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.dialects.dialect import Dialect 10from sqlglot.errors import ParseError, SchemaError 11from sqlglot.helper import dict_depth 12from sqlglot.trie import TrieResult, in_trie, new_trie 13 14if t.TYPE_CHECKING: 15 from sqlglot.dataframe.sql.types import StructType 16 from sqlglot.dialects.dialect import DialectType 17 18 ColumnMapping = t.Union[t.Dict, str, StructType, t.List] 19 20TABLE_ARGS = ("this", "db", "catalog") 21 22 23class Schema(abc.ABC): 24 """Abstract base class for database schemas""" 25 26 dialect: DialectType 27 28 @abc.abstractmethod 29 def add_table( 30 self, 31 table: exp.Table | str, 32 column_mapping: t.Optional[ColumnMapping] = None, 33 dialect: DialectType = None, 34 ) -> None: 35 """ 36 Register or update a table. Some implementing classes may require column information to also be provided. 37 38 Args: 39 table: the `Table` expression instance or string representing the table. 40 column_mapping: a column mapping that describes the structure of the table. 41 dialect: the SQL dialect that will be used to parse `table` if it's a string. 42 """ 43 44 @abc.abstractmethod 45 def column_names( 46 self, 47 table: exp.Table | str, 48 only_visible: bool = False, 49 dialect: DialectType = None, 50 ) -> t.List[str]: 51 """ 52 Get the column names for a table. 53 54 Args: 55 table: the `Table` expression instance. 56 only_visible: whether to include invisible columns. 57 dialect: the SQL dialect that will be used to parse `table` if it's a string. 58 59 Returns: 60 The list of column names. 61 """ 62 63 @abc.abstractmethod 64 def get_column_type( 65 self, 66 table: exp.Table | str, 67 column: exp.Column, 68 dialect: DialectType = None, 69 ) -> exp.DataType: 70 """ 71 Get the `sqlglot.exp.DataType` type of a column in the schema. 72 73 Args: 74 table: the source table. 75 column: the target column. 76 dialect: the SQL dialect that will be used to parse `table` if it's a string. 77 78 Returns: 79 The resulting column type. 80 """ 81 82 @property 83 @abc.abstractmethod 84 def supported_table_args(self) -> t.Tuple[str, ...]: 85 """ 86 Table arguments this schema support, e.g. `("this", "db", "catalog")` 87 """ 88 89 @property 90 def empty(self) -> bool: 91 """Returns whether or not the schema is empty.""" 92 return True 93 94 95class AbstractMappingSchema(t.Generic[T]): 96 def __init__( 97 self, 98 mapping: t.Optional[t.Dict] = None, 99 ) -> None: 100 self.mapping = mapping or {} 101 self.mapping_trie = new_trie( 102 tuple(reversed(t)) for t in flatten_schema(self.mapping, depth=self._depth()) 103 ) 104 self._supported_table_args: t.Tuple[str, ...] = tuple() 105 106 @property 107 def empty(self) -> bool: 108 return not self.mapping 109 110 def _depth(self) -> int: 111 return dict_depth(self.mapping) 112 113 @property 114 def supported_table_args(self) -> t.Tuple[str, ...]: 115 if not self._supported_table_args and self.mapping: 116 depth = self._depth() 117 118 if not depth: # None 119 self._supported_table_args = tuple() 120 elif 1 <= depth <= 3: 121 self._supported_table_args = TABLE_ARGS[:depth] 122 else: 123 raise SchemaError(f"Invalid mapping shape. Depth: {depth}") 124 125 return self._supported_table_args 126 127 def table_parts(self, table: exp.Table) -> t.List[str]: 128 if isinstance(table.this, exp.ReadCSV): 129 return [table.this.name] 130 return [table.text(part) for part in TABLE_ARGS if table.text(part)] 131 132 def find( 133 self, table: exp.Table, trie: t.Optional[t.Dict] = None, raise_on_missing: bool = True 134 ) -> t.Optional[T]: 135 parts = self.table_parts(table)[0 : len(self.supported_table_args)] 136 value, trie = in_trie(self.mapping_trie if trie is None else trie, parts) 137 138 if value == TrieResult.FAILED: 139 return None 140 141 if value == TrieResult.PREFIX: 142 possibilities = flatten_schema(trie, depth=dict_depth(trie) - 1) 143 144 if len(possibilities) == 1: 145 parts.extend(possibilities[0]) 146 else: 147 message = ", ".join(".".join(parts) for parts in possibilities) 148 if raise_on_missing: 149 raise SchemaError(f"Ambiguous mapping for {table}: {message}.") 150 return None 151 152 return self.nested_get(parts, raise_on_missing=raise_on_missing) 153 154 def nested_get( 155 self, parts: t.Sequence[str], d: t.Optional[t.Dict] = None, raise_on_missing=True 156 ) -> t.Optional[t.Any]: 157 return nested_get( 158 d or self.mapping, 159 *zip(self.supported_table_args, reversed(parts)), 160 raise_on_missing=raise_on_missing, 161 ) 162 163 164class MappingSchema(AbstractMappingSchema[t.Dict[str, str]], Schema): 165 """ 166 Schema based on a nested mapping. 167 168 Args: 169 schema: Mapping in one of the following forms: 170 1. {table: {col: type}} 171 2. {db: {table: {col: type}}} 172 3. {catalog: {db: {table: {col: type}}}} 173 4. None - Tables will be added later 174 visible: Optional mapping of which columns in the schema are visible. If not provided, all columns 175 are assumed to be visible. The nesting should mirror that of the schema: 176 1. {table: set(*cols)}} 177 2. {db: {table: set(*cols)}}} 178 3. {catalog: {db: {table: set(*cols)}}}} 179 dialect: The dialect to be used for custom type mappings & parsing string arguments. 180 normalize: Whether to normalize identifier names according to the given dialect or not. 181 """ 182 183 def __init__( 184 self, 185 schema: t.Optional[t.Dict] = None, 186 visible: t.Optional[t.Dict] = None, 187 dialect: DialectType = None, 188 normalize: bool = True, 189 ) -> None: 190 self.dialect = dialect 191 self.visible = visible or {} 192 self.normalize = normalize 193 self._type_mapping_cache: t.Dict[str, exp.DataType] = {} 194 195 super().__init__(self._normalize(schema or {})) 196 197 @classmethod 198 def from_mapping_schema(cls, mapping_schema: MappingSchema) -> MappingSchema: 199 return MappingSchema( 200 schema=mapping_schema.mapping, 201 visible=mapping_schema.visible, 202 dialect=mapping_schema.dialect, 203 ) 204 205 def copy(self, **kwargs) -> MappingSchema: 206 return MappingSchema( 207 **{ # type: ignore 208 "schema": self.mapping.copy(), 209 "visible": self.visible.copy(), 210 "dialect": self.dialect, 211 **kwargs, 212 } 213 ) 214 215 def add_table( 216 self, 217 table: exp.Table | str, 218 column_mapping: t.Optional[ColumnMapping] = None, 219 dialect: DialectType = None, 220 ) -> None: 221 """ 222 Register or update a table. Updates are only performed if a new column mapping is provided. 223 224 Args: 225 table: the `Table` expression instance or string representing the table. 226 column_mapping: a column mapping that describes the structure of the table. 227 dialect: the SQL dialect that will be used to parse `table` if it's a string. 228 """ 229 normalized_table = self._normalize_table( 230 self._ensure_table(table, dialect=dialect), dialect=dialect 231 ) 232 normalized_column_mapping = { 233 self._normalize_name(key, dialect=dialect): value 234 for key, value in ensure_column_mapping(column_mapping).items() 235 } 236 237 schema = self.find(normalized_table, raise_on_missing=False) 238 if schema and not normalized_column_mapping: 239 return 240 241 parts = self.table_parts(normalized_table) 242 243 nested_set(self.mapping, tuple(reversed(parts)), normalized_column_mapping) 244 new_trie([parts], self.mapping_trie) 245 246 def column_names( 247 self, 248 table: exp.Table | str, 249 only_visible: bool = False, 250 dialect: DialectType = None, 251 ) -> t.List[str]: 252 normalized_table = self._normalize_table( 253 self._ensure_table(table, dialect=dialect), dialect=dialect 254 ) 255 256 schema = self.find(normalized_table) 257 if schema is None: 258 return [] 259 260 if not only_visible or not self.visible: 261 return list(schema) 262 263 visible = self.nested_get(self.table_parts(normalized_table), self.visible) or [] 264 return [col for col in schema if col in visible] 265 266 def get_column_type( 267 self, 268 table: exp.Table | str, 269 column: exp.Column, 270 dialect: DialectType = None, 271 ) -> exp.DataType: 272 normalized_table = self._normalize_table( 273 self._ensure_table(table, dialect=dialect), dialect=dialect 274 ) 275 normalized_column_name = self._normalize_name( 276 column if isinstance(column, str) else column.this, dialect=dialect 277 ) 278 279 table_schema = self.find(normalized_table, raise_on_missing=False) 280 if table_schema: 281 column_type = table_schema.get(normalized_column_name) 282 283 if isinstance(column_type, exp.DataType): 284 return column_type 285 elif isinstance(column_type, str): 286 return self._to_data_type(column_type.upper(), dialect=dialect) 287 288 return exp.DataType.build("unknown") 289 290 def _normalize(self, schema: t.Dict) -> t.Dict: 291 """ 292 Normalizes all identifiers in the schema. 293 294 Args: 295 schema: the schema to normalize. 296 297 Returns: 298 The normalized schema mapping. 299 """ 300 flattened_schema = flatten_schema(schema, depth=dict_depth(schema) - 1) 301 302 normalized_mapping: t.Dict = {} 303 for keys in flattened_schema: 304 columns = nested_get(schema, *zip(keys, keys)) 305 assert columns is not None 306 307 normalized_keys = [ 308 self._normalize_name(key, dialect=self.dialect, is_table=True) for key in keys 309 ] 310 for column_name, column_type in columns.items(): 311 nested_set( 312 normalized_mapping, 313 normalized_keys + [self._normalize_name(column_name, dialect=self.dialect)], 314 column_type, 315 ) 316 317 return normalized_mapping 318 319 def _normalize_table(self, table: exp.Table, dialect: DialectType = None) -> exp.Table: 320 normalized_table = table.copy() 321 322 for arg in TABLE_ARGS: 323 value = normalized_table.args.get(arg) 324 if isinstance(value, (str, exp.Identifier)): 325 normalized_table.set( 326 arg, 327 exp.to_identifier(self._normalize_name(value, dialect=dialect, is_table=True)), 328 ) 329 330 return normalized_table 331 332 def _normalize_name( 333 self, name: str | exp.Identifier, dialect: DialectType = None, is_table: bool = False 334 ) -> str: 335 dialect = dialect or self.dialect 336 337 try: 338 identifier = sqlglot.maybe_parse(name, dialect=dialect, into=exp.Identifier) 339 except ParseError: 340 return name if isinstance(name, str) else name.name 341 342 name = identifier.name 343 if not self.normalize: 344 return name 345 346 # This can be useful for normalize_identifier 347 identifier.meta["is_table"] = is_table 348 return Dialect.get_or_raise(dialect).normalize_identifier(identifier).name 349 350 def _depth(self) -> int: 351 # The columns themselves are a mapping, but we don't want to include those 352 return super()._depth() - 1 353 354 def _ensure_table(self, table: exp.Table | str, dialect: DialectType = None) -> exp.Table: 355 return exp.maybe_parse(table, into=exp.Table, dialect=dialect or self.dialect) 356 357 def _to_data_type(self, schema_type: str, dialect: DialectType = None) -> exp.DataType: 358 """ 359 Convert a type represented as a string to the corresponding `sqlglot.exp.DataType` object. 360 361 Args: 362 schema_type: the type we want to convert. 363 dialect: the SQL dialect that will be used to parse `schema_type`, if needed. 364 365 Returns: 366 The resulting expression type. 367 """ 368 if schema_type not in self._type_mapping_cache: 369 dialect = dialect or self.dialect 370 371 try: 372 expression = exp.DataType.build(schema_type, dialect=dialect) 373 self._type_mapping_cache[schema_type] = expression 374 except AttributeError: 375 in_dialect = f" in dialect {dialect}" if dialect else "" 376 raise SchemaError(f"Failed to build type '{schema_type}'{in_dialect}.") 377 378 return self._type_mapping_cache[schema_type] 379 380 381def ensure_schema(schema: Schema | t.Optional[t.Dict], **kwargs: t.Any) -> Schema: 382 if isinstance(schema, Schema): 383 return schema 384 385 return MappingSchema(schema, **kwargs) 386 387 388def ensure_column_mapping(mapping: t.Optional[ColumnMapping]) -> t.Dict: 389 if mapping is None: 390 return {} 391 elif isinstance(mapping, dict): 392 return mapping 393 elif isinstance(mapping, str): 394 col_name_type_strs = [x.strip() for x in mapping.split(",")] 395 return { 396 name_type_str.split(":")[0].strip(): name_type_str.split(":")[1].strip() 397 for name_type_str in col_name_type_strs 398 } 399 # Check if mapping looks like a DataFrame StructType 400 elif hasattr(mapping, "simpleString"): 401 return {struct_field.name: struct_field.dataType.simpleString() for struct_field in mapping} 402 elif isinstance(mapping, list): 403 return {x.strip(): None for x in mapping} 404 405 raise ValueError(f"Invalid mapping provided: {type(mapping)}") 406 407 408def flatten_schema( 409 schema: t.Dict, depth: int, keys: t.Optional[t.List[str]] = None 410) -> t.List[t.List[str]]: 411 tables = [] 412 keys = keys or [] 413 414 for k, v in schema.items(): 415 if depth >= 2: 416 tables.extend(flatten_schema(v, depth - 1, keys + [k])) 417 elif depth == 1: 418 tables.append(keys + [k]) 419 420 return tables 421 422 423def nested_get( 424 d: t.Dict, *path: t.Tuple[str, str], raise_on_missing: bool = True 425) -> t.Optional[t.Any]: 426 """ 427 Get a value for a nested dictionary. 428 429 Args: 430 d: the dictionary to search. 431 *path: tuples of (name, key), where: 432 `key` is the key in the dictionary to get. 433 `name` is a string to use in the error if `key` isn't found. 434 435 Returns: 436 The value or None if it doesn't exist. 437 """ 438 for name, key in path: 439 d = d.get(key) # type: ignore 440 if d is None: 441 if raise_on_missing: 442 name = "table" if name == "this" else name 443 raise ValueError(f"Unknown {name}: {key}") 444 return None 445 446 return d 447 448 449def nested_set(d: t.Dict, keys: t.Sequence[str], value: t.Any) -> t.Dict: 450 """ 451 In-place set a value for a nested dictionary 452 453 Example: 454 >>> nested_set({}, ["top_key", "second_key"], "value") 455 {'top_key': {'second_key': 'value'}} 456 457 >>> nested_set({"top_key": {"third_key": "third_value"}}, ["top_key", "second_key"], "value") 458 {'top_key': {'third_key': 'third_value', 'second_key': 'value'}} 459 460 Args: 461 d: dictionary to update. 462 keys: the keys that makeup the path to `value`. 463 value: the value to set in the dictionary for the given key path. 464 465 Returns: 466 The (possibly) updated dictionary. 467 """ 468 if not keys: 469 return d 470 471 if len(keys) == 1: 472 d[keys[0]] = value 473 return d 474 475 subd = d 476 for key in keys[:-1]: 477 if key not in subd: 478 subd = subd.setdefault(key, {}) 479 else: 480 subd = subd[key] 481 482 subd[keys[-1]] = value 483 return d
24class Schema(abc.ABC): 25 """Abstract base class for database schemas""" 26 27 dialect: DialectType 28 29 @abc.abstractmethod 30 def add_table( 31 self, 32 table: exp.Table | str, 33 column_mapping: t.Optional[ColumnMapping] = None, 34 dialect: DialectType = None, 35 ) -> None: 36 """ 37 Register or update a table. Some implementing classes may require column information to also be provided. 38 39 Args: 40 table: the `Table` expression instance or string representing the table. 41 column_mapping: a column mapping that describes the structure of the table. 42 dialect: the SQL dialect that will be used to parse `table` if it's a string. 43 """ 44 45 @abc.abstractmethod 46 def column_names( 47 self, 48 table: exp.Table | str, 49 only_visible: bool = False, 50 dialect: DialectType = None, 51 ) -> t.List[str]: 52 """ 53 Get the column names for a table. 54 55 Args: 56 table: the `Table` expression instance. 57 only_visible: whether to include invisible columns. 58 dialect: the SQL dialect that will be used to parse `table` if it's a string. 59 60 Returns: 61 The list of column names. 62 """ 63 64 @abc.abstractmethod 65 def get_column_type( 66 self, 67 table: exp.Table | str, 68 column: exp.Column, 69 dialect: DialectType = None, 70 ) -> exp.DataType: 71 """ 72 Get the `sqlglot.exp.DataType` type of a column in the schema. 73 74 Args: 75 table: the source table. 76 column: the target column. 77 dialect: the SQL dialect that will be used to parse `table` if it's a string. 78 79 Returns: 80 The resulting column type. 81 """ 82 83 @property 84 @abc.abstractmethod 85 def supported_table_args(self) -> t.Tuple[str, ...]: 86 """ 87 Table arguments this schema support, e.g. `("this", "db", "catalog")` 88 """ 89 90 @property 91 def empty(self) -> bool: 92 """Returns whether or not the schema is empty.""" 93 return True
Abstract base class for database schemas
29 @abc.abstractmethod 30 def add_table( 31 self, 32 table: exp.Table | str, 33 column_mapping: t.Optional[ColumnMapping] = None, 34 dialect: DialectType = None, 35 ) -> None: 36 """ 37 Register or update a table. Some implementing classes may require column information to also be provided. 38 39 Args: 40 table: the `Table` expression instance or string representing the table. 41 column_mapping: a column mapping that describes the structure of the table. 42 dialect: the SQL dialect that will be used to parse `table` if it's a string. 43 """
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.
45 @abc.abstractmethod 46 def column_names( 47 self, 48 table: exp.Table | str, 49 only_visible: bool = False, 50 dialect: DialectType = None, 51 ) -> t.List[str]: 52 """ 53 Get the column names for a table. 54 55 Args: 56 table: the `Table` expression instance. 57 only_visible: whether to include invisible columns. 58 dialect: the SQL dialect that will be used to parse `table` if it's a string. 59 60 Returns: 61 The list of column names. 62 """
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.
64 @abc.abstractmethod 65 def get_column_type( 66 self, 67 table: exp.Table | str, 68 column: exp.Column, 69 dialect: DialectType = None, 70 ) -> exp.DataType: 71 """ 72 Get the `sqlglot.exp.DataType` type of a column in the schema. 73 74 Args: 75 table: the source table. 76 column: the target column. 77 dialect: the SQL dialect that will be used to parse `table` if it's a string. 78 79 Returns: 80 The resulting column type. 81 """
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.
96class AbstractMappingSchema(t.Generic[T]): 97 def __init__( 98 self, 99 mapping: t.Optional[t.Dict] = None, 100 ) -> None: 101 self.mapping = mapping or {} 102 self.mapping_trie = new_trie( 103 tuple(reversed(t)) for t in flatten_schema(self.mapping, depth=self._depth()) 104 ) 105 self._supported_table_args: t.Tuple[str, ...] = tuple() 106 107 @property 108 def empty(self) -> bool: 109 return not self.mapping 110 111 def _depth(self) -> int: 112 return dict_depth(self.mapping) 113 114 @property 115 def supported_table_args(self) -> t.Tuple[str, ...]: 116 if not self._supported_table_args and self.mapping: 117 depth = self._depth() 118 119 if not depth: # None 120 self._supported_table_args = tuple() 121 elif 1 <= depth <= 3: 122 self._supported_table_args = TABLE_ARGS[:depth] 123 else: 124 raise SchemaError(f"Invalid mapping shape. Depth: {depth}") 125 126 return self._supported_table_args 127 128 def table_parts(self, table: exp.Table) -> t.List[str]: 129 if isinstance(table.this, exp.ReadCSV): 130 return [table.this.name] 131 return [table.text(part) for part in TABLE_ARGS if table.text(part)] 132 133 def find( 134 self, table: exp.Table, trie: t.Optional[t.Dict] = None, raise_on_missing: bool = True 135 ) -> t.Optional[T]: 136 parts = self.table_parts(table)[0 : len(self.supported_table_args)] 137 value, trie = in_trie(self.mapping_trie if trie is None else trie, parts) 138 139 if value == TrieResult.FAILED: 140 return None 141 142 if value == TrieResult.PREFIX: 143 possibilities = flatten_schema(trie, depth=dict_depth(trie) - 1) 144 145 if len(possibilities) == 1: 146 parts.extend(possibilities[0]) 147 else: 148 message = ", ".join(".".join(parts) for parts in possibilities) 149 if raise_on_missing: 150 raise SchemaError(f"Ambiguous mapping for {table}: {message}.") 151 return None 152 153 return self.nested_get(parts, raise_on_missing=raise_on_missing) 154 155 def nested_get( 156 self, parts: t.Sequence[str], d: t.Optional[t.Dict] = None, raise_on_missing=True 157 ) -> t.Optional[t.Any]: 158 return nested_get( 159 d or self.mapping, 160 *zip(self.supported_table_args, reversed(parts)), 161 raise_on_missing=raise_on_missing, 162 )
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
133 def find( 134 self, table: exp.Table, trie: t.Optional[t.Dict] = None, raise_on_missing: bool = True 135 ) -> t.Optional[T]: 136 parts = self.table_parts(table)[0 : len(self.supported_table_args)] 137 value, trie = in_trie(self.mapping_trie if trie is None else trie, parts) 138 139 if value == TrieResult.FAILED: 140 return None 141 142 if value == TrieResult.PREFIX: 143 possibilities = flatten_schema(trie, depth=dict_depth(trie) - 1) 144 145 if len(possibilities) == 1: 146 parts.extend(possibilities[0]) 147 else: 148 message = ", ".join(".".join(parts) for parts in possibilities) 149 if raise_on_missing: 150 raise SchemaError(f"Ambiguous mapping for {table}: {message}.") 151 return None 152 153 return self.nested_get(parts, raise_on_missing=raise_on_missing)
165class MappingSchema(AbstractMappingSchema[t.Dict[str, str]], Schema): 166 """ 167 Schema based on a nested mapping. 168 169 Args: 170 schema: Mapping in one of the following forms: 171 1. {table: {col: type}} 172 2. {db: {table: {col: type}}} 173 3. {catalog: {db: {table: {col: type}}}} 174 4. None - Tables will be added later 175 visible: Optional mapping of which columns in the schema are visible. If not provided, all columns 176 are assumed to be visible. The nesting should mirror that of the schema: 177 1. {table: set(*cols)}} 178 2. {db: {table: set(*cols)}}} 179 3. {catalog: {db: {table: set(*cols)}}}} 180 dialect: The dialect to be used for custom type mappings & parsing string arguments. 181 normalize: Whether to normalize identifier names according to the given dialect or not. 182 """ 183 184 def __init__( 185 self, 186 schema: t.Optional[t.Dict] = None, 187 visible: t.Optional[t.Dict] = None, 188 dialect: DialectType = None, 189 normalize: bool = True, 190 ) -> None: 191 self.dialect = dialect 192 self.visible = visible or {} 193 self.normalize = normalize 194 self._type_mapping_cache: t.Dict[str, exp.DataType] = {} 195 196 super().__init__(self._normalize(schema or {})) 197 198 @classmethod 199 def from_mapping_schema(cls, mapping_schema: MappingSchema) -> MappingSchema: 200 return MappingSchema( 201 schema=mapping_schema.mapping, 202 visible=mapping_schema.visible, 203 dialect=mapping_schema.dialect, 204 ) 205 206 def copy(self, **kwargs) -> MappingSchema: 207 return MappingSchema( 208 **{ # type: ignore 209 "schema": self.mapping.copy(), 210 "visible": self.visible.copy(), 211 "dialect": self.dialect, 212 **kwargs, 213 } 214 ) 215 216 def add_table( 217 self, 218 table: exp.Table | str, 219 column_mapping: t.Optional[ColumnMapping] = None, 220 dialect: DialectType = None, 221 ) -> None: 222 """ 223 Register or update a table. Updates are only performed if a new column mapping is provided. 224 225 Args: 226 table: the `Table` expression instance or string representing the table. 227 column_mapping: a column mapping that describes the structure of the table. 228 dialect: the SQL dialect that will be used to parse `table` if it's a string. 229 """ 230 normalized_table = self._normalize_table( 231 self._ensure_table(table, dialect=dialect), dialect=dialect 232 ) 233 normalized_column_mapping = { 234 self._normalize_name(key, dialect=dialect): value 235 for key, value in ensure_column_mapping(column_mapping).items() 236 } 237 238 schema = self.find(normalized_table, raise_on_missing=False) 239 if schema and not normalized_column_mapping: 240 return 241 242 parts = self.table_parts(normalized_table) 243 244 nested_set(self.mapping, tuple(reversed(parts)), normalized_column_mapping) 245 new_trie([parts], self.mapping_trie) 246 247 def column_names( 248 self, 249 table: exp.Table | str, 250 only_visible: bool = False, 251 dialect: DialectType = None, 252 ) -> t.List[str]: 253 normalized_table = self._normalize_table( 254 self._ensure_table(table, dialect=dialect), dialect=dialect 255 ) 256 257 schema = self.find(normalized_table) 258 if schema is None: 259 return [] 260 261 if not only_visible or not self.visible: 262 return list(schema) 263 264 visible = self.nested_get(self.table_parts(normalized_table), self.visible) or [] 265 return [col for col in schema if col in visible] 266 267 def get_column_type( 268 self, 269 table: exp.Table | str, 270 column: exp.Column, 271 dialect: DialectType = None, 272 ) -> exp.DataType: 273 normalized_table = self._normalize_table( 274 self._ensure_table(table, dialect=dialect), dialect=dialect 275 ) 276 normalized_column_name = self._normalize_name( 277 column if isinstance(column, str) else column.this, dialect=dialect 278 ) 279 280 table_schema = self.find(normalized_table, raise_on_missing=False) 281 if table_schema: 282 column_type = table_schema.get(normalized_column_name) 283 284 if isinstance(column_type, exp.DataType): 285 return column_type 286 elif isinstance(column_type, str): 287 return self._to_data_type(column_type.upper(), dialect=dialect) 288 289 return exp.DataType.build("unknown") 290 291 def _normalize(self, schema: t.Dict) -> t.Dict: 292 """ 293 Normalizes all identifiers in the schema. 294 295 Args: 296 schema: the schema to normalize. 297 298 Returns: 299 The normalized schema mapping. 300 """ 301 flattened_schema = flatten_schema(schema, depth=dict_depth(schema) - 1) 302 303 normalized_mapping: t.Dict = {} 304 for keys in flattened_schema: 305 columns = nested_get(schema, *zip(keys, keys)) 306 assert columns is not None 307 308 normalized_keys = [ 309 self._normalize_name(key, dialect=self.dialect, is_table=True) for key in keys 310 ] 311 for column_name, column_type in columns.items(): 312 nested_set( 313 normalized_mapping, 314 normalized_keys + [self._normalize_name(column_name, dialect=self.dialect)], 315 column_type, 316 ) 317 318 return normalized_mapping 319 320 def _normalize_table(self, table: exp.Table, dialect: DialectType = None) -> exp.Table: 321 normalized_table = table.copy() 322 323 for arg in TABLE_ARGS: 324 value = normalized_table.args.get(arg) 325 if isinstance(value, (str, exp.Identifier)): 326 normalized_table.set( 327 arg, 328 exp.to_identifier(self._normalize_name(value, dialect=dialect, is_table=True)), 329 ) 330 331 return normalized_table 332 333 def _normalize_name( 334 self, name: str | exp.Identifier, dialect: DialectType = None, is_table: bool = False 335 ) -> str: 336 dialect = dialect or self.dialect 337 338 try: 339 identifier = sqlglot.maybe_parse(name, dialect=dialect, into=exp.Identifier) 340 except ParseError: 341 return name if isinstance(name, str) else name.name 342 343 name = identifier.name 344 if not self.normalize: 345 return name 346 347 # This can be useful for normalize_identifier 348 identifier.meta["is_table"] = is_table 349 return Dialect.get_or_raise(dialect).normalize_identifier(identifier).name 350 351 def _depth(self) -> int: 352 # The columns themselves are a mapping, but we don't want to include those 353 return super()._depth() - 1 354 355 def _ensure_table(self, table: exp.Table | str, dialect: DialectType = None) -> exp.Table: 356 return exp.maybe_parse(table, into=exp.Table, dialect=dialect or self.dialect) 357 358 def _to_data_type(self, schema_type: str, dialect: DialectType = None) -> exp.DataType: 359 """ 360 Convert a type represented as a string to the corresponding `sqlglot.exp.DataType` object. 361 362 Args: 363 schema_type: the type we want to convert. 364 dialect: the SQL dialect that will be used to parse `schema_type`, if needed. 365 366 Returns: 367 The resulting expression type. 368 """ 369 if schema_type not in self._type_mapping_cache: 370 dialect = dialect or self.dialect 371 372 try: 373 expression = exp.DataType.build(schema_type, dialect=dialect) 374 self._type_mapping_cache[schema_type] = expression 375 except AttributeError: 376 in_dialect = f" in dialect {dialect}" if dialect else "" 377 raise SchemaError(f"Failed to build type '{schema_type}'{in_dialect}.") 378 379 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.
- normalize: Whether to normalize identifier names according to the given dialect or not.
184 def __init__( 185 self, 186 schema: t.Optional[t.Dict] = None, 187 visible: t.Optional[t.Dict] = None, 188 dialect: DialectType = None, 189 normalize: bool = True, 190 ) -> None: 191 self.dialect = dialect 192 self.visible = visible or {} 193 self.normalize = normalize 194 self._type_mapping_cache: t.Dict[str, exp.DataType] = {} 195 196 super().__init__(self._normalize(schema or {}))
389def ensure_column_mapping(mapping: t.Optional[ColumnMapping]) -> t.Dict: 390 if mapping is None: 391 return {} 392 elif isinstance(mapping, dict): 393 return mapping 394 elif isinstance(mapping, str): 395 col_name_type_strs = [x.strip() for x in mapping.split(",")] 396 return { 397 name_type_str.split(":")[0].strip(): name_type_str.split(":")[1].strip() 398 for name_type_str in col_name_type_strs 399 } 400 # Check if mapping looks like a DataFrame StructType 401 elif hasattr(mapping, "simpleString"): 402 return {struct_field.name: struct_field.dataType.simpleString() for struct_field in mapping} 403 elif isinstance(mapping, list): 404 return {x.strip(): None for x in mapping} 405 406 raise ValueError(f"Invalid mapping provided: {type(mapping)}")
409def flatten_schema( 410 schema: t.Dict, depth: int, keys: t.Optional[t.List[str]] = None 411) -> t.List[t.List[str]]: 412 tables = [] 413 keys = keys or [] 414 415 for k, v in schema.items(): 416 if depth >= 2: 417 tables.extend(flatten_schema(v, depth - 1, keys + [k])) 418 elif depth == 1: 419 tables.append(keys + [k]) 420 421 return tables
424def nested_get( 425 d: t.Dict, *path: t.Tuple[str, str], raise_on_missing: bool = True 426) -> t.Optional[t.Any]: 427 """ 428 Get a value for a nested dictionary. 429 430 Args: 431 d: the dictionary to search. 432 *path: tuples of (name, key), where: 433 `key` is the key in the dictionary to get. 434 `name` is a string to use in the error if `key` isn't found. 435 436 Returns: 437 The value or None if it doesn't exist. 438 """ 439 for name, key in path: 440 d = d.get(key) # type: ignore 441 if d is None: 442 if raise_on_missing: 443 name = "table" if name == "this" else name 444 raise ValueError(f"Unknown {name}: {key}") 445 return None 446 447 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.
450def nested_set(d: t.Dict, keys: t.Sequence[str], value: t.Any) -> t.Dict: 451 """ 452 In-place set a value for a nested dictionary 453 454 Example: 455 >>> nested_set({}, ["top_key", "second_key"], "value") 456 {'top_key': {'second_key': 'value'}} 457 458 >>> nested_set({"top_key": {"third_key": "third_value"}}, ["top_key", "second_key"], "value") 459 {'top_key': {'third_key': 'third_value', 'second_key': 'value'}} 460 461 Args: 462 d: dictionary to update. 463 keys: the keys that makeup the path to `value`. 464 value: the value to set in the dictionary for the given key path. 465 466 Returns: 467 The (possibly) updated dictionary. 468 """ 469 if not keys: 470 return d 471 472 if len(keys) == 1: 473 d[keys[0]] = value 474 return d 475 476 subd = d 477 for key in keys[:-1]: 478 if key not in subd: 479 subd = subd.setdefault(key, {}) 480 else: 481 subd = subd[key] 482 483 subd[keys[-1]] = value 484 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.