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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2022/11/25 15:49:56 UTC
[GitHub] [spark] grundprinzip commented on a diff in pull request #38801: [SPARK-40539] [CONNECT] [PYTHON] Add basic support for DataFrameWriter
grundprinzip commented on code in PR #38801:
URL: https://github.com/apache/spark/pull/38801#discussion_r1032566396
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python/pyspark/sql/connect/writer.py:
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@@ -0,0 +1,922 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+from typing import Optional, Union, List, overload, Tuple, cast, Any
+from typing import TYPE_CHECKING
+
+from pyspark.sql.connect.plan import WriteOperation, LogicalPlan
+from pyspark.sql.types import StructType
+from pyspark.sql.utils import to_str
+
+if TYPE_CHECKING:
+ from pyspark.sql.connect._typing import OptionalPrimitiveType
+ from pyspark.sql.connect.client import RemoteSparkSession
+
+
+PathOrPaths = Union[str, List[str]]
+TupleOrListOfString = Union[List[str], Tuple[str, ...]]
+
+
+class OptionUtils:
+ def _set_opts(
+ self,
+ schema: Optional[Union[StructType, str]] = None,
+ **options: "OptionalPrimitiveType",
+ ) -> None:
+ """
+ Set named options (filter out those the value is None)
+ """
+ if schema is not None:
+ self.schema(schema) # type: ignore[attr-defined]
+ for k, v in options.items():
+ if v is not None:
+ self.option(k, v) # type: ignore[attr-defined]
+
+
+class DataFrameWriter(OptionUtils):
+ """
+ Interface used to write a :class:`DataFrame` to external storage systems
+ (e.g. file systems, key-value stores, etc). Use :attr:`DataFrame.write`
+ to access this.
+
+ .. versionadded:: 3.4.0
+ """
+
+ def __init__(self, plan: "LogicalPlan", session: "RemoteSparkSession"):
+ self._df: "LogicalPlan" = plan
+ self._spark: "RemoteSparkSession" = session
+ self._write: "WriteOperation" = WriteOperation(self._df)
+
+ def mode(self, saveMode: Optional[str]) -> "DataFrameWriter":
+ """Specifies the behavior when data or table already exists.
+
+ Options include:
+
+ * `append`: Append contents of this :class:`DataFrame` to existing data.
+ * `overwrite`: Overwrite existing data.
+ * `error` or `errorifexists`: Throw an exception if data already exists.
+ * `ignore`: Silently ignore this operation if data already exists.
+
+ .. versionadded:: 3.4.0
+
+ Examples
+ --------
+ Raise an error when writing to an existing path.
+
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... spark.createDataFrame(
+ ... [{"age": 80, "name": "Xinrong Meng"}]
+ ... ).write.mode("error").format("parquet").save(d)
+ Traceback (most recent call last):
+ ...
+ pyspark.sql.utils.AnalysisException: ...
+
+ Write a Parquet file back with various options, and read it back.
+
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Overwrite the path with a new Parquet file
+ ... spark.createDataFrame(
+ ... [{"age": 100, "name": "Hyukjin Kwon"}]
+ ... ).write.mode("overwrite").format("parquet").save(d)
+ ...
+ ... # Append another DataFrame into the Parquet file
+ ... spark.createDataFrame(
+ ... [{"age": 120, "name": "Takuya Ueshin"}]
+ ... ).write.mode("append").format("parquet").save(d)
+ ...
+ ... # Append another DataFrame into the Parquet file
+ ... spark.createDataFrame(
+ ... [{"age": 140, "name": "Haejoon Lee"}]
+ ... ).write.mode("ignore").format("parquet").save(d)
+ ...
+ ... # Read the Parquet file as a DataFrame.
+ ... spark.read.parquet(d).show()
+ +---+-------------+
+ |age| name|
+ +---+-------------+
+ |120|Takuya Ueshin|
+ |100| Hyukjin Kwon|
+ +---+-------------+
+ """
+ # At the JVM side, the default value of mode is already set to "error".
+ # So, if the given saveMode is None, we will not call JVM-side's mode method.
+ if saveMode is not None:
+ self._write.mode = saveMode
+ return self
+
+ def format(self, source: str) -> "DataFrameWriter":
+ """Specifies the underlying output data source.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ source : str
+ string, name of the data source, e.g. 'json', 'parquet'.
+
+ Examples
+ --------
+ >>> spark.range(1).write.format('parquet')
+ <pyspark.sql.readwriter.DataFrameWriter object ...>
+
+ Write a DataFrame into a Parquet file and read it back.
+
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Write a DataFrame into a Parquet file
+ ... spark.createDataFrame(
+ ... [{"age": 100, "name": "Hyukjin Kwon"}]
+ ... ).write.mode("overwrite").format("parquet").save(d)
+ ...
+ ... # Read the Parquet file as a DataFrame.
+ ... spark.read.format('parquet').load(d).show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ +---+------------+
+ """
+ self._write.source = source
+ return self
+
+ def option(self, key: str, value: "OptionalPrimitiveType") -> "DataFrameWriter":
+ """
+ Adds a output option for the underlying data source.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ key : str
+ The key for the option to set.
+ value
+ The value for the option to set.
+
+ Examples
+ --------
+ >>> spark.range(1).write.option("key", "value")
+ <pyspark.sql.readwriter.DataFrameWriter object ...>
+
+ Specify the option 'nullValue' with writing a CSV file.
+
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Write a DataFrame into a CSV file with 'nullValue' option set to 'Hyukjin Kwon'.
+ ... df = spark.createDataFrame([(100, None)], "age INT, name STRING")
+ ... df.write.option("nullValue", "Hyukjin Kwon").mode("overwrite").format("csv").save(d)
+ ...
+ ... # Read the CSV file as a DataFrame.
+ ... spark.read.schema(df.schema).format('csv').load(d).show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ +---+------------+
+ """
+ self._write.options[key] = to_str(value)
+ return self
+
+ def options(self, **options: "OptionalPrimitiveType") -> "DataFrameWriter":
+ """
+ Adds output options for the underlying data source.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ **options : dict
+ The dictionary of string keys and prmitive-type values.
+
+ Examples
+ --------
+ >>> spark.range(1).write.option("key", "value")
+ <pyspark.sql.readwriter.DataFrameWriter object ...>
+
+ Specify the option 'nullValue' and 'header' with writing a CSV file.
+
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Write a DataFrame into a CSV file with 'nullValue' option set to 'Hyukjin Kwon',
+ ... # and 'header' option set to `True`.
+ ... df = spark.createDataFrame([(100, "Hyukjin Kwon")], ["age", "name"])
+ ... df.write.options(nullValue="Hyukjin Kwon", header=True).mode(
+ ... "overwrite").format("csv").save(d)
+ ...
+ ... # Read the CSV file as a DataFrame.
+ ... spark.read.option("header", True).format('csv').load(d).show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ +---+------------+
+ """
+ for k in options:
+ self._write.options[k] = to_str(options[k])
+ return self
+
+ @overload
+ def partitionBy(self, *cols: str) -> "DataFrameWriter":
+ ...
+
+ @overload
+ def partitionBy(self, *cols: List[str]) -> "DataFrameWriter":
+ ...
+
+ def partitionBy(self, *cols: Union[str, List[str]]) -> "DataFrameWriter":
+ """Partitions the output by the given columns on the file system.
+
+ If specified, the output is laid out on the file system similar
+ to Hive's partitioning scheme.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ cols : str or list
+ name of columns
+
+ Examples
+ --------
+ Write a DataFrame into a Parquet file in a partitioned manner, and read it back.
+
+ >>> import tempfile
+ >>> import os
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Write a DataFrame into a Parquet file in a partitioned manner.
+ ... spark.createDataFrame(
+ ... [{"age": 100, "name": "Hyukjin Kwon"}, {"age": 120, "name": "Ruifeng Zheng"}]
+ ... ).write.partitionBy("name").mode("overwrite").format("parquet").save(d)
+ ...
+ ... # Read the Parquet file as a DataFrame.
+ ... spark.read.parquet(d).sort("age").show()
+ ...
+ ... # Read one partition as a DataFrame.
+ ... spark.read.parquet(f"{d}{os.path.sep}name=Hyukjin Kwon").show()
+ +---+-------------+
+ |age| name|
+ +---+-------------+
+ |100| Hyukjin Kwon|
+ |120|Ruifeng Zheng|
+ +---+-------------+
+ +---+
+ |age|
+ +---+
+ |100|
+ +---+
+ """
+ if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
+ cols = cols[0] # type: ignore[assignment]
+
+ self._write.partitioning_cols = cast(List[str], cols)
+ return self
+
+ @overload
+ def bucketBy(self, numBuckets: int, col: str, *cols: str) -> "DataFrameWriter":
+ ...
+
+ @overload
+ def bucketBy(self, numBuckets: int, col: TupleOrListOfString) -> "DataFrameWriter":
+ ...
+
+ def bucketBy(
+ self, numBuckets: int, col: Union[str, TupleOrListOfString], *cols: Optional[str]
+ ) -> "DataFrameWriter":
+ """Buckets the output by the given columns. If specified,
+ the output is laid out on the file system similar to Hive's bucketing scheme,
+ but with a different bucket hash function and is not compatible with Hive's bucketing.
+
+ .. versionadded:: 2.3.0
+
+ Parameters
+ ----------
+ numBuckets : int
+ the number of buckets to save
+ col : str, list or tuple
+ a name of a column, or a list of names.
+ cols : str
+ additional names (optional). If `col` is a list it should be empty.
+
+ Notes
+ -----
+ Applicable for file-based data sources in combination with
+ :py:meth:`DataFrameWriter.saveAsTable`.
+
+ Examples
+ --------
+ Write a DataFrame into a Parquet file in a buckted manner, and read it back.
+
+ >>> from pyspark.sql.functions import input_file_name
+ >>> # Write a DataFrame into a Parquet file in a bucketed manner.
+ ... _ = spark.sql("DROP TABLE IF EXISTS bucketed_table")
+ >>> spark.createDataFrame([
+ ... (100, "Hyukjin Kwon"), (120, "Hyukjin Kwon"), (140, "Haejoon Lee")],
+ ... schema=["age", "name"]
+ ... ).write.bucketBy(2, "name").mode("overwrite").saveAsTable("bucketed_table")
+ >>> # Read the Parquet file as a DataFrame.
+ ... spark.read.table("bucketed_table").sort("age").show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ |120|Hyukjin Kwon|
+ |140| Haejoon Lee|
+ +---+------------+
+ >>> _ = spark.sql("DROP TABLE bucketed_table")
+ """
+ if not isinstance(numBuckets, int):
+ raise TypeError("numBuckets should be an int, got {0}.".format(type(numBuckets)))
+
+ if isinstance(col, (list, tuple)):
+ if cols:
+ raise ValueError("col is a {0} but cols are not empty".format(type(col)))
+
+ col, cols = col[0], col[1:] # type: ignore[assignment]
+
+ if not all(isinstance(c, str) for c in cols) or not (isinstance(col, str)):
+ raise TypeError("all names should be `str`")
+
+ self._write.num_buckets = numBuckets
+ self._write.bucket_cols = cast(List[str], cols)
+ return self
+
+ @overload
+ def sortBy(self, col: str, *cols: str) -> "DataFrameWriter":
+ ...
+
+ @overload
+ def sortBy(self, col: TupleOrListOfString) -> "DataFrameWriter":
+ ...
+
+ def sortBy(
+ self, col: Union[str, TupleOrListOfString], *cols: Optional[str]
+ ) -> "DataFrameWriter":
+ """Sorts the output in each bucket by the given columns on the file system.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col : str, tuple or list
+ a name of a column, or a list of names.
+ cols : str
+ additional names (optional). If `col` is a list it should be empty.
+
+ Examples
+ --------
+ Write a DataFrame into a Parquet file in a sorted-buckted manner, and read it back.
+
+ >>> from pyspark.sql.functions import input_file_name
+ >>> # Write a DataFrame into a Parquet file in a sorted-bucketed manner.
+ ... _ = spark.sql("DROP TABLE IF EXISTS sorted_bucketed_table")
+ >>> spark.createDataFrame([
+ ... (100, "Hyukjin Kwon"), (120, "Hyukjin Kwon"), (140, "Haejoon Lee")],
+ ... schema=["age", "name"]
+ ... ).write.bucketBy(1, "name").sortBy("age").mode(
+ ... "overwrite").saveAsTable("sorted_bucketed_table")
+ >>> # Read the Parquet file as a DataFrame.
+ ... spark.read.table("sorted_bucketed_table").sort("age").show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ |120|Hyukjin Kwon|
+ |140| Haejoon Lee|
+ +---+------------+
+ >>> _ = spark.sql("DROP TABLE sorted_bucketed_table")
+ """
+ if isinstance(col, (list, tuple)):
+ if cols:
+ raise ValueError("col is a {0} but cols are not empty".format(type(col)))
+
+ col, cols = col[0], col[1:] # type: ignore[assignment]
+
+ if not all(isinstance(c, str) for c in cols) or not (isinstance(col, str)):
+ raise TypeError("all names should be `str`")
+
+ self._write.sort_cols = cast(List[str], cols)
+ return self
+
+ def save(
+ self,
+ path: Optional[str] = None,
+ format: Optional[str] = None,
+ mode: Optional[str] = None,
+ partitionBy: Optional[Union[str, List[str]]] = None,
+ **options: "OptionalPrimitiveType",
+ ) -> None:
+ """Saves the contents of the :class:`DataFrame` to a data source.
+
+ The data source is specified by the ``format`` and a set of ``options``.
+ If ``format`` is not specified, the default data source configured by
+ ``spark.sql.sources.default`` will be used.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ path : str, optional
+ the path in a Hadoop supported file system
+ format : str, optional
+ the format used to save
+ mode : str, optional
+ specifies the behavior of the save operation when data already exists.
+
+ * ``append``: Append contents of this :class:`DataFrame` to existing data.
+ * ``overwrite``: Overwrite existing data.
+ * ``ignore``: Silently ignore this operation if data already exists.
+ * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
+ exists.
+ partitionBy : list, optional
+ names of partitioning columns
+ **options : dict
+ all other string options
+
+ Examples
+ --------
+ Write a DataFrame into a JSON file and read it back.
+
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Write a DataFrame into a JSON file
+ ... spark.createDataFrame(
+ ... [{"age": 100, "name": "Hyukjin Kwon"}]
+ ... ).write.mode("overwrite").format("json").save(d)
+ ...
+ ... # Read the JSON file as a DataFrame.
+ ... spark.read.format('json').load(d).show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ +---+------------+
+ """
+ self.mode(mode).options(**options)
+ if partitionBy is not None:
+ self.partitionBy(partitionBy)
+ if format is not None:
+ self.format(format)
+ self._write.path = path
+ self._spark.execute_command(self._write.command(self._spark))
+
+ def insertInto(self, tableName: str, overwrite: Optional[bool] = None) -> None:
+ """Inserts the content of the :class:`DataFrame` to the specified table.
+
+ It requires that the schema of the :class:`DataFrame` is the same as the
+ schema of the table.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ overwrite : bool, optional
+ If true, overwrites existing data. Disabled by default
+
+ Notes
+ -----
+ Unlike :meth:`DataFrameWriter.saveAsTable`, :meth:`DataFrameWriter.insertInto` ignores
+ the column names and just uses position-based resolution.
+
+ Examples
+ --------
+ >>> _ = spark.sql("DROP TABLE IF EXISTS tblA")
+ >>> df = spark.createDataFrame([
+ ... (100, "Hyukjin Kwon"), (120, "Hyukjin Kwon"), (140, "Haejoon Lee")],
+ ... schema=["age", "name"]
+ ... )
+ >>> df.write.saveAsTable("tblA")
+
+ Insert the data into 'tblA' table but with different column names.
+
+ >>> df.selectExpr("age AS col1", "name AS col2").write.insertInto("tblA")
+ >>> spark.read.table("tblA").sort("age").show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ |100|Hyukjin Kwon|
+ |120|Hyukjin Kwon|
+ |120|Hyukjin Kwon|
+ |140| Haejoon Lee|
+ |140| Haejoon Lee|
+ +---+------------+
+ >>> _ = spark.sql("DROP TABLE tblA")
+ """
+ if overwrite is not None:
+ self.mode("overwrite" if overwrite else "append")
+ self.saveAsTable(tableName)
+
+ def saveAsTable(
+ self,
+ name: str,
+ format: Optional[str] = None,
+ mode: Optional[str] = None,
+ partitionBy: Optional[Union[str, List[str]]] = None,
+ **options: "OptionalPrimitiveType",
+ ) -> None:
+ """Saves the content of the :class:`DataFrame` as the specified table.
+
+ In the case the table already exists, behavior of this function depends on the
+ save mode, specified by the `mode` function (default to throwing an exception).
+ When `mode` is `Overwrite`, the schema of the :class:`DataFrame` does not need to be
+ the same as that of the existing table.
+
+ * `append`: Append contents of this :class:`DataFrame` to existing data.
+ * `overwrite`: Overwrite existing data.
+ * `error` or `errorifexists`: Throw an exception if data already exists.
+ * `ignore`: Silently ignore this operation if data already exists.
+
+ .. versionadded:: 3.4.0
+
+ Notes
+ -----
+ When `mode` is `Append`, if there is an existing table, we will use the format and
+ options of the existing table. The column order in the schema of the :class:`DataFrame`
+ doesn't need to be same as that of the existing table. Unlike
+ :meth:`DataFrameWriter.insertInto`, :meth:`DataFrameWriter.saveAsTable` will use the
+ column names to find the correct column positions.
+
+ Parameters
+ ----------
+ name : str
+ the table name
+ format : str, optional
+ the format used to save
+ mode : str, optional
+ one of `append`, `overwrite`, `error`, `errorifexists`, `ignore` \
+ (default: error)
+ partitionBy : str or list
+ names of partitioning columns
+ **options : dict
+ all other string options
+
+ Examples
+ --------
+ Creates a table from a DataFrame, and read it back.
+
+ >>> _ = spark.sql("DROP TABLE IF EXISTS tblA")
+ >>> spark.createDataFrame([
+ ... (100, "Hyukjin Kwon"), (120, "Hyukjin Kwon"), (140, "Haejoon Lee")],
+ ... schema=["age", "name"]
+ ... ).write.saveAsTable("tblA")
+ >>> spark.read.table("tblA").sort("age").show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ |120|Hyukjin Kwon|
+ |140| Haejoon Lee|
+ +---+------------+
+ >>> _ = spark.sql("DROP TABLE tblA")
+ """
+ self.mode(mode).options(**options)
+ if partitionBy is not None:
+ self.partitionBy(partitionBy)
+ if format is not None:
+ self.format(format)
+ self._write.table_name = name
+ self._spark.execute_command(self._write.command(self._spark))
+
+ def json(
+ self,
+ path: str,
+ mode: Optional[str] = None,
+ compression: Optional[str] = None,
+ dateFormat: Optional[str] = None,
+ timestampFormat: Optional[str] = None,
+ lineSep: Optional[str] = None,
+ encoding: Optional[str] = None,
+ ignoreNullFields: Optional[Union[bool, str]] = None,
+ ) -> None:
+ """Saves the content of the :class:`DataFrame` in JSON format
+ (`JSON Lines text format or newline-delimited JSON <http://jsonlines.org/>`_) at the
+ specified path.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ path : str
+ the path in any Hadoop supported file system
+ mode : str, optional
+ specifies the behavior of the save operation when data already exists.
+
+ * ``append``: Append contents of this :class:`DataFrame` to existing data.
+ * ``overwrite``: Overwrite existing data.
+ * ``ignore``: Silently ignore this operation if data already exists.
+ * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
+ exists.
+
+ Other Parameters
+ ----------------
+ Extra options
+ For the extra options, refer to
+ `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option>`_
+ in the version you use.
+
+ .. # noqa
+
+ Examples
+ --------
+ Write a DataFrame into a JSON file and read it back.
+
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Write a DataFrame into a JSON file
+ ... spark.createDataFrame(
+ ... [{"age": 100, "name": "Hyukjin Kwon"}]
+ ... ).write.json(d, mode="overwrite")
+ ...
+ ... # Read the JSON file as a DataFrame.
+ ... spark.read.format("json").load(d).show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ +---+------------+
+ """
+ self.mode(mode)
+ self._set_opts(
+ compression=compression,
+ dateFormat=dateFormat,
+ timestampFormat=timestampFormat,
+ lineSep=lineSep,
+ encoding=encoding,
+ ignoreNullFields=ignoreNullFields,
+ )
+ self.format("json").save(path)
+
+ def parquet(
+ self,
+ path: str,
+ mode: Optional[str] = None,
+ partitionBy: Optional[Union[str, List[str]]] = None,
+ compression: Optional[str] = None,
+ ) -> None:
+ """Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ path : str
+ the path in any Hadoop supported file system
+ mode : str, optional
+ specifies the behavior of the save operation when data already exists.
+
+ * ``append``: Append contents of this :class:`DataFrame` to existing data.
+ * ``overwrite``: Overwrite existing data.
+ * ``ignore``: Silently ignore this operation if data already exists.
+ * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
+ exists.
+ partitionBy : str or list, optional
+ names of partitioning columns
+
+ Other Parameters
+ ----------------
+ Extra options
+ For the extra options, refer to
+ `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#data-source-option>`_
+ in the version you use.
+
+ .. # noqa
+
+ Examples
+ --------
+ Write a DataFrame into a Parquet file and read it back.
+
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Write a DataFrame into a Parquet file
+ ... spark.createDataFrame(
+ ... [{"age": 100, "name": "Hyukjin Kwon"}]
+ ... ).write.parquet(d, mode="overwrite")
+ ...
+ ... # Read the Parquet file as a DataFrame.
+ ... spark.read.format("parquet").load(d).show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ +---+------------+
+ """
+ self.mode(mode)
+ if partitionBy is not None:
+ self.partitionBy(partitionBy)
+ self.option("compression", compression)
+ self.format("parquet").save(path)
+
+ def text(
+ self, path: str, compression: Optional[str] = None, lineSep: Optional[str] = None
+ ) -> None:
+ """Saves the content of the DataFrame in a text file at the specified path.
+ The text files will be encoded as UTF-8.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ path : str
+ the path in any Hadoop supported file system
+
+ Other Parameters
+ ----------------
+ Extra options
+ For the extra options, refer to
+ `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-text.html#data-source-option>`_
+ in the version you use.
+
+ .. # noqa
+
+ Notes
+ -----
+ The DataFrame must have only one column that is of string type.
+ Each row becomes a new line in the output file.
+
+ Examples
+ --------
+ Write a DataFrame into a text file and read it back.
+
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Write a DataFrame into a text file
+ ... df = spark.createDataFrame([("a",), ("b",), ("c",)], schema=["alphabets"])
+ ... df.write.mode("overwrite").text(d)
+ ...
+ ... # Read the text file as a DataFrame.
+ ... spark.read.schema(df.schema).format("text").load(d).sort("alphabets").show()
+ +---------+
+ |alphabets|
+ +---------+
+ | a|
+ | b|
+ | c|
+ +---------+
+ """
+ self._set_opts(compression=compression, lineSep=lineSep)
+ self.format("text").save(path)
+
+ def csv(
+ self,
+ path: str,
+ mode: Optional[str] = None,
+ compression: Optional[str] = None,
+ sep: Optional[str] = None,
+ quote: Optional[str] = None,
+ escape: Optional[str] = None,
+ header: Optional[Union[bool, str]] = None,
+ nullValue: Optional[str] = None,
+ escapeQuotes: Optional[Union[bool, str]] = None,
+ quoteAll: Optional[Union[bool, str]] = None,
+ dateFormat: Optional[str] = None,
+ timestampFormat: Optional[str] = None,
+ ignoreLeadingWhiteSpace: Optional[Union[bool, str]] = None,
+ ignoreTrailingWhiteSpace: Optional[Union[bool, str]] = None,
+ charToEscapeQuoteEscaping: Optional[str] = None,
+ encoding: Optional[str] = None,
+ emptyValue: Optional[str] = None,
+ lineSep: Optional[str] = None,
+ ) -> None:
+ r"""Saves the content of the :class:`DataFrame` in CSV format at the specified path.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ path : str
+ the path in any Hadoop supported file system
+ mode : str, optional
+ specifies the behavior of the save operation when data already exists.
+
+ * ``append``: Append contents of this :class:`DataFrame` to existing data.
+ * ``overwrite``: Overwrite existing data.
+ * ``ignore``: Silently ignore this operation if data already exists.
+ * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
+ exists.
+
+ Other Parameters
+ ----------------
+ Extra options
+ For the extra options, refer to
+ `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option>`_
+ in the version you use.
+
+ .. # noqa
+
+ Examples
+ --------
+ Write a DataFrame into a CSV file and read it back.
+
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Write a DataFrame into a CSV file
+ ... df = spark.createDataFrame([{"age": 100, "name": "Hyukjin Kwon"}])
+ ... df.write.csv(d, mode="overwrite")
+ ...
+ ... # Read the CSV file as a DataFrame with 'nullValue' option set to 'Hyukjin Kwon'.
+ ... spark.read.schema(df.schema).format("csv").option(
+ ... "nullValue", "Hyukjin Kwon").load(d).show()
+ +---+----+
+ |age|name|
+ +---+----+
+ |100|null|
+ +---+----+
+ """
+ self.mode(mode)
+ self._set_opts(
+ compression=compression,
+ sep=sep,
+ quote=quote,
+ escape=escape,
+ header=header,
+ nullValue=nullValue,
+ escapeQuotes=escapeQuotes,
+ quoteAll=quoteAll,
+ dateFormat=dateFormat,
+ timestampFormat=timestampFormat,
+ ignoreLeadingWhiteSpace=ignoreLeadingWhiteSpace,
+ ignoreTrailingWhiteSpace=ignoreTrailingWhiteSpace,
+ charToEscapeQuoteEscaping=charToEscapeQuoteEscaping,
+ encoding=encoding,
+ emptyValue=emptyValue,
+ lineSep=lineSep,
+ )
+ self.format("csv").save(path)
+
+ def orc(
+ self,
+ path: str,
+ mode: Optional[str] = None,
+ partitionBy: Optional[Union[str, List[str]]] = None,
+ compression: Optional[str] = None,
+ ) -> None:
+ """Saves the content of the :class:`DataFrame` in ORC format at the specified path.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ path : str
+ the path in any Hadoop supported file system
+ mode : str, optional
+ specifies the behavior of the save operation when data already exists.
+
+ * ``append``: Append contents of this :class:`DataFrame` to existing data.
+ * ``overwrite``: Overwrite existing data.
+ * ``ignore``: Silently ignore this operation if data already exists.
+ * ``error`` or ``errorifexists`` (default case): Throw an exception if data already \
+ exists.
+ partitionBy : str or list, optional
+ names of partitioning columns
+
+ Other Parameters
+ ----------------
+ Extra options
+ For the extra options, refer to
+ `Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-orc.html#data-source-option>`_
+ in the version you use.
+
+ .. # noqa
+
+ Examples
+ --------
+ Write a DataFrame into a ORC file and read it back.
+
+ >>> import tempfile
+ >>> with tempfile.TemporaryDirectory() as d:
+ ... # Write a DataFrame into a ORC file
+ ... spark.createDataFrame(
+ ... [{"age": 100, "name": "Hyukjin Kwon"}]
+ ... ).write.orc(d, mode="overwrite")
+ ...
+ ... # Read the Parquet file as a DataFrame.
+ ... spark.read.format("orc").load(d).show()
+ +---+------------+
+ |age| name|
+ +---+------------+
+ |100|Hyukjin Kwon|
+ +---+------------+
+ """
+ self.mode(mode)
+ if partitionBy is not None:
+ self.partitionBy(partitionBy)
+ self._set_opts(compression=compression)
+ self.format("orc").save(path)
+
+ def jdbc(self, *args: Any, **kwargs: Any) -> None:
Review Comment:
We've been discussing this and the conclusion was that it's better to give a clear error message than failing in a weird way. For Scala it's a bit different because you will get this error at compile time, but in Python you will get a method not found at runtime error that might not be as clear.
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