sqlglot.schema
1from __future__ import annotations 2 3import abc 4import typing as t 5 6import sqlglot 7from sqlglot import expressions as exp 8from sqlglot._typing import T 9from sqlglot.errors import ParseError, SchemaError 10from sqlglot.helper import dict_depth 11from sqlglot.trie import in_trie, new_trie 12 13if t.TYPE_CHECKING: 14 from sqlglot.dataframe.sql.types import StructType 15 from sqlglot.dialects.dialect import DialectType 16 17 ColumnMapping = t.Union[t.Dict, str, StructType, t.List] 18 19TABLE_ARGS = ("this", "db", "catalog") 20 21 22class Schema(abc.ABC): 23 """Abstract base class for database schemas""" 24 25 @abc.abstractmethod 26 def add_table( 27 self, 28 table: exp.Table | str, 29 column_mapping: t.Optional[ColumnMapping] = None, 30 dialect: DialectType = None, 31 ) -> None: 32 """ 33 Register or update a table. Some implementing classes may require column information to also be provided. 34 35 Args: 36 table: the `Table` expression instance or string representing the table. 37 column_mapping: a column mapping that describes the structure of the table. 38 dialect: the SQL dialect that will be used to parse `table` if it's a string. 39 """ 40 41 @abc.abstractmethod 42 def column_names( 43 self, 44 table: exp.Table | str, 45 only_visible: bool = False, 46 dialect: DialectType = None, 47 ) -> t.List[str]: 48 """ 49 Get the column names for a table. 50 51 Args: 52 table: the `Table` expression instance. 53 only_visible: whether to include invisible columns. 54 dialect: the SQL dialect that will be used to parse `table` if it's a string. 55 56 Returns: 57 The list of column names. 58 """ 59 60 @abc.abstractmethod 61 def get_column_type( 62 self, 63 table: exp.Table | str, 64 column: exp.Column, 65 dialect: DialectType = None, 66 ) -> exp.DataType: 67 """ 68 Get the `sqlglot.exp.DataType` type of a column in the schema. 69 70 Args: 71 table: the source table. 72 column: the target column. 73 dialect: the SQL dialect that will be used to parse `table` if it's a string. 74 75 Returns: 76 The resulting column type. 77 """ 78 79 @property 80 @abc.abstractmethod 81 def supported_table_args(self) -> t.Tuple[str, ...]: 82 """ 83 Table arguments this schema support, e.g. `("this", "db", "catalog")` 84 """ 85 86 @property 87 def empty(self) -> bool: 88 """Returns whether or not the schema is empty.""" 89 return True 90 91 92class AbstractMappingSchema(t.Generic[T]): 93 def __init__( 94 self, 95 mapping: t.Optional[t.Dict] = None, 96 ) -> None: 97 self.mapping = mapping or {} 98 self.mapping_trie = new_trie( 99 tuple(reversed(t)) for t in flatten_schema(self.mapping, depth=self._depth()) 100 ) 101 self._supported_table_args: t.Tuple[str, ...] = tuple() 102 103 @property 104 def empty(self) -> bool: 105 return not self.mapping 106 107 def _depth(self) -> int: 108 return dict_depth(self.mapping) 109 110 @property 111 def supported_table_args(self) -> t.Tuple[str, ...]: 112 if not self._supported_table_args and self.mapping: 113 depth = self._depth() 114 115 if not depth: # None 116 self._supported_table_args = tuple() 117 elif 1 <= depth <= 3: 118 self._supported_table_args = TABLE_ARGS[:depth] 119 else: 120 raise SchemaError(f"Invalid mapping shape. Depth: {depth}") 121 122 return self._supported_table_args 123 124 def table_parts(self, table: exp.Table) -> t.List[str]: 125 if isinstance(table.this, exp.ReadCSV): 126 return [table.this.name] 127 return [table.text(part) for part in TABLE_ARGS if table.text(part)] 128 129 def find( 130 self, table: exp.Table, trie: t.Optional[t.Dict] = None, raise_on_missing: bool = True 131 ) -> t.Optional[T]: 132 parts = self.table_parts(table)[0 : len(self.supported_table_args)] 133 value, trie = in_trie(self.mapping_trie if trie is None else trie, parts) 134 135 if value == 0: 136 return None 137 138 if value == 1: 139 possibilities = flatten_schema(trie, depth=dict_depth(trie) - 1) 140 141 if len(possibilities) == 1: 142 parts.extend(possibilities[0]) 143 else: 144 message = ", ".join(".".join(parts) for parts in possibilities) 145 if raise_on_missing: 146 raise SchemaError(f"Ambiguous mapping for {table}: {message}.") 147 return None 148 149 return self.nested_get(parts, raise_on_missing=raise_on_missing) 150 151 def nested_get( 152 self, parts: t.Sequence[str], d: t.Optional[t.Dict] = None, raise_on_missing=True 153 ) -> t.Optional[t.Any]: 154 return nested_get( 155 d or self.mapping, 156 *zip(self.supported_table_args, reversed(parts)), 157 raise_on_missing=raise_on_missing, 158 ) 159 160 161class MappingSchema(AbstractMappingSchema[t.Dict[str, str]], Schema): 162 """ 163 Schema based on a nested mapping. 164 165 Args: 166 schema: Mapping in one of the following forms: 167 1. {table: {col: type}} 168 2. {db: {table: {col: type}}} 169 3. {catalog: {db: {table: {col: type}}}} 170 4. None - Tables will be added later 171 visible: Optional mapping of which columns in the schema are visible. If not provided, all columns 172 are assumed to be visible. The nesting should mirror that of the schema: 173 1. {table: set(*cols)}} 174 2. {db: {table: set(*cols)}}} 175 3. {catalog: {db: {table: set(*cols)}}}} 176 dialect: The dialect to be used for custom type mappings & parsing string arguments. 177 """ 178 179 def __init__( 180 self, 181 schema: t.Optional[t.Dict] = None, 182 visible: t.Optional[t.Dict] = None, 183 dialect: DialectType = None, 184 ) -> None: 185 self.dialect = dialect 186 self.visible = visible or {} 187 self._type_mapping_cache: t.Dict[str, exp.DataType] = {} 188 189 super().__init__(self._normalize(schema or {})) 190 191 @classmethod 192 def from_mapping_schema(cls, mapping_schema: MappingSchema) -> MappingSchema: 193 return MappingSchema( 194 schema=mapping_schema.mapping, 195 visible=mapping_schema.visible, 196 dialect=mapping_schema.dialect, 197 ) 198 199 def copy(self, **kwargs) -> MappingSchema: 200 return MappingSchema( 201 **{ # type: ignore 202 "schema": self.mapping.copy(), 203 "visible": self.visible.copy(), 204 "dialect": self.dialect, 205 **kwargs, 206 } 207 ) 208 209 def add_table( 210 self, 211 table: exp.Table | str, 212 column_mapping: t.Optional[ColumnMapping] = None, 213 dialect: DialectType = None, 214 ) -> None: 215 """ 216 Register or update a table. Updates are only performed if a new column mapping is provided. 217 218 Args: 219 table: the `Table` expression instance or string representing the table. 220 column_mapping: a column mapping that describes the structure of the table. 221 dialect: the SQL dialect that will be used to parse `table` if it's a string. 222 """ 223 normalized_table = self._normalize_table( 224 self._ensure_table(table, dialect=dialect), dialect=dialect 225 ) 226 normalized_column_mapping = { 227 self._normalize_name(key, dialect=dialect): value 228 for key, value in ensure_column_mapping(column_mapping).items() 229 } 230 231 schema = self.find(normalized_table, raise_on_missing=False) 232 if schema and not normalized_column_mapping: 233 return 234 235 parts = self.table_parts(normalized_table) 236 237 nested_set(self.mapping, tuple(reversed(parts)), normalized_column_mapping) 238 new_trie([parts], self.mapping_trie) 239 240 def column_names( 241 self, 242 table: exp.Table | str, 243 only_visible: bool = False, 244 dialect: DialectType = None, 245 ) -> t.List[str]: 246 normalized_table = self._normalize_table( 247 self._ensure_table(table, dialect=dialect), dialect=dialect 248 ) 249 250 schema = self.find(normalized_table) 251 if schema is None: 252 return [] 253 254 if not only_visible or not self.visible: 255 return list(schema) 256 257 visible = self.nested_get(self.table_parts(normalized_table), self.visible) or [] 258 return [col for col in schema if col in visible] 259 260 def get_column_type( 261 self, 262 table: exp.Table | str, 263 column: exp.Column, 264 dialect: DialectType = None, 265 ) -> exp.DataType: 266 normalized_table = self._normalize_table( 267 self._ensure_table(table, dialect=dialect), dialect=dialect 268 ) 269 normalized_column_name = self._normalize_name( 270 column if isinstance(column, str) else column.this, dialect=dialect 271 ) 272 273 table_schema = self.find(normalized_table, raise_on_missing=False) 274 if table_schema: 275 column_type = table_schema.get(normalized_column_name) 276 277 if isinstance(column_type, exp.DataType): 278 return column_type 279 elif isinstance(column_type, str): 280 return self._to_data_type(column_type.upper(), dialect=dialect) 281 282 raise SchemaError(f"Unknown column type '{column_type}'") 283 284 return exp.DataType.build("unknown") 285 286 def _normalize(self, schema: t.Dict) -> t.Dict: 287 """ 288 Converts all identifiers in the schema into lowercase, unless they're quoted. 289 290 Args: 291 schema: the schema to normalize. 292 293 Returns: 294 The normalized schema mapping. 295 """ 296 flattened_schema = flatten_schema(schema, depth=dict_depth(schema) - 1) 297 298 normalized_mapping: t.Dict = {} 299 for keys in flattened_schema: 300 columns = nested_get(schema, *zip(keys, keys)) 301 assert columns is not None 302 303 normalized_keys = [self._normalize_name(key, dialect=self.dialect) for key in keys] 304 for column_name, column_type in columns.items(): 305 nested_set( 306 normalized_mapping, 307 normalized_keys + [self._normalize_name(column_name, dialect=self.dialect)], 308 column_type, 309 ) 310 311 return normalized_mapping 312 313 def _normalize_table(self, table: exp.Table, dialect: DialectType = None) -> exp.Table: 314 normalized_table = table.copy() 315 316 for arg in TABLE_ARGS: 317 value = normalized_table.args.get(arg) 318 if isinstance(value, (str, exp.Identifier)): 319 normalized_table.set(arg, self._normalize_name(value, dialect=dialect)) 320 321 return normalized_table 322 323 def _normalize_name(self, name: str | exp.Identifier, dialect: DialectType = None) -> str: 324 dialect = dialect or self.dialect 325 326 try: 327 identifier = sqlglot.maybe_parse(name, dialect=dialect, into=exp.Identifier) 328 except ParseError: 329 return name if isinstance(name, str) else name.name 330 331 return identifier.name if identifier.quoted else identifier.name.lower() 332 333 def _depth(self) -> int: 334 # The columns themselves are a mapping, but we don't want to include those 335 return super()._depth() - 1 336 337 def _ensure_table(self, table: exp.Table | str, dialect: DialectType = None) -> exp.Table: 338 if isinstance(table, exp.Table): 339 return table 340 341 dialect = dialect or self.dialect 342 parsed_table = sqlglot.parse_one(table, read=dialect, into=exp.Table) 343 344 if not parsed_table: 345 in_dialect = f" in dialect {dialect}" if dialect else "" 346 raise SchemaError(f"Failed to parse table '{table}'{in_dialect}.") 347 348 return parsed_table 349 350 def _to_data_type(self, schema_type: str, dialect: DialectType = None) -> exp.DataType: 351 """ 352 Convert a type represented as a string to the corresponding `sqlglot.exp.DataType` object. 353 354 Args: 355 schema_type: the type we want to convert. 356 dialect: the SQL dialect that will be used to parse `schema_type`, if needed. 357 358 Returns: 359 The resulting expression type. 360 """ 361 if schema_type not in self._type_mapping_cache: 362 dialect = dialect or self.dialect 363 364 try: 365 expression = exp.DataType.build(schema_type, dialect=dialect) 366 self._type_mapping_cache[schema_type] = expression 367 except AttributeError: 368 in_dialect = f" in dialect {dialect}" if dialect else "" 369 raise SchemaError(f"Failed to build type '{schema_type}'{in_dialect}.") 370 371 return self._type_mapping_cache[schema_type] 372 373 374def ensure_schema(schema: t.Any, dialect: DialectType = None) -> Schema: 375 if isinstance(schema, Schema): 376 return schema 377 378 return MappingSchema(schema, dialect=dialect) 379 380 381def ensure_column_mapping(mapping: t.Optional[ColumnMapping]) -> t.Dict: 382 if mapping is None: 383 return {} 384 elif isinstance(mapping, dict): 385 return mapping 386 elif isinstance(mapping, str): 387 col_name_type_strs = [x.strip() for x in mapping.split(",")] 388 return { 389 name_type_str.split(":")[0].strip(): name_type_str.split(":")[1].strip() 390 for name_type_str in col_name_type_strs 391 } 392 # Check if mapping looks like a DataFrame StructType 393 elif hasattr(mapping, "simpleString"): 394 return {struct_field.name: struct_field.dataType.simpleString() for struct_field in mapping} 395 elif isinstance(mapping, list): 396 return {x.strip(): None for x in mapping} 397 398 raise ValueError(f"Invalid mapping provided: {type(mapping)}") 399 400 401def flatten_schema( 402 schema: t.Dict, depth: int, keys: t.Optional[t.List[str]] = None 403) -> t.List[t.List[str]]: 404 tables = [] 405 keys = keys or [] 406 407 for k, v in schema.items(): 408 if depth >= 2: 409 tables.extend(flatten_schema(v, depth - 1, keys + [k])) 410 elif depth == 1: 411 tables.append(keys + [k]) 412 413 return tables 414 415 416def nested_get( 417 d: t.Dict, *path: t.Tuple[str, str], raise_on_missing: bool = True 418) -> t.Optional[t.Any]: 419 """ 420 Get a value for a nested dictionary. 421 422 Args: 423 d: the dictionary to search. 424 *path: tuples of (name, key), where: 425 `key` is the key in the dictionary to get. 426 `name` is a string to use in the error if `key` isn't found. 427 428 Returns: 429 The value or None if it doesn't exist. 430 """ 431 for name, key in path: 432 d = d.get(key) # type: ignore 433 if d is None: 434 if raise_on_missing: 435 name = "table" if name == "this" else name 436 raise ValueError(f"Unknown {name}: {key}") 437 return None 438 439 return d 440 441 442def nested_set(d: t.Dict, keys: t.Sequence[str], value: t.Any) -> t.Dict: 443 """ 444 In-place set a value for a nested dictionary 445 446 Example: 447 >>> nested_set({}, ["top_key", "second_key"], "value") 448 {'top_key': {'second_key': 'value'}} 449 450 >>> nested_set({"top_key": {"third_key": "third_value"}}, ["top_key", "second_key"], "value") 451 {'top_key': {'third_key': 'third_value', 'second_key': 'value'}} 452 453 Args: 454 d: dictionary to update. 455 keys: the keys that makeup the path to `value`. 456 value: the value to set in the dictionary for the given key path. 457 458 Returns: 459 The (possibly) updated dictionary. 460 """ 461 if not keys: 462 return d 463 464 if len(keys) == 1: 465 d[keys[0]] = value 466 return d 467 468 subd = d 469 for key in keys[:-1]: 470 if key not in subd: 471 subd = subd.setdefault(key, {}) 472 else: 473 subd = subd[key] 474 475 subd[keys[-1]] = value 476 return d
23class Schema(abc.ABC): 24 """Abstract base class for database schemas""" 25 26 @abc.abstractmethod 27 def add_table( 28 self, 29 table: exp.Table | str, 30 column_mapping: t.Optional[ColumnMapping] = None, 31 dialect: DialectType = None, 32 ) -> None: 33 """ 34 Register or update a table. Some implementing classes may require column information to also be provided. 35 36 Args: 37 table: the `Table` expression instance or string representing the table. 38 column_mapping: a column mapping that describes the structure of the table. 39 dialect: the SQL dialect that will be used to parse `table` if it's a string. 40 """ 41 42 @abc.abstractmethod 43 def column_names( 44 self, 45 table: exp.Table | str, 46 only_visible: bool = False, 47 dialect: DialectType = None, 48 ) -> t.List[str]: 49 """ 50 Get the column names for a table. 51 52 Args: 53 table: the `Table` expression instance. 54 only_visible: whether to include invisible columns. 55 dialect: the SQL dialect that will be used to parse `table` if it's a string. 56 57 Returns: 58 The list of column names. 59 """ 60 61 @abc.abstractmethod 62 def get_column_type( 63 self, 64 table: exp.Table | str, 65 column: exp.Column, 66 dialect: DialectType = None, 67 ) -> exp.DataType: 68 """ 69 Get the `sqlglot.exp.DataType` type of a column in the schema. 70 71 Args: 72 table: the source table. 73 column: the target column. 74 dialect: the SQL dialect that will be used to parse `table` if it's a string. 75 76 Returns: 77 The resulting column type. 78 """ 79 80 @property 81 @abc.abstractmethod 82 def supported_table_args(self) -> t.Tuple[str, ...]: 83 """ 84 Table arguments this schema support, e.g. `("this", "db", "catalog")` 85 """ 86 87 @property 88 def empty(self) -> bool: 89 """Returns whether or not the schema is empty.""" 90 return True
Abstract base class for database schemas
26 @abc.abstractmethod 27 def add_table( 28 self, 29 table: exp.Table | str, 30 column_mapping: t.Optional[ColumnMapping] = None, 31 dialect: DialectType = None, 32 ) -> None: 33 """ 34 Register or update a table. Some implementing classes may require column information to also be provided. 35 36 Args: 37 table: the `Table` expression instance or string representing the table. 38 column_mapping: a column mapping that describes the structure of the table. 39 dialect: the SQL dialect that will be used to parse `table` if it's a string. 40 """
Register or update a table. Some implementing classes may require column information to also be provided.
Arguments:
- table: the
Table
expression instance or string representing the table. - column_mapping: a column mapping that describes the structure of the table.
- dialect: the SQL dialect that will be used to parse
table
if it's a string.
42 @abc.abstractmethod 43 def column_names( 44 self, 45 table: exp.Table | str, 46 only_visible: bool = False, 47 dialect: DialectType = None, 48 ) -> t.List[str]: 49 """ 50 Get the column names for a table. 51 52 Args: 53 table: the `Table` expression instance. 54 only_visible: whether to include invisible columns. 55 dialect: the SQL dialect that will be used to parse `table` if it's a string. 56 57 Returns: 58 The list of column names. 59 """
Get the column names for a table.
Arguments:
- table: the
Table
expression instance. - only_visible: whether to include invisible columns.
- dialect: the SQL dialect that will be used to parse
table
if it's a string.
Returns:
The list of column names.
61 @abc.abstractmethod 62 def get_column_type( 63 self, 64 table: exp.Table | str, 65 column: exp.Column, 66 dialect: DialectType = None, 67 ) -> exp.DataType: 68 """ 69 Get the `sqlglot.exp.DataType` type of a column in the schema. 70 71 Args: 72 table: the source table. 73 column: the target column. 74 dialect: the SQL dialect that will be used to parse `table` if it's a string. 75 76 Returns: 77 The resulting column type. 78 """
Get the sqlglot.exp.DataType
type of a column in the schema.
Arguments:
- table: the source table.
- column: the target column.
- dialect: the SQL dialect that will be used to parse
table
if it's a string.
Returns:
The resulting column type.
93class AbstractMappingSchema(t.Generic[T]): 94 def __init__( 95 self, 96 mapping: t.Optional[t.Dict] = None, 97 ) -> None: 98 self.mapping = mapping or {} 99 self.mapping_trie = new_trie( 100 tuple(reversed(t)) for t in flatten_schema(self.mapping, depth=self._depth()) 101 ) 102 self._supported_table_args: t.Tuple[str, ...] = tuple() 103 104 @property 105 def empty(self) -> bool: 106 return not self.mapping 107 108 def _depth(self) -> int: 109 return dict_depth(self.mapping) 110 111 @property 112 def supported_table_args(self) -> t.Tuple[str, ...]: 113 if not self._supported_table_args and self.mapping: 114 depth = self._depth() 115 116 if not depth: # None 117 self._supported_table_args = tuple() 118 elif 1 <= depth <= 3: 119 self._supported_table_args = TABLE_ARGS[:depth] 120 else: 121 raise SchemaError(f"Invalid mapping shape. Depth: {depth}") 122 123 return self._supported_table_args 124 125 def table_parts(self, table: exp.Table) -> t.List[str]: 126 if isinstance(table.this, exp.ReadCSV): 127 return [table.this.name] 128 return [table.text(part) for part in TABLE_ARGS if table.text(part)] 129 130 def find( 131 self, table: exp.Table, trie: t.Optional[t.Dict] = None, raise_on_missing: bool = True 132 ) -> t.Optional[T]: 133 parts = self.table_parts(table)[0 : len(self.supported_table_args)] 134 value, trie = in_trie(self.mapping_trie if trie is None else trie, parts) 135 136 if value == 0: 137 return None 138 139 if value == 1: 140 possibilities = flatten_schema(trie, depth=dict_depth(trie) - 1) 141 142 if len(possibilities) == 1: 143 parts.extend(possibilities[0]) 144 else: 145 message = ", ".join(".".join(parts) for parts in possibilities) 146 if raise_on_missing: 147 raise SchemaError(f"Ambiguous mapping for {table}: {message}.") 148 return None 149 150 return self.nested_get(parts, raise_on_missing=raise_on_missing) 151 152 def nested_get( 153 self, parts: t.Sequence[str], d: t.Optional[t.Dict] = None, raise_on_missing=True 154 ) -> t.Optional[t.Any]: 155 return nested_get( 156 d or self.mapping, 157 *zip(self.supported_table_args, reversed(parts)), 158 raise_on_missing=raise_on_missing, 159 )
Abstract base class for generic types.
A generic type is typically declared by inheriting from this class parameterized with one or more type variables. For example, a generic mapping type might be defined as::
class Mapping(Generic[KT, VT]): def __getitem__(self, key: KT) -> VT: ... # Etc.
This class can then be used as follows::
def lookup_name(mapping: Mapping[KT, VT], key: KT, default: VT) -> VT: try: return mapping[key] except KeyError: return default
130 def find( 131 self, table: exp.Table, trie: t.Optional[t.Dict] = None, raise_on_missing: bool = True 132 ) -> t.Optional[T]: 133 parts = self.table_parts(table)[0 : len(self.supported_table_args)] 134 value, trie = in_trie(self.mapping_trie if trie is None else trie, parts) 135 136 if value == 0: 137 return None 138 139 if value == 1: 140 possibilities = flatten_schema(trie, depth=dict_depth(trie) - 1) 141 142 if len(possibilities) == 1: 143 parts.extend(possibilities[0]) 144 else: 145 message = ", ".join(".".join(parts) for parts in possibilities) 146 if raise_on_missing: 147 raise SchemaError(f"Ambiguous mapping for {table}: {message}.") 148 return None 149 150 return self.nested_get(parts, raise_on_missing=raise_on_missing)
162class MappingSchema(AbstractMappingSchema[t.Dict[str, str]], Schema): 163 """ 164 Schema based on a nested mapping. 165 166 Args: 167 schema: Mapping in one of the following forms: 168 1. {table: {col: type}} 169 2. {db: {table: {col: type}}} 170 3. {catalog: {db: {table: {col: type}}}} 171 4. None - Tables will be added later 172 visible: Optional mapping of which columns in the schema are visible. If not provided, all columns 173 are assumed to be visible. The nesting should mirror that of the schema: 174 1. {table: set(*cols)}} 175 2. {db: {table: set(*cols)}}} 176 3. {catalog: {db: {table: set(*cols)}}}} 177 dialect: The dialect to be used for custom type mappings & parsing string arguments. 178 """ 179 180 def __init__( 181 self, 182 schema: t.Optional[t.Dict] = None, 183 visible: t.Optional[t.Dict] = None, 184 dialect: DialectType = None, 185 ) -> None: 186 self.dialect = dialect 187 self.visible = visible or {} 188 self._type_mapping_cache: t.Dict[str, exp.DataType] = {} 189 190 super().__init__(self._normalize(schema or {})) 191 192 @classmethod 193 def from_mapping_schema(cls, mapping_schema: MappingSchema) -> MappingSchema: 194 return MappingSchema( 195 schema=mapping_schema.mapping, 196 visible=mapping_schema.visible, 197 dialect=mapping_schema.dialect, 198 ) 199 200 def copy(self, **kwargs) -> MappingSchema: 201 return MappingSchema( 202 **{ # type: ignore 203 "schema": self.mapping.copy(), 204 "visible": self.visible.copy(), 205 "dialect": self.dialect, 206 **kwargs, 207 } 208 ) 209 210 def add_table( 211 self, 212 table: exp.Table | str, 213 column_mapping: t.Optional[ColumnMapping] = None, 214 dialect: DialectType = None, 215 ) -> None: 216 """ 217 Register or update a table. Updates are only performed if a new column mapping is provided. 218 219 Args: 220 table: the `Table` expression instance or string representing the table. 221 column_mapping: a column mapping that describes the structure of the table. 222 dialect: the SQL dialect that will be used to parse `table` if it's a string. 223 """ 224 normalized_table = self._normalize_table( 225 self._ensure_table(table, dialect=dialect), dialect=dialect 226 ) 227 normalized_column_mapping = { 228 self._normalize_name(key, dialect=dialect): value 229 for key, value in ensure_column_mapping(column_mapping).items() 230 } 231 232 schema = self.find(normalized_table, raise_on_missing=False) 233 if schema and not normalized_column_mapping: 234 return 235 236 parts = self.table_parts(normalized_table) 237 238 nested_set(self.mapping, tuple(reversed(parts)), normalized_column_mapping) 239 new_trie([parts], self.mapping_trie) 240 241 def column_names( 242 self, 243 table: exp.Table | str, 244 only_visible: bool = False, 245 dialect: DialectType = None, 246 ) -> t.List[str]: 247 normalized_table = self._normalize_table( 248 self._ensure_table(table, dialect=dialect), dialect=dialect 249 ) 250 251 schema = self.find(normalized_table) 252 if schema is None: 253 return [] 254 255 if not only_visible or not self.visible: 256 return list(schema) 257 258 visible = self.nested_get(self.table_parts(normalized_table), self.visible) or [] 259 return [col for col in schema if col in visible] 260 261 def get_column_type( 262 self, 263 table: exp.Table | str, 264 column: exp.Column, 265 dialect: DialectType = None, 266 ) -> exp.DataType: 267 normalized_table = self._normalize_table( 268 self._ensure_table(table, dialect=dialect), dialect=dialect 269 ) 270 normalized_column_name = self._normalize_name( 271 column if isinstance(column, str) else column.this, dialect=dialect 272 ) 273 274 table_schema = self.find(normalized_table, raise_on_missing=False) 275 if table_schema: 276 column_type = table_schema.get(normalized_column_name) 277 278 if isinstance(column_type, exp.DataType): 279 return column_type 280 elif isinstance(column_type, str): 281 return self._to_data_type(column_type.upper(), dialect=dialect) 282 283 raise SchemaError(f"Unknown column type '{column_type}'") 284 285 return exp.DataType.build("unknown") 286 287 def _normalize(self, schema: t.Dict) -> t.Dict: 288 """ 289 Converts all identifiers in the schema into lowercase, unless they're quoted. 290 291 Args: 292 schema: the schema to normalize. 293 294 Returns: 295 The normalized schema mapping. 296 """ 297 flattened_schema = flatten_schema(schema, depth=dict_depth(schema) - 1) 298 299 normalized_mapping: t.Dict = {} 300 for keys in flattened_schema: 301 columns = nested_get(schema, *zip(keys, keys)) 302 assert columns is not None 303 304 normalized_keys = [self._normalize_name(key, dialect=self.dialect) for key in keys] 305 for column_name, column_type in columns.items(): 306 nested_set( 307 normalized_mapping, 308 normalized_keys + [self._normalize_name(column_name, dialect=self.dialect)], 309 column_type, 310 ) 311 312 return normalized_mapping 313 314 def _normalize_table(self, table: exp.Table, dialect: DialectType = None) -> exp.Table: 315 normalized_table = table.copy() 316 317 for arg in TABLE_ARGS: 318 value = normalized_table.args.get(arg) 319 if isinstance(value, (str, exp.Identifier)): 320 normalized_table.set(arg, self._normalize_name(value, dialect=dialect)) 321 322 return normalized_table 323 324 def _normalize_name(self, name: str | exp.Identifier, dialect: DialectType = None) -> str: 325 dialect = dialect or self.dialect 326 327 try: 328 identifier = sqlglot.maybe_parse(name, dialect=dialect, into=exp.Identifier) 329 except ParseError: 330 return name if isinstance(name, str) else name.name 331 332 return identifier.name if identifier.quoted else identifier.name.lower() 333 334 def _depth(self) -> int: 335 # The columns themselves are a mapping, but we don't want to include those 336 return super()._depth() - 1 337 338 def _ensure_table(self, table: exp.Table | str, dialect: DialectType = None) -> exp.Table: 339 if isinstance(table, exp.Table): 340 return table 341 342 dialect = dialect or self.dialect 343 parsed_table = sqlglot.parse_one(table, read=dialect, into=exp.Table) 344 345 if not parsed_table: 346 in_dialect = f" in dialect {dialect}" if dialect else "" 347 raise SchemaError(f"Failed to parse table '{table}'{in_dialect}.") 348 349 return parsed_table 350 351 def _to_data_type(self, schema_type: str, dialect: DialectType = None) -> exp.DataType: 352 """ 353 Convert a type represented as a string to the corresponding `sqlglot.exp.DataType` object. 354 355 Args: 356 schema_type: the type we want to convert. 357 dialect: the SQL dialect that will be used to parse `schema_type`, if needed. 358 359 Returns: 360 The resulting expression type. 361 """ 362 if schema_type not in self._type_mapping_cache: 363 dialect = dialect or self.dialect 364 365 try: 366 expression = exp.DataType.build(schema_type, dialect=dialect) 367 self._type_mapping_cache[schema_type] = expression 368 except AttributeError: 369 in_dialect = f" in dialect {dialect}" if dialect else "" 370 raise SchemaError(f"Failed to build type '{schema_type}'{in_dialect}.") 371 372 return self._type_mapping_cache[schema_type]
Schema based on a nested mapping.
Arguments:
- schema: Mapping in one of the following forms:
- {table: {col: type}}
- {db: {table: {col: type}}}
- {catalog: {db: {table: {col: type}}}}
- None - Tables will be added later
- visible: Optional mapping of which columns in the schema are visible. If not provided, all columns
are assumed to be visible. The nesting should mirror that of the schema:
- {table: set(cols)}}
- {db: {table: set(cols)}}}
- {catalog: {db: {table: set(*cols)}}}}
- dialect: The dialect to be used for custom type mappings & parsing string arguments.
180 def __init__( 181 self, 182 schema: t.Optional[t.Dict] = None, 183 visible: t.Optional[t.Dict] = None, 184 dialect: DialectType = None, 185 ) -> None: 186 self.dialect = dialect 187 self.visible = visible or {} 188 self._type_mapping_cache: t.Dict[str, exp.DataType] = {} 189 190 super().__init__(self._normalize(schema or {}))
210 def add_table( 211 self, 212 table: exp.Table | str, 213 column_mapping: t.Optional[ColumnMapping] = None, 214 dialect: DialectType = None, 215 ) -> None: 216 """ 217 Register or update a table. Updates are only performed if a new column mapping is provided. 218 219 Args: 220 table: the `Table` expression instance or string representing the table. 221 column_mapping: a column mapping that describes the structure of the table. 222 dialect: the SQL dialect that will be used to parse `table` if it's a string. 223 """ 224 normalized_table = self._normalize_table( 225 self._ensure_table(table, dialect=dialect), dialect=dialect 226 ) 227 normalized_column_mapping = { 228 self._normalize_name(key, dialect=dialect): value 229 for key, value in ensure_column_mapping(column_mapping).items() 230 } 231 232 schema = self.find(normalized_table, raise_on_missing=False) 233 if schema and not normalized_column_mapping: 234 return 235 236 parts = self.table_parts(normalized_table) 237 238 nested_set(self.mapping, tuple(reversed(parts)), normalized_column_mapping) 239 new_trie([parts], self.mapping_trie)
Register or update a table. Updates are only performed if a new column mapping is provided.
Arguments:
- table: the
Table
expression instance or string representing the table. - column_mapping: a column mapping that describes the structure of the table.
- dialect: the SQL dialect that will be used to parse
table
if it's a string.
241 def column_names( 242 self, 243 table: exp.Table | str, 244 only_visible: bool = False, 245 dialect: DialectType = None, 246 ) -> t.List[str]: 247 normalized_table = self._normalize_table( 248 self._ensure_table(table, dialect=dialect), dialect=dialect 249 ) 250 251 schema = self.find(normalized_table) 252 if schema is None: 253 return [] 254 255 if not only_visible or not self.visible: 256 return list(schema) 257 258 visible = self.nested_get(self.table_parts(normalized_table), self.visible) or [] 259 return [col for col in schema if col in visible]
Get the column names for a table.
Arguments:
- table: the
Table
expression instance. - only_visible: whether to include invisible columns.
- dialect: the SQL dialect that will be used to parse
table
if it's a string.
Returns:
The list of column names.
261 def get_column_type( 262 self, 263 table: exp.Table | str, 264 column: exp.Column, 265 dialect: DialectType = None, 266 ) -> exp.DataType: 267 normalized_table = self._normalize_table( 268 self._ensure_table(table, dialect=dialect), dialect=dialect 269 ) 270 normalized_column_name = self._normalize_name( 271 column if isinstance(column, str) else column.this, dialect=dialect 272 ) 273 274 table_schema = self.find(normalized_table, raise_on_missing=False) 275 if table_schema: 276 column_type = table_schema.get(normalized_column_name) 277 278 if isinstance(column_type, exp.DataType): 279 return column_type 280 elif isinstance(column_type, str): 281 return self._to_data_type(column_type.upper(), dialect=dialect) 282 283 raise SchemaError(f"Unknown column type '{column_type}'") 284 285 return exp.DataType.build("unknown")
Get the sqlglot.exp.DataType
type of a column in the schema.
Arguments:
- table: the source table.
- column: the target column.
- dialect: the SQL dialect that will be used to parse
table
if it's a string.
Returns:
The resulting column type.
Inherited Members
382def ensure_column_mapping(mapping: t.Optional[ColumnMapping]) -> t.Dict: 383 if mapping is None: 384 return {} 385 elif isinstance(mapping, dict): 386 return mapping 387 elif isinstance(mapping, str): 388 col_name_type_strs = [x.strip() for x in mapping.split(",")] 389 return { 390 name_type_str.split(":")[0].strip(): name_type_str.split(":")[1].strip() 391 for name_type_str in col_name_type_strs 392 } 393 # Check if mapping looks like a DataFrame StructType 394 elif hasattr(mapping, "simpleString"): 395 return {struct_field.name: struct_field.dataType.simpleString() for struct_field in mapping} 396 elif isinstance(mapping, list): 397 return {x.strip(): None for x in mapping} 398 399 raise ValueError(f"Invalid mapping provided: {type(mapping)}")
402def flatten_schema( 403 schema: t.Dict, depth: int, keys: t.Optional[t.List[str]] = None 404) -> t.List[t.List[str]]: 405 tables = [] 406 keys = keys or [] 407 408 for k, v in schema.items(): 409 if depth >= 2: 410 tables.extend(flatten_schema(v, depth - 1, keys + [k])) 411 elif depth == 1: 412 tables.append(keys + [k]) 413 414 return tables
417def nested_get( 418 d: t.Dict, *path: t.Tuple[str, str], raise_on_missing: bool = True 419) -> t.Optional[t.Any]: 420 """ 421 Get a value for a nested dictionary. 422 423 Args: 424 d: the dictionary to search. 425 *path: tuples of (name, key), where: 426 `key` is the key in the dictionary to get. 427 `name` is a string to use in the error if `key` isn't found. 428 429 Returns: 430 The value or None if it doesn't exist. 431 """ 432 for name, key in path: 433 d = d.get(key) # type: ignore 434 if d is None: 435 if raise_on_missing: 436 name = "table" if name == "this" else name 437 raise ValueError(f"Unknown {name}: {key}") 438 return None 439 440 return d
Get a value for a nested dictionary.
Arguments:
- d: the dictionary to search.
- *path: tuples of (name, key), where:
key
is the key in the dictionary to get.name
is a string to use in the error ifkey
isn't found.
Returns:
The value or None if it doesn't exist.
443def nested_set(d: t.Dict, keys: t.Sequence[str], value: t.Any) -> t.Dict: 444 """ 445 In-place set a value for a nested dictionary 446 447 Example: 448 >>> nested_set({}, ["top_key", "second_key"], "value") 449 {'top_key': {'second_key': 'value'}} 450 451 >>> nested_set({"top_key": {"third_key": "third_value"}}, ["top_key", "second_key"], "value") 452 {'top_key': {'third_key': 'third_value', 'second_key': 'value'}} 453 454 Args: 455 d: dictionary to update. 456 keys: the keys that makeup the path to `value`. 457 value: the value to set in the dictionary for the given key path. 458 459 Returns: 460 The (possibly) updated dictionary. 461 """ 462 if not keys: 463 return d 464 465 if len(keys) == 1: 466 d[keys[0]] = value 467 return d 468 469 subd = d 470 for key in keys[:-1]: 471 if key not in subd: 472 subd = subd.setdefault(key, {}) 473 else: 474 subd = subd[key] 475 476 subd[keys[-1]] = value 477 return d
In-place set a value for a nested dictionary
Example:
>>> nested_set({}, ["top_key", "second_key"], "value") {'top_key': {'second_key': 'value'}}
>>> nested_set({"top_key": {"third_key": "third_value"}}, ["top_key", "second_key"], "value") {'top_key': {'third_key': 'third_value', 'second_key': 'value'}}
Arguments:
- d: dictionary to update.
- keys: the keys that makeup the path to
value
. - value: the value to set in the dictionary for the given key path.
Returns:
The (possibly) updated dictionary.