sqlglot.schema API documentation

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# sqlglot.schema

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      1from __future__ import annotations
      2
      3import abc
      4import typing as t
      5
      6from sqlglot import expressions as exp
      7from sqlglot.dialects.dialect import Dialect
      8from sqlglot.errors import SchemaError
      9from sqlglot.helper import dict_depth
     10from sqlglot.trie import TrieResult, in_trie, new_trie
     11
     12if t.TYPE_CHECKING:
     13    from sqlglot.dataframe.sql.types import StructType
     14    from sqlglot.dialects.dialect import DialectType
     15
     16    ColumnMapping = t.Union[t.Dict, str, StructType, t.List]
     17
     18
     19class Schema(abc.ABC):
     20    """Abstract base class for database schemas"""
     21
     22    dialect: DialectType
     23
     24    @abc.abstractmethod
     25    def add_table(
     26        self,
     27        table: exp.Table | str,
     28        column_mapping: t.Optional[ColumnMapping] = None,
     29        dialect: DialectType = None,
     30        normalize: t.Optional[bool] = None,
     31        match_depth: bool = True,
     32    ) -> None:
     33        """
     34        Register or update a table. Some implementing classes may require column information to also be provided.
     35        The added table must have the necessary number of qualifiers in its path to match the schema's nesting level.
     36
     37        Args:
     38            table: the `Table` expression instance or string representing the table.
     39            column_mapping: a column mapping that describes the structure of the table.
     40            dialect: the SQL dialect that will be used to parse `table` if it's a string.
     41            normalize: whether to normalize identifiers according to the dialect of interest.
     42            match_depth: whether to enforce that the table must match the schema's depth or not.
     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        normalize: t.Optional[bool] = None,
     52    ) -> t.Sequence[str]:
     53        """
     54        Get the column names for a table.
     55
     56        Args:
     57            table: the `Table` expression instance.
     58            only_visible: whether to include invisible columns.
     59            dialect: the SQL dialect that will be used to parse `table` if it's a string.
     60            normalize: whether to normalize identifiers according to the dialect of interest.
     61
     62        Returns:
     63            The sequence of column names.
     64        """
     65
     66    @abc.abstractmethod
     67    def get_column_type(
     68        self,
     69        table: exp.Table | str,
     70        column: exp.Column | str,
     71        dialect: DialectType = None,
     72        normalize: t.Optional[bool] = None,
     73    ) -> exp.DataType:
     74        """
     75        Get the `sqlglot.exp.DataType` type of a column in the schema.
     76
     77        Args:
     78            table: the source table.
     79            column: the target column.
     80            dialect: the SQL dialect that will be used to parse `table` if it's a string.
     81            normalize: whether to normalize identifiers according to the dialect of interest.
     82
     83        Returns:
     84            The resulting column type.
     85        """
     86
     87    def has_column(
     88        self,
     89        table: exp.Table | str,
     90        column: exp.Column | str,
     91        dialect: DialectType = None,
     92        normalize: t.Optional[bool] = None,
     93    ) -> bool:
     94        """
     95        Returns whether or not `column` appears in `table`'s schema.
     96
     97        Args:
     98            table: the source table.
     99            column: the target column.
    100            dialect: the SQL dialect that will be used to parse `table` if it's a string.
    101            normalize: whether to normalize identifiers according to the dialect of interest.
    102
    103        Returns:
    104            True if the column appears in the schema, False otherwise.
    105        """
    106        name = column if isinstance(column, str) else column.name
    107        return name in self.column_names(table, dialect=dialect, normalize=normalize)
    108
    109    @property
    110    @abc.abstractmethod
    111    def supported_table_args(self) -> t.Tuple[str, ...]:
    112        """
    113        Table arguments this schema support, e.g. `("this", "db", "catalog")`
    114        """
    115
    116    @property
    117    def empty(self) -> bool:
    118        """Returns whether or not the schema is empty."""
    119        return True
    120
    121
    122class AbstractMappingSchema:
    123    def __init__(
    124        self,
    125        mapping: t.Optional[t.Dict] = None,
    126    ) -> None:
    127        self.mapping = mapping or {}
    128        self.mapping_trie = new_trie(
    129            tuple(reversed(t)) for t in flatten_schema(self.mapping, depth=self.depth())
    130        )
    131        self._supported_table_args: t.Tuple[str, ...] = tuple()
    132
    133    @property
    134    def empty(self) -> bool:
    135        return not self.mapping
    136
    137    def depth(self) -> int:
    138        return dict_depth(self.mapping)
    139
    140    @property
    141    def supported_table_args(self) -> t.Tuple[str, ...]:
    142        if not self._supported_table_args and self.mapping:
    143            depth = self.depth()
    144
    145            if not depth:  # None
    146                self._supported_table_args = tuple()
    147            elif 1 <= depth <= 3:
    148                self._supported_table_args = exp.TABLE_PARTS[:depth]
    149            else:
    150                raise SchemaError(f"Invalid mapping shape. Depth: {depth}")
    151
    152        return self._supported_table_args
    153
    154    def table_parts(self, table: exp.Table) -> t.List[str]:
    155        if isinstance(table.this, exp.ReadCSV):
    156            return [table.this.name]
    157        return [table.text(part) for part in exp.TABLE_PARTS if table.text(part)]
    158
    159    def find(self, table: exp.Table, raise_on_missing: bool = True) -> t.Optional[t.Any]:
    160        """
    161        Returns the schema of a given table.
    162
    163        Args:
    164            table: the target table.
    165            raise_on_missing: whether or not to raise in case the schema is not found.
    166
    167        Returns:
    168            The schema of the target table.
    169        """
    170        parts = self.table_parts(table)[0 : len(self.supported_table_args)]
    171        value, trie = in_trie(self.mapping_trie, parts)
    172
    173        if value == TrieResult.FAILED:
    174            return None
    175
    176        if value == TrieResult.PREFIX:
    177            possibilities = flatten_schema(trie, depth=dict_depth(trie) - 1)
    178
    179            if len(possibilities) == 1:
    180                parts.extend(possibilities[0])
    181            else:
    182                message = ", ".join(".".join(parts) for parts in possibilities)
    183                if raise_on_missing:
    184                    raise SchemaError(f"Ambiguous mapping for {table}: {message}.")
    185                return None
    186
    187        return self.nested_get(parts, raise_on_missing=raise_on_missing)
    188
    189    def nested_get(
    190        self, parts: t.Sequence[str], d: t.Optional[t.Dict] = None, raise_on_missing=True
    191    ) -> t.Optional[t.Any]:
    192        return nested_get(
    193            d or self.mapping,
    194            *zip(self.supported_table_args, reversed(parts)),
    195            raise_on_missing=raise_on_missing,
    196        )
    197
    198
    199class MappingSchema(AbstractMappingSchema, Schema):
    200    """
    201    Schema based on a nested mapping.
    202
    203    Args:
    204        schema: Mapping in one of the following forms:
    205            1. {table: {col: type}}
    206            2. {db: {table: {col: type}}}
    207            3. {catalog: {db: {table: {col: type}}}}
    208            4. None - Tables will be added later
    209        visible: Optional mapping of which columns in the schema are visible. If not provided, all columns
    210            are assumed to be visible. The nesting should mirror that of the schema:
    211            1. {table: set(*cols)}}
    212            2. {db: {table: set(*cols)}}}
    213            3. {catalog: {db: {table: set(*cols)}}}}
    214        dialect: The dialect to be used for custom type mappings & parsing string arguments.
    215        normalize: Whether to normalize identifier names according to the given dialect or not.
    216    """
    217
    218    def __init__(
    219        self,
    220        schema: t.Optional[t.Dict] = None,
    221        visible: t.Optional[t.Dict] = None,
    222        dialect: DialectType = None,
    223        normalize: bool = True,
    224    ) -> None:
    225        self.dialect = dialect
    226        self.visible = {} if visible is None else visible
    227        self.normalize = normalize
    228        self._type_mapping_cache: t.Dict[str, exp.DataType] = {}
    229        self._depth = 0
    230        schema = {} if schema is None else schema
    231
    232        super().__init__(self._normalize(schema) if self.normalize else schema)
    233
    234    @classmethod
    235    def from_mapping_schema(cls, mapping_schema: MappingSchema) -> MappingSchema:
    236        return MappingSchema(
    237            schema=mapping_schema.mapping,
    238            visible=mapping_schema.visible,
    239            dialect=mapping_schema.dialect,
    240            normalize=mapping_schema.normalize,
    241        )
    242
    243    def copy(self, **kwargs) -> MappingSchema:
    244        return MappingSchema(
    245            **{  # type: ignore
    246                "schema": self.mapping.copy(),
    247                "visible": self.visible.copy(),
    248                "dialect": self.dialect,
    249                "normalize": self.normalize,
    250                **kwargs,
    251            }
    252        )
    253
    254    def add_table(
    255        self,
    256        table: exp.Table | str,
    257        column_mapping: t.Optional[ColumnMapping] = None,
    258        dialect: DialectType = None,
    259        normalize: t.Optional[bool] = None,
    260        match_depth: bool = True,
    261    ) -> None:
    262        """
    263        Register or update a table. Updates are only performed if a new column mapping is provided.
    264        The added table must have the necessary number of qualifiers in its path to match the schema's nesting level.
    265
    266        Args:
    267            table: the `Table` expression instance or string representing the table.
    268            column_mapping: a column mapping that describes the structure of the table.
    269            dialect: the SQL dialect that will be used to parse `table` if it's a string.
    270            normalize: whether to normalize identifiers according to the dialect of interest.
    271            match_depth: whether to enforce that the table must match the schema's depth or not.
    272        """
    273        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    274
    275        if match_depth and not self.empty and len(normalized_table.parts) != self.depth():
    276            raise SchemaError(
    277                f"Table {normalized_table.sql(dialect=self.dialect)} must match the "
    278                f"schema's nesting level: {self.depth()}."
    279            )
    280
    281        normalized_column_mapping = {
    282            self._normalize_name(key, dialect=dialect, normalize=normalize): value
    283            for key, value in ensure_column_mapping(column_mapping).items()
    284        }
    285
    286        schema = self.find(normalized_table, raise_on_missing=False)
    287        if schema and not normalized_column_mapping:
    288            return
    289
    290        parts = self.table_parts(normalized_table)
    291
    292        nested_set(self.mapping, tuple(reversed(parts)), normalized_column_mapping)
    293        new_trie([parts], self.mapping_trie)
    294
    295    def column_names(
    296        self,
    297        table: exp.Table | str,
    298        only_visible: bool = False,
    299        dialect: DialectType = None,
    300        normalize: t.Optional[bool] = None,
    301    ) -> t.List[str]:
    302        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    303
    304        schema = self.find(normalized_table)
    305        if schema is None:
    306            return []
    307
    308        if not only_visible or not self.visible:
    309            return list(schema)
    310
    311        visible = self.nested_get(self.table_parts(normalized_table), self.visible) or []
    312        return [col for col in schema if col in visible]
    313
    314    def get_column_type(
    315        self,
    316        table: exp.Table | str,
    317        column: exp.Column | str,
    318        dialect: DialectType = None,
    319        normalize: t.Optional[bool] = None,
    320    ) -> exp.DataType:
    321        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    322
    323        normalized_column_name = self._normalize_name(
    324            column if isinstance(column, str) else column.this, dialect=dialect, normalize=normalize
    325        )
    326
    327        table_schema = self.find(normalized_table, raise_on_missing=False)
    328        if table_schema:
    329            column_type = table_schema.get(normalized_column_name)
    330
    331            if isinstance(column_type, exp.DataType):
    332                return column_type
    333            elif isinstance(column_type, str):
    334                return self._to_data_type(column_type, dialect=dialect)
    335
    336        return exp.DataType.build("unknown")
    337
    338    def has_column(
    339        self,
    340        table: exp.Table | str,
    341        column: exp.Column | str,
    342        dialect: DialectType = None,
    343        normalize: t.Optional[bool] = None,
    344    ) -> bool:
    345        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    346
    347        normalized_column_name = self._normalize_name(
    348            column if isinstance(column, str) else column.this, dialect=dialect, normalize=normalize
    349        )
    350
    351        table_schema = self.find(normalized_table, raise_on_missing=False)
    352        return normalized_column_name in table_schema if table_schema else False
    353
    354    def _normalize(self, schema: t.Dict) -> t.Dict:
    355        """
    356        Normalizes all identifiers in the schema.
    357
    358        Args:
    359            schema: the schema to normalize.
    360
    361        Returns:
    362            The normalized schema mapping.
    363        """
    364        normalized_mapping: t.Dict = {}
    365        flattened_schema = flatten_schema(schema, depth=dict_depth(schema) - 1)
    366
    367        for keys in flattened_schema:
    368            columns = nested_get(schema, *zip(keys, keys))
    369
    370            if not isinstance(columns, dict):
    371                raise SchemaError(
    372                    f"Table {'.'.join(keys[:-1])} must match the schema's nesting level: {len(flattened_schema[0])}."
    373                )
    374
    375            normalized_keys = [self._normalize_name(key, is_table=True) for key in keys]
    376            for column_name, column_type in columns.items():
    377                nested_set(
    378                    normalized_mapping,
    379                    normalized_keys + [self._normalize_name(column_name)],
    380                    column_type,
    381                )
    382
    383        return normalized_mapping
    384
    385    def _normalize_table(
    386        self,
    387        table: exp.Table | str,
    388        dialect: DialectType = None,
    389        normalize: t.Optional[bool] = None,
    390    ) -> exp.Table:
    391        dialect = dialect or self.dialect
    392        normalize = self.normalize if normalize is None else normalize
    393
    394        normalized_table = exp.maybe_parse(table, into=exp.Table, dialect=dialect, copy=normalize)
    395
    396        if normalize:
    397            for arg in exp.TABLE_PARTS:
    398                value = normalized_table.args.get(arg)
    399                if isinstance(value, exp.Identifier):
    400                    normalized_table.set(
    401                        arg,
    402                        normalize_name(value, dialect=dialect, is_table=True, normalize=normalize),
    403                    )
    404
    405        return normalized_table
    406
    407    def _normalize_name(
    408        self,
    409        name: str | exp.Identifier,
    410        dialect: DialectType = None,
    411        is_table: bool = False,
    412        normalize: t.Optional[bool] = None,
    413    ) -> str:
    414        return normalize_name(
    415            name,
    416            dialect=dialect or self.dialect,
    417            is_table=is_table,
    418            normalize=self.normalize if normalize is None else normalize,
    419        ).name
    420
    421    def depth(self) -> int:
    422        if not self.empty and not self._depth:
    423            # The columns themselves are a mapping, but we don't want to include those
    424            self._depth = super().depth() - 1
    425        return self._depth
    426
    427    def _to_data_type(self, schema_type: str, dialect: DialectType = None) -> exp.DataType:
    428        """
    429        Convert a type represented as a string to the corresponding `sqlglot.exp.DataType` object.
    430
    431        Args:
    432            schema_type: the type we want to convert.
    433            dialect: the SQL dialect that will be used to parse `schema_type`, if needed.
    434
    435        Returns:
    436            The resulting expression type.
    437        """
    438        if schema_type not in self._type_mapping_cache:
    439            dialect = dialect or self.dialect
    440            udt = Dialect.get_or_raise(dialect).SUPPORTS_USER_DEFINED_TYPES
    441
    442            try:
    443                expression = exp.DataType.build(schema_type, dialect=dialect, udt=udt)
    444                self._type_mapping_cache[schema_type] = expression
    445            except AttributeError:
    446                in_dialect = f" in dialect {dialect}" if dialect else ""
    447                raise SchemaError(f"Failed to build type '{schema_type}'{in_dialect}.")
    448
    449        return self._type_mapping_cache[schema_type]
    450
    451
    452def normalize_name(
    453    identifier: str | exp.Identifier,
    454    dialect: DialectType = None,
    455    is_table: bool = False,
    456    normalize: t.Optional[bool] = True,
    457) -> exp.Identifier:
    458    if isinstance(identifier, str):
    459        identifier = exp.parse_identifier(identifier, dialect=dialect)
    460
    461    if not normalize:
    462        return identifier
    463
    464    # this is used for normalize_identifier, bigquery has special rules pertaining tables
    465    identifier.meta["is_table"] = is_table
    466    return Dialect.get_or_raise(dialect).normalize_identifier(identifier)
    467
    468
    469def ensure_schema(schema: Schema | t.Optional[t.Dict], **kwargs: t.Any) -> Schema:
    470    if isinstance(schema, Schema):
    471        return schema
    472
    473    return MappingSchema(schema, **kwargs)
    474
    475
    476def ensure_column_mapping(mapping: t.Optional[ColumnMapping]) -> t.Dict:
    477    if mapping is None:
    478        return {}
    479    elif isinstance(mapping, dict):
    480        return mapping
    481    elif isinstance(mapping, str):
    482        col_name_type_strs = [x.strip() for x in mapping.split(",")]
    483        return {
    484            name_type_str.split(":")[0].strip(): name_type_str.split(":")[1].strip()
    485            for name_type_str in col_name_type_strs
    486        }
    487    # Check if mapping looks like a DataFrame StructType
    488    elif hasattr(mapping, "simpleString"):
    489        return {struct_field.name: struct_field.dataType.simpleString() for struct_field in mapping}
    490    elif isinstance(mapping, list):
    491        return {x.strip(): None for x in mapping}
    492
    493    raise ValueError(f"Invalid mapping provided: {type(mapping)}")
    494
    495
    496def flatten_schema(
    497    schema: t.Dict, depth: int, keys: t.Optional[t.List[str]] = None
    498) -> t.List[t.List[str]]:
    499    tables = []
    500    keys = keys or []
    501
    502    for k, v in schema.items():
    503        if depth >= 2:
    504            tables.extend(flatten_schema(v, depth - 1, keys + [k]))
    505        elif depth == 1:
    506            tables.append(keys + [k])
    507
    508    return tables
    509
    510
    511def nested_get(
    512    d: t.Dict, *path: t.Tuple[str, str], raise_on_missing: bool = True
    513) -> t.Optional[t.Any]:
    514    """
    515    Get a value for a nested dictionary.
    516
    517    Args:
    518        d: the dictionary to search.
    519        *path: tuples of (name, key), where:
    520            `key` is the key in the dictionary to get.
    521            `name` is a string to use in the error if `key` isn't found.
    522
    523    Returns:
    524        The value or None if it doesn't exist.
    525    """
    526    for name, key in path:
    527        d = d.get(key)  # type: ignore
    528        if d is None:
    529            if raise_on_missing:
    530                name = "table" if name == "this" else name
    531                raise ValueError(f"Unknown {name}: {key}")
    532            return None
    533
    534    return d
    535
    536
    537def nested_set(d: t.Dict, keys: t.Sequence[str], value: t.Any) -> t.Dict:
    538    """
    539    In-place set a value for a nested dictionary
    540
    541    Example:
    542        >>> nested_set({}, ["top_key", "second_key"], "value")
    543        {'top_key': {'second_key': 'value'}}
    544
    545        >>> nested_set({"top_key": {"third_key": "third_value"}}, ["top_key", "second_key"], "value")
    546        {'top_key': {'third_key': 'third_value', 'second_key': 'value'}}
    547
    548    Args:
    549        d: dictionary to update.
    550        keys: the keys that makeup the path to `value`.
    551        value: the value to set in the dictionary for the given key path.
    552
    553    Returns:
    554        The (possibly) updated dictionary.
    555    """
    556    if not keys:
    557        return d
    558
    559    if len(keys) == 1:
    560        d[keys[0]] = value
    561        return d
    562
    563    subd = d
    564    for key in keys[:-1]:
    565        if key not in subd:
    566            subd = subd.setdefault(key, {})
    567        else:
    568            subd = subd[key]
    569
    570    subd[keys[-1]] = value
    571    return d

class Schema(abc.ABC): View Source

     20class Schema(abc.ABC):
     21    """Abstract base class for database schemas"""
     22
     23    dialect: DialectType
     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        normalize: t.Optional[bool] = None,
     32        match_depth: bool = True,
     33    ) -> None:
     34        """
     35        Register or update a table. Some implementing classes may require column information to also be provided.
     36        The added table must have the necessary number of qualifiers in its path to match the schema's nesting level.
     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            normalize: whether to normalize identifiers according to the dialect of interest.
     43            match_depth: whether to enforce that the table must match the schema's depth or not.
     44        """
     45
     46    @abc.abstractmethod
     47    def column_names(
     48        self,
     49        table: exp.Table | str,
     50        only_visible: bool = False,
     51        dialect: DialectType = None,
     52        normalize: t.Optional[bool] = None,
     53    ) -> t.Sequence[str]:
     54        """
     55        Get the column names for a table.
     56
     57        Args:
     58            table: the `Table` expression instance.
     59            only_visible: whether to include invisible columns.
     60            dialect: the SQL dialect that will be used to parse `table` if it's a string.
     61            normalize: whether to normalize identifiers according to the dialect of interest.
     62
     63        Returns:
     64            The sequence of column names.
     65        """
     66
     67    @abc.abstractmethod
     68    def get_column_type(
     69        self,
     70        table: exp.Table | str,
     71        column: exp.Column | str,
     72        dialect: DialectType = None,
     73        normalize: t.Optional[bool] = None,
     74    ) -> exp.DataType:
     75        """
     76        Get the `sqlglot.exp.DataType` type of a column in the schema.
     77
     78        Args:
     79            table: the source table.
     80            column: the target column.
     81            dialect: the SQL dialect that will be used to parse `table` if it's a string.
     82            normalize: whether to normalize identifiers according to the dialect of interest.
     83
     84        Returns:
     85            The resulting column type.
     86        """
     87
     88    def has_column(
     89        self,
     90        table: exp.Table | str,
     91        column: exp.Column | str,
     92        dialect: DialectType = None,
     93        normalize: t.Optional[bool] = None,
     94    ) -> bool:
     95        """
     96        Returns whether or not `column` appears in `table`'s schema.
     97
     98        Args:
     99            table: the source table.
    100            column: the target column.
    101            dialect: the SQL dialect that will be used to parse `table` if it's a string.
    102            normalize: whether to normalize identifiers according to the dialect of interest.
    103
    104        Returns:
    105            True if the column appears in the schema, False otherwise.
    106        """
    107        name = column if isinstance(column, str) else column.name
    108        return name in self.column_names(table, dialect=dialect, normalize=normalize)
    109
    110    @property
    111    @abc.abstractmethod
    112    def supported_table_args(self) -> t.Tuple[str, ...]:
    113        """
    114        Table arguments this schema support, e.g. `("this", "db", "catalog")`
    115        """
    116
    117    @property
    118    def empty(self) -> bool:
    119        """Returns whether or not the schema is empty."""
    120        return True

Abstract base class for database schemas

dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType]
@abc.abstractmethod
def add_table( self, table: sqlglot.expressions.Table | str, column_mapping: Union[Dict, str, sqlglot.dataframe.sql.types.StructType, List, NoneType] = None, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, normalize: Optional[bool] = None, match_depth: bool = True) -> None: View Source

    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        normalize: t.Optional[bool] = None,
    32        match_depth: bool = True,
    33    ) -> None:
    34        """
    35        Register or update a table. Some implementing classes may require column information to also be provided.
    36        The added table must have the necessary number of qualifiers in its path to match the schema's nesting level.
    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            normalize: whether to normalize identifiers according to the dialect of interest.
    43            match_depth: whether to enforce that the table must match the schema's depth or not.
    44        """

Register or update a table. Some implementing classes may require column information to also be provided. The added table must have the necessary number of qualifiers in its path to match the schema's nesting level.

###### 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.
- normalize: whether to normalize identifiers according to the dialect of interest.
- match_depth: whether to enforce that the table must match the schema's depth or not.

@abc.abstractmethod
def column_names( self, table: sqlglot.expressions.Table | str, only_visible: bool = False, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, normalize: Optional[bool] = None) -> Sequence[str]: View Source

    46    @abc.abstractmethod
    47    def column_names(
    48        self,
    49        table: exp.Table | str,
    50        only_visible: bool = False,
    51        dialect: DialectType = None,
    52        normalize: t.Optional[bool] = None,
    53    ) -> t.Sequence[str]:
    54        """
    55        Get the column names for a table.
    56
    57        Args:
    58            table: the `Table` expression instance.
    59            only_visible: whether to include invisible columns.
    60            dialect: the SQL dialect that will be used to parse `table` if it's a string.
    61            normalize: whether to normalize identifiers according to the dialect of interest.
    62
    63        Returns:
    64            The sequence of column names.
    65        """

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.
- normalize: whether to normalize identifiers according to the dialect of interest.

###### Returns:

> The sequence of column names.

@abc.abstractmethod
def get_column_type( self, table: sqlglot.expressions.Table | str, column: sqlglot.expressions.Column | str, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, normalize: Optional[bool] = None) -> sqlglot.expressions.DataType: View Source

    67    @abc.abstractmethod
    68    def get_column_type(
    69        self,
    70        table: exp.Table | str,
    71        column: exp.Column | str,
    72        dialect: DialectType = None,
    73        normalize: t.Optional[bool] = None,
    74    ) -> exp.DataType:
    75        """
    76        Get the `sqlglot.exp.DataType` type of a column in the schema.
    77
    78        Args:
    79            table: the source table.
    80            column: the target column.
    81            dialect: the SQL dialect that will be used to parse `table` if it's a string.
    82            normalize: whether to normalize identifiers according to the dialect of interest.
    83
    84        Returns:
    85            The resulting column type.
    86        """

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.
- normalize: whether to normalize identifiers according to the dialect of interest.

###### Returns:

> The resulting column type.

def has_column( self, table: sqlglot.expressions.Table | str, column: sqlglot.expressions.Column | str, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, normalize: Optional[bool] = None) -> bool: View Source

     88    def has_column(
     89        self,
     90        table: exp.Table | str,
     91        column: exp.Column | str,
     92        dialect: DialectType = None,
     93        normalize: t.Optional[bool] = None,
     94    ) -> bool:
     95        """
     96        Returns whether or not `column` appears in `table`'s schema.
     97
     98        Args:
     99            table: the source table.
    100            column: the target column.
    101            dialect: the SQL dialect that will be used to parse `table` if it's a string.
    102            normalize: whether to normalize identifiers according to the dialect of interest.
    103
    104        Returns:
    105            True if the column appears in the schema, False otherwise.
    106        """
    107        name = column if isinstance(column, str) else column.name
    108        return name in self.column_names(table, dialect=dialect, normalize=normalize)

Returns whether or not `column` appears in `table`'s 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.
- normalize: whether to normalize identifiers according to the dialect of interest.

###### Returns:

> True if the column appears in the schema, False otherwise.

supported_table_args: Tuple[str, ...] View Source

    110    @property
    111    @abc.abstractmethod
    112    def supported_table_args(self) -> t.Tuple[str, ...]:
    113        """
    114        Table arguments this schema support, e.g. `("this", "db", "catalog")`
    115        """

Table arguments this schema support, e.g. `("this", "db", "catalog")`

empty: bool View Source

    117    @property
    118    def empty(self) -> bool:
    119        """Returns whether or not the schema is empty."""
    120        return True

Returns whether or not the schema is empty.

class AbstractMappingSchema: View Source

    123class AbstractMappingSchema:
    124    def __init__(
    125        self,
    126        mapping: t.Optional[t.Dict] = None,
    127    ) -> None:
    128        self.mapping = mapping or {}
    129        self.mapping_trie = new_trie(
    130            tuple(reversed(t)) for t in flatten_schema(self.mapping, depth=self.depth())
    131        )
    132        self._supported_table_args: t.Tuple[str, ...] = tuple()
    133
    134    @property
    135    def empty(self) -> bool:
    136        return not self.mapping
    137
    138    def depth(self) -> int:
    139        return dict_depth(self.mapping)
    140
    141    @property
    142    def supported_table_args(self) -> t.Tuple[str, ...]:
    143        if not self._supported_table_args and self.mapping:
    144            depth = self.depth()
    145
    146            if not depth:  # None
    147                self._supported_table_args = tuple()
    148            elif 1 <= depth <= 3:
    149                self._supported_table_args = exp.TABLE_PARTS[:depth]
    150            else:
    151                raise SchemaError(f"Invalid mapping shape. Depth: {depth}")
    152
    153        return self._supported_table_args
    154
    155    def table_parts(self, table: exp.Table) -> t.List[str]:
    156        if isinstance(table.this, exp.ReadCSV):
    157            return [table.this.name]
    158        return [table.text(part) for part in exp.TABLE_PARTS if table.text(part)]
    159
    160    def find(self, table: exp.Table, raise_on_missing: bool = True) -> t.Optional[t.Any]:
    161        """
    162        Returns the schema of a given table.
    163
    164        Args:
    165            table: the target table.
    166            raise_on_missing: whether or not to raise in case the schema is not found.
    167
    168        Returns:
    169            The schema of the target table.
    170        """
    171        parts = self.table_parts(table)[0 : len(self.supported_table_args)]
    172        value, trie = in_trie(self.mapping_trie, parts)
    173
    174        if value == TrieResult.FAILED:
    175            return None
    176
    177        if value == TrieResult.PREFIX:
    178            possibilities = flatten_schema(trie, depth=dict_depth(trie) - 1)
    179
    180            if len(possibilities) == 1:
    181                parts.extend(possibilities[0])
    182            else:
    183                message = ", ".join(".".join(parts) for parts in possibilities)
    184                if raise_on_missing:
    185                    raise SchemaError(f"Ambiguous mapping for {table}: {message}.")
    186                return None
    187
    188        return self.nested_get(parts, raise_on_missing=raise_on_missing)
    189
    190    def nested_get(
    191        self, parts: t.Sequence[str], d: t.Optional[t.Dict] = None, raise_on_missing=True
    192    ) -> t.Optional[t.Any]:
    193        return nested_get(
    194            d or self.mapping,
    195            *zip(self.supported_table_args, reversed(parts)),
    196            raise_on_missing=raise_on_missing,
    197        )

AbstractMappingSchema(mapping: Optional[Dict] = None) View Source

    124    def __init__(
    125        self,
    126        mapping: t.Optional[t.Dict] = None,
    127    ) -> None:
    128        self.mapping = mapping or {}
    129        self.mapping_trie = new_trie(
    130            tuple(reversed(t)) for t in flatten_schema(self.mapping, depth=self.depth())
    131        )
    132        self._supported_table_args: t.Tuple[str, ...] = tuple()

mapping
mapping_trie
empty: bool View Source

    134    @property
    135    def empty(self) -> bool:
    136        return not self.mapping

def depth(self) -> int: View Source

    138    def depth(self) -> int:
    139        return dict_depth(self.mapping)

supported_table_args: Tuple[str, ...] View Source

    141    @property
    142    def supported_table_args(self) -> t.Tuple[str, ...]:
    143        if not self._supported_table_args and self.mapping:
    144            depth = self.depth()
    145
    146            if not depth:  # None
    147                self._supported_table_args = tuple()
    148            elif 1 <= depth <= 3:
    149                self._supported_table_args = exp.TABLE_PARTS[:depth]
    150            else:
    151                raise SchemaError(f"Invalid mapping shape. Depth: {depth}")
    152
    153        return self._supported_table_args

def table_parts(self, table: sqlglot.expressions.Table) -> List[str]: View Source

    155    def table_parts(self, table: exp.Table) -> t.List[str]:
    156        if isinstance(table.this, exp.ReadCSV):
    157            return [table.this.name]
    158        return [table.text(part) for part in exp.TABLE_PARTS if table.text(part)]

def find( self, table: sqlglot.expressions.Table, raise_on_missing: bool = True) -> Optional[Any]: View Source

    160    def find(self, table: exp.Table, raise_on_missing: bool = True) -> t.Optional[t.Any]:
    161        """
    162        Returns the schema of a given table.
    163
    164        Args:
    165            table: the target table.
    166            raise_on_missing: whether or not to raise in case the schema is not found.
    167
    168        Returns:
    169            The schema of the target table.
    170        """
    171        parts = self.table_parts(table)[0 : len(self.supported_table_args)]
    172        value, trie = in_trie(self.mapping_trie, parts)
    173
    174        if value == TrieResult.FAILED:
    175            return None
    176
    177        if value == TrieResult.PREFIX:
    178            possibilities = flatten_schema(trie, depth=dict_depth(trie) - 1)
    179
    180            if len(possibilities) == 1:
    181                parts.extend(possibilities[0])
    182            else:
    183                message = ", ".join(".".join(parts) for parts in possibilities)
    184                if raise_on_missing:
    185                    raise SchemaError(f"Ambiguous mapping for {table}: {message}.")
    186                return None
    187
    188        return self.nested_get(parts, raise_on_missing=raise_on_missing)

Returns the schema of a given table.

###### Arguments:

- table: the target table.
- raise_on_missing: whether or not to raise in case the schema is not found.

###### Returns:

> The schema of the target table.

def nested_get( self, parts: Sequence[str], d: Optional[Dict] = None, raise_on_missing=True) -> Optional[Any]: View Source

    190    def nested_get(
    191        self, parts: t.Sequence[str], d: t.Optional[t.Dict] = None, raise_on_missing=True
    192    ) -> t.Optional[t.Any]:
    193        return nested_get(
    194            d or self.mapping,
    195            *zip(self.supported_table_args, reversed(parts)),
    196            raise_on_missing=raise_on_missing,
    197        )

class MappingSchema(AbstractMappingSchema, Schema): View Source

    200class MappingSchema(AbstractMappingSchema, Schema):
    201    """
    202    Schema based on a nested mapping.
    203
    204    Args:
    205        schema: Mapping in one of the following forms:
    206            1. {table: {col: type}}
    207            2. {db: {table: {col: type}}}
    208            3. {catalog: {db: {table: {col: type}}}}
    209            4. None - Tables will be added later
    210        visible: Optional mapping of which columns in the schema are visible. If not provided, all columns
    211            are assumed to be visible. The nesting should mirror that of the schema:
    212            1. {table: set(*cols)}}
    213            2. {db: {table: set(*cols)}}}
    214            3. {catalog: {db: {table: set(*cols)}}}}
    215        dialect: The dialect to be used for custom type mappings & parsing string arguments.
    216        normalize: Whether to normalize identifier names according to the given dialect or not.
    217    """
    218
    219    def __init__(
    220        self,
    221        schema: t.Optional[t.Dict] = None,
    222        visible: t.Optional[t.Dict] = None,
    223        dialect: DialectType = None,
    224        normalize: bool = True,
    225    ) -> None:
    226        self.dialect = dialect
    227        self.visible = {} if visible is None else visible
    228        self.normalize = normalize
    229        self._type_mapping_cache: t.Dict[str, exp.DataType] = {}
    230        self._depth = 0
    231        schema = {} if schema is None else schema
    232
    233        super().__init__(self._normalize(schema) if self.normalize else schema)
    234
    235    @classmethod
    236    def from_mapping_schema(cls, mapping_schema: MappingSchema) -> MappingSchema:
    237        return MappingSchema(
    238            schema=mapping_schema.mapping,
    239            visible=mapping_schema.visible,
    240            dialect=mapping_schema.dialect,
    241            normalize=mapping_schema.normalize,
    242        )
    243
    244    def copy(self, **kwargs) -> MappingSchema:
    245        return MappingSchema(
    246            **{  # type: ignore
    247                "schema": self.mapping.copy(),
    248                "visible": self.visible.copy(),
    249                "dialect": self.dialect,
    250                "normalize": self.normalize,
    251                **kwargs,
    252            }
    253        )
    254
    255    def add_table(
    256        self,
    257        table: exp.Table | str,
    258        column_mapping: t.Optional[ColumnMapping] = None,
    259        dialect: DialectType = None,
    260        normalize: t.Optional[bool] = None,
    261        match_depth: bool = True,
    262    ) -> None:
    263        """
    264        Register or update a table. Updates are only performed if a new column mapping is provided.
    265        The added table must have the necessary number of qualifiers in its path to match the schema's nesting level.
    266
    267        Args:
    268            table: the `Table` expression instance or string representing the table.
    269            column_mapping: a column mapping that describes the structure of the table.
    270            dialect: the SQL dialect that will be used to parse `table` if it's a string.
    271            normalize: whether to normalize identifiers according to the dialect of interest.
    272            match_depth: whether to enforce that the table must match the schema's depth or not.
    273        """
    274        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    275
    276        if match_depth and not self.empty and len(normalized_table.parts) != self.depth():
    277            raise SchemaError(
    278                f"Table {normalized_table.sql(dialect=self.dialect)} must match the "
    279                f"schema's nesting level: {self.depth()}."
    280            )
    281
    282        normalized_column_mapping = {
    283            self._normalize_name(key, dialect=dialect, normalize=normalize): value
    284            for key, value in ensure_column_mapping(column_mapping).items()
    285        }
    286
    287        schema = self.find(normalized_table, raise_on_missing=False)
    288        if schema and not normalized_column_mapping:
    289            return
    290
    291        parts = self.table_parts(normalized_table)
    292
    293        nested_set(self.mapping, tuple(reversed(parts)), normalized_column_mapping)
    294        new_trie([parts], self.mapping_trie)
    295
    296    def column_names(
    297        self,
    298        table: exp.Table | str,
    299        only_visible: bool = False,
    300        dialect: DialectType = None,
    301        normalize: t.Optional[bool] = None,
    302    ) -> t.List[str]:
    303        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    304
    305        schema = self.find(normalized_table)
    306        if schema is None:
    307            return []
    308
    309        if not only_visible or not self.visible:
    310            return list(schema)
    311
    312        visible = self.nested_get(self.table_parts(normalized_table), self.visible) or []
    313        return [col for col in schema if col in visible]
    314
    315    def get_column_type(
    316        self,
    317        table: exp.Table | str,
    318        column: exp.Column | str,
    319        dialect: DialectType = None,
    320        normalize: t.Optional[bool] = None,
    321    ) -> exp.DataType:
    322        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    323
    324        normalized_column_name = self._normalize_name(
    325            column if isinstance(column, str) else column.this, dialect=dialect, normalize=normalize
    326        )
    327
    328        table_schema = self.find(normalized_table, raise_on_missing=False)
    329        if table_schema:
    330            column_type = table_schema.get(normalized_column_name)
    331
    332            if isinstance(column_type, exp.DataType):
    333                return column_type
    334            elif isinstance(column_type, str):
    335                return self._to_data_type(column_type, dialect=dialect)
    336
    337        return exp.DataType.build("unknown")
    338
    339    def has_column(
    340        self,
    341        table: exp.Table | str,
    342        column: exp.Column | str,
    343        dialect: DialectType = None,
    344        normalize: t.Optional[bool] = None,
    345    ) -> bool:
    346        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    347
    348        normalized_column_name = self._normalize_name(
    349            column if isinstance(column, str) else column.this, dialect=dialect, normalize=normalize
    350        )
    351
    352        table_schema = self.find(normalized_table, raise_on_missing=False)
    353        return normalized_column_name in table_schema if table_schema else False
    354
    355    def _normalize(self, schema: t.Dict) -> t.Dict:
    356        """
    357        Normalizes all identifiers in the schema.
    358
    359        Args:
    360            schema: the schema to normalize.
    361
    362        Returns:
    363            The normalized schema mapping.
    364        """
    365        normalized_mapping: t.Dict = {}
    366        flattened_schema = flatten_schema(schema, depth=dict_depth(schema) - 1)
    367
    368        for keys in flattened_schema:
    369            columns = nested_get(schema, *zip(keys, keys))
    370
    371            if not isinstance(columns, dict):
    372                raise SchemaError(
    373                    f"Table {'.'.join(keys[:-1])} must match the schema's nesting level: {len(flattened_schema[0])}."
    374                )
    375
    376            normalized_keys = [self._normalize_name(key, is_table=True) for key in keys]
    377            for column_name, column_type in columns.items():
    378                nested_set(
    379                    normalized_mapping,
    380                    normalized_keys + [self._normalize_name(column_name)],
    381                    column_type,
    382                )
    383
    384        return normalized_mapping
    385
    386    def _normalize_table(
    387        self,
    388        table: exp.Table | str,
    389        dialect: DialectType = None,
    390        normalize: t.Optional[bool] = None,
    391    ) -> exp.Table:
    392        dialect = dialect or self.dialect
    393        normalize = self.normalize if normalize is None else normalize
    394
    395        normalized_table = exp.maybe_parse(table, into=exp.Table, dialect=dialect, copy=normalize)
    396
    397        if normalize:
    398            for arg in exp.TABLE_PARTS:
    399                value = normalized_table.args.get(arg)
    400                if isinstance(value, exp.Identifier):
    401                    normalized_table.set(
    402                        arg,
    403                        normalize_name(value, dialect=dialect, is_table=True, normalize=normalize),
    404                    )
    405
    406        return normalized_table
    407
    408    def _normalize_name(
    409        self,
    410        name: str | exp.Identifier,
    411        dialect: DialectType = None,
    412        is_table: bool = False,
    413        normalize: t.Optional[bool] = None,
    414    ) -> str:
    415        return normalize_name(
    416            name,
    417            dialect=dialect or self.dialect,
    418            is_table=is_table,
    419            normalize=self.normalize if normalize is None else normalize,
    420        ).name
    421
    422    def depth(self) -> int:
    423        if not self.empty and not self._depth:
    424            # The columns themselves are a mapping, but we don't want to include those
    425            self._depth = super().depth() - 1
    426        return self._depth
    427
    428    def _to_data_type(self, schema_type: str, dialect: DialectType = None) -> exp.DataType:
    429        """
    430        Convert a type represented as a string to the corresponding `sqlglot.exp.DataType` object.
    431
    432        Args:
    433            schema_type: the type we want to convert.
    434            dialect: the SQL dialect that will be used to parse `schema_type`, if needed.
    435
    436        Returns:
    437            The resulting expression type.
    438        """
    439        if schema_type not in self._type_mapping_cache:
    440            dialect = dialect or self.dialect
    441            udt = Dialect.get_or_raise(dialect).SUPPORTS_USER_DEFINED_TYPES
    442
    443            try:
    444                expression = exp.DataType.build(schema_type, dialect=dialect, udt=udt)
    445                self._type_mapping_cache[schema_type] = expression
    446            except AttributeError:
    447                in_dialect = f" in dialect {dialect}" if dialect else ""
    448                raise SchemaError(f"Failed to build type '{schema_type}'{in_dialect}.")
    449
    450        return self._type_mapping_cache[schema_type]

Schema based on a nested mapping.

###### Arguments:

- schema: Mapping in one of the following forms:
  1. {table: {col: type}}
  2. {db: {table: {col: type}}}
  3. {catalog: {db: {table: {col: type}}}}
  4. 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:
  1. {table: set(cols)}}
  2. {db: {table: set(cols)}}}
  3. {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.

MappingSchema( schema: Optional[Dict] = None, visible: Optional[Dict] = None, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, normalize: bool = True) View Source

    219    def __init__(
    220        self,
    221        schema: t.Optional[t.Dict] = None,
    222        visible: t.Optional[t.Dict] = None,
    223        dialect: DialectType = None,
    224        normalize: bool = True,
    225    ) -> None:
    226        self.dialect = dialect
    227        self.visible = {} if visible is None else visible
    228        self.normalize = normalize
    229        self._type_mapping_cache: t.Dict[str, exp.DataType] = {}
    230        self._depth = 0
    231        schema = {} if schema is None else schema
    232
    233        super().__init__(self._normalize(schema) if self.normalize else schema)

dialect
visible
normalize
@classmethod
def from_mapping_schema( cls, mapping_schema: MappingSchema) -> MappingSchema: View Source

    235    @classmethod
    236    def from_mapping_schema(cls, mapping_schema: MappingSchema) -> MappingSchema:
    237        return MappingSchema(
    238            schema=mapping_schema.mapping,
    239            visible=mapping_schema.visible,
    240            dialect=mapping_schema.dialect,
    241            normalize=mapping_schema.normalize,
    242        )

def copy(self, **kwargs) -> MappingSchema: View Source

    244    def copy(self, **kwargs) -> MappingSchema:
    245        return MappingSchema(
    246            **{  # type: ignore
    247                "schema": self.mapping.copy(),
    248                "visible": self.visible.copy(),
    249                "dialect": self.dialect,
    250                "normalize": self.normalize,
    251                **kwargs,
    252            }
    253        )

def add_table( self, table: sqlglot.expressions.Table | str, column_mapping: Union[Dict, str, sqlglot.dataframe.sql.types.StructType, List, NoneType] = None, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, normalize: Optional[bool] = None, match_depth: bool = True) -> None: View Source

    255    def add_table(
    256        self,
    257        table: exp.Table | str,
    258        column_mapping: t.Optional[ColumnMapping] = None,
    259        dialect: DialectType = None,
    260        normalize: t.Optional[bool] = None,
    261        match_depth: bool = True,
    262    ) -> None:
    263        """
    264        Register or update a table. Updates are only performed if a new column mapping is provided.
    265        The added table must have the necessary number of qualifiers in its path to match the schema's nesting level.
    266
    267        Args:
    268            table: the `Table` expression instance or string representing the table.
    269            column_mapping: a column mapping that describes the structure of the table.
    270            dialect: the SQL dialect that will be used to parse `table` if it's a string.
    271            normalize: whether to normalize identifiers according to the dialect of interest.
    272            match_depth: whether to enforce that the table must match the schema's depth or not.
    273        """
    274        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    275
    276        if match_depth and not self.empty and len(normalized_table.parts) != self.depth():
    277            raise SchemaError(
    278                f"Table {normalized_table.sql(dialect=self.dialect)} must match the "
    279                f"schema's nesting level: {self.depth()}."
    280            )
    281
    282        normalized_column_mapping = {
    283            self._normalize_name(key, dialect=dialect, normalize=normalize): value
    284            for key, value in ensure_column_mapping(column_mapping).items()
    285        }
    286
    287        schema = self.find(normalized_table, raise_on_missing=False)
    288        if schema and not normalized_column_mapping:
    289            return
    290
    291        parts = self.table_parts(normalized_table)
    292
    293        nested_set(self.mapping, tuple(reversed(parts)), normalized_column_mapping)
    294        new_trie([parts], self.mapping_trie)

Register or update a table. Updates are only performed if a new column mapping is provided. The added table must have the necessary number of qualifiers in its path to match the schema's nesting level.

###### 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.
- normalize: whether to normalize identifiers according to the dialect of interest.
- match_depth: whether to enforce that the table must match the schema's depth or not.

def column_names( self, table: sqlglot.expressions.Table | str, only_visible: bool = False, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, normalize: Optional[bool] = None) -> List[str]: View Source

    296    def column_names(
    297        self,
    298        table: exp.Table | str,
    299        only_visible: bool = False,
    300        dialect: DialectType = None,
    301        normalize: t.Optional[bool] = None,
    302    ) -> t.List[str]:
    303        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    304
    305        schema = self.find(normalized_table)
    306        if schema is None:
    307            return []
    308
    309        if not only_visible or not self.visible:
    310            return list(schema)
    311
    312        visible = self.nested_get(self.table_parts(normalized_table), self.visible) or []
    313        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.
- normalize: whether to normalize identifiers according to the dialect of interest.

###### Returns:

> The sequence of column names.

def get_column_type( self, table: sqlglot.expressions.Table | str, column: sqlglot.expressions.Column | str, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, normalize: Optional[bool] = None) -> sqlglot.expressions.DataType: View Source

    315    def get_column_type(
    316        self,
    317        table: exp.Table | str,
    318        column: exp.Column | str,
    319        dialect: DialectType = None,
    320        normalize: t.Optional[bool] = None,
    321    ) -> exp.DataType:
    322        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    323
    324        normalized_column_name = self._normalize_name(
    325            column if isinstance(column, str) else column.this, dialect=dialect, normalize=normalize
    326        )
    327
    328        table_schema = self.find(normalized_table, raise_on_missing=False)
    329        if table_schema:
    330            column_type = table_schema.get(normalized_column_name)
    331
    332            if isinstance(column_type, exp.DataType):
    333                return column_type
    334            elif isinstance(column_type, str):
    335                return self._to_data_type(column_type, dialect=dialect)
    336
    337        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.
- normalize: whether to normalize identifiers according to the dialect of interest.

###### Returns:

> The resulting column type.

def has_column( self, table: sqlglot.expressions.Table | str, column: sqlglot.expressions.Column | str, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, normalize: Optional[bool] = None) -> bool: View Source

    339    def has_column(
    340        self,
    341        table: exp.Table | str,
    342        column: exp.Column | str,
    343        dialect: DialectType = None,
    344        normalize: t.Optional[bool] = None,
    345    ) -> bool:
    346        normalized_table = self._normalize_table(table, dialect=dialect, normalize=normalize)
    347
    348        normalized_column_name = self._normalize_name(
    349            column if isinstance(column, str) else column.this, dialect=dialect, normalize=normalize
    350        )
    351
    352        table_schema = self.find(normalized_table, raise_on_missing=False)
    353        return normalized_column_name in table_schema if table_schema else False

Returns whether or not `column` appears in `table`'s 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.
- normalize: whether to normalize identifiers according to the dialect of interest.

###### Returns:

> True if the column appears in the schema, False otherwise.

def depth(self) -> int: View Source

    422    def depth(self) -> int:
    423        if not self.empty and not self._depth:
    424            # The columns themselves are a mapping, but we don't want to include those
    425            self._depth = super().depth() - 1
    426        return self._depth

##### Inherited Members

AbstractMappingSchema
  mapping
  mapping_trie
  empty
  supported_table_args
  table_parts
  find
  nested_get

def normalize_name( identifier: str | sqlglot.expressions.Identifier, dialect: Union[str, sqlglot.dialects.dialect.Dialect, Type[sqlglot.dialects.dialect.Dialect], NoneType] = None, is_table: bool = False, normalize: Optional[bool] = True) -> sqlglot.expressions.Identifier: View Source

    453def normalize_name(
    454    identifier: str | exp.Identifier,
    455    dialect: DialectType = None,
    456    is_table: bool = False,
    457    normalize: t.Optional[bool] = True,
    458) -> exp.Identifier:
    459    if isinstance(identifier, str):
    460        identifier = exp.parse_identifier(identifier, dialect=dialect)
    461
    462    if not normalize:
    463        return identifier
    464
    465    # this is used for normalize_identifier, bigquery has special rules pertaining tables
    466    identifier.meta["is_table"] = is_table
    467    return Dialect.get_or_raise(dialect).normalize_identifier(identifier)

def ensure_schema( schema: Union[Schema, Dict, NoneType], **kwargs: Any) -> Schema: View Source

    470def ensure_schema(schema: Schema | t.Optional[t.Dict], **kwargs: t.Any) -> Schema:
    471    if isinstance(schema, Schema):
    472        return schema
    473
    474    return MappingSchema(schema, **kwargs)

def ensure_column_mapping( mapping: Union[Dict, str, sqlglot.dataframe.sql.types.StructType, List, NoneType]) -> Dict: View Source

    477def ensure_column_mapping(mapping: t.Optional[ColumnMapping]) -> t.Dict:
    478    if mapping is None:
    479        return {}
    480    elif isinstance(mapping, dict):
    481        return mapping
    482    elif isinstance(mapping, str):
    483        col_name_type_strs = [x.strip() for x in mapping.split(",")]
    484        return {
    485            name_type_str.split(":")[0].strip(): name_type_str.split(":")[1].strip()
    486            for name_type_str in col_name_type_strs
    487        }
    488    # Check if mapping looks like a DataFrame StructType
    489    elif hasattr(mapping, "simpleString"):
    490        return {struct_field.name: struct_field.dataType.simpleString() for struct_field in mapping}
    491    elif isinstance(mapping, list):
    492        return {x.strip(): None for x in mapping}
    493
    494    raise ValueError(f"Invalid mapping provided: {type(mapping)}")

def flatten_schema( schema: Dict, depth: int, keys: Optional[List[str]] = None) -> List[List[str]]: View Source

    497def flatten_schema(
    498    schema: t.Dict, depth: int, keys: t.Optional[t.List[str]] = None
    499) -> t.List[t.List[str]]:
    500    tables = []
    501    keys = keys or []
    502
    503    for k, v in schema.items():
    504        if depth >= 2:
    505            tables.extend(flatten_schema(v, depth - 1, keys + [k]))
    506        elif depth == 1:
    507            tables.append(keys + [k])
    508
    509    return tables

def nested_get( d: Dict, *path: Tuple[str, str], raise_on_missing: bool = True) -> Optional[Any]: View Source

    512def nested_get(
    513    d: t.Dict, *path: t.Tuple[str, str], raise_on_missing: bool = True
    514) -> t.Optional[t.Any]:
    515    """
    516    Get a value for a nested dictionary.
    517
    518    Args:
    519        d: the dictionary to search.
    520        *path: tuples of (name, key), where:
    521            `key` is the key in the dictionary to get.
    522            `name` is a string to use in the error if `key` isn't found.
    523
    524    Returns:
    525        The value or None if it doesn't exist.
    526    """
    527    for name, key in path:
    528        d = d.get(key)  # type: ignore
    529        if d is None:
    530            if raise_on_missing:
    531                name = "table" if name == "this" else name
    532                raise ValueError(f"Unknown {name}: {key}")
    533            return None
    534
    535    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 if `key` isn't found.

###### Returns:

> The value or None if it doesn't exist.

def nested_set(d: Dict, keys: Sequence[str], value: Any) -> Dict: View Source

    538def nested_set(d: t.Dict, keys: t.Sequence[str], value: t.Any) -> t.Dict:
    539    """
    540    In-place set a value for a nested dictionary
    541
    542    Example:
    543        >>> nested_set({}, ["top_key", "second_key"], "value")
    544        {'top_key': {'second_key': 'value'}}
    545
    546        >>> nested_set({"top_key": {"third_key": "third_value"}}, ["top_key", "second_key"], "value")
    547        {'top_key': {'third_key': 'third_value', 'second_key': 'value'}}
    548
    549    Args:
    550        d: dictionary to update.
    551        keys: the keys that makeup the path to `value`.
    552        value: the value to set in the dictionary for the given key path.
    553
    554    Returns:
    555        The (possibly) updated dictionary.
    556    """
    557    if not keys:
    558        return d
    559
    560    if len(keys) == 1:
    561        d[keys[0]] = value
    562        return d
    563
    564    subd = d
    565    for key in keys[:-1]:
    566        if key not in subd:
    567            subd = subd.setdefault(key, {})
    568        else:
    569            subd = subd[key]
    570
    571    subd[keys[-1]] = value
    572    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.