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# sqlglot.dataframe.sql

View Source

     1from sqlglot.dataframe.sql.column import Column
     2from sqlglot.dataframe.sql.dataframe import DataFrame, DataFrameNaFunctions
     3from sqlglot.dataframe.sql.group import GroupedData
     4from sqlglot.dataframe.sql.readwriter import DataFrameReader, DataFrameWriter
     5from sqlglot.dataframe.sql.session import SparkSession
     6from sqlglot.dataframe.sql.window import Window, WindowSpec
     7
     8__all__ = [
     9    "SparkSession",
    10    "DataFrame",
    11    "GroupedData",
    12    "Column",
    13    "DataFrameNaFunctions",
    14    "Window",
    15    "WindowSpec",
    16    "DataFrameReader",
    17    "DataFrameWriter",
    18]

class SparkSession: View Source

     20class SparkSession:
     21    known_ids: t.ClassVar[t.Set[str]] = set()
     22    known_branch_ids: t.ClassVar[t.Set[str]] = set()
     23    known_sequence_ids: t.ClassVar[t.Set[str]] = set()
     24    name_to_sequence_id_mapping: t.ClassVar[t.Dict[str, t.List[str]]] = defaultdict(list)
     25
     26    def __init__(self):
     27        self.incrementing_id = 1
     28
     29    def __getattr__(self, name: str) -> SparkSession:
     30        return self
     31
     32    def __call__(self, *args, **kwargs) -> SparkSession:
     33        return self
     34
     35    @property
     36    def read(self) -> DataFrameReader:
     37        return DataFrameReader(self)
     38
     39    def table(self, tableName: str) -> DataFrame:
     40        return self.read.table(tableName)
     41
     42    def createDataFrame(
     43        self,
     44        data: t.Sequence[t.Union[t.Dict[str, ColumnLiterals], t.List[ColumnLiterals], t.Tuple]],
     45        schema: t.Optional[SchemaInput] = None,
     46        samplingRatio: t.Optional[float] = None,
     47        verifySchema: bool = False,
     48    ) -> DataFrame:
     49        from sqlglot.dataframe.sql.dataframe import DataFrame
     50
     51        if samplingRatio is not None or verifySchema:
     52            raise NotImplementedError("Sampling Ratio and Verify Schema are not supported")
     53        if schema is not None and (
     54            not isinstance(schema, (StructType, str, list))
     55            or (isinstance(schema, list) and not isinstance(schema[0], str))
     56        ):
     57            raise NotImplementedError("Only schema of either list or string of list supported")
     58        if not data:
     59            raise ValueError("Must provide data to create into a DataFrame")
     60
     61        column_mapping: t.Dict[str, t.Optional[str]]
     62        if schema is not None:
     63            column_mapping = get_column_mapping_from_schema_input(schema)
     64        elif isinstance(data[0], dict):
     65            column_mapping = {col_name.strip(): None for col_name in data[0]}
     66        else:
     67            column_mapping = {f"_{i}": None for i in range(1, len(data[0]) + 1)}
     68
     69        data_expressions = [
     70            exp.Tuple(
     71                expressions=list(
     72                    map(
     73                        lambda x: F.lit(x).expression,
     74                        row if not isinstance(row, dict) else row.values(),
     75                    )
     76                )
     77            )
     78            for row in data
     79        ]
     80
     81        sel_columns = [
     82            F.col(name).cast(data_type).alias(name).expression
     83            if data_type is not None
     84            else F.col(name).expression
     85            for name, data_type in column_mapping.items()
     86        ]
     87
     88        select_kwargs = {
     89            "expressions": sel_columns,
     90            "from": exp.From(
     91                this=exp.Values(
     92                    expressions=data_expressions,
     93                    alias=exp.TableAlias(
     94                        this=exp.to_identifier(self._auto_incrementing_name),
     95                        columns=[exp.to_identifier(col_name) for col_name in column_mapping],
     96                    ),
     97                ),
     98            ),
     99        }
    100
    101        sel_expression = exp.Select(**select_kwargs)
    102        return DataFrame(self, sel_expression)
    103
    104    def sql(self, sqlQuery: str) -> DataFrame:
    105        expression = sqlglot.parse_one(sqlQuery, read="spark")
    106        if isinstance(expression, exp.Select):
    107            df = DataFrame(self, expression)
    108            df = df._convert_leaf_to_cte()
    109        elif isinstance(expression, (exp.Create, exp.Insert)):
    110            select_expression = expression.expression.copy()
    111            if isinstance(expression, exp.Insert):
    112                select_expression.set("with", expression.args.get("with"))
    113                expression.set("with", None)
    114            del expression.args["expression"]
    115            df = DataFrame(self, select_expression, output_expression_container=expression)  # type: ignore
    116            df = df._convert_leaf_to_cte()
    117        else:
    118            raise ValueError(
    119                "Unknown expression type provided in the SQL. Please create an issue with the SQL."
    120            )
    121        return df
    122
    123    @property
    124    def _auto_incrementing_name(self) -> str:
    125        name = f"a{self.incrementing_id}"
    126        self.incrementing_id += 1
    127        return name
    128
    129    @property
    130    def _random_branch_id(self) -> str:
    131        id = self._random_id
    132        self.known_branch_ids.add(id)
    133        return id
    134
    135    @property
    136    def _random_sequence_id(self):
    137        id = self._random_id
    138        self.known_sequence_ids.add(id)
    139        return id
    140
    141    @property
    142    def _random_id(self) -> str:
    143        id = "r" + uuid.uuid4().hex
    144        self.known_ids.add(id)
    145        return id
    146
    147    @property
    148    def _join_hint_names(self) -> t.Set[str]:
    149        return {"BROADCAST", "MERGE", "SHUFFLE_HASH", "SHUFFLE_REPLICATE_NL"}
    150
    151    def _add_alias_to_mapping(self, name: str, sequence_id: str):
    152        self.name_to_sequence_id_mapping[name].append(sequence_id)

known_ids: ClassVar[Set[str]] = set()
known_branch_ids: ClassVar[Set[str]] = set()
known_sequence_ids: ClassVar[Set[str]] = set()
name_to_sequence_id_mapping: ClassVar[Dict[str, List[str]]] = defaultdict(<class 'list'>, {})
incrementing_id
read: sqlglot.dataframe.sql.DataFrameReader
def table(self, tableName: str) -> sqlglot.dataframe.sql.DataFrame: View Source

    39    def table(self, tableName: str) -> DataFrame:
    40        return self.read.table(tableName)

def createDataFrame( self, data: Sequence[Union[Dict[str, <MagicMock id='140430583414944'>], List[<MagicMock id='140430583414944'>], Tuple]], schema: Optional[<MagicMock id='140430585007696'>] = None, samplingRatio: Optional[float] = None, verifySchema: bool = False) -> sqlglot.dataframe.sql.DataFrame: View Source

     42    def createDataFrame(
     43        self,
     44        data: t.Sequence[t.Union[t.Dict[str, ColumnLiterals], t.List[ColumnLiterals], t.Tuple]],
     45        schema: t.Optional[SchemaInput] = None,
     46        samplingRatio: t.Optional[float] = None,
     47        verifySchema: bool = False,
     48    ) -> DataFrame:
     49        from sqlglot.dataframe.sql.dataframe import DataFrame
     50
     51        if samplingRatio is not None or verifySchema:
     52            raise NotImplementedError("Sampling Ratio and Verify Schema are not supported")
     53        if schema is not None and (
     54            not isinstance(schema, (StructType, str, list))
     55            or (isinstance(schema, list) and not isinstance(schema[0], str))
     56        ):
     57            raise NotImplementedError("Only schema of either list or string of list supported")
     58        if not data:
     59            raise ValueError("Must provide data to create into a DataFrame")
     60
     61        column_mapping: t.Dict[str, t.Optional[str]]
     62        if schema is not None:
     63            column_mapping = get_column_mapping_from_schema_input(schema)
     64        elif isinstance(data[0], dict):
     65            column_mapping = {col_name.strip(): None for col_name in data[0]}
     66        else:
     67            column_mapping = {f"_{i}": None for i in range(1, len(data[0]) + 1)}
     68
     69        data_expressions = [
     70            exp.Tuple(
     71                expressions=list(
     72                    map(
     73                        lambda x: F.lit(x).expression,
     74                        row if not isinstance(row, dict) else row.values(),
     75                    )
     76                )
     77            )
     78            for row in data
     79        ]
     80
     81        sel_columns = [
     82            F.col(name).cast(data_type).alias(name).expression
     83            if data_type is not None
     84            else F.col(name).expression
     85            for name, data_type in column_mapping.items()
     86        ]
     87
     88        select_kwargs = {
     89            "expressions": sel_columns,
     90            "from": exp.From(
     91                this=exp.Values(
     92                    expressions=data_expressions,
     93                    alias=exp.TableAlias(
     94                        this=exp.to_identifier(self._auto_incrementing_name),
     95                        columns=[exp.to_identifier(col_name) for col_name in column_mapping],
     96                    ),
     97                ),
     98            ),
     99        }
    100
    101        sel_expression = exp.Select(**select_kwargs)
    102        return DataFrame(self, sel_expression)

def sql(self, sqlQuery: str) -> sqlglot.dataframe.sql.DataFrame: View Source

    104    def sql(self, sqlQuery: str) -> DataFrame:
    105        expression = sqlglot.parse_one(sqlQuery, read="spark")
    106        if isinstance(expression, exp.Select):
    107            df = DataFrame(self, expression)
    108            df = df._convert_leaf_to_cte()
    109        elif isinstance(expression, (exp.Create, exp.Insert)):
    110            select_expression = expression.expression.copy()
    111            if isinstance(expression, exp.Insert):
    112                select_expression.set("with", expression.args.get("with"))
    113                expression.set("with", None)
    114            del expression.args["expression"]
    115            df = DataFrame(self, select_expression, output_expression_container=expression)  # type: ignore
    116            df = df._convert_leaf_to_cte()
    117        else:
    118            raise ValueError(
    119                "Unknown expression type provided in the SQL. Please create an issue with the SQL."
    120            )
    121        return df

class DataFrame: View Source

     45class DataFrame:
     46    def __init__(
     47        self,
     48        spark: SparkSession,
     49        expression: exp.Select,
     50        branch_id: t.Optional[str] = None,
     51        sequence_id: t.Optional[str] = None,
     52        last_op: Operation = Operation.INIT,
     53        pending_hints: t.Optional[t.List[exp.Expression]] = None,
     54        output_expression_container: t.Optional[OutputExpressionContainer] = None,
     55        **kwargs,
     56    ):
     57        self.spark = spark
     58        self.expression = expression
     59        self.branch_id = branch_id or self.spark._random_branch_id
     60        self.sequence_id = sequence_id or self.spark._random_sequence_id
     61        self.last_op = last_op
     62        self.pending_hints = pending_hints or []
     63        self.output_expression_container = output_expression_container or exp.Select()
     64
     65    def __getattr__(self, column_name: str) -> Column:
     66        return self[column_name]
     67
     68    def __getitem__(self, column_name: str) -> Column:
     69        column_name = f"{self.branch_id}.{column_name}"
     70        return Column(column_name)
     71
     72    def __copy__(self):
     73        return self.copy()
     74
     75    @property
     76    def sparkSession(self):
     77        return self.spark
     78
     79    @property
     80    def write(self):
     81        return DataFrameWriter(self)
     82
     83    @property
     84    def latest_cte_name(self) -> str:
     85        if not self.expression.ctes:
     86            from_exp = self.expression.args["from"]
     87            if from_exp.alias_or_name:
     88                return from_exp.alias_or_name
     89            table_alias = from_exp.find(exp.TableAlias)
     90            if not table_alias:
     91                raise RuntimeError(
     92                    f"Could not find an alias name for this expression: {self.expression}"
     93                )
     94            return table_alias.alias_or_name
     95        return self.expression.ctes[-1].alias
     96
     97    @property
     98    def pending_join_hints(self):
     99        return [hint for hint in self.pending_hints if isinstance(hint, exp.JoinHint)]
    100
    101    @property
    102    def pending_partition_hints(self):
    103        return [hint for hint in self.pending_hints if isinstance(hint, exp.Anonymous)]
    104
    105    @property
    106    def columns(self) -> t.List[str]:
    107        return self.expression.named_selects
    108
    109    @property
    110    def na(self) -> DataFrameNaFunctions:
    111        return DataFrameNaFunctions(self)
    112
    113    def _replace_cte_names_with_hashes(self, expression: exp.Select):
    114        replacement_mapping = {}
    115        for cte in expression.ctes:
    116            old_name_id = cte.args["alias"].this
    117            new_hashed_id = exp.to_identifier(
    118                self._create_hash_from_expression(cte.this), quoted=old_name_id.args["quoted"]
    119            )
    120            replacement_mapping[old_name_id] = new_hashed_id
    121            expression = expression.transform(replace_id_value, replacement_mapping)
    122        return expression
    123
    124    def _create_cte_from_expression(
    125        self,
    126        expression: exp.Expression,
    127        branch_id: t.Optional[str] = None,
    128        sequence_id: t.Optional[str] = None,
    129        **kwargs,
    130    ) -> t.Tuple[exp.CTE, str]:
    131        name = self._create_hash_from_expression(expression)
    132        expression_to_cte = expression.copy()
    133        expression_to_cte.set("with", None)
    134        cte = exp.Select().with_(name, as_=expression_to_cte, **kwargs).ctes[0]
    135        cte.set("branch_id", branch_id or self.branch_id)
    136        cte.set("sequence_id", sequence_id or self.sequence_id)
    137        return cte, name
    138
    139    @t.overload
    140    def _ensure_list_of_columns(self, cols: t.Collection[ColumnOrLiteral]) -> t.List[Column]:
    141        ...
    142
    143    @t.overload
    144    def _ensure_list_of_columns(self, cols: ColumnOrLiteral) -> t.List[Column]:
    145        ...
    146
    147    def _ensure_list_of_columns(self, cols):
    148        return Column.ensure_cols(ensure_list(cols))
    149
    150    def _ensure_and_normalize_cols(self, cols, expression: t.Optional[exp.Select] = None):
    151        cols = self._ensure_list_of_columns(cols)
    152        normalize(self.spark, expression or self.expression, cols)
    153        return cols
    154
    155    def _ensure_and_normalize_col(self, col):
    156        col = Column.ensure_col(col)
    157        normalize(self.spark, self.expression, col)
    158        return col
    159
    160    def _convert_leaf_to_cte(self, sequence_id: t.Optional[str] = None) -> DataFrame:
    161        df = self._resolve_pending_hints()
    162        sequence_id = sequence_id or df.sequence_id
    163        expression = df.expression.copy()
    164        cte_expression, cte_name = df._create_cte_from_expression(
    165            expression=expression, sequence_id=sequence_id
    166        )
    167        new_expression = df._add_ctes_to_expression(
    168            exp.Select(), expression.ctes + [cte_expression]
    169        )
    170        sel_columns = df._get_outer_select_columns(cte_expression)
    171        new_expression = new_expression.from_(cte_name).select(
    172            *[x.alias_or_name for x in sel_columns]
    173        )
    174        return df.copy(expression=new_expression, sequence_id=sequence_id)
    175
    176    def _resolve_pending_hints(self) -> DataFrame:
    177        df = self.copy()
    178        if not self.pending_hints:
    179            return df
    180        expression = df.expression
    181        hint_expression = expression.args.get("hint") or exp.Hint(expressions=[])
    182        for hint in df.pending_partition_hints:
    183            hint_expression.append("expressions", hint)
    184            df.pending_hints.remove(hint)
    185
    186        join_aliases = {
    187            join_table.alias_or_name
    188            for join_table in get_tables_from_expression_with_join(expression)
    189        }
    190        if join_aliases:
    191            for hint in df.pending_join_hints:
    192                for sequence_id_expression in hint.expressions:
    193                    sequence_id_or_name = sequence_id_expression.alias_or_name
    194                    sequence_ids_to_match = [sequence_id_or_name]
    195                    if sequence_id_or_name in df.spark.name_to_sequence_id_mapping:
    196                        sequence_ids_to_match = df.spark.name_to_sequence_id_mapping[
    197                            sequence_id_or_name
    198                        ]
    199                    matching_ctes = [
    200                        cte
    201                        for cte in reversed(expression.ctes)
    202                        if cte.args["sequence_id"] in sequence_ids_to_match
    203                    ]
    204                    for matching_cte in matching_ctes:
    205                        if matching_cte.alias_or_name in join_aliases:
    206                            sequence_id_expression.set("this", matching_cte.args["alias"].this)
    207                            df.pending_hints.remove(hint)
    208                            break
    209                hint_expression.append("expressions", hint)
    210        if hint_expression.expressions:
    211            expression.set("hint", hint_expression)
    212        return df
    213
    214    def _hint(self, hint_name: str, args: t.List[Column]) -> DataFrame:
    215        hint_name = hint_name.upper()
    216        hint_expression = (
    217            exp.JoinHint(
    218                this=hint_name,
    219                expressions=[exp.to_table(parameter.alias_or_name) for parameter in args],
    220            )
    221            if hint_name in JOIN_HINTS
    222            else exp.Anonymous(
    223                this=hint_name, expressions=[parameter.expression for parameter in args]
    224            )
    225        )
    226        new_df = self.copy()
    227        new_df.pending_hints.append(hint_expression)
    228        return new_df
    229
    230    def _set_operation(self, klass: t.Callable, other: DataFrame, distinct: bool):
    231        other_df = other._convert_leaf_to_cte()
    232        base_expression = self.expression.copy()
    233        base_expression = self._add_ctes_to_expression(base_expression, other_df.expression.ctes)
    234        all_ctes = base_expression.ctes
    235        other_df.expression.set("with", None)
    236        base_expression.set("with", None)
    237        operation = klass(this=base_expression, distinct=distinct, expression=other_df.expression)
    238        operation.set("with", exp.With(expressions=all_ctes))
    239        return self.copy(expression=operation)._convert_leaf_to_cte()
    240
    241    def _cache(self, storage_level: str):
    242        df = self._convert_leaf_to_cte()
    243        df.expression.ctes[-1].set("cache_storage_level", storage_level)
    244        return df
    245
    246    @classmethod
    247    def _add_ctes_to_expression(cls, expression: exp.Select, ctes: t.List[exp.CTE]) -> exp.Select:
    248        expression = expression.copy()
    249        with_expression = expression.args.get("with")
    250        if with_expression:
    251            existing_ctes = with_expression.expressions
    252            existsing_cte_names = {x.alias_or_name for x in existing_ctes}
    253            for cte in ctes:
    254                if cte.alias_or_name not in existsing_cte_names:
    255                    existing_ctes.append(cte)
    256        else:
    257            existing_ctes = ctes
    258        expression.set("with", exp.With(expressions=existing_ctes))
    259        return expression
    260
    261    @classmethod
    262    def _get_outer_select_columns(cls, item: t.Union[exp.Expression, DataFrame]) -> t.List[Column]:
    263        expression = item.expression if isinstance(item, DataFrame) else item
    264        return [Column(x) for x in (expression.find(exp.Select) or exp.Select()).expressions]
    265
    266    @classmethod
    267    def _create_hash_from_expression(cls, expression: exp.Expression) -> str:
    268        value = expression.sql(dialect="spark").encode("utf-8")
    269        return f"t{zlib.crc32(value)}"[:6]
    270
    271    def _get_select_expressions(
    272        self,
    273    ) -> t.List[t.Tuple[t.Union[t.Type[exp.Cache], OutputExpressionContainer], exp.Select]]:
    274        select_expressions: t.List[
    275            t.Tuple[t.Union[t.Type[exp.Cache], OutputExpressionContainer], exp.Select]
    276        ] = []
    277        main_select_ctes: t.List[exp.CTE] = []
    278        for cte in self.expression.ctes:
    279            cache_storage_level = cte.args.get("cache_storage_level")
    280            if cache_storage_level:
    281                select_expression = cte.this.copy()
    282                select_expression.set("with", exp.With(expressions=copy(main_select_ctes)))
    283                select_expression.set("cte_alias_name", cte.alias_or_name)
    284                select_expression.set("cache_storage_level", cache_storage_level)
    285                select_expressions.append((exp.Cache, select_expression))
    286            else:
    287                main_select_ctes.append(cte)
    288        main_select = self.expression.copy()
    289        if main_select_ctes:
    290            main_select.set("with", exp.With(expressions=main_select_ctes))
    291        expression_select_pair = (type(self.output_expression_container), main_select)
    292        select_expressions.append(expression_select_pair)  # type: ignore
    293        return select_expressions
    294
    295    def sql(self, dialect="spark", optimize=True, **kwargs) -> t.List[str]:
    296        df = self._resolve_pending_hints()
    297        select_expressions = df._get_select_expressions()
    298        output_expressions: t.List[t.Union[exp.Select, exp.Cache, exp.Drop]] = []
    299        replacement_mapping: t.Dict[exp.Identifier, exp.Identifier] = {}
    300        for expression_type, select_expression in select_expressions:
    301            select_expression = select_expression.transform(replace_id_value, replacement_mapping)
    302            if optimize:
    303                select_expression = t.cast(exp.Select, optimize_func(select_expression))
    304            select_expression = df._replace_cte_names_with_hashes(select_expression)
    305            expression: t.Union[exp.Select, exp.Cache, exp.Drop]
    306            if expression_type == exp.Cache:
    307                cache_table_name = df._create_hash_from_expression(select_expression)
    308                cache_table = exp.to_table(cache_table_name)
    309                original_alias_name = select_expression.args["cte_alias_name"]
    310
    311                replacement_mapping[exp.to_identifier(original_alias_name)] = exp.to_identifier(  # type: ignore
    312                    cache_table_name
    313                )
    314                sqlglot.schema.add_table(
    315                    cache_table_name,
    316                    {
    317                        expression.alias_or_name: expression.type.sql("spark")
    318                        for expression in select_expression.expressions
    319                    },
    320                    dialect="spark",
    321                )
    322                cache_storage_level = select_expression.args["cache_storage_level"]
    323                options = [
    324                    exp.Literal.string("storageLevel"),
    325                    exp.Literal.string(cache_storage_level),
    326                ]
    327                expression = exp.Cache(
    328                    this=cache_table, expression=select_expression, lazy=True, options=options
    329                )
    330                # We will drop the "view" if it exists before running the cache table
    331                output_expressions.append(exp.Drop(this=cache_table, exists=True, kind="VIEW"))
    332            elif expression_type == exp.Create:
    333                expression = df.output_expression_container.copy()
    334                expression.set("expression", select_expression)
    335            elif expression_type == exp.Insert:
    336                expression = df.output_expression_container.copy()
    337                select_without_ctes = select_expression.copy()
    338                select_without_ctes.set("with", None)
    339                expression.set("expression", select_without_ctes)
    340                if select_expression.ctes:
    341                    expression.set("with", exp.With(expressions=select_expression.ctes))
    342            elif expression_type == exp.Select:
    343                expression = select_expression
    344            else:
    345                raise ValueError(f"Invalid expression type: {expression_type}")
    346            output_expressions.append(expression)
    347
    348        return [
    349            expression.sql(**{"dialect": dialect, **kwargs}) for expression in output_expressions
    350        ]
    351
    352    def copy(self, **kwargs) -> DataFrame:
    353        return DataFrame(**object_to_dict(self, **kwargs))
    354
    355    @operation(Operation.SELECT)
    356    def select(self, *cols, **kwargs) -> DataFrame:
    357        cols = self._ensure_and_normalize_cols(cols)
    358        kwargs["append"] = kwargs.get("append", False)
    359        if self.expression.args.get("joins"):
    360            ambiguous_cols = [
    361                col
    362                for col in cols
    363                if isinstance(col.column_expression, exp.Column) and not col.column_expression.table
    364            ]
    365            if ambiguous_cols:
    366                join_table_identifiers = [
    367                    x.this for x in get_tables_from_expression_with_join(self.expression)
    368                ]
    369                cte_names_in_join = [x.this for x in join_table_identifiers]
    370                # If we have columns that resolve to multiple CTE expressions then we want to use each CTE left-to-right
    371                # and therefore we allow multiple columns with the same name in the result. This matches the behavior
    372                # of Spark.
    373                resolved_column_position: t.Dict[Column, int] = {col: -1 for col in ambiguous_cols}
    374                for ambiguous_col in ambiguous_cols:
    375                    ctes_with_column = [
    376                        cte
    377                        for cte in self.expression.ctes
    378                        if cte.alias_or_name in cte_names_in_join
    379                        and ambiguous_col.alias_or_name in cte.this.named_selects
    380                    ]
    381                    # Check if there is a CTE with this column that we haven't used before. If so, use it. Otherwise,
    382                    # use the same CTE we used before
    383                    cte = seq_get(ctes_with_column, resolved_column_position[ambiguous_col] + 1)
    384                    if cte:
    385                        resolved_column_position[ambiguous_col] += 1
    386                    else:
    387                        cte = ctes_with_column[resolved_column_position[ambiguous_col]]
    388                    ambiguous_col.expression.set("table", cte.alias_or_name)
    389        return self.copy(
    390            expression=self.expression.select(*[x.expression for x in cols], **kwargs), **kwargs
    391        )
    392
    393    @operation(Operation.NO_OP)
    394    def alias(self, name: str, **kwargs) -> DataFrame:
    395        new_sequence_id = self.spark._random_sequence_id
    396        df = self.copy()
    397        for join_hint in df.pending_join_hints:
    398            for expression in join_hint.expressions:
    399                if expression.alias_or_name == self.sequence_id:
    400                    expression.set("this", Column.ensure_col(new_sequence_id).expression)
    401        df.spark._add_alias_to_mapping(name, new_sequence_id)
    402        return df._convert_leaf_to_cte(sequence_id=new_sequence_id)
    403
    404    @operation(Operation.WHERE)
    405    def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
    406        col = self._ensure_and_normalize_col(column)
    407        return self.copy(expression=self.expression.where(col.expression))
    408
    409    filter = where
    410
    411    @operation(Operation.GROUP_BY)
    412    def groupBy(self, *cols, **kwargs) -> GroupedData:
    413        columns = self._ensure_and_normalize_cols(cols)
    414        return GroupedData(self, columns, self.last_op)
    415
    416    @operation(Operation.SELECT)
    417    def agg(self, *exprs, **kwargs) -> DataFrame:
    418        cols = self._ensure_and_normalize_cols(exprs)
    419        return self.groupBy().agg(*cols)
    420
    421    @operation(Operation.FROM)
    422    def join(
    423        self,
    424        other_df: DataFrame,
    425        on: t.Union[str, t.List[str], Column, t.List[Column]],
    426        how: str = "inner",
    427        **kwargs,
    428    ) -> DataFrame:
    429        other_df = other_df._convert_leaf_to_cte()
    430        join_columns = self._ensure_list_of_columns(on)
    431        # We will determine actual "join on" expression later so we don't provide it at first
    432        join_expression = self.expression.join(
    433            other_df.latest_cte_name, join_type=how.replace("_", " ")
    434        )
    435        join_expression = self._add_ctes_to_expression(join_expression, other_df.expression.ctes)
    436        self_columns = self._get_outer_select_columns(join_expression)
    437        other_columns = self._get_outer_select_columns(other_df)
    438        # Determines the join clause and select columns to be used passed on what type of columns were provided for
    439        # the join. The columns returned changes based on how the on expression is provided.
    440        if isinstance(join_columns[0].expression, exp.Column):
    441            """
    442            Unique characteristics of join on column names only:
    443            * The column names are put at the front of the select list
    444            * The column names are deduplicated across the entire select list and only the column names (other dups are allowed)
    445            """
    446            table_names = [
    447                table.alias_or_name
    448                for table in get_tables_from_expression_with_join(join_expression)
    449            ]
    450            potential_ctes = [
    451                cte
    452                for cte in join_expression.ctes
    453                if cte.alias_or_name in table_names
    454                and cte.alias_or_name != other_df.latest_cte_name
    455            ]
    456            # Determine the table to reference for the left side of the join by checking each of the left side
    457            # tables and see if they have the column being referenced.
    458            join_column_pairs = []
    459            for join_column in join_columns:
    460                num_matching_ctes = 0
    461                for cte in potential_ctes:
    462                    if join_column.alias_or_name in cte.this.named_selects:
    463                        left_column = join_column.copy().set_table_name(cte.alias_or_name)
    464                        right_column = join_column.copy().set_table_name(other_df.latest_cte_name)
    465                        join_column_pairs.append((left_column, right_column))
    466                        num_matching_ctes += 1
    467                if num_matching_ctes > 1:
    468                    raise ValueError(
    469                        f"Column {join_column.alias_or_name} is ambiguous. Please specify the table name."
    470                    )
    471                elif num_matching_ctes == 0:
    472                    raise ValueError(
    473                        f"Column {join_column.alias_or_name} does not exist in any of the tables."
    474                    )
    475            join_clause = functools.reduce(
    476                lambda x, y: x & y,
    477                [left_column == right_column for left_column, right_column in join_column_pairs],
    478            )
    479            join_column_names = [left_col.alias_or_name for left_col, _ in join_column_pairs]
    480            # To match spark behavior only the join clause gets deduplicated and it gets put in the front of the column list
    481            select_column_names = [
    482                column.alias_or_name
    483                if not isinstance(column.expression.this, exp.Star)
    484                else column.sql()
    485                for column in self_columns + other_columns
    486            ]
    487            select_column_names = [
    488                column_name
    489                for column_name in select_column_names
    490                if column_name not in join_column_names
    491            ]
    492            select_column_names = join_column_names + select_column_names
    493        else:
    494            """
    495            Unique characteristics of join on expressions:
    496            * There is no deduplication of the results.
    497            * The left join dataframe columns go first and right come after. No sort preference is given to join columns
    498            """
    499            join_columns = self._ensure_and_normalize_cols(join_columns, join_expression)
    500            if len(join_columns) > 1:
    501                join_columns = [functools.reduce(lambda x, y: x & y, join_columns)]
    502            join_clause = join_columns[0]
    503            select_column_names = [column.alias_or_name for column in self_columns + other_columns]
    504
    505        # Update the on expression with the actual join clause to replace the dummy one from before
    506        join_expression.args["joins"][-1].set("on", join_clause.expression)
    507        new_df = self.copy(expression=join_expression)
    508        new_df.pending_join_hints.extend(self.pending_join_hints)
    509        new_df.pending_hints.extend(other_df.pending_hints)
    510        new_df = new_df.select.__wrapped__(new_df, *select_column_names)
    511        return new_df
    512
    513    @operation(Operation.ORDER_BY)
    514    def orderBy(
    515        self,
    516        *cols: t.Union[str, Column],
    517        ascending: t.Optional[t.Union[t.Any, t.List[t.Any]]] = None,
    518    ) -> DataFrame:
    519        """
    520        This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark
    521        has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this
    522        is unlikely to come up.
    523        """
    524        columns = self._ensure_and_normalize_cols(cols)
    525        pre_ordered_col_indexes = [
    526            x
    527            for x in [
    528                i if isinstance(col.expression, exp.Ordered) else None
    529                for i, col in enumerate(columns)
    530            ]
    531            if x is not None
    532        ]
    533        if ascending is None:
    534            ascending = [True] * len(columns)
    535        elif not isinstance(ascending, list):
    536            ascending = [ascending] * len(columns)
    537        ascending = [bool(x) for i, x in enumerate(ascending)]
    538        assert len(columns) == len(
    539            ascending
    540        ), "The length of items in ascending must equal the number of columns provided"
    541        col_and_ascending = list(zip(columns, ascending))
    542        order_by_columns = [
    543            exp.Ordered(this=col.expression, desc=not asc)
    544            if i not in pre_ordered_col_indexes
    545            else columns[i].column_expression
    546            for i, (col, asc) in enumerate(col_and_ascending)
    547        ]
    548        return self.copy(expression=self.expression.order_by(*order_by_columns))
    549
    550    sort = orderBy
    551
    552    @operation(Operation.FROM)
    553    def union(self, other: DataFrame) -> DataFrame:
    554        return self._set_operation(exp.Union, other, False)
    555
    556    unionAll = union
    557
    558    @operation(Operation.FROM)
    559    def unionByName(self, other: DataFrame, allowMissingColumns: bool = False):
    560        l_columns = self.columns
    561        r_columns = other.columns
    562        if not allowMissingColumns:
    563            l_expressions = l_columns
    564            r_expressions = l_columns
    565        else:
    566            l_expressions = []
    567            r_expressions = []
    568            r_columns_unused = copy(r_columns)
    569            for l_column in l_columns:
    570                l_expressions.append(l_column)
    571                if l_column in r_columns:
    572                    r_expressions.append(l_column)
    573                    r_columns_unused.remove(l_column)
    574                else:
    575                    r_expressions.append(exp.alias_(exp.Null(), l_column, copy=False))
    576            for r_column in r_columns_unused:
    577                l_expressions.append(exp.alias_(exp.Null(), r_column, copy=False))
    578                r_expressions.append(r_column)
    579        r_df = (
    580            other.copy()._convert_leaf_to_cte().select(*self._ensure_list_of_columns(r_expressions))
    581        )
    582        l_df = self.copy()
    583        if allowMissingColumns:
    584            l_df = l_df._convert_leaf_to_cte().select(*self._ensure_list_of_columns(l_expressions))
    585        return l_df._set_operation(exp.Union, r_df, False)
    586
    587    @operation(Operation.FROM)
    588    def intersect(self, other: DataFrame) -> DataFrame:
    589        return self._set_operation(exp.Intersect, other, True)
    590
    591    @operation(Operation.FROM)
    592    def intersectAll(self, other: DataFrame) -> DataFrame:
    593        return self._set_operation(exp.Intersect, other, False)
    594
    595    @operation(Operation.FROM)
    596    def exceptAll(self, other: DataFrame) -> DataFrame:
    597        return self._set_operation(exp.Except, other, False)
    598
    599    @operation(Operation.SELECT)
    600    def distinct(self) -> DataFrame:
    601        return self.copy(expression=self.expression.distinct())
    602
    603    @operation(Operation.SELECT)
    604    def dropDuplicates(self, subset: t.Optional[t.List[str]] = None):
    605        if not subset:
    606            return self.distinct()
    607        column_names = ensure_list(subset)
    608        window = Window.partitionBy(*column_names).orderBy(*column_names)
    609        return (
    610            self.copy()
    611            .withColumn("row_num", F.row_number().over(window))
    612            .where(F.col("row_num") == F.lit(1))
    613            .drop("row_num")
    614        )
    615
    616    @operation(Operation.FROM)
    617    def dropna(
    618        self,
    619        how: str = "any",
    620        thresh: t.Optional[int] = None,
    621        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    622    ) -> DataFrame:
    623        minimum_non_null = thresh or 0  # will be determined later if thresh is null
    624        new_df = self.copy()
    625        all_columns = self._get_outer_select_columns(new_df.expression)
    626        if subset:
    627            null_check_columns = self._ensure_and_normalize_cols(subset)
    628        else:
    629            null_check_columns = all_columns
    630        if thresh is None:
    631            minimum_num_nulls = 1 if how == "any" else len(null_check_columns)
    632        else:
    633            minimum_num_nulls = len(null_check_columns) - minimum_non_null + 1
    634        if minimum_num_nulls > len(null_check_columns):
    635            raise RuntimeError(
    636                f"The minimum num nulls for dropna must be less than or equal to the number of columns. "
    637                f"Minimum num nulls: {minimum_num_nulls}, Num Columns: {len(null_check_columns)}"
    638            )
    639        if_null_checks = [
    640            F.when(column.isNull(), F.lit(1)).otherwise(F.lit(0)) for column in null_check_columns
    641        ]
    642        nulls_added_together = functools.reduce(lambda x, y: x + y, if_null_checks)
    643        num_nulls = nulls_added_together.alias("num_nulls")
    644        new_df = new_df.select(num_nulls, append=True)
    645        filtered_df = new_df.where(F.col("num_nulls") < F.lit(minimum_num_nulls))
    646        final_df = filtered_df.select(*all_columns)
    647        return final_df
    648
    649    @operation(Operation.FROM)
    650    def fillna(
    651        self,
    652        value: t.Union[ColumnLiterals],
    653        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    654    ) -> DataFrame:
    655        """
    656        Functionality Difference: If you provide a value to replace a null and that type conflicts
    657        with the type of the column then PySpark will just ignore your replacement.
    658        This will try to cast them to be the same in some cases. So they won't always match.
    659        Best to not mix types so make sure replacement is the same type as the column
    660
    661        Possibility for improvement: Use `typeof` function to get the type of the column
    662        and check if it matches the type of the value provided. If not then make it null.
    663        """
    664        from sqlglot.dataframe.sql.functions import lit
    665
    666        values = None
    667        columns = None
    668        new_df = self.copy()
    669        all_columns = self._get_outer_select_columns(new_df.expression)
    670        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    671        if isinstance(value, dict):
    672            values = list(value.values())
    673            columns = self._ensure_and_normalize_cols(list(value))
    674        if not columns:
    675            columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    676        if not values:
    677            values = [value] * len(columns)
    678        value_columns = [lit(value) for value in values]
    679
    680        null_replacement_mapping = {
    681            column.alias_or_name: (
    682                F.when(column.isNull(), value).otherwise(column).alias(column.alias_or_name)
    683            )
    684            for column, value in zip(columns, value_columns)
    685        }
    686        null_replacement_mapping = {**all_column_mapping, **null_replacement_mapping}
    687        null_replacement_columns = [
    688            null_replacement_mapping[column.alias_or_name] for column in all_columns
    689        ]
    690        new_df = new_df.select(*null_replacement_columns)
    691        return new_df
    692
    693    @operation(Operation.FROM)
    694    def replace(
    695        self,
    696        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    697        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    698        subset: t.Optional[t.Collection[ColumnOrName] | ColumnOrName] = None,
    699    ) -> DataFrame:
    700        from sqlglot.dataframe.sql.functions import lit
    701
    702        old_values = None
    703        new_df = self.copy()
    704        all_columns = self._get_outer_select_columns(new_df.expression)
    705        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    706
    707        columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    708        if isinstance(to_replace, dict):
    709            old_values = list(to_replace)
    710            new_values = list(to_replace.values())
    711        elif not old_values and isinstance(to_replace, list):
    712            assert isinstance(value, list), "value must be a list since the replacements are a list"
    713            assert len(to_replace) == len(
    714                value
    715            ), "the replacements and values must be the same length"
    716            old_values = to_replace
    717            new_values = value
    718        else:
    719            old_values = [to_replace] * len(columns)
    720            new_values = [value] * len(columns)
    721        old_values = [lit(value) for value in old_values]
    722        new_values = [lit(value) for value in new_values]
    723
    724        replacement_mapping = {}
    725        for column in columns:
    726            expression = Column(None)
    727            for i, (old_value, new_value) in enumerate(zip(old_values, new_values)):
    728                if i == 0:
    729                    expression = F.when(column == old_value, new_value)
    730                else:
    731                    expression = expression.when(column == old_value, new_value)  # type: ignore
    732            replacement_mapping[column.alias_or_name] = expression.otherwise(column).alias(
    733                column.expression.alias_or_name
    734            )
    735
    736        replacement_mapping = {**all_column_mapping, **replacement_mapping}
    737        replacement_columns = [replacement_mapping[column.alias_or_name] for column in all_columns]
    738        new_df = new_df.select(*replacement_columns)
    739        return new_df
    740
    741    @operation(Operation.SELECT)
    742    def withColumn(self, colName: str, col: Column) -> DataFrame:
    743        col = self._ensure_and_normalize_col(col)
    744        existing_col_names = self.expression.named_selects
    745        existing_col_index = (
    746            existing_col_names.index(colName) if colName in existing_col_names else None
    747        )
    748        if existing_col_index:
    749            expression = self.expression.copy()
    750            expression.expressions[existing_col_index] = col.expression
    751            return self.copy(expression=expression)
    752        return self.copy().select(col.alias(colName), append=True)
    753
    754    @operation(Operation.SELECT)
    755    def withColumnRenamed(self, existing: str, new: str):
    756        expression = self.expression.copy()
    757        existing_columns = [
    758            expression
    759            for expression in expression.expressions
    760            if expression.alias_or_name == existing
    761        ]
    762        if not existing_columns:
    763            raise ValueError("Tried to rename a column that doesn't exist")
    764        for existing_column in existing_columns:
    765            if isinstance(existing_column, exp.Column):
    766                existing_column.replace(exp.alias_(existing_column, new))
    767            else:
    768                existing_column.set("alias", exp.to_identifier(new))
    769        return self.copy(expression=expression)
    770
    771    @operation(Operation.SELECT)
    772    def drop(self, *cols: t.Union[str, Column]) -> DataFrame:
    773        all_columns = self._get_outer_select_columns(self.expression)
    774        drop_cols = self._ensure_and_normalize_cols(cols)
    775        new_columns = [
    776            col
    777            for col in all_columns
    778            if col.alias_or_name not in [drop_column.alias_or_name for drop_column in drop_cols]
    779        ]
    780        return self.copy().select(*new_columns, append=False)
    781
    782    @operation(Operation.LIMIT)
    783    def limit(self, num: int) -> DataFrame:
    784        return self.copy(expression=self.expression.limit(num))
    785
    786    @operation(Operation.NO_OP)
    787    def hint(self, name: str, *parameters: t.Optional[t.Union[str, int]]) -> DataFrame:
    788        parameter_list = ensure_list(parameters)
    789        parameter_columns = (
    790            self._ensure_list_of_columns(parameter_list)
    791            if parameters
    792            else Column.ensure_cols([self.sequence_id])
    793        )
    794        return self._hint(name, parameter_columns)
    795
    796    @operation(Operation.NO_OP)
    797    def repartition(
    798        self, numPartitions: t.Union[int, ColumnOrName], *cols: ColumnOrName
    799    ) -> DataFrame:
    800        num_partition_cols = self._ensure_list_of_columns(numPartitions)
    801        columns = self._ensure_and_normalize_cols(cols)
    802        args = num_partition_cols + columns
    803        return self._hint("repartition", args)
    804
    805    @operation(Operation.NO_OP)
    806    def coalesce(self, numPartitions: int) -> DataFrame:
    807        num_partitions = Column.ensure_cols([numPartitions])
    808        return self._hint("coalesce", num_partitions)
    809
    810    @operation(Operation.NO_OP)
    811    def cache(self) -> DataFrame:
    812        return self._cache(storage_level="MEMORY_AND_DISK")
    813
    814    @operation(Operation.NO_OP)
    815    def persist(self, storageLevel: str = "MEMORY_AND_DISK_SER") -> DataFrame:
    816        """
    817        Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html
    818        """
    819        return self._cache(storageLevel)

DataFrame( spark: <MagicMock id='140430588014224'>, expression: sqlglot.expressions.Select, branch_id: Optional[str] = None, sequence_id: Optional[str] = None, last_op: sqlglot.dataframe.sql.operations.Operation = <Operation.INIT: -1>, pending_hints: Optional[List[sqlglot.expressions.Expression]] = None, output_expression_container: Optional[<MagicMock id='140430588117776'>] = None, **kwargs) View Source

    46    def __init__(
    47        self,
    48        spark: SparkSession,
    49        expression: exp.Select,
    50        branch_id: t.Optional[str] = None,
    51        sequence_id: t.Optional[str] = None,
    52        last_op: Operation = Operation.INIT,
    53        pending_hints: t.Optional[t.List[exp.Expression]] = None,
    54        output_expression_container: t.Optional[OutputExpressionContainer] = None,
    55        **kwargs,
    56    ):
    57        self.spark = spark
    58        self.expression = expression
    59        self.branch_id = branch_id or self.spark._random_branch_id
    60        self.sequence_id = sequence_id or self.spark._random_sequence_id
    61        self.last_op = last_op
    62        self.pending_hints = pending_hints or []
    63        self.output_expression_container = output_expression_container or exp.Select()

spark
expression
branch_id
sequence_id
last_op
pending_hints
output_expression_container
sparkSession
write
latest_cte_name: str
pending_join_hints
pending_partition_hints
columns: List[str]
na: sqlglot.dataframe.sql.DataFrameNaFunctions
def sql(self, dialect='spark', optimize=True, **kwargs) -> List[str]: View Source

    295    def sql(self, dialect="spark", optimize=True, **kwargs) -> t.List[str]:
    296        df = self._resolve_pending_hints()
    297        select_expressions = df._get_select_expressions()
    298        output_expressions: t.List[t.Union[exp.Select, exp.Cache, exp.Drop]] = []
    299        replacement_mapping: t.Dict[exp.Identifier, exp.Identifier] = {}
    300        for expression_type, select_expression in select_expressions:
    301            select_expression = select_expression.transform(replace_id_value, replacement_mapping)
    302            if optimize:
    303                select_expression = t.cast(exp.Select, optimize_func(select_expression))
    304            select_expression = df._replace_cte_names_with_hashes(select_expression)
    305            expression: t.Union[exp.Select, exp.Cache, exp.Drop]
    306            if expression_type == exp.Cache:
    307                cache_table_name = df._create_hash_from_expression(select_expression)
    308                cache_table = exp.to_table(cache_table_name)
    309                original_alias_name = select_expression.args["cte_alias_name"]
    310
    311                replacement_mapping[exp.to_identifier(original_alias_name)] = exp.to_identifier(  # type: ignore
    312                    cache_table_name
    313                )
    314                sqlglot.schema.add_table(
    315                    cache_table_name,
    316                    {
    317                        expression.alias_or_name: expression.type.sql("spark")
    318                        for expression in select_expression.expressions
    319                    },
    320                    dialect="spark",
    321                )
    322                cache_storage_level = select_expression.args["cache_storage_level"]
    323                options = [
    324                    exp.Literal.string("storageLevel"),
    325                    exp.Literal.string(cache_storage_level),
    326                ]
    327                expression = exp.Cache(
    328                    this=cache_table, expression=select_expression, lazy=True, options=options
    329                )
    330                # We will drop the "view" if it exists before running the cache table
    331                output_expressions.append(exp.Drop(this=cache_table, exists=True, kind="VIEW"))
    332            elif expression_type == exp.Create:
    333                expression = df.output_expression_container.copy()
    334                expression.set("expression", select_expression)
    335            elif expression_type == exp.Insert:
    336                expression = df.output_expression_container.copy()
    337                select_without_ctes = select_expression.copy()
    338                select_without_ctes.set("with", None)
    339                expression.set("expression", select_without_ctes)
    340                if select_expression.ctes:
    341                    expression.set("with", exp.With(expressions=select_expression.ctes))
    342            elif expression_type == exp.Select:
    343                expression = select_expression
    344            else:
    345                raise ValueError(f"Invalid expression type: {expression_type}")
    346            output_expressions.append(expression)
    347
    348        return [
    349            expression.sql(**{"dialect": dialect, **kwargs}) for expression in output_expressions
    350        ]

def copy(self, **kwargs) -> sqlglot.dataframe.sql.DataFrame: View Source

    352    def copy(self, **kwargs) -> DataFrame:
    353        return DataFrame(**object_to_dict(self, **kwargs))

@operation(Operation.SELECT)
def select(self, *cols, **kwargs) -> sqlglot.dataframe.sql.DataFrame: View Source

    355    @operation(Operation.SELECT)
    356    def select(self, *cols, **kwargs) -> DataFrame:
    357        cols = self._ensure_and_normalize_cols(cols)
    358        kwargs["append"] = kwargs.get("append", False)
    359        if self.expression.args.get("joins"):
    360            ambiguous_cols = [
    361                col
    362                for col in cols
    363                if isinstance(col.column_expression, exp.Column) and not col.column_expression.table
    364            ]
    365            if ambiguous_cols:
    366                join_table_identifiers = [
    367                    x.this for x in get_tables_from_expression_with_join(self.expression)
    368                ]
    369                cte_names_in_join = [x.this for x in join_table_identifiers]
    370                # If we have columns that resolve to multiple CTE expressions then we want to use each CTE left-to-right
    371                # and therefore we allow multiple columns with the same name in the result. This matches the behavior
    372                # of Spark.
    373                resolved_column_position: t.Dict[Column, int] = {col: -1 for col in ambiguous_cols}
    374                for ambiguous_col in ambiguous_cols:
    375                    ctes_with_column = [
    376                        cte
    377                        for cte in self.expression.ctes
    378                        if cte.alias_or_name in cte_names_in_join
    379                        and ambiguous_col.alias_or_name in cte.this.named_selects
    380                    ]
    381                    # Check if there is a CTE with this column that we haven't used before. If so, use it. Otherwise,
    382                    # use the same CTE we used before
    383                    cte = seq_get(ctes_with_column, resolved_column_position[ambiguous_col] + 1)
    384                    if cte:
    385                        resolved_column_position[ambiguous_col] += 1
    386                    else:
    387                        cte = ctes_with_column[resolved_column_position[ambiguous_col]]
    388                    ambiguous_col.expression.set("table", cte.alias_or_name)
    389        return self.copy(
    390            expression=self.expression.select(*[x.expression for x in cols], **kwargs), **kwargs
    391        )

@operation(Operation.NO_OP)
def alias(self, name: str, **kwargs) -> sqlglot.dataframe.sql.DataFrame: View Source

    393    @operation(Operation.NO_OP)
    394    def alias(self, name: str, **kwargs) -> DataFrame:
    395        new_sequence_id = self.spark._random_sequence_id
    396        df = self.copy()
    397        for join_hint in df.pending_join_hints:
    398            for expression in join_hint.expressions:
    399                if expression.alias_or_name == self.sequence_id:
    400                    expression.set("this", Column.ensure_col(new_sequence_id).expression)
    401        df.spark._add_alias_to_mapping(name, new_sequence_id)
    402        return df._convert_leaf_to_cte(sequence_id=new_sequence_id)

@operation(Operation.WHERE)
def where( self, column: Union[sqlglot.dataframe.sql.Column, bool], **kwargs) -> sqlglot.dataframe.sql.DataFrame: View Source

    404    @operation(Operation.WHERE)
    405    def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
    406        col = self._ensure_and_normalize_col(column)
    407        return self.copy(expression=self.expression.where(col.expression))

@operation(Operation.WHERE)
def filter( self, column: Union[sqlglot.dataframe.sql.Column, bool], **kwargs) -> sqlglot.dataframe.sql.DataFrame: View Source

    404    @operation(Operation.WHERE)
    405    def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
    406        col = self._ensure_and_normalize_col(column)
    407        return self.copy(expression=self.expression.where(col.expression))

@operation(Operation.GROUP_BY)
def groupBy(self, *cols, **kwargs) -> sqlglot.dataframe.sql.GroupedData: View Source

    411    @operation(Operation.GROUP_BY)
    412    def groupBy(self, *cols, **kwargs) -> GroupedData:
    413        columns = self._ensure_and_normalize_cols(cols)
    414        return GroupedData(self, columns, self.last_op)

@operation(Operation.SELECT)
def agg(self, *exprs, **kwargs) -> sqlglot.dataframe.sql.DataFrame: View Source

    416    @operation(Operation.SELECT)
    417    def agg(self, *exprs, **kwargs) -> DataFrame:
    418        cols = self._ensure_and_normalize_cols(exprs)
    419        return self.groupBy().agg(*cols)

@operation(Operation.FROM)
def join( self, other_df: sqlglot.dataframe.sql.DataFrame, on: Union[str, List[str], sqlglot.dataframe.sql.Column, List[sqlglot.dataframe.sql.Column]], how: str = 'inner', **kwargs) -> sqlglot.dataframe.sql.DataFrame: View Source

    421    @operation(Operation.FROM)
    422    def join(
    423        self,
    424        other_df: DataFrame,
    425        on: t.Union[str, t.List[str], Column, t.List[Column]],
    426        how: str = "inner",
    427        **kwargs,
    428    ) -> DataFrame:
    429        other_df = other_df._convert_leaf_to_cte()
    430        join_columns = self._ensure_list_of_columns(on)
    431        # We will determine actual "join on" expression later so we don't provide it at first
    432        join_expression = self.expression.join(
    433            other_df.latest_cte_name, join_type=how.replace("_", " ")
    434        )
    435        join_expression = self._add_ctes_to_expression(join_expression, other_df.expression.ctes)
    436        self_columns = self._get_outer_select_columns(join_expression)
    437        other_columns = self._get_outer_select_columns(other_df)
    438        # Determines the join clause and select columns to be used passed on what type of columns were provided for
    439        # the join. The columns returned changes based on how the on expression is provided.
    440        if isinstance(join_columns[0].expression, exp.Column):
    441            """
    442            Unique characteristics of join on column names only:
    443            * The column names are put at the front of the select list
    444            * The column names are deduplicated across the entire select list and only the column names (other dups are allowed)
    445            """
    446            table_names = [
    447                table.alias_or_name
    448                for table in get_tables_from_expression_with_join(join_expression)
    449            ]
    450            potential_ctes = [
    451                cte
    452                for cte in join_expression.ctes
    453                if cte.alias_or_name in table_names
    454                and cte.alias_or_name != other_df.latest_cte_name
    455            ]
    456            # Determine the table to reference for the left side of the join by checking each of the left side
    457            # tables and see if they have the column being referenced.
    458            join_column_pairs = []
    459            for join_column in join_columns:
    460                num_matching_ctes = 0
    461                for cte in potential_ctes:
    462                    if join_column.alias_or_name in cte.this.named_selects:
    463                        left_column = join_column.copy().set_table_name(cte.alias_or_name)
    464                        right_column = join_column.copy().set_table_name(other_df.latest_cte_name)
    465                        join_column_pairs.append((left_column, right_column))
    466                        num_matching_ctes += 1
    467                if num_matching_ctes > 1:
    468                    raise ValueError(
    469                        f"Column {join_column.alias_or_name} is ambiguous. Please specify the table name."
    470                    )
    471                elif num_matching_ctes == 0:
    472                    raise ValueError(
    473                        f"Column {join_column.alias_or_name} does not exist in any of the tables."
    474                    )
    475            join_clause = functools.reduce(
    476                lambda x, y: x & y,
    477                [left_column == right_column for left_column, right_column in join_column_pairs],
    478            )
    479            join_column_names = [left_col.alias_or_name for left_col, _ in join_column_pairs]
    480            # To match spark behavior only the join clause gets deduplicated and it gets put in the front of the column list
    481            select_column_names = [
    482                column.alias_or_name
    483                if not isinstance(column.expression.this, exp.Star)
    484                else column.sql()
    485                for column in self_columns + other_columns
    486            ]
    487            select_column_names = [
    488                column_name
    489                for column_name in select_column_names
    490                if column_name not in join_column_names
    491            ]
    492            select_column_names = join_column_names + select_column_names
    493        else:
    494            """
    495            Unique characteristics of join on expressions:
    496            * There is no deduplication of the results.
    497            * The left join dataframe columns go first and right come after. No sort preference is given to join columns
    498            """
    499            join_columns = self._ensure_and_normalize_cols(join_columns, join_expression)
    500            if len(join_columns) > 1:
    501                join_columns = [functools.reduce(lambda x, y: x & y, join_columns)]
    502            join_clause = join_columns[0]
    503            select_column_names = [column.alias_or_name for column in self_columns + other_columns]
    504
    505        # Update the on expression with the actual join clause to replace the dummy one from before
    506        join_expression.args["joins"][-1].set("on", join_clause.expression)
    507        new_df = self.copy(expression=join_expression)
    508        new_df.pending_join_hints.extend(self.pending_join_hints)
    509        new_df.pending_hints.extend(other_df.pending_hints)
    510        new_df = new_df.select.__wrapped__(new_df, *select_column_names)
    511        return new_df

@operation(Operation.ORDER_BY)
def orderBy( self, *cols: Union[str, sqlglot.dataframe.sql.Column], ascending: Union[Any, List[Any], NoneType] = None) -> sqlglot.dataframe.sql.DataFrame: View Source

    513    @operation(Operation.ORDER_BY)
    514    def orderBy(
    515        self,
    516        *cols: t.Union[str, Column],
    517        ascending: t.Optional[t.Union[t.Any, t.List[t.Any]]] = None,
    518    ) -> DataFrame:
    519        """
    520        This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark
    521        has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this
    522        is unlikely to come up.
    523        """
    524        columns = self._ensure_and_normalize_cols(cols)
    525        pre_ordered_col_indexes = [
    526            x
    527            for x in [
    528                i if isinstance(col.expression, exp.Ordered) else None
    529                for i, col in enumerate(columns)
    530            ]
    531            if x is not None
    532        ]
    533        if ascending is None:
    534            ascending = [True] * len(columns)
    535        elif not isinstance(ascending, list):
    536            ascending = [ascending] * len(columns)
    537        ascending = [bool(x) for i, x in enumerate(ascending)]
    538        assert len(columns) == len(
    539            ascending
    540        ), "The length of items in ascending must equal the number of columns provided"
    541        col_and_ascending = list(zip(columns, ascending))
    542        order_by_columns = [
    543            exp.Ordered(this=col.expression, desc=not asc)
    544            if i not in pre_ordered_col_indexes
    545            else columns[i].column_expression
    546            for i, (col, asc) in enumerate(col_and_ascending)
    547        ]
    548        return self.copy(expression=self.expression.order_by(*order_by_columns))

This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this is unlikely to come up.

@operation(Operation.ORDER_BY)
def sort( self, *cols: Union[str, sqlglot.dataframe.sql.Column], ascending: Union[Any, List[Any], NoneType] = None) -> sqlglot.dataframe.sql.DataFrame: View Source

    513    @operation(Operation.ORDER_BY)
    514    def orderBy(
    515        self,
    516        *cols: t.Union[str, Column],
    517        ascending: t.Optional[t.Union[t.Any, t.List[t.Any]]] = None,
    518    ) -> DataFrame:
    519        """
    520        This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark
    521        has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this
    522        is unlikely to come up.
    523        """
    524        columns = self._ensure_and_normalize_cols(cols)
    525        pre_ordered_col_indexes = [
    526            x
    527            for x in [
    528                i if isinstance(col.expression, exp.Ordered) else None
    529                for i, col in enumerate(columns)
    530            ]
    531            if x is not None
    532        ]
    533        if ascending is None:
    534            ascending = [True] * len(columns)
    535        elif not isinstance(ascending, list):
    536            ascending = [ascending] * len(columns)
    537        ascending = [bool(x) for i, x in enumerate(ascending)]
    538        assert len(columns) == len(
    539            ascending
    540        ), "The length of items in ascending must equal the number of columns provided"
    541        col_and_ascending = list(zip(columns, ascending))
    542        order_by_columns = [
    543            exp.Ordered(this=col.expression, desc=not asc)
    544            if i not in pre_ordered_col_indexes
    545            else columns[i].column_expression
    546            for i, (col, asc) in enumerate(col_and_ascending)
    547        ]
    548        return self.copy(expression=self.expression.order_by(*order_by_columns))

This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this is unlikely to come up.

@operation(Operation.FROM)
def union( self, other: sqlglot.dataframe.sql.DataFrame) -> sqlglot.dataframe.sql.DataFrame: View Source

    552    @operation(Operation.FROM)
    553    def union(self, other: DataFrame) -> DataFrame:
    554        return self._set_operation(exp.Union, other, False)

@operation(Operation.FROM)
def unionAll( self, other: sqlglot.dataframe.sql.DataFrame) -> sqlglot.dataframe.sql.DataFrame: View Source

    552    @operation(Operation.FROM)
    553    def union(self, other: DataFrame) -> DataFrame:
    554        return self._set_operation(exp.Union, other, False)

@operation(Operation.FROM)
def unionByName( self, other: sqlglot.dataframe.sql.DataFrame, allowMissingColumns: bool = False): View Source

    558    @operation(Operation.FROM)
    559    def unionByName(self, other: DataFrame, allowMissingColumns: bool = False):
    560        l_columns = self.columns
    561        r_columns = other.columns
    562        if not allowMissingColumns:
    563            l_expressions = l_columns
    564            r_expressions = l_columns
    565        else:
    566            l_expressions = []
    567            r_expressions = []
    568            r_columns_unused = copy(r_columns)
    569            for l_column in l_columns:
    570                l_expressions.append(l_column)
    571                if l_column in r_columns:
    572                    r_expressions.append(l_column)
    573                    r_columns_unused.remove(l_column)
    574                else:
    575                    r_expressions.append(exp.alias_(exp.Null(), l_column, copy=False))
    576            for r_column in r_columns_unused:
    577                l_expressions.append(exp.alias_(exp.Null(), r_column, copy=False))
    578                r_expressions.append(r_column)
    579        r_df = (
    580            other.copy()._convert_leaf_to_cte().select(*self._ensure_list_of_columns(r_expressions))
    581        )
    582        l_df = self.copy()
    583        if allowMissingColumns:
    584            l_df = l_df._convert_leaf_to_cte().select(*self._ensure_list_of_columns(l_expressions))
    585        return l_df._set_operation(exp.Union, r_df, False)

@operation(Operation.FROM)
def intersect( self, other: sqlglot.dataframe.sql.DataFrame) -> sqlglot.dataframe.sql.DataFrame: View Source

    587    @operation(Operation.FROM)
    588    def intersect(self, other: DataFrame) -> DataFrame:
    589        return self._set_operation(exp.Intersect, other, True)

@operation(Operation.FROM)
def intersectAll( self, other: sqlglot.dataframe.sql.DataFrame) -> sqlglot.dataframe.sql.DataFrame: View Source

    591    @operation(Operation.FROM)
    592    def intersectAll(self, other: DataFrame) -> DataFrame:
    593        return self._set_operation(exp.Intersect, other, False)

@operation(Operation.FROM)
def exceptAll( self, other: sqlglot.dataframe.sql.DataFrame) -> sqlglot.dataframe.sql.DataFrame: View Source

    595    @operation(Operation.FROM)
    596    def exceptAll(self, other: DataFrame) -> DataFrame:
    597        return self._set_operation(exp.Except, other, False)

@operation(Operation.SELECT)
def distinct(self) -> sqlglot.dataframe.sql.DataFrame: View Source

    599    @operation(Operation.SELECT)
    600    def distinct(self) -> DataFrame:
    601        return self.copy(expression=self.expression.distinct())

@operation(Operation.SELECT)
def dropDuplicates(self, subset: Optional[List[str]] = None): View Source

    603    @operation(Operation.SELECT)
    604    def dropDuplicates(self, subset: t.Optional[t.List[str]] = None):
    605        if not subset:
    606            return self.distinct()
    607        column_names = ensure_list(subset)
    608        window = Window.partitionBy(*column_names).orderBy(*column_names)
    609        return (
    610            self.copy()
    611            .withColumn("row_num", F.row_number().over(window))
    612            .where(F.col("row_num") == F.lit(1))
    613            .drop("row_num")
    614        )

@operation(Operation.FROM)
def dropna( self, how: str = 'any', thresh: Optional[int] = None, subset: Union[str, Tuple[str, ...], List[str], NoneType] = None) -> sqlglot.dataframe.sql.DataFrame: View Source

    616    @operation(Operation.FROM)
    617    def dropna(
    618        self,
    619        how: str = "any",
    620        thresh: t.Optional[int] = None,
    621        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    622    ) -> DataFrame:
    623        minimum_non_null = thresh or 0  # will be determined later if thresh is null
    624        new_df = self.copy()
    625        all_columns = self._get_outer_select_columns(new_df.expression)
    626        if subset:
    627            null_check_columns = self._ensure_and_normalize_cols(subset)
    628        else:
    629            null_check_columns = all_columns
    630        if thresh is None:
    631            minimum_num_nulls = 1 if how == "any" else len(null_check_columns)
    632        else:
    633            minimum_num_nulls = len(null_check_columns) - minimum_non_null + 1
    634        if minimum_num_nulls > len(null_check_columns):
    635            raise RuntimeError(
    636                f"The minimum num nulls for dropna must be less than or equal to the number of columns. "
    637                f"Minimum num nulls: {minimum_num_nulls}, Num Columns: {len(null_check_columns)}"
    638            )
    639        if_null_checks = [
    640            F.when(column.isNull(), F.lit(1)).otherwise(F.lit(0)) for column in null_check_columns
    641        ]
    642        nulls_added_together = functools.reduce(lambda x, y: x + y, if_null_checks)
    643        num_nulls = nulls_added_together.alias("num_nulls")
    644        new_df = new_df.select(num_nulls, append=True)
    645        filtered_df = new_df.where(F.col("num_nulls") < F.lit(minimum_num_nulls))
    646        final_df = filtered_df.select(*all_columns)
    647        return final_df

@operation(Operation.FROM)
def fillna( self, value: <MagicMock id='140430583371888'>, subset: Union[str, Tuple[str, ...], List[str], NoneType] = None) -> sqlglot.dataframe.sql.DataFrame: View Source

    649    @operation(Operation.FROM)
    650    def fillna(
    651        self,
    652        value: t.Union[ColumnLiterals],
    653        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    654    ) -> DataFrame:
    655        """
    656        Functionality Difference: If you provide a value to replace a null and that type conflicts
    657        with the type of the column then PySpark will just ignore your replacement.
    658        This will try to cast them to be the same in some cases. So they won't always match.
    659        Best to not mix types so make sure replacement is the same type as the column
    660
    661        Possibility for improvement: Use `typeof` function to get the type of the column
    662        and check if it matches the type of the value provided. If not then make it null.
    663        """
    664        from sqlglot.dataframe.sql.functions import lit
    665
    666        values = None
    667        columns = None
    668        new_df = self.copy()
    669        all_columns = self._get_outer_select_columns(new_df.expression)
    670        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    671        if isinstance(value, dict):
    672            values = list(value.values())
    673            columns = self._ensure_and_normalize_cols(list(value))
    674        if not columns:
    675            columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    676        if not values:
    677            values = [value] * len(columns)
    678        value_columns = [lit(value) for value in values]
    679
    680        null_replacement_mapping = {
    681            column.alias_or_name: (
    682                F.when(column.isNull(), value).otherwise(column).alias(column.alias_or_name)
    683            )
    684            for column, value in zip(columns, value_columns)
    685        }
    686        null_replacement_mapping = {**all_column_mapping, **null_replacement_mapping}
    687        null_replacement_columns = [
    688            null_replacement_mapping[column.alias_or_name] for column in all_columns
    689        ]
    690        new_df = new_df.select(*null_replacement_columns)
    691        return new_df

Functionality Difference: If you provide a value to replace a null and that type conflicts with the type of the column then PySpark will just ignore your replacement. This will try to cast them to be the same in some cases. So they won't always match. Best to not mix types so make sure replacement is the same type as the column

Possibility for improvement: Use `typeof` function to get the type of the column and check if it matches the type of the value provided. If not then make it null.

@operation(Operation.FROM)
def replace( self, to_replace: Union[bool, int, float, str, List, Dict], value: Union[bool, int, float, str, List, NoneType] = None, subset: Union[Collection[<MagicMock id='140430583980064'>], <MagicMock id='140430583980064'>, NoneType] = None) -> sqlglot.dataframe.sql.DataFrame: View Source

    693    @operation(Operation.FROM)
    694    def replace(
    695        self,
    696        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    697        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    698        subset: t.Optional[t.Collection[ColumnOrName] | ColumnOrName] = None,
    699    ) -> DataFrame:
    700        from sqlglot.dataframe.sql.functions import lit
    701
    702        old_values = None
    703        new_df = self.copy()
    704        all_columns = self._get_outer_select_columns(new_df.expression)
    705        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    706
    707        columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    708        if isinstance(to_replace, dict):
    709            old_values = list(to_replace)
    710            new_values = list(to_replace.values())
    711        elif not old_values and isinstance(to_replace, list):
    712            assert isinstance(value, list), "value must be a list since the replacements are a list"
    713            assert len(to_replace) == len(
    714                value
    715            ), "the replacements and values must be the same length"
    716            old_values = to_replace
    717            new_values = value
    718        else:
    719            old_values = [to_replace] * len(columns)
    720            new_values = [value] * len(columns)
    721        old_values = [lit(value) for value in old_values]
    722        new_values = [lit(value) for value in new_values]
    723
    724        replacement_mapping = {}
    725        for column in columns:
    726            expression = Column(None)
    727            for i, (old_value, new_value) in enumerate(zip(old_values, new_values)):
    728                if i == 0:
    729                    expression = F.when(column == old_value, new_value)
    730                else:
    731                    expression = expression.when(column == old_value, new_value)  # type: ignore
    732            replacement_mapping[column.alias_or_name] = expression.otherwise(column).alias(
    733                column.expression.alias_or_name
    734            )
    735
    736        replacement_mapping = {**all_column_mapping, **replacement_mapping}
    737        replacement_columns = [replacement_mapping[column.alias_or_name] for column in all_columns]
    738        new_df = new_df.select(*replacement_columns)
    739        return new_df

@operation(Operation.SELECT)
def withColumn( self, colName: str, col: sqlglot.dataframe.sql.Column) -> sqlglot.dataframe.sql.DataFrame: View Source

    741    @operation(Operation.SELECT)
    742    def withColumn(self, colName: str, col: Column) -> DataFrame:
    743        col = self._ensure_and_normalize_col(col)
    744        existing_col_names = self.expression.named_selects
    745        existing_col_index = (
    746            existing_col_names.index(colName) if colName in existing_col_names else None
    747        )
    748        if existing_col_index:
    749            expression = self.expression.copy()
    750            expression.expressions[existing_col_index] = col.expression
    751            return self.copy(expression=expression)
    752        return self.copy().select(col.alias(colName), append=True)

@operation(Operation.SELECT)
def withColumnRenamed(self, existing: str, new: str): View Source

    754    @operation(Operation.SELECT)
    755    def withColumnRenamed(self, existing: str, new: str):
    756        expression = self.expression.copy()
    757        existing_columns = [
    758            expression
    759            for expression in expression.expressions
    760            if expression.alias_or_name == existing
    761        ]
    762        if not existing_columns:
    763            raise ValueError("Tried to rename a column that doesn't exist")
    764        for existing_column in existing_columns:
    765            if isinstance(existing_column, exp.Column):
    766                existing_column.replace(exp.alias_(existing_column, new))
    767            else:
    768                existing_column.set("alias", exp.to_identifier(new))
    769        return self.copy(expression=expression)

@operation(Operation.SELECT)
def drop( self, *cols: Union[str, sqlglot.dataframe.sql.Column]) -> sqlglot.dataframe.sql.DataFrame: View Source

    771    @operation(Operation.SELECT)
    772    def drop(self, *cols: t.Union[str, Column]) -> DataFrame:
    773        all_columns = self._get_outer_select_columns(self.expression)
    774        drop_cols = self._ensure_and_normalize_cols(cols)
    775        new_columns = [
    776            col
    777            for col in all_columns
    778            if col.alias_or_name not in [drop_column.alias_or_name for drop_column in drop_cols]
    779        ]
    780        return self.copy().select(*new_columns, append=False)

@operation(Operation.LIMIT)
def limit(self, num: int) -> sqlglot.dataframe.sql.DataFrame: View Source

    782    @operation(Operation.LIMIT)
    783    def limit(self, num: int) -> DataFrame:
    784        return self.copy(expression=self.expression.limit(num))

@operation(Operation.NO_OP)
def hint( self, name: str, *parameters: Union[str, int, NoneType]) -> sqlglot.dataframe.sql.DataFrame: View Source

    786    @operation(Operation.NO_OP)
    787    def hint(self, name: str, *parameters: t.Optional[t.Union[str, int]]) -> DataFrame:
    788        parameter_list = ensure_list(parameters)
    789        parameter_columns = (
    790            self._ensure_list_of_columns(parameter_list)
    791            if parameters
    792            else Column.ensure_cols([self.sequence_id])
    793        )
    794        return self._hint(name, parameter_columns)

@operation(Operation.NO_OP)
def repartition( self, numPartitions: Union[int, <MagicMock id='140430581926688'>], *cols: <MagicMock id='140430581982224'>) -> sqlglot.dataframe.sql.DataFrame: View Source

    796    @operation(Operation.NO_OP)
    797    def repartition(
    798        self, numPartitions: t.Union[int, ColumnOrName], *cols: ColumnOrName
    799    ) -> DataFrame:
    800        num_partition_cols = self._ensure_list_of_columns(numPartitions)
    801        columns = self._ensure_and_normalize_cols(cols)
    802        args = num_partition_cols + columns
    803        return self._hint("repartition", args)

@operation(Operation.NO_OP)
def coalesce(self, numPartitions: int) -> sqlglot.dataframe.sql.DataFrame: View Source

    805    @operation(Operation.NO_OP)
    806    def coalesce(self, numPartitions: int) -> DataFrame:
    807        num_partitions = Column.ensure_cols([numPartitions])
    808        return self._hint("coalesce", num_partitions)

@operation(Operation.NO_OP)
def cache(self) -> sqlglot.dataframe.sql.DataFrame: View Source

    810    @operation(Operation.NO_OP)
    811    def cache(self) -> DataFrame:
    812        return self._cache(storage_level="MEMORY_AND_DISK")

@operation(Operation.NO_OP)
def persist( self, storageLevel: str = 'MEMORY_AND_DISK_SER') -> sqlglot.dataframe.sql.DataFrame: View Source

    814    @operation(Operation.NO_OP)
    815    def persist(self, storageLevel: str = "MEMORY_AND_DISK_SER") -> DataFrame:
    816        """
    817        Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html
    818        """
    819        return self._cache(storageLevel)

Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html

class GroupedData: View Source

    14class GroupedData:
    15    def __init__(self, df: DataFrame, group_by_cols: t.List[Column], last_op: Operation):
    16        self._df = df.copy()
    17        self.spark = df.spark
    18        self.last_op = last_op
    19        self.group_by_cols = group_by_cols
    20
    21    def _get_function_applied_columns(
    22        self, func_name: str, cols: t.Tuple[str, ...]
    23    ) -> t.List[Column]:
    24        func_name = func_name.lower()
    25        return [getattr(F, func_name)(name).alias(f"{func_name}({name})") for name in cols]
    26
    27    @operation(Operation.SELECT)
    28    def agg(self, *exprs: t.Union[Column, t.Dict[str, str]]) -> DataFrame:
    29        columns = (
    30            [Column(f"{agg_func}({column_name})") for column_name, agg_func in exprs[0].items()]
    31            if isinstance(exprs[0], dict)
    32            else exprs
    33        )
    34        cols = self._df._ensure_and_normalize_cols(columns)
    35
    36        expression = self._df.expression.group_by(
    37            *[x.expression for x in self.group_by_cols]
    38        ).select(*[x.expression for x in self.group_by_cols + cols], append=False)
    39        return self._df.copy(expression=expression)
    40
    41    def count(self) -> DataFrame:
    42        return self.agg(F.count("*").alias("count"))
    43
    44    def mean(self, *cols: str) -> DataFrame:
    45        return self.avg(*cols)
    46
    47    def avg(self, *cols: str) -> DataFrame:
    48        return self.agg(*self._get_function_applied_columns("avg", cols))
    49
    50    def max(self, *cols: str) -> DataFrame:
    51        return self.agg(*self._get_function_applied_columns("max", cols))
    52
    53    def min(self, *cols: str) -> DataFrame:
    54        return self.agg(*self._get_function_applied_columns("min", cols))
    55
    56    def sum(self, *cols: str) -> DataFrame:
    57        return self.agg(*self._get_function_applied_columns("sum", cols))
    58
    59    def pivot(self, *cols: str) -> DataFrame:
    60        raise NotImplementedError("Sum distinct is not currently implemented")

GroupedData( df: sqlglot.dataframe.sql.DataFrame, group_by_cols: List[sqlglot.dataframe.sql.Column], last_op: sqlglot.dataframe.sql.operations.Operation) View Source

    15    def __init__(self, df: DataFrame, group_by_cols: t.List[Column], last_op: Operation):
    16        self._df = df.copy()
    17        self.spark = df.spark
    18        self.last_op = last_op
    19        self.group_by_cols = group_by_cols

spark
last_op
group_by_cols
@operation(Operation.SELECT)
def agg( self, *exprs: Union[sqlglot.dataframe.sql.Column, Dict[str, str]]) -> sqlglot.dataframe.sql.DataFrame: View Source

    27    @operation(Operation.SELECT)
    28    def agg(self, *exprs: t.Union[Column, t.Dict[str, str]]) -> DataFrame:
    29        columns = (
    30            [Column(f"{agg_func}({column_name})") for column_name, agg_func in exprs[0].items()]
    31            if isinstance(exprs[0], dict)
    32            else exprs
    33        )
    34        cols = self._df._ensure_and_normalize_cols(columns)
    35
    36        expression = self._df.expression.group_by(
    37            *[x.expression for x in self.group_by_cols]
    38        ).select(*[x.expression for x in self.group_by_cols + cols], append=False)
    39        return self._df.copy(expression=expression)

def count(self) -> sqlglot.dataframe.sql.DataFrame: View Source

    41    def count(self) -> DataFrame:
    42        return self.agg(F.count("*").alias("count"))

def mean(self, *cols: str) -> sqlglot.dataframe.sql.DataFrame: View Source

    44    def mean(self, *cols: str) -> DataFrame:
    45        return self.avg(*cols)

def avg(self, *cols: str) -> sqlglot.dataframe.sql.DataFrame: View Source

    47    def avg(self, *cols: str) -> DataFrame:
    48        return self.agg(*self._get_function_applied_columns("avg", cols))

def max(self, *cols: str) -> sqlglot.dataframe.sql.DataFrame: View Source

    50    def max(self, *cols: str) -> DataFrame:
    51        return self.agg(*self._get_function_applied_columns("max", cols))

def min(self, *cols: str) -> sqlglot.dataframe.sql.DataFrame: View Source

    53    def min(self, *cols: str) -> DataFrame:
    54        return self.agg(*self._get_function_applied_columns("min", cols))

def sum(self, *cols: str) -> sqlglot.dataframe.sql.DataFrame: View Source

    56    def sum(self, *cols: str) -> DataFrame:
    57        return self.agg(*self._get_function_applied_columns("sum", cols))

def pivot(self, *cols: str) -> sqlglot.dataframe.sql.DataFrame: View Source

    59    def pivot(self, *cols: str) -> DataFrame:
    60        raise NotImplementedError("Sum distinct is not currently implemented")

class Column: View Source

     17class Column:
     18    def __init__(self, expression: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]):
     19        if isinstance(expression, Column):
     20            expression = expression.expression  # type: ignore
     21        elif expression is None or not isinstance(expression, (str, exp.Expression)):
     22            expression = self._lit(expression).expression  # type: ignore
     23
     24        expression = sqlglot.maybe_parse(expression, dialect="spark")
     25        if expression is None:
     26            raise ValueError(f"Could not parse {expression}")
     27
     28        if isinstance(expression, exp.Column):
     29            expression.transform(Spark.normalize_identifier, copy=False)
     30
     31        self.expression: exp.Expression = expression
     32
     33    def __repr__(self):
     34        return repr(self.expression)
     35
     36    def __hash__(self):
     37        return hash(self.expression)
     38
     39    def __eq__(self, other: ColumnOrLiteral) -> Column:  # type: ignore
     40        return self.binary_op(exp.EQ, other)
     41
     42    def __ne__(self, other: ColumnOrLiteral) -> Column:  # type: ignore
     43        return self.binary_op(exp.NEQ, other)
     44
     45    def __gt__(self, other: ColumnOrLiteral) -> Column:
     46        return self.binary_op(exp.GT, other)
     47
     48    def __ge__(self, other: ColumnOrLiteral) -> Column:
     49        return self.binary_op(exp.GTE, other)
     50
     51    def __lt__(self, other: ColumnOrLiteral) -> Column:
     52        return self.binary_op(exp.LT, other)
     53
     54    def __le__(self, other: ColumnOrLiteral) -> Column:
     55        return self.binary_op(exp.LTE, other)
     56
     57    def __and__(self, other: ColumnOrLiteral) -> Column:
     58        return self.binary_op(exp.And, other)
     59
     60    def __or__(self, other: ColumnOrLiteral) -> Column:
     61        return self.binary_op(exp.Or, other)
     62
     63    def __mod__(self, other: ColumnOrLiteral) -> Column:
     64        return self.binary_op(exp.Mod, other)
     65
     66    def __add__(self, other: ColumnOrLiteral) -> Column:
     67        return self.binary_op(exp.Add, other)
     68
     69    def __sub__(self, other: ColumnOrLiteral) -> Column:
     70        return self.binary_op(exp.Sub, other)
     71
     72    def __mul__(self, other: ColumnOrLiteral) -> Column:
     73        return self.binary_op(exp.Mul, other)
     74
     75    def __truediv__(self, other: ColumnOrLiteral) -> Column:
     76        return self.binary_op(exp.Div, other)
     77
     78    def __div__(self, other: ColumnOrLiteral) -> Column:
     79        return self.binary_op(exp.Div, other)
     80
     81    def __neg__(self) -> Column:
     82        return self.unary_op(exp.Neg)
     83
     84    def __radd__(self, other: ColumnOrLiteral) -> Column:
     85        return self.inverse_binary_op(exp.Add, other)
     86
     87    def __rsub__(self, other: ColumnOrLiteral) -> Column:
     88        return self.inverse_binary_op(exp.Sub, other)
     89
     90    def __rmul__(self, other: ColumnOrLiteral) -> Column:
     91        return self.inverse_binary_op(exp.Mul, other)
     92
     93    def __rdiv__(self, other: ColumnOrLiteral) -> Column:
     94        return self.inverse_binary_op(exp.Div, other)
     95
     96    def __rtruediv__(self, other: ColumnOrLiteral) -> Column:
     97        return self.inverse_binary_op(exp.Div, other)
     98
     99    def __rmod__(self, other: ColumnOrLiteral) -> Column:
    100        return self.inverse_binary_op(exp.Mod, other)
    101
    102    def __pow__(self, power: ColumnOrLiteral, modulo=None):
    103        return Column(exp.Pow(this=self.expression, expression=Column(power).expression))
    104
    105    def __rpow__(self, power: ColumnOrLiteral):
    106        return Column(exp.Pow(this=Column(power).expression, expression=self.expression))
    107
    108    def __invert__(self):
    109        return self.unary_op(exp.Not)
    110
    111    def __rand__(self, other: ColumnOrLiteral) -> Column:
    112        return self.inverse_binary_op(exp.And, other)
    113
    114    def __ror__(self, other: ColumnOrLiteral) -> Column:
    115        return self.inverse_binary_op(exp.Or, other)
    116
    117    @classmethod
    118    def ensure_col(cls, value: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]):
    119        return cls(value)
    120
    121    @classmethod
    122    def ensure_cols(cls, args: t.List[t.Union[ColumnOrLiteral, exp.Expression]]) -> t.List[Column]:
    123        return [cls.ensure_col(x) if not isinstance(x, Column) else x for x in args]
    124
    125    @classmethod
    126    def _lit(cls, value: ColumnOrLiteral) -> Column:
    127        if isinstance(value, dict):
    128            columns = [cls._lit(v).alias(k).expression for k, v in value.items()]
    129            return cls(exp.Struct(expressions=columns))
    130        return cls(exp.convert(value))
    131
    132    @classmethod
    133    def invoke_anonymous_function(
    134        cls, column: t.Optional[ColumnOrLiteral], func_name: str, *args: t.Optional[ColumnOrLiteral]
    135    ) -> Column:
    136        columns = [] if column is None else [cls.ensure_col(column)]
    137        column_args = [cls.ensure_col(arg) for arg in args]
    138        expressions = [x.expression for x in columns + column_args]
    139        new_expression = exp.Anonymous(this=func_name.upper(), expressions=expressions)
    140        return Column(new_expression)
    141
    142    @classmethod
    143    def invoke_expression_over_column(
    144        cls, column: t.Optional[ColumnOrLiteral], callable_expression: t.Callable, **kwargs
    145    ) -> Column:
    146        ensured_column = None if column is None else cls.ensure_col(column)
    147        ensure_expression_values = {
    148            k: [Column.ensure_col(x).expression for x in v]
    149            if is_iterable(v)
    150            else Column.ensure_col(v).expression
    151            for k, v in kwargs.items()
    152            if v is not None
    153        }
    154        new_expression = (
    155            callable_expression(**ensure_expression_values)
    156            if ensured_column is None
    157            else callable_expression(
    158                this=ensured_column.column_expression, **ensure_expression_values
    159            )
    160        )
    161        return Column(new_expression)
    162
    163    def binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    164        return Column(
    165            klass(this=self.column_expression, expression=Column(other).column_expression, **kwargs)
    166        )
    167
    168    def inverse_binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    169        return Column(
    170            klass(this=Column(other).column_expression, expression=self.column_expression, **kwargs)
    171        )
    172
    173    def unary_op(self, klass: t.Callable, **kwargs) -> Column:
    174        return Column(klass(this=self.column_expression, **kwargs))
    175
    176    @property
    177    def is_alias(self):
    178        return isinstance(self.expression, exp.Alias)
    179
    180    @property
    181    def is_column(self):
    182        return isinstance(self.expression, exp.Column)
    183
    184    @property
    185    def column_expression(self) -> t.Union[exp.Column, exp.Literal]:
    186        return self.expression.unalias()
    187
    188    @property
    189    def alias_or_name(self) -> str:
    190        return self.expression.alias_or_name
    191
    192    @classmethod
    193    def ensure_literal(cls, value) -> Column:
    194        from sqlglot.dataframe.sql.functions import lit
    195
    196        if isinstance(value, cls):
    197            value = value.expression
    198        if not isinstance(value, exp.Literal):
    199            return lit(value)
    200        return Column(value)
    201
    202    def copy(self) -> Column:
    203        return Column(self.expression.copy())
    204
    205    def set_table_name(self, table_name: str, copy=False) -> Column:
    206        expression = self.expression.copy() if copy else self.expression
    207        expression.set("table", exp.to_identifier(table_name))
    208        return Column(expression)
    209
    210    def sql(self, **kwargs) -> str:
    211        return self.expression.sql(**{"dialect": "spark", **kwargs})
    212
    213    def alias(self, name: str) -> Column:
    214        new_expression = exp.alias_(self.column_expression, name)
    215        return Column(new_expression)
    216
    217    def asc(self) -> Column:
    218        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
    219        return Column(new_expression)
    220
    221    def desc(self) -> Column:
    222        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
    223        return Column(new_expression)
    224
    225    asc_nulls_first = asc
    226
    227    def asc_nulls_last(self) -> Column:
    228        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=False)
    229        return Column(new_expression)
    230
    231    def desc_nulls_first(self) -> Column:
    232        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=True)
    233        return Column(new_expression)
    234
    235    desc_nulls_last = desc
    236
    237    def when(self, condition: Column, value: t.Any) -> Column:
    238        from sqlglot.dataframe.sql.functions import when
    239
    240        column_with_if = when(condition, value)
    241        if not isinstance(self.expression, exp.Case):
    242            return column_with_if
    243        new_column = self.copy()
    244        new_column.expression.args["ifs"].extend(column_with_if.expression.args["ifs"])
    245        return new_column
    246
    247    def otherwise(self, value: t.Any) -> Column:
    248        from sqlglot.dataframe.sql.functions import lit
    249
    250        true_value = value if isinstance(value, Column) else lit(value)
    251        new_column = self.copy()
    252        new_column.expression.set("default", true_value.column_expression)
    253        return new_column
    254
    255    def isNull(self) -> Column:
    256        new_expression = exp.Is(this=self.column_expression, expression=exp.Null())
    257        return Column(new_expression)
    258
    259    def isNotNull(self) -> Column:
    260        new_expression = exp.Not(this=exp.Is(this=self.column_expression, expression=exp.Null()))
    261        return Column(new_expression)
    262
    263    def cast(self, dataType: t.Union[str, DataType]):
    264        """
    265        Functionality Difference: PySpark cast accepts a datatype instance of the datatype class
    266        Sqlglot doesn't currently replicate this class so it only accepts a string
    267        """
    268        if isinstance(dataType, DataType):
    269            dataType = dataType.simpleString()
    270        return Column(exp.cast(self.column_expression, dataType, dialect="spark"))
    271
    272    def startswith(self, value: t.Union[str, Column]) -> Column:
    273        value = self._lit(value) if not isinstance(value, Column) else value
    274        return self.invoke_anonymous_function(self, "STARTSWITH", value)
    275
    276    def endswith(self, value: t.Union[str, Column]) -> Column:
    277        value = self._lit(value) if not isinstance(value, Column) else value
    278        return self.invoke_anonymous_function(self, "ENDSWITH", value)
    279
    280    def rlike(self, regexp: str) -> Column:
    281        return self.invoke_expression_over_column(
    282            column=self, callable_expression=exp.RegexpLike, expression=self._lit(regexp).expression
    283        )
    284
    285    def like(self, other: str):
    286        return self.invoke_expression_over_column(
    287            self, exp.Like, expression=self._lit(other).expression
    288        )
    289
    290    def ilike(self, other: str):
    291        return self.invoke_expression_over_column(
    292            self, exp.ILike, expression=self._lit(other).expression
    293        )
    294
    295    def substr(self, startPos: t.Union[int, Column], length: t.Union[int, Column]) -> Column:
    296        startPos = self._lit(startPos) if not isinstance(startPos, Column) else startPos
    297        length = self._lit(length) if not isinstance(length, Column) else length
    298        return Column.invoke_expression_over_column(
    299            self, exp.Substring, start=startPos.expression, length=length.expression
    300        )
    301
    302    def isin(self, *cols: t.Union[ColumnOrLiteral, t.Iterable[ColumnOrLiteral]]):
    303        columns = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
    304        expressions = [self._lit(x).expression for x in columns]
    305        return Column.invoke_expression_over_column(self, exp.In, expressions=expressions)  # type: ignore
    306
    307    def between(
    308        self,
    309        lowerBound: t.Union[ColumnOrLiteral],
    310        upperBound: t.Union[ColumnOrLiteral],
    311    ) -> Column:
    312        lower_bound_exp = (
    313            self._lit(lowerBound) if not isinstance(lowerBound, Column) else lowerBound
    314        )
    315        upper_bound_exp = (
    316            self._lit(upperBound) if not isinstance(upperBound, Column) else upperBound
    317        )
    318        return Column(
    319            exp.Between(
    320                this=self.column_expression,
    321                low=lower_bound_exp.expression,
    322                high=upper_bound_exp.expression,
    323            )
    324        )
    325
    326    def over(self, window: WindowSpec) -> Column:
    327        window_expression = window.expression.copy()
    328        window_expression.set("this", self.column_expression)
    329        return Column(window_expression)

Column( expression: Union[<MagicMock id='140430586079424'>, sqlglot.expressions.Expression, NoneType]) View Source

    18    def __init__(self, expression: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]):
    19        if isinstance(expression, Column):
    20            expression = expression.expression  # type: ignore
    21        elif expression is None or not isinstance(expression, (str, exp.Expression)):
    22            expression = self._lit(expression).expression  # type: ignore
    23
    24        expression = sqlglot.maybe_parse(expression, dialect="spark")
    25        if expression is None:
    26            raise ValueError(f"Could not parse {expression}")
    27
    28        if isinstance(expression, exp.Column):
    29            expression.transform(Spark.normalize_identifier, copy=False)
    30
    31        self.expression: exp.Expression = expression

expression: sqlglot.expressions.Expression
@classmethod
def ensure_col( cls, value: Union[<MagicMock id='140430582454624'>, sqlglot.expressions.Expression, NoneType]): View Source

    117    @classmethod
    118    def ensure_col(cls, value: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]):
    119        return cls(value)

@classmethod
def ensure_cols( cls, args: List[Union[<MagicMock id='140430582612608'>, sqlglot.expressions.Expression]]) -> List[sqlglot.dataframe.sql.Column]: View Source

    121    @classmethod
    122    def ensure_cols(cls, args: t.List[t.Union[ColumnOrLiteral, exp.Expression]]) -> t.List[Column]:
    123        return [cls.ensure_col(x) if not isinstance(x, Column) else x for x in args]

@classmethod
def invoke_anonymous_function( cls, column: Optional[<MagicMock id='140430582366016'>], func_name: str, *args: Optional[<MagicMock id='140430582236640'>]) -> sqlglot.dataframe.sql.Column: View Source

    132    @classmethod
    133    def invoke_anonymous_function(
    134        cls, column: t.Optional[ColumnOrLiteral], func_name: str, *args: t.Optional[ColumnOrLiteral]
    135    ) -> Column:
    136        columns = [] if column is None else [cls.ensure_col(column)]
    137        column_args = [cls.ensure_col(arg) for arg in args]
    138        expressions = [x.expression for x in columns + column_args]
    139        new_expression = exp.Anonymous(this=func_name.upper(), expressions=expressions)
    140        return Column(new_expression)

@classmethod
def invoke_expression_over_column( cls, column: Optional[<MagicMock id='140430582194784'>], callable_expression: Callable, **kwargs) -> sqlglot.dataframe.sql.Column: View Source

    142    @classmethod
    143    def invoke_expression_over_column(
    144        cls, column: t.Optional[ColumnOrLiteral], callable_expression: t.Callable, **kwargs
    145    ) -> Column:
    146        ensured_column = None if column is None else cls.ensure_col(column)
    147        ensure_expression_values = {
    148            k: [Column.ensure_col(x).expression for x in v]
    149            if is_iterable(v)
    150            else Column.ensure_col(v).expression
    151            for k, v in kwargs.items()
    152            if v is not None
    153        }
    154        new_expression = (
    155            callable_expression(**ensure_expression_values)
    156            if ensured_column is None
    157            else callable_expression(
    158                this=ensured_column.column_expression, **ensure_expression_values
    159            )
    160        )
    161        return Column(new_expression)

def binary_op( self, klass: Callable, other: <MagicMock id='140430582382656'>, **kwargs) -> sqlglot.dataframe.sql.Column: View Source

    163    def binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    164        return Column(
    165            klass(this=self.column_expression, expression=Column(other).column_expression, **kwargs)
    166        )

def inverse_binary_op( self, klass: Callable, other: <MagicMock id='140430582392304'>, **kwargs) -> sqlglot.dataframe.sql.Column: View Source

    168    def inverse_binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    169        return Column(
    170            klass(this=Column(other).column_expression, expression=self.column_expression, **kwargs)
    171        )

def unary_op(self, klass: Callable, **kwargs) -> sqlglot.dataframe.sql.Column: View Source

    173    def unary_op(self, klass: t.Callable, **kwargs) -> Column:
    174        return Column(klass(this=self.column_expression, **kwargs))

is_alias
is_column
column_expression: Union[sqlglot.expressions.Column, sqlglot.expressions.Literal]
alias_or_name: str
@classmethod
def ensure_literal(cls, value) -> sqlglot.dataframe.sql.Column: View Source

    192    @classmethod
    193    def ensure_literal(cls, value) -> Column:
    194        from sqlglot.dataframe.sql.functions import lit
    195
    196        if isinstance(value, cls):
    197            value = value.expression
    198        if not isinstance(value, exp.Literal):
    199            return lit(value)
    200        return Column(value)

def copy(self) -> sqlglot.dataframe.sql.Column: View Source

    202    def copy(self) -> Column:
    203        return Column(self.expression.copy())

def set_table_name(self, table_name: str, copy=False) -> sqlglot.dataframe.sql.Column: View Source

    205    def set_table_name(self, table_name: str, copy=False) -> Column:
    206        expression = self.expression.copy() if copy else self.expression
    207        expression.set("table", exp.to_identifier(table_name))
    208        return Column(expression)

def sql(self, **kwargs) -> str: View Source

    210    def sql(self, **kwargs) -> str:
    211        return self.expression.sql(**{"dialect": "spark", **kwargs})

def alias(self, name: str) -> sqlglot.dataframe.sql.Column: View Source

    213    def alias(self, name: str) -> Column:
    214        new_expression = exp.alias_(self.column_expression, name)
    215        return Column(new_expression)

def asc(self) -> sqlglot.dataframe.sql.Column: View Source

    217    def asc(self) -> Column:
    218        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
    219        return Column(new_expression)

def desc(self) -> sqlglot.dataframe.sql.Column: View Source

    221    def desc(self) -> Column:
    222        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
    223        return Column(new_expression)

def asc_nulls_first(self) -> sqlglot.dataframe.sql.Column: View Source

    217    def asc(self) -> Column:
    218        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
    219        return Column(new_expression)

def asc_nulls_last(self) -> sqlglot.dataframe.sql.Column: View Source

    227    def asc_nulls_last(self) -> Column:
    228        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=False)
    229        return Column(new_expression)

def desc_nulls_first(self) -> sqlglot.dataframe.sql.Column: View Source

    231    def desc_nulls_first(self) -> Column:
    232        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=True)
    233        return Column(new_expression)

def desc_nulls_last(self) -> sqlglot.dataframe.sql.Column: View Source

    221    def desc(self) -> Column:
    222        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
    223        return Column(new_expression)

def when( self, condition: sqlglot.dataframe.sql.Column, value: Any) -> sqlglot.dataframe.sql.Column: View Source

    237    def when(self, condition: Column, value: t.Any) -> Column:
    238        from sqlglot.dataframe.sql.functions import when
    239
    240        column_with_if = when(condition, value)
    241        if not isinstance(self.expression, exp.Case):
    242            return column_with_if
    243        new_column = self.copy()
    244        new_column.expression.args["ifs"].extend(column_with_if.expression.args["ifs"])
    245        return new_column

def otherwise(self, value: Any) -> sqlglot.dataframe.sql.Column: View Source

    247    def otherwise(self, value: t.Any) -> Column:
    248        from sqlglot.dataframe.sql.functions import lit
    249
    250        true_value = value if isinstance(value, Column) else lit(value)
    251        new_column = self.copy()
    252        new_column.expression.set("default", true_value.column_expression)
    253        return new_column

def isNull(self) -> sqlglot.dataframe.sql.Column: View Source

    255    def isNull(self) -> Column:
    256        new_expression = exp.Is(this=self.column_expression, expression=exp.Null())
    257        return Column(new_expression)

def isNotNull(self) -> sqlglot.dataframe.sql.Column: View Source

    259    def isNotNull(self) -> Column:
    260        new_expression = exp.Not(this=exp.Is(this=self.column_expression, expression=exp.Null()))
    261        return Column(new_expression)

def cast(self, dataType: Union[str, sqlglot.dataframe.sql.types.DataType]): View Source

    263    def cast(self, dataType: t.Union[str, DataType]):
    264        """
    265        Functionality Difference: PySpark cast accepts a datatype instance of the datatype class
    266        Sqlglot doesn't currently replicate this class so it only accepts a string
    267        """
    268        if isinstance(dataType, DataType):
    269            dataType = dataType.simpleString()
    270        return Column(exp.cast(self.column_expression, dataType, dialect="spark"))

Functionality Difference: PySpark cast accepts a datatype instance of the datatype class Sqlglot doesn't currently replicate this class so it only accepts a string

def startswith( self, value: Union[str, sqlglot.dataframe.sql.Column]) -> sqlglot.dataframe.sql.Column: View Source

    272    def startswith(self, value: t.Union[str, Column]) -> Column:
    273        value = self._lit(value) if not isinstance(value, Column) else value
    274        return self.invoke_anonymous_function(self, "STARTSWITH", value)

def endswith( self, value: Union[str, sqlglot.dataframe.sql.Column]) -> sqlglot.dataframe.sql.Column: View Source

    276    def endswith(self, value: t.Union[str, Column]) -> Column:
    277        value = self._lit(value) if not isinstance(value, Column) else value
    278        return self.invoke_anonymous_function(self, "ENDSWITH", value)

def rlike(self, regexp: str) -> sqlglot.dataframe.sql.Column: View Source

    280    def rlike(self, regexp: str) -> Column:
    281        return self.invoke_expression_over_column(
    282            column=self, callable_expression=exp.RegexpLike, expression=self._lit(regexp).expression
    283        )

def like(self, other: str): View Source

    285    def like(self, other: str):
    286        return self.invoke_expression_over_column(
    287            self, exp.Like, expression=self._lit(other).expression
    288        )

def ilike(self, other: str): View Source

    290    def ilike(self, other: str):
    291        return self.invoke_expression_over_column(
    292            self, exp.ILike, expression=self._lit(other).expression
    293        )

def substr( self, startPos: Union[int, sqlglot.dataframe.sql.Column], length: Union[int, sqlglot.dataframe.sql.Column]) -> sqlglot.dataframe.sql.Column: View Source

    295    def substr(self, startPos: t.Union[int, Column], length: t.Union[int, Column]) -> Column:
    296        startPos = self._lit(startPos) if not isinstance(startPos, Column) else startPos
    297        length = self._lit(length) if not isinstance(length, Column) else length
    298        return Column.invoke_expression_over_column(
    299            self, exp.Substring, start=startPos.expression, length=length.expression
    300        )

def isin( self, *cols: Union[<MagicMock id='140430582714512'>, Iterable[<MagicMock id='140430582714512'>]]): View Source

    302    def isin(self, *cols: t.Union[ColumnOrLiteral, t.Iterable[ColumnOrLiteral]]):
    303        columns = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
    304        expressions = [self._lit(x).expression for x in columns]
    305        return Column.invoke_expression_over_column(self, exp.In, expressions=expressions)  # type: ignore

def between( self, lowerBound: <MagicMock id='140430582771536'>, upperBound: <MagicMock id='140430582832176'>) -> sqlglot.dataframe.sql.Column: View Source

    307    def between(
    308        self,
    309        lowerBound: t.Union[ColumnOrLiteral],
    310        upperBound: t.Union[ColumnOrLiteral],
    311    ) -> Column:
    312        lower_bound_exp = (
    313            self._lit(lowerBound) if not isinstance(lowerBound, Column) else lowerBound
    314        )
    315        upper_bound_exp = (
    316            self._lit(upperBound) if not isinstance(upperBound, Column) else upperBound
    317        )
    318        return Column(
    319            exp.Between(
    320                this=self.column_expression,
    321                low=lower_bound_exp.expression,
    322                high=upper_bound_exp.expression,
    323            )
    324        )

def over( self, window: <MagicMock id='140430582891472'>) -> sqlglot.dataframe.sql.Column: View Source

    326    def over(self, window: WindowSpec) -> Column:
    327        window_expression = window.expression.copy()
    328        window_expression.set("this", self.column_expression)
    329        return Column(window_expression)

class DataFrameNaFunctions: View Source

    822class DataFrameNaFunctions:
    823    def __init__(self, df: DataFrame):
    824        self.df = df
    825
    826    def drop(
    827        self,
    828        how: str = "any",
    829        thresh: t.Optional[int] = None,
    830        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    831    ) -> DataFrame:
    832        return self.df.dropna(how=how, thresh=thresh, subset=subset)
    833
    834    def fill(
    835        self,
    836        value: t.Union[int, bool, float, str, t.Dict[str, t.Any]],
    837        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    838    ) -> DataFrame:
    839        return self.df.fillna(value=value, subset=subset)
    840
    841    def replace(
    842        self,
    843        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    844        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    845        subset: t.Optional[t.Union[str, t.List[str]]] = None,
    846    ) -> DataFrame:
    847        return self.df.replace(to_replace=to_replace, value=value, subset=subset)

DataFrameNaFunctions(df: sqlglot.dataframe.sql.DataFrame) View Source

    823    def __init__(self, df: DataFrame):
    824        self.df = df

df
def drop( self, how: str = 'any', thresh: Optional[int] = None, subset: Union[str, Tuple[str, ...], List[str], NoneType] = None) -> sqlglot.dataframe.sql.DataFrame: View Source

    826    def drop(
    827        self,
    828        how: str = "any",
    829        thresh: t.Optional[int] = None,
    830        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    831    ) -> DataFrame:
    832        return self.df.dropna(how=how, thresh=thresh, subset=subset)

def fill( self, value: Union[int, bool, float, str, Dict[str, Any]], subset: Union[str, Tuple[str, ...], List[str], NoneType] = None) -> sqlglot.dataframe.sql.DataFrame: View Source

    834    def fill(
    835        self,
    836        value: t.Union[int, bool, float, str, t.Dict[str, t.Any]],
    837        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    838    ) -> DataFrame:
    839        return self.df.fillna(value=value, subset=subset)

def replace( self, to_replace: Union[bool, int, float, str, List, Dict], value: Union[bool, int, float, str, List, NoneType] = None, subset: Union[str, List[str], NoneType] = None) -> sqlglot.dataframe.sql.DataFrame: View Source

    841    def replace(
    842        self,
    843        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    844        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    845        subset: t.Optional[t.Union[str, t.List[str]]] = None,
    846    ) -> DataFrame:
    847        return self.df.replace(to_replace=to_replace, value=value, subset=subset)

class Window: View Source

    15class Window:
    16    _JAVA_MIN_LONG = -(1 << 63)  # -9223372036854775808
    17    _JAVA_MAX_LONG = (1 << 63) - 1  # 9223372036854775807
    18    _PRECEDING_THRESHOLD = max(-sys.maxsize, _JAVA_MIN_LONG)
    19    _FOLLOWING_THRESHOLD = min(sys.maxsize, _JAVA_MAX_LONG)
    20
    21    unboundedPreceding: int = _JAVA_MIN_LONG
    22
    23    unboundedFollowing: int = _JAVA_MAX_LONG
    24
    25    currentRow: int = 0
    26
    27    @classmethod
    28    def partitionBy(cls, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    29        return WindowSpec().partitionBy(*cols)
    30
    31    @classmethod
    32    def orderBy(cls, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    33        return WindowSpec().orderBy(*cols)
    34
    35    @classmethod
    36    def rowsBetween(cls, start: int, end: int) -> WindowSpec:
    37        return WindowSpec().rowsBetween(start, end)
    38
    39    @classmethod
    40    def rangeBetween(cls, start: int, end: int) -> WindowSpec:
    41        return WindowSpec().rangeBetween(start, end)

unboundedPreceding: int = -9223372036854775808
unboundedFollowing: int = 9223372036854775807
currentRow: int = 0
@classmethod
def partitionBy( cls, *cols: Union[<MagicMock id='140430579900976'>, List[<MagicMock id='140430579900976'>]]) -> sqlglot.dataframe.sql.WindowSpec: View Source

    27    @classmethod
    28    def partitionBy(cls, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    29        return WindowSpec().partitionBy(*cols)

@classmethod
def orderBy( cls, *cols: Union[<MagicMock id='140430582923168'>, List[<MagicMock id='140430582923168'>]]) -> sqlglot.dataframe.sql.WindowSpec: View Source

    31    @classmethod
    32    def orderBy(cls, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    33        return WindowSpec().orderBy(*cols)

@classmethod
def rowsBetween(cls, start: int, end: int) -> sqlglot.dataframe.sql.WindowSpec: View Source

    35    @classmethod
    36    def rowsBetween(cls, start: int, end: int) -> WindowSpec:
    37        return WindowSpec().rowsBetween(start, end)

@classmethod
def rangeBetween(cls, start: int, end: int) -> sqlglot.dataframe.sql.WindowSpec: View Source

    39    @classmethod
    40    def rangeBetween(cls, start: int, end: int) -> WindowSpec:
    41        return WindowSpec().rangeBetween(start, end)

class WindowSpec: View Source

     44class WindowSpec:
     45    def __init__(self, expression: exp.Expression = exp.Window()):
     46        self.expression = expression
     47
     48    def copy(self):
     49        return WindowSpec(self.expression.copy())
     50
     51    def sql(self, **kwargs) -> str:
     52        return self.expression.sql(dialect="spark", **kwargs)
     53
     54    def partitionBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
     55        from sqlglot.dataframe.sql.column import Column
     56
     57        cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
     58        expressions = [Column.ensure_col(x).expression for x in cols]
     59        window_spec = self.copy()
     60        partition_by_expressions = window_spec.expression.args.get("partition_by", [])
     61        partition_by_expressions.extend(expressions)
     62        window_spec.expression.set("partition_by", partition_by_expressions)
     63        return window_spec
     64
     65    def orderBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
     66        from sqlglot.dataframe.sql.column import Column
     67
     68        cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
     69        expressions = [Column.ensure_col(x).expression for x in cols]
     70        window_spec = self.copy()
     71        if window_spec.expression.args.get("order") is None:
     72            window_spec.expression.set("order", exp.Order(expressions=[]))
     73        order_by = window_spec.expression.args["order"].expressions
     74        order_by.extend(expressions)
     75        window_spec.expression.args["order"].set("expressions", order_by)
     76        return window_spec
     77
     78    def _calc_start_end(
     79        self, start: int, end: int
     80    ) -> t.Dict[str, t.Optional[t.Union[str, exp.Expression]]]:
     81        kwargs: t.Dict[str, t.Optional[t.Union[str, exp.Expression]]] = {
     82            "start_side": None,
     83            "end_side": None,
     84        }
     85        if start == Window.currentRow:
     86            kwargs["start"] = "CURRENT ROW"
     87        else:
     88            kwargs = {
     89                **kwargs,
     90                **{
     91                    "start_side": "PRECEDING",
     92                    "start": "UNBOUNDED"
     93                    if start <= Window.unboundedPreceding
     94                    else F.lit(start).expression,
     95                },
     96            }
     97        if end == Window.currentRow:
     98            kwargs["end"] = "CURRENT ROW"
     99        else:
    100            kwargs = {
    101                **kwargs,
    102                **{
    103                    "end_side": "FOLLOWING",
    104                    "end": "UNBOUNDED"
    105                    if end >= Window.unboundedFollowing
    106                    else F.lit(end).expression,
    107                },
    108            }
    109        return kwargs
    110
    111    def rowsBetween(self, start: int, end: int) -> WindowSpec:
    112        window_spec = self.copy()
    113        spec = self._calc_start_end(start, end)
    114        spec["kind"] = "ROWS"
    115        window_spec.expression.set(
    116            "spec",
    117            exp.WindowSpec(
    118                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    119            ),
    120        )
    121        return window_spec
    122
    123    def rangeBetween(self, start: int, end: int) -> WindowSpec:
    124        window_spec = self.copy()
    125        spec = self._calc_start_end(start, end)
    126        spec["kind"] = "RANGE"
    127        window_spec.expression.set(
    128            "spec",
    129            exp.WindowSpec(
    130                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    131            ),
    132        )
    133        return window_spec

WindowSpec(expression: sqlglot.expressions.Expression = (WINDOW )) View Source

    45    def __init__(self, expression: exp.Expression = exp.Window()):
    46        self.expression = expression

expression
def copy(self): View Source

    48    def copy(self):
    49        return WindowSpec(self.expression.copy())

def sql(self, **kwargs) -> str: View Source

    51    def sql(self, **kwargs) -> str:
    52        return self.expression.sql(dialect="spark", **kwargs)

def partitionBy( self, *cols: Union[<MagicMock id='140430580080384'>, List[<MagicMock id='140430580080384'>]]) -> sqlglot.dataframe.sql.WindowSpec: View Source

    54    def partitionBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    55        from sqlglot.dataframe.sql.column import Column
    56
    57        cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
    58        expressions = [Column.ensure_col(x).expression for x in cols]
    59        window_spec = self.copy()
    60        partition_by_expressions = window_spec.expression.args.get("partition_by", [])
    61        partition_by_expressions.extend(expressions)
    62        window_spec.expression.set("partition_by", partition_by_expressions)
    63        return window_spec

def orderBy( self, *cols: Union[<MagicMock id='140430580145632'>, List[<MagicMock id='140430580145632'>]]) -> sqlglot.dataframe.sql.WindowSpec: View Source

    65    def orderBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
    66        from sqlglot.dataframe.sql.column import Column
    67
    68        cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
    69        expressions = [Column.ensure_col(x).expression for x in cols]
    70        window_spec = self.copy()
    71        if window_spec.expression.args.get("order") is None:
    72            window_spec.expression.set("order", exp.Order(expressions=[]))
    73        order_by = window_spec.expression.args["order"].expressions
    74        order_by.extend(expressions)
    75        window_spec.expression.args["order"].set("expressions", order_by)
    76        return window_spec

def rowsBetween(self, start: int, end: int) -> sqlglot.dataframe.sql.WindowSpec: View Source

    111    def rowsBetween(self, start: int, end: int) -> WindowSpec:
    112        window_spec = self.copy()
    113        spec = self._calc_start_end(start, end)
    114        spec["kind"] = "ROWS"
    115        window_spec.expression.set(
    116            "spec",
    117            exp.WindowSpec(
    118                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    119            ),
    120        )
    121        return window_spec

def rangeBetween(self, start: int, end: int) -> sqlglot.dataframe.sql.WindowSpec: View Source

    123    def rangeBetween(self, start: int, end: int) -> WindowSpec:
    124        window_spec = self.copy()
    125        spec = self._calc_start_end(start, end)
    126        spec["kind"] = "RANGE"
    127        window_spec.expression.set(
    128            "spec",
    129            exp.WindowSpec(
    130                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    131            ),
    132        )
    133        return window_spec

class DataFrameReader: View Source

    16class DataFrameReader:
    17    def __init__(self, spark: SparkSession):
    18        self.spark = spark
    19
    20    def table(self, tableName: str) -> DataFrame:
    21        from sqlglot.dataframe.sql.dataframe import DataFrame
    22
    23        sqlglot.schema.add_table(tableName, dialect="spark")
    24
    25        return DataFrame(
    26            self.spark,
    27            exp.Select()
    28            .from_(exp.to_table(tableName, dialect="spark").transform(Spark.normalize_identifier))
    29            .select(
    30                *(column for column in sqlglot.schema.column_names(tableName, dialect="spark"))
    31            ),
    32        )

DataFrameReader(spark: sqlglot.dataframe.sql.SparkSession) View Source

    17    def __init__(self, spark: SparkSession):
    18        self.spark = spark

spark
def table(self, tableName: str) -> sqlglot.dataframe.sql.DataFrame: View Source

    20    def table(self, tableName: str) -> DataFrame:
    21        from sqlglot.dataframe.sql.dataframe import DataFrame
    22
    23        sqlglot.schema.add_table(tableName, dialect="spark")
    24
    25        return DataFrame(
    26            self.spark,
    27            exp.Select()
    28            .from_(exp.to_table(tableName, dialect="spark").transform(Spark.normalize_identifier))
    29            .select(
    30                *(column for column in sqlglot.schema.column_names(tableName, dialect="spark"))
    31            ),
    32        )

class DataFrameWriter: View Source

    35class DataFrameWriter:
    36    def __init__(
    37        self,
    38        df: DataFrame,
    39        spark: t.Optional[SparkSession] = None,
    40        mode: t.Optional[str] = None,
    41        by_name: bool = False,
    42    ):
    43        self._df = df
    44        self._spark = spark or df.spark
    45        self._mode = mode
    46        self._by_name = by_name
    47
    48    def copy(self, **kwargs) -> DataFrameWriter:
    49        return DataFrameWriter(
    50            **{
    51                k[1:] if k.startswith("_") else k: v
    52                for k, v in object_to_dict(self, **kwargs).items()
    53            }
    54        )
    55
    56    def sql(self, **kwargs) -> t.List[str]:
    57        return self._df.sql(**kwargs)
    58
    59    def mode(self, saveMode: t.Optional[str]) -> DataFrameWriter:
    60        return self.copy(_mode=saveMode)
    61
    62    @property
    63    def byName(self):
    64        return self.copy(by_name=True)
    65
    66    def insertInto(self, tableName: str, overwrite: t.Optional[bool] = None) -> DataFrameWriter:
    67        output_expression_container = exp.Insert(
    68            **{
    69                "this": exp.to_table(tableName),
    70                "overwrite": overwrite,
    71            }
    72        )
    73        df = self._df.copy(output_expression_container=output_expression_container)
    74        if self._by_name:
    75            columns = sqlglot.schema.column_names(tableName, only_visible=True, dialect="spark")
    76            df = df._convert_leaf_to_cte().select(*columns)
    77
    78        return self.copy(_df=df)
    79
    80    def saveAsTable(self, name: str, format: t.Optional[str] = None, mode: t.Optional[str] = None):
    81        if format is not None:
    82            raise NotImplementedError("Providing Format in the save as table is not supported")
    83        exists, replace, mode = None, None, mode or str(self._mode)
    84        if mode == "append":
    85            return self.insertInto(name)
    86        if mode == "ignore":
    87            exists = True
    88        if mode == "overwrite":
    89            replace = True
    90        output_expression_container = exp.Create(
    91            this=exp.to_table(name),
    92            kind="TABLE",
    93            exists=exists,
    94            replace=replace,
    95        )
    96        return self.copy(_df=self._df.copy(output_expression_container=output_expression_container))

DataFrameWriter( df: sqlglot.dataframe.sql.DataFrame, spark: Optional[sqlglot.dataframe.sql.SparkSession] = None, mode: Optional[str] = None, by_name: bool = False) View Source

    36    def __init__(
    37        self,
    38        df: DataFrame,
    39        spark: t.Optional[SparkSession] = None,
    40        mode: t.Optional[str] = None,
    41        by_name: bool = False,
    42    ):
    43        self._df = df
    44        self._spark = spark or df.spark
    45        self._mode = mode
    46        self._by_name = by_name

def copy(self, **kwargs) -> sqlglot.dataframe.sql.DataFrameWriter: View Source

    48    def copy(self, **kwargs) -> DataFrameWriter:
    49        return DataFrameWriter(
    50            **{
    51                k[1:] if k.startswith("_") else k: v
    52                for k, v in object_to_dict(self, **kwargs).items()
    53            }
    54        )

def sql(self, **kwargs) -> List[str]: View Source

    56    def sql(self, **kwargs) -> t.List[str]:
    57        return self._df.sql(**kwargs)

def mode( self, saveMode: Optional[str]) -> sqlglot.dataframe.sql.DataFrameWriter: View Source

    59    def mode(self, saveMode: t.Optional[str]) -> DataFrameWriter:
    60        return self.copy(_mode=saveMode)

byName
def insertInto( self, tableName: str, overwrite: Optional[bool] = None) -> sqlglot.dataframe.sql.DataFrameWriter: View Source

    66    def insertInto(self, tableName: str, overwrite: t.Optional[bool] = None) -> DataFrameWriter:
    67        output_expression_container = exp.Insert(
    68            **{
    69                "this": exp.to_table(tableName),
    70                "overwrite": overwrite,
    71            }
    72        )
    73        df = self._df.copy(output_expression_container=output_expression_container)
    74        if self._by_name:
    75            columns = sqlglot.schema.column_names(tableName, only_visible=True, dialect="spark")
    76            df = df._convert_leaf_to_cte().select(*columns)
    77
    78        return self.copy(_df=df)

def saveAsTable( self, name: str, format: Optional[str] = None, mode: Optional[str] = None): View Source

    80    def saveAsTable(self, name: str, format: t.Optional[str] = None, mode: t.Optional[str] = None):
    81        if format is not None:
    82            raise NotImplementedError("Providing Format in the save as table is not supported")
    83        exists, replace, mode = None, None, mode or str(self._mode)
    84        if mode == "append":
    85            return self.insertInto(name)
    86        if mode == "ignore":
    87            exists = True
    88        if mode == "overwrite":
    89            replace = True
    90        output_expression_container = exp.Create(
    91            this=exp.to_table(name),
    92            kind="TABLE",
    93            exists=exists,
    94            replace=replace,
    95        )
    96        return self.copy(_df=self._df.copy(output_expression_container=output_expression_container))