sqlglot.dataframe.sql API documentation

Edit on GitHub

# 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

     23class SparkSession:
     24    DEFAULT_DIALECT = "spark"
     25    _instance = None
     26
     27    def __init__(self):
     28        if not hasattr(self, "known_ids"):
     29            self.known_ids = set()
     30            self.known_branch_ids = set()
     31            self.known_sequence_ids = set()
     32            self.name_to_sequence_id_mapping = defaultdict(list)
     33            self.incrementing_id = 1
     34            self.dialect = Dialect.get_or_raise(self.DEFAULT_DIALECT)
     35
     36    def __new__(cls, *args, **kwargs) -> SparkSession:
     37        if cls._instance is None:
     38            cls._instance = super().__new__(cls)
     39        return cls._instance
     40
     41    @property
     42    def read(self) -> DataFrameReader:
     43        return DataFrameReader(self)
     44
     45    def table(self, tableName: str) -> DataFrame:
     46        return self.read.table(tableName)
     47
     48    def createDataFrame(
     49        self,
     50        data: t.Sequence[t.Union[t.Dict[str, ColumnLiterals], t.List[ColumnLiterals], t.Tuple]],
     51        schema: t.Optional[SchemaInput] = None,
     52        samplingRatio: t.Optional[float] = None,
     53        verifySchema: bool = False,
     54    ) -> DataFrame:
     55        from sqlglot.dataframe.sql.dataframe import DataFrame
     56
     57        if samplingRatio is not None or verifySchema:
     58            raise NotImplementedError("Sampling Ratio and Verify Schema are not supported")
     59        if schema is not None and (
     60            not isinstance(schema, (StructType, str, list))
     61            or (isinstance(schema, list) and not isinstance(schema[0], str))
     62        ):
     63            raise NotImplementedError("Only schema of either list or string of list supported")
     64        if not data:
     65            raise ValueError("Must provide data to create into a DataFrame")
     66
     67        column_mapping: t.Dict[str, t.Optional[str]]
     68        if schema is not None:
     69            column_mapping = get_column_mapping_from_schema_input(schema)
     70        elif isinstance(data[0], dict):
     71            column_mapping = {col_name.strip(): None for col_name in data[0]}
     72        else:
     73            column_mapping = {f"_{i}": None for i in range(1, len(data[0]) + 1)}
     74
     75        data_expressions = [
     76            exp.tuple_(
     77                *map(
     78                    lambda x: F.lit(x).expression,
     79                    row if not isinstance(row, dict) else row.values(),
     80                )
     81            )
     82            for row in data
     83        ]
     84
     85        sel_columns = [
     86            (
     87                F.col(name).cast(data_type).alias(name).expression
     88                if data_type is not None
     89                else F.col(name).expression
     90            )
     91            for name, data_type in column_mapping.items()
     92        ]
     93
     94        select_kwargs = {
     95            "expressions": sel_columns,
     96            "from": exp.From(
     97                this=exp.Values(
     98                    expressions=data_expressions,
     99                    alias=exp.TableAlias(
    100                        this=exp.to_identifier(self._auto_incrementing_name),
    101                        columns=[exp.to_identifier(col_name) for col_name in column_mapping],
    102                    ),
    103                ),
    104            ),
    105        }
    106
    107        sel_expression = exp.Select(**select_kwargs)
    108        return DataFrame(self, sel_expression)
    109
    110    def _optimize(
    111        self, expression: exp.Expression, dialect: t.Optional[Dialect] = None
    112    ) -> exp.Expression:
    113        dialect = dialect or self.dialect
    114        quote_identifiers(expression, dialect=dialect)
    115        return optimize(expression, dialect=dialect)
    116
    117    def sql(self, sqlQuery: str) -> DataFrame:
    118        expression = self._optimize(sqlglot.parse_one(sqlQuery, read=self.dialect))
    119        if isinstance(expression, exp.Select):
    120            df = DataFrame(self, expression)
    121            df = df._convert_leaf_to_cte()
    122        elif isinstance(expression, (exp.Create, exp.Insert)):
    123            select_expression = expression.expression.copy()
    124            if isinstance(expression, exp.Insert):
    125                select_expression.set("with", expression.args.get("with"))
    126                expression.set("with", None)
    127            del expression.args["expression"]
    128            df = DataFrame(self, select_expression, output_expression_container=expression)  # type: ignore
    129            df = df._convert_leaf_to_cte()
    130        else:
    131            raise ValueError(
    132                "Unknown expression type provided in the SQL. Please create an issue with the SQL."
    133            )
    134        return df
    135
    136    @property
    137    def _auto_incrementing_name(self) -> str:
    138        name = f"a{self.incrementing_id}"
    139        self.incrementing_id += 1
    140        return name
    141
    142    @property
    143    def _random_branch_id(self) -> str:
    144        id = self._random_id
    145        self.known_branch_ids.add(id)
    146        return id
    147
    148    @property
    149    def _random_sequence_id(self):
    150        id = self._random_id
    151        self.known_sequence_ids.add(id)
    152        return id
    153
    154    @property
    155    def _random_id(self) -> str:
    156        id = "r" + uuid.uuid4().hex
    157        self.known_ids.add(id)
    158        return id
    159
    160    @property
    161    def _join_hint_names(self) -> t.Set[str]:
    162        return {"BROADCAST", "MERGE", "SHUFFLE_HASH", "SHUFFLE_REPLICATE_NL"}
    163
    164    def _add_alias_to_mapping(self, name: str, sequence_id: str):
    165        self.name_to_sequence_id_mapping[name].append(sequence_id)
    166
    167    class Builder:
    168        SQLFRAME_DIALECT_KEY = "sqlframe.dialect"
    169
    170        def __init__(self):
    171            self.dialect = "spark"
    172
    173        def __getattr__(self, item) -> SparkSession.Builder:
    174            return self
    175
    176        def __call__(self, *args, **kwargs):
    177            return self
    178
    179        def config(
    180            self,
    181            key: t.Optional[str] = None,
    182            value: t.Optional[t.Any] = None,
    183            *,
    184            map: t.Optional[t.Dict[str, t.Any]] = None,
    185            **kwargs: t.Any,
    186        ) -> SparkSession.Builder:
    187            if key == self.SQLFRAME_DIALECT_KEY:
    188                self.dialect = value
    189            elif map and self.SQLFRAME_DIALECT_KEY in map:
    190                self.dialect = map[self.SQLFRAME_DIALECT_KEY]
    191            return self
    192
    193        def getOrCreate(self) -> SparkSession:
    194            spark = SparkSession()
    195            spark.dialect = Dialect.get_or_raise(self.dialect)
    196            return spark
    197
    198    @classproperty
    199    def builder(cls) -> Builder:
    200        return cls.Builder()

DEFAULT_DIALECT = 'spark'
read: DataFrameReader View Source

    41    @property
    42    def read(self) -> DataFrameReader:
    43        return DataFrameReader(self)

def table(self, tableName: str) -> DataFrame: View Source

    45    def table(self, tableName: str) -> DataFrame:
    46        return self.read.table(tableName)

def createDataFrame( self, data: Sequence[Union[Dict[str, <MagicMock id='139980125852944'>], List[<MagicMock id='139980125852944'>], Tuple]], schema: Optional[<MagicMock id='139980125405248'>] = None, samplingRatio: Optional[float] = None, verifySchema: bool = False) -> DataFrame: View Source

     48    def createDataFrame(
     49        self,
     50        data: t.Sequence[t.Union[t.Dict[str, ColumnLiterals], t.List[ColumnLiterals], t.Tuple]],
     51        schema: t.Optional[SchemaInput] = None,
     52        samplingRatio: t.Optional[float] = None,
     53        verifySchema: bool = False,
     54    ) -> DataFrame:
     55        from sqlglot.dataframe.sql.dataframe import DataFrame
     56
     57        if samplingRatio is not None or verifySchema:
     58            raise NotImplementedError("Sampling Ratio and Verify Schema are not supported")
     59        if schema is not None and (
     60            not isinstance(schema, (StructType, str, list))
     61            or (isinstance(schema, list) and not isinstance(schema[0], str))
     62        ):
     63            raise NotImplementedError("Only schema of either list or string of list supported")
     64        if not data:
     65            raise ValueError("Must provide data to create into a DataFrame")
     66
     67        column_mapping: t.Dict[str, t.Optional[str]]
     68        if schema is not None:
     69            column_mapping = get_column_mapping_from_schema_input(schema)
     70        elif isinstance(data[0], dict):
     71            column_mapping = {col_name.strip(): None for col_name in data[0]}
     72        else:
     73            column_mapping = {f"_{i}": None for i in range(1, len(data[0]) + 1)}
     74
     75        data_expressions = [
     76            exp.tuple_(
     77                *map(
     78                    lambda x: F.lit(x).expression,
     79                    row if not isinstance(row, dict) else row.values(),
     80                )
     81            )
     82            for row in data
     83        ]
     84
     85        sel_columns = [
     86            (
     87                F.col(name).cast(data_type).alias(name).expression
     88                if data_type is not None
     89                else F.col(name).expression
     90            )
     91            for name, data_type in column_mapping.items()
     92        ]
     93
     94        select_kwargs = {
     95            "expressions": sel_columns,
     96            "from": exp.From(
     97                this=exp.Values(
     98                    expressions=data_expressions,
     99                    alias=exp.TableAlias(
    100                        this=exp.to_identifier(self._auto_incrementing_name),
    101                        columns=[exp.to_identifier(col_name) for col_name in column_mapping],
    102                    ),
    103                ),
    104            ),
    105        }
    106
    107        sel_expression = exp.Select(**select_kwargs)
    108        return DataFrame(self, sel_expression)

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

    117    def sql(self, sqlQuery: str) -> DataFrame:
    118        expression = self._optimize(sqlglot.parse_one(sqlQuery, read=self.dialect))
    119        if isinstance(expression, exp.Select):
    120            df = DataFrame(self, expression)
    121            df = df._convert_leaf_to_cte()
    122        elif isinstance(expression, (exp.Create, exp.Insert)):
    123            select_expression = expression.expression.copy()
    124            if isinstance(expression, exp.Insert):
    125                select_expression.set("with", expression.args.get("with"))
    126                expression.set("with", None)
    127            del expression.args["expression"]
    128            df = DataFrame(self, select_expression, output_expression_container=expression)  # type: ignore
    129            df = df._convert_leaf_to_cte()
    130        else:
    131            raise ValueError(
    132                "Unknown expression type provided in the SQL. Please create an issue with the SQL."
    133            )
    134        return df

builder: SparkSession.Builder View Source

    198    @classproperty
    199    def builder(cls) -> Builder:
    200        return cls.Builder()

class SparkSession.Builder: View Source

    167    class Builder:
    168        SQLFRAME_DIALECT_KEY = "sqlframe.dialect"
    169
    170        def __init__(self):
    171            self.dialect = "spark"
    172
    173        def __getattr__(self, item) -> SparkSession.Builder:
    174            return self
    175
    176        def __call__(self, *args, **kwargs):
    177            return self
    178
    179        def config(
    180            self,
    181            key: t.Optional[str] = None,
    182            value: t.Optional[t.Any] = None,
    183            *,
    184            map: t.Optional[t.Dict[str, t.Any]] = None,
    185            **kwargs: t.Any,
    186        ) -> SparkSession.Builder:
    187            if key == self.SQLFRAME_DIALECT_KEY:
    188                self.dialect = value
    189            elif map and self.SQLFRAME_DIALECT_KEY in map:
    190                self.dialect = map[self.SQLFRAME_DIALECT_KEY]
    191            return self
    192
    193        def getOrCreate(self) -> SparkSession:
    194            spark = SparkSession()
    195            spark.dialect = Dialect.get_or_raise(self.dialect)
    196            return spark

SQLFRAME_DIALECT_KEY = 'sqlframe.dialect'
dialect
def config( self, key: Optional[str] = None, value: Optional[Any] = None, *, map: Optional[Dict[str, Any]] = None, **kwargs: Any) -> SparkSession.Builder: View Source

    179        def config(
    180            self,
    181            key: t.Optional[str] = None,
    182            value: t.Optional[t.Any] = None,
    183            *,
    184            map: t.Optional[t.Dict[str, t.Any]] = None,
    185            **kwargs: t.Any,
    186        ) -> SparkSession.Builder:
    187            if key == self.SQLFRAME_DIALECT_KEY:
    188                self.dialect = value
    189            elif map and self.SQLFRAME_DIALECT_KEY in map:
    190                self.dialect = map[self.SQLFRAME_DIALECT_KEY]
    191            return self

def getOrCreate(self) -> SparkSession: View Source

    193        def getOrCreate(self) -> SparkSession:
    194            spark = SparkSession()
    195            spark.dialect = Dialect.get_or_raise(self.dialect)
    196            return spark

class DataFrame: View Source

     47class DataFrame:
     48    def __init__(
     49        self,
     50        spark: SparkSession,
     51        expression: exp.Select,
     52        branch_id: t.Optional[str] = None,
     53        sequence_id: t.Optional[str] = None,
     54        last_op: Operation = Operation.INIT,
     55        pending_hints: t.Optional[t.List[exp.Expression]] = None,
     56        output_expression_container: t.Optional[OutputExpressionContainer] = None,
     57        **kwargs,
     58    ):
     59        self.spark = spark
     60        self.expression = expression
     61        self.branch_id = branch_id or self.spark._random_branch_id
     62        self.sequence_id = sequence_id or self.spark._random_sequence_id
     63        self.last_op = last_op
     64        self.pending_hints = pending_hints or []
     65        self.output_expression_container = output_expression_container or exp.Select()
     66
     67    def __getattr__(self, column_name: str) -> Column:
     68        return self[column_name]
     69
     70    def __getitem__(self, column_name: str) -> Column:
     71        column_name = f"{self.branch_id}.{column_name}"
     72        return Column(column_name)
     73
     74    def __copy__(self):
     75        return self.copy()
     76
     77    @property
     78    def sparkSession(self):
     79        return self.spark
     80
     81    @property
     82    def write(self):
     83        return DataFrameWriter(self)
     84
     85    @property
     86    def latest_cte_name(self) -> str:
     87        if not self.expression.ctes:
     88            from_exp = self.expression.args["from"]
     89            if from_exp.alias_or_name:
     90                return from_exp.alias_or_name
     91            table_alias = from_exp.find(exp.TableAlias)
     92            if not table_alias:
     93                raise RuntimeError(
     94                    f"Could not find an alias name for this expression: {self.expression}"
     95                )
     96            return table_alias.alias_or_name
     97        return self.expression.ctes[-1].alias
     98
     99    @property
    100    def pending_join_hints(self):
    101        return [hint for hint in self.pending_hints if isinstance(hint, exp.JoinHint)]
    102
    103    @property
    104    def pending_partition_hints(self):
    105        return [hint for hint in self.pending_hints if isinstance(hint, exp.Anonymous)]
    106
    107    @property
    108    def columns(self) -> t.List[str]:
    109        return self.expression.named_selects
    110
    111    @property
    112    def na(self) -> DataFrameNaFunctions:
    113        return DataFrameNaFunctions(self)
    114
    115    def _replace_cte_names_with_hashes(self, expression: exp.Select):
    116        replacement_mapping = {}
    117        for cte in expression.ctes:
    118            old_name_id = cte.args["alias"].this
    119            new_hashed_id = exp.to_identifier(
    120                self._create_hash_from_expression(cte.this), quoted=old_name_id.args["quoted"]
    121            )
    122            replacement_mapping[old_name_id] = new_hashed_id
    123            expression = expression.transform(replace_id_value, replacement_mapping).assert_is(
    124                exp.Select
    125            )
    126        return expression
    127
    128    def _create_cte_from_expression(
    129        self,
    130        expression: exp.Expression,
    131        branch_id: t.Optional[str] = None,
    132        sequence_id: t.Optional[str] = None,
    133        **kwargs,
    134    ) -> t.Tuple[exp.CTE, str]:
    135        name = self._create_hash_from_expression(expression)
    136        expression_to_cte = expression.copy()
    137        expression_to_cte.set("with", None)
    138        cte = exp.Select().with_(name, as_=expression_to_cte, **kwargs).ctes[0]
    139        cte.set("branch_id", branch_id or self.branch_id)
    140        cte.set("sequence_id", sequence_id or self.sequence_id)
    141        return cte, name
    142
    143    @t.overload
    144    def _ensure_list_of_columns(self, cols: t.Collection[ColumnOrLiteral]) -> t.List[Column]: ...
    145
    146    @t.overload
    147    def _ensure_list_of_columns(self, cols: ColumnOrLiteral) -> t.List[Column]: ...
    148
    149    def _ensure_list_of_columns(self, cols):
    150        return Column.ensure_cols(ensure_list(cols))
    151
    152    def _ensure_and_normalize_cols(self, cols, expression: t.Optional[exp.Select] = None):
    153        cols = self._ensure_list_of_columns(cols)
    154        normalize(self.spark, expression or self.expression, cols)
    155        return cols
    156
    157    def _ensure_and_normalize_col(self, col):
    158        col = Column.ensure_col(col)
    159        normalize(self.spark, self.expression, col)
    160        return col
    161
    162    def _convert_leaf_to_cte(self, sequence_id: t.Optional[str] = None) -> DataFrame:
    163        df = self._resolve_pending_hints()
    164        sequence_id = sequence_id or df.sequence_id
    165        expression = df.expression.copy()
    166        cte_expression, cte_name = df._create_cte_from_expression(
    167            expression=expression, sequence_id=sequence_id
    168        )
    169        new_expression = df._add_ctes_to_expression(
    170            exp.Select(), expression.ctes + [cte_expression]
    171        )
    172        sel_columns = df._get_outer_select_columns(cte_expression)
    173        new_expression = new_expression.from_(cte_name).select(
    174            *[x.alias_or_name for x in sel_columns]
    175        )
    176        return df.copy(expression=new_expression, sequence_id=sequence_id)
    177
    178    def _resolve_pending_hints(self) -> DataFrame:
    179        df = self.copy()
    180        if not self.pending_hints:
    181            return df
    182        expression = df.expression
    183        hint_expression = expression.args.get("hint") or exp.Hint(expressions=[])
    184        for hint in df.pending_partition_hints:
    185            hint_expression.append("expressions", hint)
    186            df.pending_hints.remove(hint)
    187
    188        join_aliases = {
    189            join_table.alias_or_name
    190            for join_table in get_tables_from_expression_with_join(expression)
    191        }
    192        if join_aliases:
    193            for hint in df.pending_join_hints:
    194                for sequence_id_expression in hint.expressions:
    195                    sequence_id_or_name = sequence_id_expression.alias_or_name
    196                    sequence_ids_to_match = [sequence_id_or_name]
    197                    if sequence_id_or_name in df.spark.name_to_sequence_id_mapping:
    198                        sequence_ids_to_match = df.spark.name_to_sequence_id_mapping[
    199                            sequence_id_or_name
    200                        ]
    201                    matching_ctes = [
    202                        cte
    203                        for cte in reversed(expression.ctes)
    204                        if cte.args["sequence_id"] in sequence_ids_to_match
    205                    ]
    206                    for matching_cte in matching_ctes:
    207                        if matching_cte.alias_or_name in join_aliases:
    208                            sequence_id_expression.set("this", matching_cte.args["alias"].this)
    209                            df.pending_hints.remove(hint)
    210                            break
    211                hint_expression.append("expressions", hint)
    212        if hint_expression.expressions:
    213            expression.set("hint", hint_expression)
    214        return df
    215
    216    def _hint(self, hint_name: str, args: t.List[Column]) -> DataFrame:
    217        hint_name = hint_name.upper()
    218        hint_expression = (
    219            exp.JoinHint(
    220                this=hint_name,
    221                expressions=[exp.to_table(parameter.alias_or_name) for parameter in args],
    222            )
    223            if hint_name in JOIN_HINTS
    224            else exp.Anonymous(
    225                this=hint_name, expressions=[parameter.expression for parameter in args]
    226            )
    227        )
    228        new_df = self.copy()
    229        new_df.pending_hints.append(hint_expression)
    230        return new_df
    231
    232    def _set_operation(self, klass: t.Callable, other: DataFrame, distinct: bool):
    233        other_df = other._convert_leaf_to_cte()
    234        base_expression = self.expression.copy()
    235        base_expression = self._add_ctes_to_expression(base_expression, other_df.expression.ctes)
    236        all_ctes = base_expression.ctes
    237        other_df.expression.set("with", None)
    238        base_expression.set("with", None)
    239        operation = klass(this=base_expression, distinct=distinct, expression=other_df.expression)
    240        operation.set("with", exp.With(expressions=all_ctes))
    241        return self.copy(expression=operation)._convert_leaf_to_cte()
    242
    243    def _cache(self, storage_level: str):
    244        df = self._convert_leaf_to_cte()
    245        df.expression.ctes[-1].set("cache_storage_level", storage_level)
    246        return df
    247
    248    @classmethod
    249    def _add_ctes_to_expression(cls, expression: exp.Select, ctes: t.List[exp.CTE]) -> exp.Select:
    250        expression = expression.copy()
    251        with_expression = expression.args.get("with")
    252        if with_expression:
    253            existing_ctes = with_expression.expressions
    254            existsing_cte_names = {x.alias_or_name for x in existing_ctes}
    255            for cte in ctes:
    256                if cte.alias_or_name not in existsing_cte_names:
    257                    existing_ctes.append(cte)
    258        else:
    259            existing_ctes = ctes
    260        expression.set("with", exp.With(expressions=existing_ctes))
    261        return expression
    262
    263    @classmethod
    264    def _get_outer_select_columns(cls, item: t.Union[exp.Expression, DataFrame]) -> t.List[Column]:
    265        expression = item.expression if isinstance(item, DataFrame) else item
    266        return [Column(x) for x in (expression.find(exp.Select) or exp.Select()).expressions]
    267
    268    @classmethod
    269    def _create_hash_from_expression(cls, expression: exp.Expression) -> str:
    270        from sqlglot.dataframe.sql.session import SparkSession
    271
    272        value = expression.sql(dialect=SparkSession().dialect).encode("utf-8")
    273        return f"t{zlib.crc32(value)}"[:6]
    274
    275    def _get_select_expressions(
    276        self,
    277    ) -> t.List[t.Tuple[t.Union[t.Type[exp.Cache], OutputExpressionContainer], exp.Select]]:
    278        select_expressions: t.List[
    279            t.Tuple[t.Union[t.Type[exp.Cache], OutputExpressionContainer], exp.Select]
    280        ] = []
    281        main_select_ctes: t.List[exp.CTE] = []
    282        for cte in self.expression.ctes:
    283            cache_storage_level = cte.args.get("cache_storage_level")
    284            if cache_storage_level:
    285                select_expression = cte.this.copy()
    286                select_expression.set("with", exp.With(expressions=copy(main_select_ctes)))
    287                select_expression.set("cte_alias_name", cte.alias_or_name)
    288                select_expression.set("cache_storage_level", cache_storage_level)
    289                select_expressions.append((exp.Cache, select_expression))
    290            else:
    291                main_select_ctes.append(cte)
    292        main_select = self.expression.copy()
    293        if main_select_ctes:
    294            main_select.set("with", exp.With(expressions=main_select_ctes))
    295        expression_select_pair = (type(self.output_expression_container), main_select)
    296        select_expressions.append(expression_select_pair)  # type: ignore
    297        return select_expressions
    298
    299    def sql(self, dialect: DialectType = None, optimize: bool = True, **kwargs) -> t.List[str]:
    300        from sqlglot.dataframe.sql.session import SparkSession
    301
    302        dialect = Dialect.get_or_raise(dialect or SparkSession().dialect)
    303
    304        df = self._resolve_pending_hints()
    305        select_expressions = df._get_select_expressions()
    306        output_expressions: t.List[t.Union[exp.Select, exp.Cache, exp.Drop]] = []
    307        replacement_mapping: t.Dict[exp.Identifier, exp.Identifier] = {}
    308
    309        for expression_type, select_expression in select_expressions:
    310            select_expression = select_expression.transform(
    311                replace_id_value, replacement_mapping
    312            ).assert_is(exp.Select)
    313            if optimize:
    314                select_expression = t.cast(
    315                    exp.Select, self.spark._optimize(select_expression, dialect=dialect)
    316                )
    317
    318            select_expression = df._replace_cte_names_with_hashes(select_expression)
    319
    320            expression: t.Union[exp.Select, exp.Cache, exp.Drop]
    321            if expression_type == exp.Cache:
    322                cache_table_name = df._create_hash_from_expression(select_expression)
    323                cache_table = exp.to_table(cache_table_name)
    324                original_alias_name = select_expression.args["cte_alias_name"]
    325
    326                replacement_mapping[exp.to_identifier(original_alias_name)] = exp.to_identifier(  # type: ignore
    327                    cache_table_name
    328                )
    329                sqlglot.schema.add_table(
    330                    cache_table_name,
    331                    {
    332                        expression.alias_or_name: expression.type.sql(dialect=dialect)
    333                        for expression in select_expression.expressions
    334                    },
    335                    dialect=dialect,
    336                )
    337
    338                cache_storage_level = select_expression.args["cache_storage_level"]
    339                options = [
    340                    exp.Literal.string("storageLevel"),
    341                    exp.Literal.string(cache_storage_level),
    342                ]
    343                expression = exp.Cache(
    344                    this=cache_table, expression=select_expression, lazy=True, options=options
    345                )
    346
    347                # We will drop the "view" if it exists before running the cache table
    348                output_expressions.append(exp.Drop(this=cache_table, exists=True, kind="VIEW"))
    349            elif expression_type == exp.Create:
    350                expression = df.output_expression_container.copy()
    351                expression.set("expression", select_expression)
    352            elif expression_type == exp.Insert:
    353                expression = df.output_expression_container.copy()
    354                select_without_ctes = select_expression.copy()
    355                select_without_ctes.set("with", None)
    356                expression.set("expression", select_without_ctes)
    357
    358                if select_expression.ctes:
    359                    expression.set("with", exp.With(expressions=select_expression.ctes))
    360            elif expression_type == exp.Select:
    361                expression = select_expression
    362            else:
    363                raise ValueError(f"Invalid expression type: {expression_type}")
    364
    365            output_expressions.append(expression)
    366
    367        return [expression.sql(dialect=dialect, **kwargs) for expression in output_expressions]
    368
    369    def copy(self, **kwargs) -> DataFrame:
    370        return DataFrame(**object_to_dict(self, **kwargs))
    371
    372    @operation(Operation.SELECT)
    373    def select(self, *cols, **kwargs) -> DataFrame:
    374        cols = self._ensure_and_normalize_cols(cols)
    375        kwargs["append"] = kwargs.get("append", False)
    376        if self.expression.args.get("joins"):
    377            ambiguous_cols = [
    378                col
    379                for col in cols
    380                if isinstance(col.column_expression, exp.Column) and not col.column_expression.table
    381            ]
    382            if ambiguous_cols:
    383                join_table_identifiers = [
    384                    x.this for x in get_tables_from_expression_with_join(self.expression)
    385                ]
    386                cte_names_in_join = [x.this for x in join_table_identifiers]
    387                # If we have columns that resolve to multiple CTE expressions then we want to use each CTE left-to-right
    388                # and therefore we allow multiple columns with the same name in the result. This matches the behavior
    389                # of Spark.
    390                resolved_column_position: t.Dict[Column, int] = {col: -1 for col in ambiguous_cols}
    391                for ambiguous_col in ambiguous_cols:
    392                    ctes_with_column = [
    393                        cte
    394                        for cte in self.expression.ctes
    395                        if cte.alias_or_name in cte_names_in_join
    396                        and ambiguous_col.alias_or_name in cte.this.named_selects
    397                    ]
    398                    # Check if there is a CTE with this column that we haven't used before. If so, use it. Otherwise,
    399                    # use the same CTE we used before
    400                    cte = seq_get(ctes_with_column, resolved_column_position[ambiguous_col] + 1)
    401                    if cte:
    402                        resolved_column_position[ambiguous_col] += 1
    403                    else:
    404                        cte = ctes_with_column[resolved_column_position[ambiguous_col]]
    405                    ambiguous_col.expression.set("table", cte.alias_or_name)
    406        return self.copy(
    407            expression=self.expression.select(*[x.expression for x in cols], **kwargs), **kwargs
    408        )
    409
    410    @operation(Operation.NO_OP)
    411    def alias(self, name: str, **kwargs) -> DataFrame:
    412        new_sequence_id = self.spark._random_sequence_id
    413        df = self.copy()
    414        for join_hint in df.pending_join_hints:
    415            for expression in join_hint.expressions:
    416                if expression.alias_or_name == self.sequence_id:
    417                    expression.set("this", Column.ensure_col(new_sequence_id).expression)
    418        df.spark._add_alias_to_mapping(name, new_sequence_id)
    419        return df._convert_leaf_to_cte(sequence_id=new_sequence_id)
    420
    421    @operation(Operation.WHERE)
    422    def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
    423        col = self._ensure_and_normalize_col(column)
    424        return self.copy(expression=self.expression.where(col.expression))
    425
    426    filter = where
    427
    428    @operation(Operation.GROUP_BY)
    429    def groupBy(self, *cols, **kwargs) -> GroupedData:
    430        columns = self._ensure_and_normalize_cols(cols)
    431        return GroupedData(self, columns, self.last_op)
    432
    433    @operation(Operation.SELECT)
    434    def agg(self, *exprs, **kwargs) -> DataFrame:
    435        cols = self._ensure_and_normalize_cols(exprs)
    436        return self.groupBy().agg(*cols)
    437
    438    @operation(Operation.FROM)
    439    def join(
    440        self,
    441        other_df: DataFrame,
    442        on: t.Union[str, t.List[str], Column, t.List[Column]],
    443        how: str = "inner",
    444        **kwargs,
    445    ) -> DataFrame:
    446        other_df = other_df._convert_leaf_to_cte()
    447        join_columns = self._ensure_list_of_columns(on)
    448        # We will determine actual "join on" expression later so we don't provide it at first
    449        join_expression = self.expression.join(
    450            other_df.latest_cte_name, join_type=how.replace("_", " ")
    451        )
    452        join_expression = self._add_ctes_to_expression(join_expression, other_df.expression.ctes)
    453        self_columns = self._get_outer_select_columns(join_expression)
    454        other_columns = self._get_outer_select_columns(other_df)
    455        # Determines the join clause and select columns to be used passed on what type of columns were provided for
    456        # the join. The columns returned changes based on how the on expression is provided.
    457        if isinstance(join_columns[0].expression, exp.Column):
    458            """
    459            Unique characteristics of join on column names only:
    460            * The column names are put at the front of the select list
    461            * The column names are deduplicated across the entire select list and only the column names (other dups are allowed)
    462            """
    463            table_names = [
    464                table.alias_or_name
    465                for table in get_tables_from_expression_with_join(join_expression)
    466            ]
    467            potential_ctes = [
    468                cte
    469                for cte in join_expression.ctes
    470                if cte.alias_or_name in table_names
    471                and cte.alias_or_name != other_df.latest_cte_name
    472            ]
    473            # Determine the table to reference for the left side of the join by checking each of the left side
    474            # tables and see if they have the column being referenced.
    475            join_column_pairs = []
    476            for join_column in join_columns:
    477                num_matching_ctes = 0
    478                for cte in potential_ctes:
    479                    if join_column.alias_or_name in cte.this.named_selects:
    480                        left_column = join_column.copy().set_table_name(cte.alias_or_name)
    481                        right_column = join_column.copy().set_table_name(other_df.latest_cte_name)
    482                        join_column_pairs.append((left_column, right_column))
    483                        num_matching_ctes += 1
    484                if num_matching_ctes > 1:
    485                    raise ValueError(
    486                        f"Column {join_column.alias_or_name} is ambiguous. Please specify the table name."
    487                    )
    488                elif num_matching_ctes == 0:
    489                    raise ValueError(
    490                        f"Column {join_column.alias_or_name} does not exist in any of the tables."
    491                    )
    492            join_clause = functools.reduce(
    493                lambda x, y: x & y,
    494                [left_column == right_column for left_column, right_column in join_column_pairs],
    495            )
    496            join_column_names = [left_col.alias_or_name for left_col, _ in join_column_pairs]
    497            # To match spark behavior only the join clause gets deduplicated and it gets put in the front of the column list
    498            select_column_names = [
    499                (
    500                    column.alias_or_name
    501                    if not isinstance(column.expression.this, exp.Star)
    502                    else column.sql()
    503                )
    504                for column in self_columns + other_columns
    505            ]
    506            select_column_names = [
    507                column_name
    508                for column_name in select_column_names
    509                if column_name not in join_column_names
    510            ]
    511            select_column_names = join_column_names + select_column_names
    512        else:
    513            """
    514            Unique characteristics of join on expressions:
    515            * There is no deduplication of the results.
    516            * The left join dataframe columns go first and right come after. No sort preference is given to join columns
    517            """
    518            join_columns = self._ensure_and_normalize_cols(join_columns, join_expression)
    519            if len(join_columns) > 1:
    520                join_columns = [functools.reduce(lambda x, y: x & y, join_columns)]
    521            join_clause = join_columns[0]
    522            select_column_names = [column.alias_or_name for column in self_columns + other_columns]
    523
    524        # Update the on expression with the actual join clause to replace the dummy one from before
    525        join_expression.args["joins"][-1].set("on", join_clause.expression)
    526        new_df = self.copy(expression=join_expression)
    527        new_df.pending_join_hints.extend(self.pending_join_hints)
    528        new_df.pending_hints.extend(other_df.pending_hints)
    529        new_df = new_df.select.__wrapped__(new_df, *select_column_names)
    530        return new_df
    531
    532    @operation(Operation.ORDER_BY)
    533    def orderBy(
    534        self,
    535        *cols: t.Union[str, Column],
    536        ascending: t.Optional[t.Union[t.Any, t.List[t.Any]]] = None,
    537    ) -> DataFrame:
    538        """
    539        This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark
    540        has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this
    541        is unlikely to come up.
    542        """
    543        columns = self._ensure_and_normalize_cols(cols)
    544        pre_ordered_col_indexes = [
    545            i for i, col in enumerate(columns) if isinstance(col.expression, exp.Ordered)
    546        ]
    547        if ascending is None:
    548            ascending = [True] * len(columns)
    549        elif not isinstance(ascending, list):
    550            ascending = [ascending] * len(columns)
    551        ascending = [bool(x) for i, x in enumerate(ascending)]
    552        assert len(columns) == len(
    553            ascending
    554        ), "The length of items in ascending must equal the number of columns provided"
    555        col_and_ascending = list(zip(columns, ascending))
    556        order_by_columns = [
    557            (
    558                exp.Ordered(this=col.expression, desc=not asc)
    559                if i not in pre_ordered_col_indexes
    560                else columns[i].column_expression
    561            )
    562            for i, (col, asc) in enumerate(col_and_ascending)
    563        ]
    564        return self.copy(expression=self.expression.order_by(*order_by_columns))
    565
    566    sort = orderBy
    567
    568    @operation(Operation.FROM)
    569    def union(self, other: DataFrame) -> DataFrame:
    570        return self._set_operation(exp.Union, other, False)
    571
    572    unionAll = union
    573
    574    @operation(Operation.FROM)
    575    def unionByName(self, other: DataFrame, allowMissingColumns: bool = False):
    576        l_columns = self.columns
    577        r_columns = other.columns
    578        if not allowMissingColumns:
    579            l_expressions = l_columns
    580            r_expressions = l_columns
    581        else:
    582            l_expressions = []
    583            r_expressions = []
    584            r_columns_unused = copy(r_columns)
    585            for l_column in l_columns:
    586                l_expressions.append(l_column)
    587                if l_column in r_columns:
    588                    r_expressions.append(l_column)
    589                    r_columns_unused.remove(l_column)
    590                else:
    591                    r_expressions.append(exp.alias_(exp.Null(), l_column, copy=False))
    592            for r_column in r_columns_unused:
    593                l_expressions.append(exp.alias_(exp.Null(), r_column, copy=False))
    594                r_expressions.append(r_column)
    595        r_df = (
    596            other.copy()._convert_leaf_to_cte().select(*self._ensure_list_of_columns(r_expressions))
    597        )
    598        l_df = self.copy()
    599        if allowMissingColumns:
    600            l_df = l_df._convert_leaf_to_cte().select(*self._ensure_list_of_columns(l_expressions))
    601        return l_df._set_operation(exp.Union, r_df, False)
    602
    603    @operation(Operation.FROM)
    604    def intersect(self, other: DataFrame) -> DataFrame:
    605        return self._set_operation(exp.Intersect, other, True)
    606
    607    @operation(Operation.FROM)
    608    def intersectAll(self, other: DataFrame) -> DataFrame:
    609        return self._set_operation(exp.Intersect, other, False)
    610
    611    @operation(Operation.FROM)
    612    def exceptAll(self, other: DataFrame) -> DataFrame:
    613        return self._set_operation(exp.Except, other, False)
    614
    615    @operation(Operation.SELECT)
    616    def distinct(self) -> DataFrame:
    617        return self.copy(expression=self.expression.distinct())
    618
    619    @operation(Operation.SELECT)
    620    def dropDuplicates(self, subset: t.Optional[t.List[str]] = None):
    621        if not subset:
    622            return self.distinct()
    623        column_names = ensure_list(subset)
    624        window = Window.partitionBy(*column_names).orderBy(*column_names)
    625        return (
    626            self.copy()
    627            .withColumn("row_num", F.row_number().over(window))
    628            .where(F.col("row_num") == F.lit(1))
    629            .drop("row_num")
    630        )
    631
    632    @operation(Operation.FROM)
    633    def dropna(
    634        self,
    635        how: str = "any",
    636        thresh: t.Optional[int] = None,
    637        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    638    ) -> DataFrame:
    639        minimum_non_null = thresh or 0  # will be determined later if thresh is null
    640        new_df = self.copy()
    641        all_columns = self._get_outer_select_columns(new_df.expression)
    642        if subset:
    643            null_check_columns = self._ensure_and_normalize_cols(subset)
    644        else:
    645            null_check_columns = all_columns
    646        if thresh is None:
    647            minimum_num_nulls = 1 if how == "any" else len(null_check_columns)
    648        else:
    649            minimum_num_nulls = len(null_check_columns) - minimum_non_null + 1
    650        if minimum_num_nulls > len(null_check_columns):
    651            raise RuntimeError(
    652                f"The minimum num nulls for dropna must be less than or equal to the number of columns. "
    653                f"Minimum num nulls: {minimum_num_nulls}, Num Columns: {len(null_check_columns)}"
    654            )
    655        if_null_checks = [
    656            F.when(column.isNull(), F.lit(1)).otherwise(F.lit(0)) for column in null_check_columns
    657        ]
    658        nulls_added_together = functools.reduce(lambda x, y: x + y, if_null_checks)
    659        num_nulls = nulls_added_together.alias("num_nulls")
    660        new_df = new_df.select(num_nulls, append=True)
    661        filtered_df = new_df.where(F.col("num_nulls") < F.lit(minimum_num_nulls))
    662        final_df = filtered_df.select(*all_columns)
    663        return final_df
    664
    665    @operation(Operation.FROM)
    666    def fillna(
    667        self,
    668        value: t.Union[ColumnLiterals],
    669        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    670    ) -> DataFrame:
    671        """
    672        Functionality Difference: If you provide a value to replace a null and that type conflicts
    673        with the type of the column then PySpark will just ignore your replacement.
    674        This will try to cast them to be the same in some cases. So they won't always match.
    675        Best to not mix types so make sure replacement is the same type as the column
    676
    677        Possibility for improvement: Use `typeof` function to get the type of the column
    678        and check if it matches the type of the value provided. If not then make it null.
    679        """
    680        from sqlglot.dataframe.sql.functions import lit
    681
    682        values = None
    683        columns = None
    684        new_df = self.copy()
    685        all_columns = self._get_outer_select_columns(new_df.expression)
    686        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    687        if isinstance(value, dict):
    688            values = list(value.values())
    689            columns = self._ensure_and_normalize_cols(list(value))
    690        if not columns:
    691            columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    692        if not values:
    693            values = [value] * len(columns)
    694        value_columns = [lit(value) for value in values]
    695
    696        null_replacement_mapping = {
    697            column.alias_or_name: (
    698                F.when(column.isNull(), value).otherwise(column).alias(column.alias_or_name)
    699            )
    700            for column, value in zip(columns, value_columns)
    701        }
    702        null_replacement_mapping = {**all_column_mapping, **null_replacement_mapping}
    703        null_replacement_columns = [
    704            null_replacement_mapping[column.alias_or_name] for column in all_columns
    705        ]
    706        new_df = new_df.select(*null_replacement_columns)
    707        return new_df
    708
    709    @operation(Operation.FROM)
    710    def replace(
    711        self,
    712        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    713        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    714        subset: t.Optional[t.Collection[ColumnOrName] | ColumnOrName] = None,
    715    ) -> DataFrame:
    716        from sqlglot.dataframe.sql.functions import lit
    717
    718        old_values = None
    719        new_df = self.copy()
    720        all_columns = self._get_outer_select_columns(new_df.expression)
    721        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    722
    723        columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    724        if isinstance(to_replace, dict):
    725            old_values = list(to_replace)
    726            new_values = list(to_replace.values())
    727        elif not old_values and isinstance(to_replace, list):
    728            assert isinstance(value, list), "value must be a list since the replacements are a list"
    729            assert len(to_replace) == len(
    730                value
    731            ), "the replacements and values must be the same length"
    732            old_values = to_replace
    733            new_values = value
    734        else:
    735            old_values = [to_replace] * len(columns)
    736            new_values = [value] * len(columns)
    737        old_values = [lit(value) for value in old_values]
    738        new_values = [lit(value) for value in new_values]
    739
    740        replacement_mapping = {}
    741        for column in columns:
    742            expression = Column(None)
    743            for i, (old_value, new_value) in enumerate(zip(old_values, new_values)):
    744                if i == 0:
    745                    expression = F.when(column == old_value, new_value)
    746                else:
    747                    expression = expression.when(column == old_value, new_value)  # type: ignore
    748            replacement_mapping[column.alias_or_name] = expression.otherwise(column).alias(
    749                column.expression.alias_or_name
    750            )
    751
    752        replacement_mapping = {**all_column_mapping, **replacement_mapping}
    753        replacement_columns = [replacement_mapping[column.alias_or_name] for column in all_columns]
    754        new_df = new_df.select(*replacement_columns)
    755        return new_df
    756
    757    @operation(Operation.SELECT)
    758    def withColumn(self, colName: str, col: Column) -> DataFrame:
    759        col = self._ensure_and_normalize_col(col)
    760        existing_col_names = self.expression.named_selects
    761        existing_col_index = (
    762            existing_col_names.index(colName) if colName in existing_col_names else None
    763        )
    764        if existing_col_index:
    765            expression = self.expression.copy()
    766            expression.expressions[existing_col_index] = col.expression
    767            return self.copy(expression=expression)
    768        return self.copy().select(col.alias(colName), append=True)
    769
    770    @operation(Operation.SELECT)
    771    def withColumnRenamed(self, existing: str, new: str):
    772        expression = self.expression.copy()
    773        existing_columns = [
    774            expression
    775            for expression in expression.expressions
    776            if expression.alias_or_name == existing
    777        ]
    778        if not existing_columns:
    779            raise ValueError("Tried to rename a column that doesn't exist")
    780        for existing_column in existing_columns:
    781            if isinstance(existing_column, exp.Column):
    782                existing_column.replace(exp.alias_(existing_column, new))
    783            else:
    784                existing_column.set("alias", exp.to_identifier(new))
    785        return self.copy(expression=expression)
    786
    787    @operation(Operation.SELECT)
    788    def drop(self, *cols: t.Union[str, Column]) -> DataFrame:
    789        all_columns = self._get_outer_select_columns(self.expression)
    790        drop_cols = self._ensure_and_normalize_cols(cols)
    791        new_columns = [
    792            col
    793            for col in all_columns
    794            if col.alias_or_name not in [drop_column.alias_or_name for drop_column in drop_cols]
    795        ]
    796        return self.copy().select(*new_columns, append=False)
    797
    798    @operation(Operation.LIMIT)
    799    def limit(self, num: int) -> DataFrame:
    800        return self.copy(expression=self.expression.limit(num))
    801
    802    @operation(Operation.NO_OP)
    803    def hint(self, name: str, *parameters: t.Optional[t.Union[str, int]]) -> DataFrame:
    804        parameter_list = ensure_list(parameters)
    805        parameter_columns = (
    806            self._ensure_list_of_columns(parameter_list)
    807            if parameters
    808            else Column.ensure_cols([self.sequence_id])
    809        )
    810        return self._hint(name, parameter_columns)
    811
    812    @operation(Operation.NO_OP)
    813    def repartition(
    814        self, numPartitions: t.Union[int, ColumnOrName], *cols: ColumnOrName
    815    ) -> DataFrame:
    816        num_partition_cols = self._ensure_list_of_columns(numPartitions)
    817        columns = self._ensure_and_normalize_cols(cols)
    818        args = num_partition_cols + columns
    819        return self._hint("repartition", args)
    820
    821    @operation(Operation.NO_OP)
    822    def coalesce(self, numPartitions: int) -> DataFrame:
    823        num_partitions = Column.ensure_cols([numPartitions])
    824        return self._hint("coalesce", num_partitions)
    825
    826    @operation(Operation.NO_OP)
    827    def cache(self) -> DataFrame:
    828        return self._cache(storage_level="MEMORY_AND_DISK")
    829
    830    @operation(Operation.NO_OP)
    831    def persist(self, storageLevel: str = "MEMORY_AND_DISK_SER") -> DataFrame:
    832        """
    833        Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html
    834        """
    835        return self._cache(storageLevel)

DataFrame( spark: <MagicMock id='139980128668496'>, 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='139980141257792'>] = None, **kwargs) View Source

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

spark
expression
branch_id
sequence_id
last_op
pending_hints
output_expression_container
sparkSession View Source

    77    @property
    78    def sparkSession(self):
    79        return self.spark

write View Source

    81    @property
    82    def write(self):
    83        return DataFrameWriter(self)

latest_cte_name: str View Source

    85    @property
    86    def latest_cte_name(self) -> str:
    87        if not self.expression.ctes:
    88            from_exp = self.expression.args["from"]
    89            if from_exp.alias_or_name:
    90                return from_exp.alias_or_name
    91            table_alias = from_exp.find(exp.TableAlias)
    92            if not table_alias:
    93                raise RuntimeError(
    94                    f"Could not find an alias name for this expression: {self.expression}"
    95                )
    96            return table_alias.alias_or_name
    97        return self.expression.ctes[-1].alias

pending_join_hints View Source

     99    @property
    100    def pending_join_hints(self):
    101        return [hint for hint in self.pending_hints if isinstance(hint, exp.JoinHint)]

pending_partition_hints View Source

    103    @property
    104    def pending_partition_hints(self):
    105        return [hint for hint in self.pending_hints if isinstance(hint, exp.Anonymous)]

columns: List[str] View Source

    107    @property
    108    def columns(self) -> t.List[str]:
    109        return self.expression.named_selects

na: DataFrameNaFunctions View Source

    111    @property
    112    def na(self) -> DataFrameNaFunctions:
    113        return DataFrameNaFunctions(self)

def sql( self, dialect: <MagicMock id='139980124350176'> = None, optimize: bool = True, **kwargs) -> List[str]: View Source

    299    def sql(self, dialect: DialectType = None, optimize: bool = True, **kwargs) -> t.List[str]:
    300        from sqlglot.dataframe.sql.session import SparkSession
    301
    302        dialect = Dialect.get_or_raise(dialect or SparkSession().dialect)
    303
    304        df = self._resolve_pending_hints()
    305        select_expressions = df._get_select_expressions()
    306        output_expressions: t.List[t.Union[exp.Select, exp.Cache, exp.Drop]] = []
    307        replacement_mapping: t.Dict[exp.Identifier, exp.Identifier] = {}
    308
    309        for expression_type, select_expression in select_expressions:
    310            select_expression = select_expression.transform(
    311                replace_id_value, replacement_mapping
    312            ).assert_is(exp.Select)
    313            if optimize:
    314                select_expression = t.cast(
    315                    exp.Select, self.spark._optimize(select_expression, dialect=dialect)
    316                )
    317
    318            select_expression = df._replace_cte_names_with_hashes(select_expression)
    319
    320            expression: t.Union[exp.Select, exp.Cache, exp.Drop]
    321            if expression_type == exp.Cache:
    322                cache_table_name = df._create_hash_from_expression(select_expression)
    323                cache_table = exp.to_table(cache_table_name)
    324                original_alias_name = select_expression.args["cte_alias_name"]
    325
    326                replacement_mapping[exp.to_identifier(original_alias_name)] = exp.to_identifier(  # type: ignore
    327                    cache_table_name
    328                )
    329                sqlglot.schema.add_table(
    330                    cache_table_name,
    331                    {
    332                        expression.alias_or_name: expression.type.sql(dialect=dialect)
    333                        for expression in select_expression.expressions
    334                    },
    335                    dialect=dialect,
    336                )
    337
    338                cache_storage_level = select_expression.args["cache_storage_level"]
    339                options = [
    340                    exp.Literal.string("storageLevel"),
    341                    exp.Literal.string(cache_storage_level),
    342                ]
    343                expression = exp.Cache(
    344                    this=cache_table, expression=select_expression, lazy=True, options=options
    345                )
    346
    347                # We will drop the "view" if it exists before running the cache table
    348                output_expressions.append(exp.Drop(this=cache_table, exists=True, kind="VIEW"))
    349            elif expression_type == exp.Create:
    350                expression = df.output_expression_container.copy()
    351                expression.set("expression", select_expression)
    352            elif expression_type == exp.Insert:
    353                expression = df.output_expression_container.copy()
    354                select_without_ctes = select_expression.copy()
    355                select_without_ctes.set("with", None)
    356                expression.set("expression", select_without_ctes)
    357
    358                if select_expression.ctes:
    359                    expression.set("with", exp.With(expressions=select_expression.ctes))
    360            elif expression_type == exp.Select:
    361                expression = select_expression
    362            else:
    363                raise ValueError(f"Invalid expression type: {expression_type}")
    364
    365            output_expressions.append(expression)
    366
    367        return [expression.sql(dialect=dialect, **kwargs) for expression in output_expressions]

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

    369    def copy(self, **kwargs) -> DataFrame:
    370        return DataFrame(**object_to_dict(self, **kwargs))

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

    372    @operation(Operation.SELECT)
    373    def select(self, *cols, **kwargs) -> DataFrame:
    374        cols = self._ensure_and_normalize_cols(cols)
    375        kwargs["append"] = kwargs.get("append", False)
    376        if self.expression.args.get("joins"):
    377            ambiguous_cols = [
    378                col
    379                for col in cols
    380                if isinstance(col.column_expression, exp.Column) and not col.column_expression.table
    381            ]
    382            if ambiguous_cols:
    383                join_table_identifiers = [
    384                    x.this for x in get_tables_from_expression_with_join(self.expression)
    385                ]
    386                cte_names_in_join = [x.this for x in join_table_identifiers]
    387                # If we have columns that resolve to multiple CTE expressions then we want to use each CTE left-to-right
    388                # and therefore we allow multiple columns with the same name in the result. This matches the behavior
    389                # of Spark.
    390                resolved_column_position: t.Dict[Column, int] = {col: -1 for col in ambiguous_cols}
    391                for ambiguous_col in ambiguous_cols:
    392                    ctes_with_column = [
    393                        cte
    394                        for cte in self.expression.ctes
    395                        if cte.alias_or_name in cte_names_in_join
    396                        and ambiguous_col.alias_or_name in cte.this.named_selects
    397                    ]
    398                    # Check if there is a CTE with this column that we haven't used before. If so, use it. Otherwise,
    399                    # use the same CTE we used before
    400                    cte = seq_get(ctes_with_column, resolved_column_position[ambiguous_col] + 1)
    401                    if cte:
    402                        resolved_column_position[ambiguous_col] += 1
    403                    else:
    404                        cte = ctes_with_column[resolved_column_position[ambiguous_col]]
    405                    ambiguous_col.expression.set("table", cte.alias_or_name)
    406        return self.copy(
    407            expression=self.expression.select(*[x.expression for x in cols], **kwargs), **kwargs
    408        )

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

    410    @operation(Operation.NO_OP)
    411    def alias(self, name: str, **kwargs) -> DataFrame:
    412        new_sequence_id = self.spark._random_sequence_id
    413        df = self.copy()
    414        for join_hint in df.pending_join_hints:
    415            for expression in join_hint.expressions:
    416                if expression.alias_or_name == self.sequence_id:
    417                    expression.set("this", Column.ensure_col(new_sequence_id).expression)
    418        df.spark._add_alias_to_mapping(name, new_sequence_id)
    419        return df._convert_leaf_to_cte(sequence_id=new_sequence_id)

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

    421    @operation(Operation.WHERE)
    422    def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
    423        col = self._ensure_and_normalize_col(column)
    424        return self.copy(expression=self.expression.where(col.expression))

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

    421    @operation(Operation.WHERE)
    422    def where(self, column: t.Union[Column, bool], **kwargs) -> DataFrame:
    423        col = self._ensure_and_normalize_col(column)
    424        return self.copy(expression=self.expression.where(col.expression))

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

    428    @operation(Operation.GROUP_BY)
    429    def groupBy(self, *cols, **kwargs) -> GroupedData:
    430        columns = self._ensure_and_normalize_cols(cols)
    431        return GroupedData(self, columns, self.last_op)

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

    433    @operation(Operation.SELECT)
    434    def agg(self, *exprs, **kwargs) -> DataFrame:
    435        cols = self._ensure_and_normalize_cols(exprs)
    436        return self.groupBy().agg(*cols)

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

    438    @operation(Operation.FROM)
    439    def join(
    440        self,
    441        other_df: DataFrame,
    442        on: t.Union[str, t.List[str], Column, t.List[Column]],
    443        how: str = "inner",
    444        **kwargs,
    445    ) -> DataFrame:
    446        other_df = other_df._convert_leaf_to_cte()
    447        join_columns = self._ensure_list_of_columns(on)
    448        # We will determine actual "join on" expression later so we don't provide it at first
    449        join_expression = self.expression.join(
    450            other_df.latest_cte_name, join_type=how.replace("_", " ")
    451        )
    452        join_expression = self._add_ctes_to_expression(join_expression, other_df.expression.ctes)
    453        self_columns = self._get_outer_select_columns(join_expression)
    454        other_columns = self._get_outer_select_columns(other_df)
    455        # Determines the join clause and select columns to be used passed on what type of columns were provided for
    456        # the join. The columns returned changes based on how the on expression is provided.
    457        if isinstance(join_columns[0].expression, exp.Column):
    458            """
    459            Unique characteristics of join on column names only:
    460            * The column names are put at the front of the select list
    461            * The column names are deduplicated across the entire select list and only the column names (other dups are allowed)
    462            """
    463            table_names = [
    464                table.alias_or_name
    465                for table in get_tables_from_expression_with_join(join_expression)
    466            ]
    467            potential_ctes = [
    468                cte
    469                for cte in join_expression.ctes
    470                if cte.alias_or_name in table_names
    471                and cte.alias_or_name != other_df.latest_cte_name
    472            ]
    473            # Determine the table to reference for the left side of the join by checking each of the left side
    474            # tables and see if they have the column being referenced.
    475            join_column_pairs = []
    476            for join_column in join_columns:
    477                num_matching_ctes = 0
    478                for cte in potential_ctes:
    479                    if join_column.alias_or_name in cte.this.named_selects:
    480                        left_column = join_column.copy().set_table_name(cte.alias_or_name)
    481                        right_column = join_column.copy().set_table_name(other_df.latest_cte_name)
    482                        join_column_pairs.append((left_column, right_column))
    483                        num_matching_ctes += 1
    484                if num_matching_ctes > 1:
    485                    raise ValueError(
    486                        f"Column {join_column.alias_or_name} is ambiguous. Please specify the table name."
    487                    )
    488                elif num_matching_ctes == 0:
    489                    raise ValueError(
    490                        f"Column {join_column.alias_or_name} does not exist in any of the tables."
    491                    )
    492            join_clause = functools.reduce(
    493                lambda x, y: x & y,
    494                [left_column == right_column for left_column, right_column in join_column_pairs],
    495            )
    496            join_column_names = [left_col.alias_or_name for left_col, _ in join_column_pairs]
    497            # To match spark behavior only the join clause gets deduplicated and it gets put in the front of the column list
    498            select_column_names = [
    499                (
    500                    column.alias_or_name
    501                    if not isinstance(column.expression.this, exp.Star)
    502                    else column.sql()
    503                )
    504                for column in self_columns + other_columns
    505            ]
    506            select_column_names = [
    507                column_name
    508                for column_name in select_column_names
    509                if column_name not in join_column_names
    510            ]
    511            select_column_names = join_column_names + select_column_names
    512        else:
    513            """
    514            Unique characteristics of join on expressions:
    515            * There is no deduplication of the results.
    516            * The left join dataframe columns go first and right come after. No sort preference is given to join columns
    517            """
    518            join_columns = self._ensure_and_normalize_cols(join_columns, join_expression)
    519            if len(join_columns) > 1:
    520                join_columns = [functools.reduce(lambda x, y: x & y, join_columns)]
    521            join_clause = join_columns[0]
    522            select_column_names = [column.alias_or_name for column in self_columns + other_columns]
    523
    524        # Update the on expression with the actual join clause to replace the dummy one from before
    525        join_expression.args["joins"][-1].set("on", join_clause.expression)
    526        new_df = self.copy(expression=join_expression)
    527        new_df.pending_join_hints.extend(self.pending_join_hints)
    528        new_df.pending_hints.extend(other_df.pending_hints)
    529        new_df = new_df.select.__wrapped__(new_df, *select_column_names)
    530        return new_df

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

    532    @operation(Operation.ORDER_BY)
    533    def orderBy(
    534        self,
    535        *cols: t.Union[str, Column],
    536        ascending: t.Optional[t.Union[t.Any, t.List[t.Any]]] = None,
    537    ) -> DataFrame:
    538        """
    539        This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark
    540        has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this
    541        is unlikely to come up.
    542        """
    543        columns = self._ensure_and_normalize_cols(cols)
    544        pre_ordered_col_indexes = [
    545            i for i, col in enumerate(columns) if isinstance(col.expression, exp.Ordered)
    546        ]
    547        if ascending is None:
    548            ascending = [True] * len(columns)
    549        elif not isinstance(ascending, list):
    550            ascending = [ascending] * len(columns)
    551        ascending = [bool(x) for i, x in enumerate(ascending)]
    552        assert len(columns) == len(
    553            ascending
    554        ), "The length of items in ascending must equal the number of columns provided"
    555        col_and_ascending = list(zip(columns, ascending))
    556        order_by_columns = [
    557            (
    558                exp.Ordered(this=col.expression, desc=not asc)
    559                if i not in pre_ordered_col_indexes
    560                else columns[i].column_expression
    561            )
    562            for i, (col, asc) in enumerate(col_and_ascending)
    563        ]
    564        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, Column], ascending: Union[Any, List[Any], NoneType] = None) -> DataFrame: View Source

    532    @operation(Operation.ORDER_BY)
    533    def orderBy(
    534        self,
    535        *cols: t.Union[str, Column],
    536        ascending: t.Optional[t.Union[t.Any, t.List[t.Any]]] = None,
    537    ) -> DataFrame:
    538        """
    539        This implementation lets any ordered columns take priority over whatever is provided in `ascending`. Spark
    540        has irregular behavior and can result in runtime errors. Users shouldn't be mixing the two anyways so this
    541        is unlikely to come up.
    542        """
    543        columns = self._ensure_and_normalize_cols(cols)
    544        pre_ordered_col_indexes = [
    545            i for i, col in enumerate(columns) if isinstance(col.expression, exp.Ordered)
    546        ]
    547        if ascending is None:
    548            ascending = [True] * len(columns)
    549        elif not isinstance(ascending, list):
    550            ascending = [ascending] * len(columns)
    551        ascending = [bool(x) for i, x in enumerate(ascending)]
    552        assert len(columns) == len(
    553            ascending
    554        ), "The length of items in ascending must equal the number of columns provided"
    555        col_and_ascending = list(zip(columns, ascending))
    556        order_by_columns = [
    557            (
    558                exp.Ordered(this=col.expression, desc=not asc)
    559                if i not in pre_ordered_col_indexes
    560                else columns[i].column_expression
    561            )
    562            for i, (col, asc) in enumerate(col_and_ascending)
    563        ]
    564        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: DataFrame) -> DataFrame: View Source

    568    @operation(Operation.FROM)
    569    def union(self, other: DataFrame) -> DataFrame:
    570        return self._set_operation(exp.Union, other, False)

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

    568    @operation(Operation.FROM)
    569    def union(self, other: DataFrame) -> DataFrame:
    570        return self._set_operation(exp.Union, other, False)

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

    574    @operation(Operation.FROM)
    575    def unionByName(self, other: DataFrame, allowMissingColumns: bool = False):
    576        l_columns = self.columns
    577        r_columns = other.columns
    578        if not allowMissingColumns:
    579            l_expressions = l_columns
    580            r_expressions = l_columns
    581        else:
    582            l_expressions = []
    583            r_expressions = []
    584            r_columns_unused = copy(r_columns)
    585            for l_column in l_columns:
    586                l_expressions.append(l_column)
    587                if l_column in r_columns:
    588                    r_expressions.append(l_column)
    589                    r_columns_unused.remove(l_column)
    590                else:
    591                    r_expressions.append(exp.alias_(exp.Null(), l_column, copy=False))
    592            for r_column in r_columns_unused:
    593                l_expressions.append(exp.alias_(exp.Null(), r_column, copy=False))
    594                r_expressions.append(r_column)
    595        r_df = (
    596            other.copy()._convert_leaf_to_cte().select(*self._ensure_list_of_columns(r_expressions))
    597        )
    598        l_df = self.copy()
    599        if allowMissingColumns:
    600            l_df = l_df._convert_leaf_to_cte().select(*self._ensure_list_of_columns(l_expressions))
    601        return l_df._set_operation(exp.Union, r_df, False)

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

    603    @operation(Operation.FROM)
    604    def intersect(self, other: DataFrame) -> DataFrame:
    605        return self._set_operation(exp.Intersect, other, True)

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

    607    @operation(Operation.FROM)
    608    def intersectAll(self, other: DataFrame) -> DataFrame:
    609        return self._set_operation(exp.Intersect, other, False)

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

    611    @operation(Operation.FROM)
    612    def exceptAll(self, other: DataFrame) -> DataFrame:
    613        return self._set_operation(exp.Except, other, False)

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

    615    @operation(Operation.SELECT)
    616    def distinct(self) -> DataFrame:
    617        return self.copy(expression=self.expression.distinct())

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

    619    @operation(Operation.SELECT)
    620    def dropDuplicates(self, subset: t.Optional[t.List[str]] = None):
    621        if not subset:
    622            return self.distinct()
    623        column_names = ensure_list(subset)
    624        window = Window.partitionBy(*column_names).orderBy(*column_names)
    625        return (
    626            self.copy()
    627            .withColumn("row_num", F.row_number().over(window))
    628            .where(F.col("row_num") == F.lit(1))
    629            .drop("row_num")
    630        )

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

    632    @operation(Operation.FROM)
    633    def dropna(
    634        self,
    635        how: str = "any",
    636        thresh: t.Optional[int] = None,
    637        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    638    ) -> DataFrame:
    639        minimum_non_null = thresh or 0  # will be determined later if thresh is null
    640        new_df = self.copy()
    641        all_columns = self._get_outer_select_columns(new_df.expression)
    642        if subset:
    643            null_check_columns = self._ensure_and_normalize_cols(subset)
    644        else:
    645            null_check_columns = all_columns
    646        if thresh is None:
    647            minimum_num_nulls = 1 if how == "any" else len(null_check_columns)
    648        else:
    649            minimum_num_nulls = len(null_check_columns) - minimum_non_null + 1
    650        if minimum_num_nulls > len(null_check_columns):
    651            raise RuntimeError(
    652                f"The minimum num nulls for dropna must be less than or equal to the number of columns. "
    653                f"Minimum num nulls: {minimum_num_nulls}, Num Columns: {len(null_check_columns)}"
    654            )
    655        if_null_checks = [
    656            F.when(column.isNull(), F.lit(1)).otherwise(F.lit(0)) for column in null_check_columns
    657        ]
    658        nulls_added_together = functools.reduce(lambda x, y: x + y, if_null_checks)
    659        num_nulls = nulls_added_together.alias("num_nulls")
    660        new_df = new_df.select(num_nulls, append=True)
    661        filtered_df = new_df.where(F.col("num_nulls") < F.lit(minimum_num_nulls))
    662        final_df = filtered_df.select(*all_columns)
    663        return final_df

@operation(Operation.FROM)
def fillna( self, value: <MagicMock id='139980124312224'>, subset: Union[str, Tuple[str, ...], List[str], NoneType] = None) -> DataFrame: View Source

    665    @operation(Operation.FROM)
    666    def fillna(
    667        self,
    668        value: t.Union[ColumnLiterals],
    669        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    670    ) -> DataFrame:
    671        """
    672        Functionality Difference: If you provide a value to replace a null and that type conflicts
    673        with the type of the column then PySpark will just ignore your replacement.
    674        This will try to cast them to be the same in some cases. So they won't always match.
    675        Best to not mix types so make sure replacement is the same type as the column
    676
    677        Possibility for improvement: Use `typeof` function to get the type of the column
    678        and check if it matches the type of the value provided. If not then make it null.
    679        """
    680        from sqlglot.dataframe.sql.functions import lit
    681
    682        values = None
    683        columns = None
    684        new_df = self.copy()
    685        all_columns = self._get_outer_select_columns(new_df.expression)
    686        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    687        if isinstance(value, dict):
    688            values = list(value.values())
    689            columns = self._ensure_and_normalize_cols(list(value))
    690        if not columns:
    691            columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    692        if not values:
    693            values = [value] * len(columns)
    694        value_columns = [lit(value) for value in values]
    695
    696        null_replacement_mapping = {
    697            column.alias_or_name: (
    698                F.when(column.isNull(), value).otherwise(column).alias(column.alias_or_name)
    699            )
    700            for column, value in zip(columns, value_columns)
    701        }
    702        null_replacement_mapping = {**all_column_mapping, **null_replacement_mapping}
    703        null_replacement_columns = [
    704            null_replacement_mapping[column.alias_or_name] for column in all_columns
    705        ]
    706        new_df = new_df.select(*null_replacement_columns)
    707        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='139980124822048'>], <MagicMock id='139980124822048'>, NoneType] = None) -> DataFrame: View Source

    709    @operation(Operation.FROM)
    710    def replace(
    711        self,
    712        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    713        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    714        subset: t.Optional[t.Collection[ColumnOrName] | ColumnOrName] = None,
    715    ) -> DataFrame:
    716        from sqlglot.dataframe.sql.functions import lit
    717
    718        old_values = None
    719        new_df = self.copy()
    720        all_columns = self._get_outer_select_columns(new_df.expression)
    721        all_column_mapping = {column.alias_or_name: column for column in all_columns}
    722
    723        columns = self._ensure_and_normalize_cols(subset) if subset else all_columns
    724        if isinstance(to_replace, dict):
    725            old_values = list(to_replace)
    726            new_values = list(to_replace.values())
    727        elif not old_values and isinstance(to_replace, list):
    728            assert isinstance(value, list), "value must be a list since the replacements are a list"
    729            assert len(to_replace) == len(
    730                value
    731            ), "the replacements and values must be the same length"
    732            old_values = to_replace
    733            new_values = value
    734        else:
    735            old_values = [to_replace] * len(columns)
    736            new_values = [value] * len(columns)
    737        old_values = [lit(value) for value in old_values]
    738        new_values = [lit(value) for value in new_values]
    739
    740        replacement_mapping = {}
    741        for column in columns:
    742            expression = Column(None)
    743            for i, (old_value, new_value) in enumerate(zip(old_values, new_values)):
    744                if i == 0:
    745                    expression = F.when(column == old_value, new_value)
    746                else:
    747                    expression = expression.when(column == old_value, new_value)  # type: ignore
    748            replacement_mapping[column.alias_or_name] = expression.otherwise(column).alias(
    749                column.expression.alias_or_name
    750            )
    751
    752        replacement_mapping = {**all_column_mapping, **replacement_mapping}
    753        replacement_columns = [replacement_mapping[column.alias_or_name] for column in all_columns]
    754        new_df = new_df.select(*replacement_columns)
    755        return new_df

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

    757    @operation(Operation.SELECT)
    758    def withColumn(self, colName: str, col: Column) -> DataFrame:
    759        col = self._ensure_and_normalize_col(col)
    760        existing_col_names = self.expression.named_selects
    761        existing_col_index = (
    762            existing_col_names.index(colName) if colName in existing_col_names else None
    763        )
    764        if existing_col_index:
    765            expression = self.expression.copy()
    766            expression.expressions[existing_col_index] = col.expression
    767            return self.copy(expression=expression)
    768        return self.copy().select(col.alias(colName), append=True)

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

    770    @operation(Operation.SELECT)
    771    def withColumnRenamed(self, existing: str, new: str):
    772        expression = self.expression.copy()
    773        existing_columns = [
    774            expression
    775            for expression in expression.expressions
    776            if expression.alias_or_name == existing
    777        ]
    778        if not existing_columns:
    779            raise ValueError("Tried to rename a column that doesn't exist")
    780        for existing_column in existing_columns:
    781            if isinstance(existing_column, exp.Column):
    782                existing_column.replace(exp.alias_(existing_column, new))
    783            else:
    784                existing_column.set("alias", exp.to_identifier(new))
    785        return self.copy(expression=expression)

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

    787    @operation(Operation.SELECT)
    788    def drop(self, *cols: t.Union[str, Column]) -> DataFrame:
    789        all_columns = self._get_outer_select_columns(self.expression)
    790        drop_cols = self._ensure_and_normalize_cols(cols)
    791        new_columns = [
    792            col
    793            for col in all_columns
    794            if col.alias_or_name not in [drop_column.alias_or_name for drop_column in drop_cols]
    795        ]
    796        return self.copy().select(*new_columns, append=False)

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

    798    @operation(Operation.LIMIT)
    799    def limit(self, num: int) -> DataFrame:
    800        return self.copy(expression=self.expression.limit(num))

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

    802    @operation(Operation.NO_OP)
    803    def hint(self, name: str, *parameters: t.Optional[t.Union[str, int]]) -> DataFrame:
    804        parameter_list = ensure_list(parameters)
    805        parameter_columns = (
    806            self._ensure_list_of_columns(parameter_list)
    807            if parameters
    808            else Column.ensure_cols([self.sequence_id])
    809        )
    810        return self._hint(name, parameter_columns)

@operation(Operation.NO_OP)
def repartition( self, numPartitions: Union[int, <MagicMock id='139980124822048'>], *cols: <MagicMock id='139980124822048'>) -> DataFrame: View Source

    812    @operation(Operation.NO_OP)
    813    def repartition(
    814        self, numPartitions: t.Union[int, ColumnOrName], *cols: ColumnOrName
    815    ) -> DataFrame:
    816        num_partition_cols = self._ensure_list_of_columns(numPartitions)
    817        columns = self._ensure_and_normalize_cols(cols)
    818        args = num_partition_cols + columns
    819        return self._hint("repartition", args)

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

    821    @operation(Operation.NO_OP)
    822    def coalesce(self, numPartitions: int) -> DataFrame:
    823        num_partitions = Column.ensure_cols([numPartitions])
    824        return self._hint("coalesce", num_partitions)

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

    826    @operation(Operation.NO_OP)
    827    def cache(self) -> DataFrame:
    828        return self._cache(storage_level="MEMORY_AND_DISK")

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

    830    @operation(Operation.NO_OP)
    831    def persist(self, storageLevel: str = "MEMORY_AND_DISK_SER") -> DataFrame:
    832        """
    833        Storage Level Options: https://spark.apache.org/docs/3.0.0-preview/sql-ref-syntax-aux-cache-cache-table.html
    834        """
    835        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: DataFrame, group_by_cols: List[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[Column, Dict[str, str]]) -> 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) -> DataFrame: View Source

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

def mean(self, *cols: str) -> DataFrame: View Source

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

def avg(self, *cols: str) -> 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) -> 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) -> 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) -> 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) -> DataFrame: View Source

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

class Column: View Source

     16class Column:
     17    def __init__(self, expression: t.Optional[t.Union[ColumnOrLiteral, exp.Expression]]):
     18        from sqlglot.dataframe.sql.session import SparkSession
     19
     20        if isinstance(expression, Column):
     21            expression = expression.expression  # type: ignore
     22        elif expression is None or not isinstance(expression, (str, exp.Expression)):
     23            expression = self._lit(expression).expression  # type: ignore
     24        elif not isinstance(expression, exp.Column):
     25            expression = sqlglot.maybe_parse(expression, dialect=SparkSession().dialect).transform(
     26                SparkSession().dialect.normalize_identifier, copy=False
     27            )
     28        if expression is None:
     29            raise ValueError(f"Could not parse {expression}")
     30
     31        self.expression: exp.Expression = expression  # type: ignore
     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]]) -> Column:
    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: (
    149                [Column.ensure_col(x).expression for x in v]
    150                if is_iterable(v)
    151                else Column.ensure_col(v).expression
    152            )
    153            for k, v in kwargs.items()
    154            if v is not None
    155        }
    156        new_expression = (
    157            callable_expression(**ensure_expression_values)
    158            if ensured_column is None
    159            else callable_expression(
    160                this=ensured_column.column_expression, **ensure_expression_values
    161            )
    162        )
    163        return Column(new_expression)
    164
    165    def binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    166        return Column(
    167            klass(this=self.column_expression, expression=Column(other).column_expression, **kwargs)
    168        )
    169
    170    def inverse_binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    171        return Column(
    172            klass(this=Column(other).column_expression, expression=self.column_expression, **kwargs)
    173        )
    174
    175    def unary_op(self, klass: t.Callable, **kwargs) -> Column:
    176        return Column(klass(this=self.column_expression, **kwargs))
    177
    178    @property
    179    def is_alias(self):
    180        return isinstance(self.expression, exp.Alias)
    181
    182    @property
    183    def is_column(self):
    184        return isinstance(self.expression, exp.Column)
    185
    186    @property
    187    def column_expression(self) -> t.Union[exp.Column, exp.Literal]:
    188        return self.expression.unalias()
    189
    190    @property
    191    def alias_or_name(self) -> str:
    192        return self.expression.alias_or_name
    193
    194    @classmethod
    195    def ensure_literal(cls, value) -> Column:
    196        from sqlglot.dataframe.sql.functions import lit
    197
    198        if isinstance(value, cls):
    199            value = value.expression
    200        if not isinstance(value, exp.Literal):
    201            return lit(value)
    202        return Column(value)
    203
    204    def copy(self) -> Column:
    205        return Column(self.expression.copy())
    206
    207    def set_table_name(self, table_name: str, copy=False) -> Column:
    208        expression = self.expression.copy() if copy else self.expression
    209        expression.set("table", exp.to_identifier(table_name))
    210        return Column(expression)
    211
    212    def sql(self, **kwargs) -> str:
    213        from sqlglot.dataframe.sql.session import SparkSession
    214
    215        return self.expression.sql(**{"dialect": SparkSession().dialect, **kwargs})
    216
    217    def alias(self, name: str) -> Column:
    218        from sqlglot.dataframe.sql.session import SparkSession
    219
    220        dialect = SparkSession().dialect
    221        alias: exp.Expression = sqlglot.maybe_parse(name, dialect=dialect)
    222        new_expression = exp.alias_(
    223            self.column_expression,
    224            alias.this if isinstance(alias, exp.Column) else name,
    225            dialect=dialect,
    226        )
    227        return Column(new_expression)
    228
    229    def asc(self) -> Column:
    230        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
    231        return Column(new_expression)
    232
    233    def desc(self) -> Column:
    234        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
    235        return Column(new_expression)
    236
    237    asc_nulls_first = asc
    238
    239    def asc_nulls_last(self) -> Column:
    240        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=False)
    241        return Column(new_expression)
    242
    243    def desc_nulls_first(self) -> Column:
    244        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=True)
    245        return Column(new_expression)
    246
    247    desc_nulls_last = desc
    248
    249    def when(self, condition: Column, value: t.Any) -> Column:
    250        from sqlglot.dataframe.sql.functions import when
    251
    252        column_with_if = when(condition, value)
    253        if not isinstance(self.expression, exp.Case):
    254            return column_with_if
    255        new_column = self.copy()
    256        new_column.expression.args["ifs"].extend(column_with_if.expression.args["ifs"])
    257        return new_column
    258
    259    def otherwise(self, value: t.Any) -> Column:
    260        from sqlglot.dataframe.sql.functions import lit
    261
    262        true_value = value if isinstance(value, Column) else lit(value)
    263        new_column = self.copy()
    264        new_column.expression.set("default", true_value.column_expression)
    265        return new_column
    266
    267    def isNull(self) -> Column:
    268        new_expression = exp.Is(this=self.column_expression, expression=exp.Null())
    269        return Column(new_expression)
    270
    271    def isNotNull(self) -> Column:
    272        new_expression = exp.Not(this=exp.Is(this=self.column_expression, expression=exp.Null()))
    273        return Column(new_expression)
    274
    275    def cast(self, dataType: t.Union[str, DataType]) -> Column:
    276        """
    277        Functionality Difference: PySpark cast accepts a datatype instance of the datatype class
    278        Sqlglot doesn't currently replicate this class so it only accepts a string
    279        """
    280        from sqlglot.dataframe.sql.session import SparkSession
    281
    282        if isinstance(dataType, DataType):
    283            dataType = dataType.simpleString()
    284        return Column(exp.cast(self.column_expression, dataType, dialect=SparkSession().dialect))
    285
    286    def startswith(self, value: t.Union[str, Column]) -> Column:
    287        value = self._lit(value) if not isinstance(value, Column) else value
    288        return self.invoke_anonymous_function(self, "STARTSWITH", value)
    289
    290    def endswith(self, value: t.Union[str, Column]) -> Column:
    291        value = self._lit(value) if not isinstance(value, Column) else value
    292        return self.invoke_anonymous_function(self, "ENDSWITH", value)
    293
    294    def rlike(self, regexp: str) -> Column:
    295        return self.invoke_expression_over_column(
    296            column=self, callable_expression=exp.RegexpLike, expression=self._lit(regexp).expression
    297        )
    298
    299    def like(self, other: str):
    300        return self.invoke_expression_over_column(
    301            self, exp.Like, expression=self._lit(other).expression
    302        )
    303
    304    def ilike(self, other: str):
    305        return self.invoke_expression_over_column(
    306            self, exp.ILike, expression=self._lit(other).expression
    307        )
    308
    309    def substr(self, startPos: t.Union[int, Column], length: t.Union[int, Column]) -> Column:
    310        startPos = self._lit(startPos) if not isinstance(startPos, Column) else startPos
    311        length = self._lit(length) if not isinstance(length, Column) else length
    312        return Column.invoke_expression_over_column(
    313            self, exp.Substring, start=startPos.expression, length=length.expression
    314        )
    315
    316    def isin(self, *cols: t.Union[ColumnOrLiteral, t.Iterable[ColumnOrLiteral]]):
    317        columns = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
    318        expressions = [self._lit(x).expression for x in columns]
    319        return Column.invoke_expression_over_column(self, exp.In, expressions=expressions)  # type: ignore
    320
    321    def between(
    322        self,
    323        lowerBound: t.Union[ColumnOrLiteral],
    324        upperBound: t.Union[ColumnOrLiteral],
    325    ) -> Column:
    326        lower_bound_exp = (
    327            self._lit(lowerBound) if not isinstance(lowerBound, Column) else lowerBound
    328        )
    329        upper_bound_exp = (
    330            self._lit(upperBound) if not isinstance(upperBound, Column) else upperBound
    331        )
    332        return Column(
    333            exp.Between(
    334                this=self.column_expression,
    335                low=lower_bound_exp.expression,
    336                high=upper_bound_exp.expression,
    337            )
    338        )
    339
    340    def over(self, window: WindowSpec) -> Column:
    341        window_expression = window.expression.copy()
    342        window_expression.set("this", self.column_expression)
    343        return Column(window_expression)

Column( expression: Union[<MagicMock id='139980126740032'>, sqlglot.expressions.Expression, NoneType]) View Source

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

expression: sqlglot.expressions.Expression
@classmethod
def ensure_col( cls, value: Union[<MagicMock id='139980126740032'>, sqlglot.expressions.Expression, NoneType]) -> Column: View Source

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

@classmethod
def ensure_cols( cls, args: List[Union[<MagicMock id='139980126740032'>, sqlglot.expressions.Expression]]) -> List[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='139980126740032'>], func_name: str, *args: Optional[<MagicMock id='139980126740032'>]) -> 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='139980126740032'>], callable_expression: Callable, **kwargs) -> 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: (
    149                [Column.ensure_col(x).expression for x in v]
    150                if is_iterable(v)
    151                else Column.ensure_col(v).expression
    152            )
    153            for k, v in kwargs.items()
    154            if v is not None
    155        }
    156        new_expression = (
    157            callable_expression(**ensure_expression_values)
    158            if ensured_column is None
    159            else callable_expression(
    160                this=ensured_column.column_expression, **ensure_expression_values
    161            )
    162        )
    163        return Column(new_expression)

def binary_op( self, klass: Callable, other: <MagicMock id='139980126740032'>, **kwargs) -> Column: View Source

    165    def binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    166        return Column(
    167            klass(this=self.column_expression, expression=Column(other).column_expression, **kwargs)
    168        )

def inverse_binary_op( self, klass: Callable, other: <MagicMock id='139980126740032'>, **kwargs) -> Column: View Source

    170    def inverse_binary_op(self, klass: t.Callable, other: ColumnOrLiteral, **kwargs) -> Column:
    171        return Column(
    172            klass(this=Column(other).column_expression, expression=self.column_expression, **kwargs)
    173        )

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

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

is_alias View Source

    178    @property
    179    def is_alias(self):
    180        return isinstance(self.expression, exp.Alias)

is_column View Source

    182    @property
    183    def is_column(self):
    184        return isinstance(self.expression, exp.Column)

column_expression: Union[sqlglot.expressions.Column, sqlglot.expressions.Literal] View Source

    186    @property
    187    def column_expression(self) -> t.Union[exp.Column, exp.Literal]:
    188        return self.expression.unalias()

alias_or_name: str View Source

    190    @property
    191    def alias_or_name(self) -> str:
    192        return self.expression.alias_or_name

@classmethod
def ensure_literal(cls, value) -> Column: View Source

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

def copy(self) -> Column: View Source

    204    def copy(self) -> Column:
    205        return Column(self.expression.copy())

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

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

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

    212    def sql(self, **kwargs) -> str:
    213        from sqlglot.dataframe.sql.session import SparkSession
    214
    215        return self.expression.sql(**{"dialect": SparkSession().dialect, **kwargs})

def alias(self, name: str) -> Column: View Source

    217    def alias(self, name: str) -> Column:
    218        from sqlglot.dataframe.sql.session import SparkSession
    219
    220        dialect = SparkSession().dialect
    221        alias: exp.Expression = sqlglot.maybe_parse(name, dialect=dialect)
    222        new_expression = exp.alias_(
    223            self.column_expression,
    224            alias.this if isinstance(alias, exp.Column) else name,
    225            dialect=dialect,
    226        )
    227        return Column(new_expression)

def asc(self) -> Column: View Source

    229    def asc(self) -> Column:
    230        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
    231        return Column(new_expression)

def desc(self) -> Column: View Source

    233    def desc(self) -> Column:
    234        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
    235        return Column(new_expression)

def asc_nulls_first(self) -> Column: View Source

    229    def asc(self) -> Column:
    230        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=True)
    231        return Column(new_expression)

def asc_nulls_last(self) -> Column: View Source

    239    def asc_nulls_last(self) -> Column:
    240        new_expression = exp.Ordered(this=self.column_expression, desc=False, nulls_first=False)
    241        return Column(new_expression)

def desc_nulls_first(self) -> Column: View Source

    243    def desc_nulls_first(self) -> Column:
    244        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=True)
    245        return Column(new_expression)

def desc_nulls_last(self) -> Column: View Source

    233    def desc(self) -> Column:
    234        new_expression = exp.Ordered(this=self.column_expression, desc=True, nulls_first=False)
    235        return Column(new_expression)

def when( self, condition: Column, value: Any) -> Column: View Source

    249    def when(self, condition: Column, value: t.Any) -> Column:
    250        from sqlglot.dataframe.sql.functions import when
    251
    252        column_with_if = when(condition, value)
    253        if not isinstance(self.expression, exp.Case):
    254            return column_with_if
    255        new_column = self.copy()
    256        new_column.expression.args["ifs"].extend(column_with_if.expression.args["ifs"])
    257        return new_column

def otherwise(self, value: Any) -> Column: View Source

    259    def otherwise(self, value: t.Any) -> Column:
    260        from sqlglot.dataframe.sql.functions import lit
    261
    262        true_value = value if isinstance(value, Column) else lit(value)
    263        new_column = self.copy()
    264        new_column.expression.set("default", true_value.column_expression)
    265        return new_column

def isNull(self) -> Column: View Source

    267    def isNull(self) -> Column:
    268        new_expression = exp.Is(this=self.column_expression, expression=exp.Null())
    269        return Column(new_expression)

def isNotNull(self) -> Column: View Source

    271    def isNotNull(self) -> Column:
    272        new_expression = exp.Not(this=exp.Is(this=self.column_expression, expression=exp.Null()))
    273        return Column(new_expression)

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

    275    def cast(self, dataType: t.Union[str, DataType]) -> Column:
    276        """
    277        Functionality Difference: PySpark cast accepts a datatype instance of the datatype class
    278        Sqlglot doesn't currently replicate this class so it only accepts a string
    279        """
    280        from sqlglot.dataframe.sql.session import SparkSession
    281
    282        if isinstance(dataType, DataType):
    283            dataType = dataType.simpleString()
    284        return Column(exp.cast(self.column_expression, dataType, dialect=SparkSession().dialect))

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, Column]) -> Column: View Source

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

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

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

def rlike(self, regexp: str) -> Column: View Source

    294    def rlike(self, regexp: str) -> Column:
    295        return self.invoke_expression_over_column(
    296            column=self, callable_expression=exp.RegexpLike, expression=self._lit(regexp).expression
    297        )

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

    299    def like(self, other: str):
    300        return self.invoke_expression_over_column(
    301            self, exp.Like, expression=self._lit(other).expression
    302        )

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

    304    def ilike(self, other: str):
    305        return self.invoke_expression_over_column(
    306            self, exp.ILike, expression=self._lit(other).expression
    307        )

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

    309    def substr(self, startPos: t.Union[int, Column], length: t.Union[int, Column]) -> Column:
    310        startPos = self._lit(startPos) if not isinstance(startPos, Column) else startPos
    311        length = self._lit(length) if not isinstance(length, Column) else length
    312        return Column.invoke_expression_over_column(
    313            self, exp.Substring, start=startPos.expression, length=length.expression
    314        )

def isin( self, *cols: Union[<MagicMock id='139980126740032'>, Iterable[<MagicMock id='139980126740032'>]]): View Source

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

def between( self, lowerBound: <MagicMock id='139980126740032'>, upperBound: <MagicMock id='139980126740032'>) -> Column: View Source

    321    def between(
    322        self,
    323        lowerBound: t.Union[ColumnOrLiteral],
    324        upperBound: t.Union[ColumnOrLiteral],
    325    ) -> Column:
    326        lower_bound_exp = (
    327            self._lit(lowerBound) if not isinstance(lowerBound, Column) else lowerBound
    328        )
    329        upper_bound_exp = (
    330            self._lit(upperBound) if not isinstance(upperBound, Column) else upperBound
    331        )
    332        return Column(
    333            exp.Between(
    334                this=self.column_expression,
    335                low=lower_bound_exp.expression,
    336                high=upper_bound_exp.expression,
    337            )
    338        )

def over( self, window: <MagicMock id='139980122981280'>) -> Column: View Source

    340    def over(self, window: WindowSpec) -> Column:
    341        window_expression = window.expression.copy()
    342        window_expression.set("this", self.column_expression)
    343        return Column(window_expression)

class DataFrameNaFunctions: View Source

    838class DataFrameNaFunctions:
    839    def __init__(self, df: DataFrame):
    840        self.df = df
    841
    842    def drop(
    843        self,
    844        how: str = "any",
    845        thresh: t.Optional[int] = None,
    846        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    847    ) -> DataFrame:
    848        return self.df.dropna(how=how, thresh=thresh, subset=subset)
    849
    850    def fill(
    851        self,
    852        value: t.Union[int, bool, float, str, t.Dict[str, t.Any]],
    853        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    854    ) -> DataFrame:
    855        return self.df.fillna(value=value, subset=subset)
    856
    857    def replace(
    858        self,
    859        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    860        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    861        subset: t.Optional[t.Union[str, t.List[str]]] = None,
    862    ) -> DataFrame:
    863        return self.df.replace(to_replace=to_replace, value=value, subset=subset)

DataFrameNaFunctions(df: DataFrame) View Source

    839    def __init__(self, df: DataFrame):
    840        self.df = df

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

    842    def drop(
    843        self,
    844        how: str = "any",
    845        thresh: t.Optional[int] = None,
    846        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    847    ) -> DataFrame:
    848        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) -> DataFrame: View Source

    850    def fill(
    851        self,
    852        value: t.Union[int, bool, float, str, t.Dict[str, t.Any]],
    853        subset: t.Optional[t.Union[str, t.Tuple[str, ...], t.List[str]]] = None,
    854    ) -> DataFrame:
    855        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) -> DataFrame: View Source

    857    def replace(
    858        self,
    859        to_replace: t.Union[bool, int, float, str, t.List, t.Dict],
    860        value: t.Optional[t.Union[bool, int, float, str, t.List]] = None,
    861        subset: t.Optional[t.Union[str, t.List[str]]] = None,
    862    ) -> DataFrame:
    863        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='139980123534768'>, List[<MagicMock id='139980123534768'>]]) -> 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='139980123534768'>, List[<MagicMock id='139980123534768'>]]) -> 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) -> 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) -> 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        from sqlglot.dataframe.sql.session import SparkSession
     53
     54        return self.expression.sql(dialect=SparkSession().dialect, **kwargs)
     55
     56    def partitionBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
     57        from sqlglot.dataframe.sql.column import Column
     58
     59        cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
     60        expressions = [Column.ensure_col(x).expression for x in cols]
     61        window_spec = self.copy()
     62        partition_by_expressions = window_spec.expression.args.get("partition_by", [])
     63        partition_by_expressions.extend(expressions)
     64        window_spec.expression.set("partition_by", partition_by_expressions)
     65        return window_spec
     66
     67    def orderBy(self, *cols: t.Union[ColumnOrName, t.List[ColumnOrName]]) -> WindowSpec:
     68        from sqlglot.dataframe.sql.column import Column
     69
     70        cols = flatten(cols) if isinstance(cols[0], (list, set, tuple)) else cols  # type: ignore
     71        expressions = [Column.ensure_col(x).expression for x in cols]
     72        window_spec = self.copy()
     73        if window_spec.expression.args.get("order") is None:
     74            window_spec.expression.set("order", exp.Order(expressions=[]))
     75        order_by = window_spec.expression.args["order"].expressions
     76        order_by.extend(expressions)
     77        window_spec.expression.args["order"].set("expressions", order_by)
     78        return window_spec
     79
     80    def _calc_start_end(
     81        self, start: int, end: int
     82    ) -> t.Dict[str, t.Optional[t.Union[str, exp.Expression]]]:
     83        kwargs: t.Dict[str, t.Optional[t.Union[str, exp.Expression]]] = {
     84            "start_side": None,
     85            "end_side": None,
     86        }
     87        if start == Window.currentRow:
     88            kwargs["start"] = "CURRENT ROW"
     89        else:
     90            kwargs = {
     91                **kwargs,
     92                **{
     93                    "start_side": "PRECEDING",
     94                    "start": (
     95                        "UNBOUNDED"
     96                        if start <= Window.unboundedPreceding
     97                        else F.lit(start).expression
     98                    ),
     99                },
    100            }
    101        if end == Window.currentRow:
    102            kwargs["end"] = "CURRENT ROW"
    103        else:
    104            kwargs = {
    105                **kwargs,
    106                **{
    107                    "end_side": "FOLLOWING",
    108                    "end": (
    109                        "UNBOUNDED" if end >= Window.unboundedFollowing else F.lit(end).expression
    110                    ),
    111                },
    112            }
    113        return kwargs
    114
    115    def rowsBetween(self, start: int, end: int) -> WindowSpec:
    116        window_spec = self.copy()
    117        spec = self._calc_start_end(start, end)
    118        spec["kind"] = "ROWS"
    119        window_spec.expression.set(
    120            "spec",
    121            exp.WindowSpec(
    122                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    123            ),
    124        )
    125        return window_spec
    126
    127    def rangeBetween(self, start: int, end: int) -> WindowSpec:
    128        window_spec = self.copy()
    129        spec = self._calc_start_end(start, end)
    130        spec["kind"] = "RANGE"
    131        window_spec.expression.set(
    132            "spec",
    133            exp.WindowSpec(
    134                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    135            ),
    136        )
    137        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        from sqlglot.dataframe.sql.session import SparkSession
    53
    54        return self.expression.sql(dialect=SparkSession().dialect, **kwargs)

def partitionBy( self, *cols: Union[<MagicMock id='139980123534768'>, List[<MagicMock id='139980123534768'>]]) -> WindowSpec: View Source

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

def orderBy( self, *cols: Union[<MagicMock id='139980123534768'>, List[<MagicMock id='139980123534768'>]]) -> WindowSpec: View Source

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

def rowsBetween(self, start: int, end: int) -> WindowSpec: View Source

    115    def rowsBetween(self, start: int, end: int) -> WindowSpec:
    116        window_spec = self.copy()
    117        spec = self._calc_start_end(start, end)
    118        spec["kind"] = "ROWS"
    119        window_spec.expression.set(
    120            "spec",
    121            exp.WindowSpec(
    122                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    123            ),
    124        )
    125        return window_spec

def rangeBetween(self, start: int, end: int) -> WindowSpec: View Source

    127    def rangeBetween(self, start: int, end: int) -> WindowSpec:
    128        window_spec = self.copy()
    129        spec = self._calc_start_end(start, end)
    130        spec["kind"] = "RANGE"
    131        window_spec.expression.set(
    132            "spec",
    133            exp.WindowSpec(
    134                **{**window_spec.expression.args.get("spec", exp.WindowSpec()).args, **spec}
    135            ),
    136        )
    137        return window_spec

class DataFrameReader: View Source

    15class DataFrameReader:
    16    def __init__(self, spark: SparkSession):
    17        self.spark = spark
    18
    19    def table(self, tableName: str) -> DataFrame:
    20        from sqlglot.dataframe.sql.dataframe import DataFrame
    21        from sqlglot.dataframe.sql.session import SparkSession
    22
    23        sqlglot.schema.add_table(tableName, dialect=SparkSession().dialect)
    24
    25        return DataFrame(
    26            self.spark,
    27            exp.Select()
    28            .from_(
    29                exp.to_table(tableName, dialect=SparkSession().dialect).transform(
    30                    SparkSession().dialect.normalize_identifier
    31                )
    32            )
    33            .select(
    34                *(
    35                    column
    36                    for column in sqlglot.schema.column_names(
    37                        tableName, dialect=SparkSession().dialect
    38                    )
    39                )
    40            ),
    41        )

DataFrameReader(spark: SparkSession) View Source

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

spark
def table(self, tableName: str) -> DataFrame: View Source

    19    def table(self, tableName: str) -> DataFrame:
    20        from sqlglot.dataframe.sql.dataframe import DataFrame
    21        from sqlglot.dataframe.sql.session import SparkSession
    22
    23        sqlglot.schema.add_table(tableName, dialect=SparkSession().dialect)
    24
    25        return DataFrame(
    26            self.spark,
    27            exp.Select()
    28            .from_(
    29                exp.to_table(tableName, dialect=SparkSession().dialect).transform(
    30                    SparkSession().dialect.normalize_identifier
    31                )
    32            )
    33            .select(
    34                *(
    35                    column
    36                    for column in sqlglot.schema.column_names(
    37                        tableName, dialect=SparkSession().dialect
    38                    )
    39                )
    40            ),
    41        )

class DataFrameWriter: View Source

     44class DataFrameWriter:
     45    def __init__(
     46        self,
     47        df: DataFrame,
     48        spark: t.Optional[SparkSession] = None,
     49        mode: t.Optional[str] = None,
     50        by_name: bool = False,
     51    ):
     52        self._df = df
     53        self._spark = spark or df.spark
     54        self._mode = mode
     55        self._by_name = by_name
     56
     57    def copy(self, **kwargs) -> DataFrameWriter:
     58        return DataFrameWriter(
     59            **{
     60                k[1:] if k.startswith("_") else k: v
     61                for k, v in object_to_dict(self, **kwargs).items()
     62            }
     63        )
     64
     65    def sql(self, **kwargs) -> t.List[str]:
     66        return self._df.sql(**kwargs)
     67
     68    def mode(self, saveMode: t.Optional[str]) -> DataFrameWriter:
     69        return self.copy(_mode=saveMode)
     70
     71    @property
     72    def byName(self):
     73        return self.copy(by_name=True)
     74
     75    def insertInto(self, tableName: str, overwrite: t.Optional[bool] = None) -> DataFrameWriter:
     76        from sqlglot.dataframe.sql.session import SparkSession
     77
     78        output_expression_container = exp.Insert(
     79            **{
     80                "this": exp.to_table(tableName),
     81                "overwrite": overwrite,
     82            }
     83        )
     84        df = self._df.copy(output_expression_container=output_expression_container)
     85        if self._by_name:
     86            columns = sqlglot.schema.column_names(
     87                tableName, only_visible=True, dialect=SparkSession().dialect
     88            )
     89            df = df._convert_leaf_to_cte().select(*columns)
     90
     91        return self.copy(_df=df)
     92
     93    def saveAsTable(self, name: str, format: t.Optional[str] = None, mode: t.Optional[str] = None):
     94        if format is not None:
     95            raise NotImplementedError("Providing Format in the save as table is not supported")
     96        exists, replace, mode = None, None, mode or str(self._mode)
     97        if mode == "append":
     98            return self.insertInto(name)
     99        if mode == "ignore":
    100            exists = True
    101        if mode == "overwrite":
    102            replace = True
    103        output_expression_container = exp.Create(
    104            this=exp.to_table(name),
    105            kind="TABLE",
    106            exists=exists,
    107            replace=replace,
    108        )
    109        return self.copy(_df=self._df.copy(output_expression_container=output_expression_container))

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

    45    def __init__(
    46        self,
    47        df: DataFrame,
    48        spark: t.Optional[SparkSession] = None,
    49        mode: t.Optional[str] = None,
    50        by_name: bool = False,
    51    ):
    52        self._df = df
    53        self._spark = spark or df.spark
    54        self._mode = mode
    55        self._by_name = by_name

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

    57    def copy(self, **kwargs) -> DataFrameWriter:
    58        return DataFrameWriter(
    59            **{
    60                k[1:] if k.startswith("_") else k: v
    61                for k, v in object_to_dict(self, **kwargs).items()
    62            }
    63        )

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

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

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

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

byName View Source

    71    @property
    72    def byName(self):
    73        return self.copy(by_name=True)

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

    75    def insertInto(self, tableName: str, overwrite: t.Optional[bool] = None) -> DataFrameWriter:
    76        from sqlglot.dataframe.sql.session import SparkSession
    77
    78        output_expression_container = exp.Insert(
    79            **{
    80                "this": exp.to_table(tableName),
    81                "overwrite": overwrite,
    82            }
    83        )
    84        df = self._df.copy(output_expression_container=output_expression_container)
    85        if self._by_name:
    86            columns = sqlglot.schema.column_names(
    87                tableName, only_visible=True, dialect=SparkSession().dialect
    88            )
    89            df = df._convert_leaf_to_cte().select(*columns)
    90
    91        return self.copy(_df=df)

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

     93    def saveAsTable(self, name: str, format: t.Optional[str] = None, mode: t.Optional[str] = None):
     94        if format is not None:
     95            raise NotImplementedError("Providing Format in the save as table is not supported")
     96        exists, replace, mode = None, None, mode or str(self._mode)
     97        if mode == "append":
     98            return self.insertInto(name)
     99        if mode == "ignore":
    100            exists = True
    101        if mode == "overwrite":
    102            replace = True
    103        output_expression_container = exp.Create(
    104            this=exp.to_table(name),
    105            kind="TABLE",
    106            exists=exists,
    107            replace=replace,
    108        )
    109        return self.copy(_df=self._df.copy(output_expression_container=output_expression_container))