in Fabric
52            exp.DataType.Type.DECIMAL: "DECIMAL",  # T-SQL uses NUMERIC
53            exp.DataType.Type.DOUBLE: "FLOAT",
54            exp.DataType.Type.INT: "INT",  # T-SQL uses INTEGER
55        }
56
57        def datatype_sql(self, expression: exp.DataType) -> str:
58            """
59            Override datatype generation to handle Fabric-specific precision limitations.
60
61            Fabric limits temporal types (TIME, DATETIME2, DATETIMEOFFSET) to max 6 digits precision.
62            When no precision is specified, we default to 6 digits.
63            """
64            if expression.is_type(
65                exp.DataType.Type.TIME,
66                exp.DataType.Type.DATETIME2,
67                exp.DataType.Type.TIMESTAMPTZ,  # DATETIMEOFFSET in Fabric
68            ):
69                # Get the current precision (first expression if it exists)
70                precision = expression.find(exp.DataTypeParam)
71
72                # Determine the target precision
73                if precision is None:
74                    # No precision specified, default to 6
75                    target_precision = 6
76                elif precision.this.is_int:
77                    # Cap precision at 6
78                    current_precision = precision.this.to_py()
79                    target_precision = min(current_precision, 6)
80
81                # Create a new expression with the target precision
82                new_expression = exp.DataType(
83                    this=expression.this,
84                    expressions=[exp.DataTypeParam(this=exp.Literal.number(target_precision))],
85                )
86
87                return super().datatype_sql(new_expression)
88
89            return super().datatype_sql(expression)

Generator converts a given syntax tree to the corresponding SQL string.

Arguments:

pretty: Whether to format the produced SQL string. Default: False.
identify: Determines when an identifier should be quoted. Possible values are: False (default): Never quote, except in cases where it's mandatory by the dialect. True or 'always': Always quote. 'safe': Only quote identifiers that are case insensitive.
normalize: Whether to normalize identifiers to lowercase. Default: False.
pad: The pad size in a formatted string. For example, this affects the indentation of a projection in a query, relative to its nesting level. Default: 2.
indent: The indentation size in a formatted string. For example, this affects the indentation of subqueries and filters under a WHERE clause. Default: 2.
normalize_functions: How to normalize function names. Possible values are: "upper" or True (default): Convert names to uppercase. "lower": Convert names to lowercase. False: Disables function name normalization.
unsupported_level: Determines the generator's behavior when it encounters unsupported expressions. Default ErrorLevel.WARN.
max_unsupported: Maximum number of unsupported messages to include in a raised UnsupportedError. This is only relevant if unsupported_level is ErrorLevel.RAISE. Default: 3
leading_comma: Whether the comma is leading or trailing in select expressions. This is only relevant when generating in pretty mode. Default: False
max_text_width: The max number of characters in a segment before creating new lines in pretty mode. The default is on the smaller end because the length only represents a segment and not the true line length. Default: 80
comments: Whether to preserve comments in the output SQL code. Default: True

TYPE_MAPPING = {<Type.DATETIME2: 'DATETIME2'>: 'DATETIME2', <Type.MEDIUMTEXT: 'MEDIUMTEXT'>: 'TEXT', <Type.LONGTEXT: 'LONGTEXT'>: 'TEXT', <Type.TINYTEXT: 'TINYTEXT'>: 'TEXT', <Type.BLOB: 'BLOB'>: 'VARBINARY', <Type.MEDIUMBLOB: 'MEDIUMBLOB'>: 'BLOB', <Type.LONGBLOB: 'LONGBLOB'>: 'BLOB', <Type.TINYBLOB: 'TINYBLOB'>: 'BLOB', <Type.INET: 'INET'>: 'INET', <Type.ROWVERSION: 'ROWVERSION'>: 'ROWVERSION', <Type.SMALLDATETIME: 'SMALLDATETIME'>: 'DATETIME2(6)', <Type.BOOLEAN: 'BOOLEAN'>: 'BIT', <Type.DECIMAL: 'DECIMAL'>: 'DECIMAL', <Type.DOUBLE: 'DOUBLE'>: 'FLOAT', <Type.INT: 'INT'>: 'INT', <Type.TEXT: 'TEXT'>: 'VARCHAR(MAX)', <Type.TIMESTAMP: 'TIMESTAMP'>: 'DATETIME2', <Type.TIMESTAMPNTZ: 'TIMESTAMPNTZ'>: 'DATETIME2', <Type.TIMESTAMPTZ: 'TIMESTAMPTZ'>: 'DATETIMEOFFSET', <Type.UTINYINT: 'UTINYINT'>: 'SMALLINT', <Type.VARIANT: 'VARIANT'>: 'SQL_VARIANT', <Type.UUID: 'UUID'>: 'VARBINARY(MAX)', <Type.MONEY: 'MONEY'>: 'DECIMAL', <Type.SMALLMONEY: 'SMALLMONEY'>: 'DECIMAL', <Type.DATETIME: 'DATETIME'>: 'DATETIME2(6)', <Type.NCHAR: 'NCHAR'>: 'CHAR', <Type.NVARCHAR: 'NVARCHAR'>: 'VARCHAR', <Type.IMAGE: 'IMAGE'>: 'VARBINARY', <Type.TINYINT: 'TINYINT'>: 'SMALLINT', <Type.JSON: 'JSON'>: 'VARCHAR', <Type.XML: 'XML'>: 'VARCHAR'}

def datatype_sql(self, expression: sqlglot.expressions.DataType) -> str: View Source

57        def datatype_sql(self, expression: exp.DataType) -> str:
58            """
59            Override datatype generation to handle Fabric-specific precision limitations.
60
61            Fabric limits temporal types (TIME, DATETIME2, DATETIMEOFFSET) to max 6 digits precision.
62            When no precision is specified, we default to 6 digits.
63            """
64            if expression.is_type(
65                exp.DataType.Type.TIME,
66                exp.DataType.Type.DATETIME2,
67                exp.DataType.Type.TIMESTAMPTZ,  # DATETIMEOFFSET in Fabric
68            ):
69                # Get the current precision (first expression if it exists)
70                precision = expression.find(exp.DataTypeParam)
71
72                # Determine the target precision
73                if precision is None:
74                    # No precision specified, default to 6
75                    target_precision = 6
76                elif precision.this.is_int:
77                    # Cap precision at 6
78                    current_precision = precision.this.to_py()
79                    target_precision = min(current_precision, 6)
80
81                # Create a new expression with the target precision
82                new_expression = exp.DataType(
83                    this=expression.this,
84                    expressions=[exp.DataTypeParam(this=exp.Literal.number(target_precision))],
85                )
86
87                return super().datatype_sql(new_expression)
88
89            return super().datatype_sql(expression)

Override datatype generation to handle Fabric-specific precision limitations.

Fabric limits temporal types (TIME, DATETIME2, DATETIMEOFFSET) to max 6 digits precision. When no precision is specified, we default to 6 digits.

SELECT_KINDS: Tuple[str, ...] = ()

TRY_SUPPORTED = False

SUPPORTS_UESCAPE = False

AFTER_HAVING_MODIFIER_TRANSFORMS = {'windows': <function Generator.<lambda>>, 'qualify': <function Generator.<lambda>>}

Inherited Members

sqlglot.generator.Generator: Generator; IGNORE_NULLS_IN_FUNC; LOCKING_READS_SUPPORTED; WRAP_DERIVED_VALUES; CREATE_FUNCTION_RETURN_AS; MATCHED_BY_SOURCE; SINGLE_STRING_INTERVAL; INTERVAL_ALLOWS_PLURAL_FORM; LIMIT_ONLY_LITERALS; RENAME_TABLE_WITH_DB; GROUPINGS_SEP; INDEX_ON; JOIN_HINTS; TABLE_HINTS; QUERY_HINT_SEP; IS_BOOL_ALLOWED; DUPLICATE_KEY_UPDATE_WITH_SET; EXTRACT_ALLOWS_QUOTES; TZ_TO_WITH_TIME_ZONE; VALUES_AS_TABLE; UNNEST_WITH_ORDINALITY; AGGREGATE_FILTER_SUPPORTED; SEMI_ANTI_JOIN_WITH_SIDE; SUPPORTS_TABLE_COPY; TABLESAMPLE_REQUIRES_PARENS; TABLESAMPLE_SIZE_IS_ROWS; TABLESAMPLE_KEYWORDS; TABLESAMPLE_WITH_METHOD; COLLATE_IS_FUNC; DATA_TYPE_SPECIFIERS_ALLOWED; LAST_DAY_SUPPORTS_DATE_PART; SUPPORTS_TABLE_ALIAS_COLUMNS; UNPIVOT_ALIASES_ARE_IDENTIFIERS; JSON_KEY_VALUE_PAIR_SEP; INSERT_OVERWRITE; SUPPORTS_UNLOGGED_TABLES; SUPPORTS_CREATE_TABLE_LIKE; LIKE_PROPERTY_INSIDE_SCHEMA; MULTI_ARG_DISTINCT; JSON_TYPE_REQUIRED_FOR_EXTRACTION; JSON_PATH_SINGLE_QUOTE_ESCAPE; CAN_IMPLEMENT_ARRAY_ANY; SUPPORTS_WINDOW_EXCLUDE; COPY_PARAMS_ARE_WRAPPED; COPY_HAS_INTO_KEYWORD; STAR_EXCEPT; HEX_FUNC; WITH_PROPERTIES_PREFIX; QUOTE_JSON_PATH; PAD_FILL_PATTERN_IS_REQUIRED; SUPPORTS_EXPLODING_PROJECTIONS; ARRAY_CONCAT_IS_VAR_LEN; SUPPORTS_CONVERT_TIMEZONE; SUPPORTS_MEDIAN; SUPPORTS_UNIX_SECONDS; NORMALIZE_EXTRACT_DATE_PARTS; ARRAY_SIZE_NAME; ARRAY_SIZE_DIM_REQUIRED; TIME_PART_SINGULARS; TOKEN_MAPPING; STRUCT_DELIMITER; PARAMETER_TOKEN; NAMED_PLACEHOLDER_TOKEN; EXPRESSION_PRECEDES_PROPERTIES_CREATABLES; RESERVED_KEYWORDS; WITH_SEPARATED_COMMENTS; EXCLUDE_COMMENTS; UNWRAPPED_INTERVAL_VALUES; PARAMETERIZABLE_TEXT_TYPES; RESPECT_IGNORE_NULLS_UNSUPPORTED_EXPRESSIONS; SENTINEL_LINE_BREAK; pretty; identify; normalize; pad; unsupported_level; max_unsupported; leading_comma; max_text_width; comments; dialect; normalize_functions; unsupported_messages; generate; preprocess; unsupported; sep; seg; sanitize_comment; maybe_comment; wrap; no_identify; normalize_func; indent; sql; uncache_sql; cache_sql; characterset_sql; column_parts; column_sql; columnposition_sql; columnconstraint_sql; computedcolumnconstraint_sql; autoincrementcolumnconstraint_sql; compresscolumnconstraint_sql; generatedasidentitycolumnconstraint_sql; generatedasrowcolumnconstraint_sql; periodforsystemtimeconstraint_sql; notnullcolumnconstraint_sql; primarykeycolumnconstraint_sql; uniquecolumnconstraint_sql; sequenceproperties_sql; clone_sql; describe_sql; heredoc_sql; prepend_ctes; with_sql; cte_sql; tablealias_sql; bitstring_sql; hexstring_sql; bytestring_sql; unicodestring_sql; rawstring_sql; datatypeparam_sql; directory_sql; delete_sql; set_operation; set_operations; fetch_sql; limitoptions_sql; filter_sql; hint_sql; indexparameters_sql; index_sql; hex_sql; lowerhex_sql; inputoutputformat_sql; national_sql; properties_sql; root_properties; properties; with_properties; locate_properties; property_name; property_sql; likeproperty_sql; fallbackproperty_sql; journalproperty_sql; freespaceproperty_sql; checksumproperty_sql; mergeblockratioproperty_sql; datablocksizeproperty_sql; blockcompressionproperty_sql; isolatedloadingproperty_sql; partitionboundspec_sql; partitionedofproperty_sql; lockingproperty_sql; withdataproperty_sql; withsystemversioningproperty_sql; insert_sql; introducer_sql; kill_sql; pseudotype_sql; objectidentifier_sql; onconflict_sql; rowformatdelimitedproperty_sql; withtablehint_sql; indextablehint_sql; historicaldata_sql; table_parts; table_sql; tablefromrows_sql; tablesample_sql; pivot_sql; tuple_sql; update_sql; values_sql; var_sql; from_sql; groupingsets_sql; rollup_sql; cube_sql; group_sql; having_sql; connect_sql; prior_sql; join_sql; lambda_sql; lateral_sql; limit_sql; set_sql; pragma_sql; lock_sql; literal_sql; escape_str; loaddata_sql; null_sql; order_sql; withfill_sql; cluster_sql; distribute_sql; sort_sql; ordered_sql; matchrecognizemeasure_sql; matchrecognize_sql; query_modifiers; for_modifiers; offset_limit_modifiers; after_limit_modifiers; schema_sql; schema_columns_sql; star_sql; parameter_sql; sessionparameter_sql; placeholder_sql; subquery_sql; qualify_sql; unnest_sql; prewhere_sql; where_sql; window_sql; partition_by_sql; windowspec_sql; withingroup_sql; between_sql; bracket_offset_expressions; bracket_sql; all_sql; any_sql; exists_sql; case_sql; nextvaluefor_sql; extract_sql; trim_sql; convert_concat_args; concat_sql; concatws_sql; check_sql; foreignkey_sql; primarykey_sql; if_sql; matchagainst_sql; jsonkeyvalue_sql; jsonpath_sql; json_path_part; formatjson_sql; jsonobject_sql; jsonobjectagg_sql; jsonarray_sql; jsonarrayagg_sql; jsoncolumndef_sql; jsonschema_sql; jsontable_sql; openjsoncolumndef_sql; openjson_sql; in_sql; in_unnest_op; interval_sql; return_sql; reference_sql; anonymous_sql; paren_sql; neg_sql; not_sql; alias_sql; pivotalias_sql; aliases_sql; atindex_sql; attimezone_sql; fromtimezone_sql; add_sql; and_sql; or_sql; xor_sql; connector_sql; bitwiseand_sql; bitwiseleftshift_sql; bitwisenot_sql; bitwiseor_sql; bitwiserightshift_sql; bitwisexor_sql; cast_sql; currentdate_sql; collate_sql; command_sql; comment_sql; mergetreettlaction_sql; mergetreettl_sql; altercolumn_sql; alterindex_sql; alterdiststyle_sql; altersortkey_sql; alterrename_sql; renamecolumn_sql; alterset_sql; add_column_sql; droppartition_sql; addconstraint_sql; addpartition_sql; distinct_sql; ignorenulls_sql; respectnulls_sql; havingmax_sql; intdiv_sql; div_sql; safedivide_sql; overlaps_sql; distance_sql; dot_sql; eq_sql; propertyeq_sql; escape_sql; glob_sql; gt_sql; gte_sql; ilike_sql; ilikeany_sql; like_sql; likeany_sql; similarto_sql; lt_sql; lte_sql; mod_sql; mul_sql; neq_sql; nullsafeeq_sql; nullsafeneq_sql; slice_sql; sub_sql; trycast_sql; jsoncast_sql; try_sql; log_sql; use_sql; binary; ceil_floor; function_fallback_sql; func; format_args; too_wide; format_time; expressions; op_expressions; naked_property; tag_sql; token_sql; userdefinedfunction_sql; joinhint_sql; kwarg_sql; when_sql; whens_sql; merge_sql; tochar_sql; tonumber_sql; dictproperty_sql; dictrange_sql; dictsubproperty_sql; duplicatekeyproperty_sql; uniquekeyproperty_sql; distributedbyproperty_sql; oncluster_sql; clusteredbyproperty_sql; anyvalue_sql; querytransform_sql; indexconstraintoption_sql; checkcolumnconstraint_sql; indexcolumnconstraint_sql; nvl2_sql; comprehension_sql; columnprefix_sql; opclass_sql; predict_sql; forin_sql; refresh_sql; toarray_sql; tsordstotime_sql; tsordstotimestamp_sql; tsordstodatetime_sql; tsordstodate_sql; unixdate_sql; lastday_sql; dateadd_sql; arrayany_sql; struct_sql; partitionrange_sql; truncatetable_sql; copyparameter_sql; credentials_sql; copy_sql; semicolon_sql; datadeletionproperty_sql; maskingpolicycolumnconstraint_sql; gapfill_sql; scoperesolution_sql; parsejson_sql; rand_sql; changes_sql; pad_sql; summarize_sql; explodinggenerateseries_sql; arrayconcat_sql; converttimezone_sql; json_sql; jsonvalue_sql; conditionalinsert_sql; multitableinserts_sql; oncondition_sql; jsonextractquote_sql; jsonexists_sql; arrayagg_sql; apply_sql; grant_sql; grantprivilege_sql; grantprincipal_sql; columns_sql; overlay_sql; todouble_sql; string_sql; median_sql; overflowtruncatebehavior_sql; unixseconds_sql; arraysize_sql; attach_sql; detach_sql; attachoption_sql; featuresattime_sql; watermarkcolumnconstraint_sql; encodeproperty_sql; includeproperty_sql; xmlelement_sql; xmlkeyvalueoption_sql; partitionbyrangeproperty_sql; partitionbyrangepropertydynamic_sql; unpivotcolumns_sql; analyzesample_sql; analyzestatistics_sql; analyzehistogram_sql; analyzedelete_sql; analyzelistchainedrows_sql; analyzevalidate_sql; analyze_sql; xmltable_sql; xmlnamespace_sql; export_sql; declare_sql; declareitem_sql; recursivewithsearch_sql; parameterizedagg_sql; anonymousaggfunc_sql; combinedaggfunc_sql; combinedparameterizedagg_sql; show_sql; get_put_sql; translatecharacters_sql
sqlglot.dialects.tsql.TSQL.Generator: LIMIT_IS_TOP; QUERY_HINTS; RETURNING_END; NVL2_SUPPORTED; ALTER_TABLE_INCLUDE_COLUMN_KEYWORD; LIMIT_FETCH; COMPUTED_COLUMN_WITH_TYPE; CTE_RECURSIVE_KEYWORD_REQUIRED; ENSURE_BOOLS; NULL_ORDERING_SUPPORTED; SUPPORTS_SINGLE_ARG_CONCAT; TABLESAMPLE_SEED_KEYWORD; SUPPORTS_SELECT_INTO; JSON_PATH_BRACKETED_KEY_SUPPORTED; SUPPORTS_TO_NUMBER; SET_OP_MODIFIERS; COPY_PARAMS_EQ_REQUIRED; PARSE_JSON_NAME; EXCEPT_INTERSECT_SUPPORT_ALL_CLAUSE; ALTER_SET_WRAPPED; ALTER_SET_TYPE; EXPRESSIONS_WITHOUT_NESTED_CTES; SUPPORTED_JSON_PATH_PARTS; TRANSFORMS; PROPERTIES_LOCATION; scope_resolution; select_sql; convert_sql; queryoption_sql; lateral_op; splitpart_sql; timefromparts_sql; timestampfromparts_sql; setitem_sql; boolean_sql; is_sql; createable_sql; create_sql; into_sql; count_sql; offset_sql; version_sql; returnsproperty_sql; returning_sql; transaction_sql; commit_sql; rollback_sql; identifier_sql; constraint_sql; length_sql; right_sql; left_sql; partition_sql; alter_sql; drop_sql; options_modifier; dpipe_sql; isascii_sql; columndef_sql; coalesce_sql