2ef8ced27a
### What changes were proposed in this pull request? Updating the pyspark sql readwriter documentation to the level of detail provided by the scala documentation ### Why are the changes needed? documentation clarity ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Only documentation change Closes #33394 from dominikgehl/feature/SPARK-36181. Authored-by: Dominik Gehl <dog@open.ch> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
1212 lines
46 KiB
Python
1212 lines
46 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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from py4j.java_gateway import JavaClass
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from pyspark import RDD, since
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from pyspark.sql.column import _to_seq, _to_java_column
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from pyspark.sql.types import StructType
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from pyspark.sql import utils
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from pyspark.sql.utils import to_str
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__all__ = ["DataFrameReader", "DataFrameWriter"]
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class OptionUtils(object):
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def _set_opts(self, schema=None, **options):
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"""
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Set named options (filter out those the value is None)
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"""
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if schema is not None:
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self.schema(schema)
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for k, v in options.items():
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if v is not None:
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self.option(k, v)
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class DataFrameReader(OptionUtils):
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"""
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Interface used to load a :class:`DataFrame` from external storage systems
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(e.g. file systems, key-value stores, etc). Use :attr:`SparkSession.read`
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to access this.
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.. versionadded:: 1.4
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"""
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def __init__(self, spark):
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self._jreader = spark._ssql_ctx.read()
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self._spark = spark
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def _df(self, jdf):
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from pyspark.sql.dataframe import DataFrame
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return DataFrame(jdf, self._spark)
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def format(self, source):
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"""Specifies the input data source format.
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.. versionadded:: 1.4.0
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Parameters
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----------
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source : str
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string, name of the data source, e.g. 'json', 'parquet'.
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Examples
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--------
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>>> df = spark.read.format('json').load('python/test_support/sql/people.json')
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>>> df.dtypes
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[('age', 'bigint'), ('name', 'string')]
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"""
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self._jreader = self._jreader.format(source)
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return self
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def schema(self, schema):
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"""Specifies the input schema.
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Some data sources (e.g. JSON) can infer the input schema automatically from data.
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By specifying the schema here, the underlying data source can skip the schema
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inference step, and thus speed up data loading.
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.. versionadded:: 1.4.0
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Parameters
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----------
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schema : :class:`pyspark.sql.types.StructType` or str
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a :class:`pyspark.sql.types.StructType` object or a DDL-formatted string
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(For example ``col0 INT, col1 DOUBLE``).
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>>> s = spark.read.schema("col0 INT, col1 DOUBLE")
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"""
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from pyspark.sql import SparkSession
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spark = SparkSession.builder.getOrCreate()
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if isinstance(schema, StructType):
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jschema = spark._jsparkSession.parseDataType(schema.json())
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self._jreader = self._jreader.schema(jschema)
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elif isinstance(schema, str):
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self._jreader = self._jreader.schema(schema)
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else:
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raise TypeError("schema should be StructType or string")
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return self
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@since(1.5)
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def option(self, key, value):
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"""Adds an input option for the underlying data source.
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"""
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self._jreader = self._jreader.option(key, to_str(value))
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return self
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@since(1.4)
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def options(self, **options):
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"""Adds input options for the underlying data source.
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"""
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for k in options:
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self._jreader = self._jreader.option(k, to_str(options[k]))
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return self
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def load(self, path=None, format=None, schema=None, **options):
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"""Loads data from a data source and returns it as a :class:`DataFrame`.
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.. versionadded:: 1.4.0
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Parameters
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----------
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path : str or list, optional
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optional string or a list of string for file-system backed data sources.
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format : str, optional
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optional string for format of the data source. Default to 'parquet'.
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schema : :class:`pyspark.sql.types.StructType` or str, optional
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optional :class:`pyspark.sql.types.StructType` for the input schema
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or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).
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**options : dict
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all other string options
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Examples
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--------
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>>> df = spark.read.format("parquet").load('python/test_support/sql/parquet_partitioned',
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... opt1=True, opt2=1, opt3='str')
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>>> df.dtypes
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[('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
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>>> df = spark.read.format('json').load(['python/test_support/sql/people.json',
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... 'python/test_support/sql/people1.json'])
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>>> df.dtypes
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[('age', 'bigint'), ('aka', 'string'), ('name', 'string')]
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"""
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if format is not None:
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self.format(format)
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if schema is not None:
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self.schema(schema)
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self.options(**options)
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if isinstance(path, str):
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return self._df(self._jreader.load(path))
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elif path is not None:
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if type(path) != list:
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path = [path]
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return self._df(self._jreader.load(self._spark._sc._jvm.PythonUtils.toSeq(path)))
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else:
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return self._df(self._jreader.load())
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def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
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allowComments=None, allowUnquotedFieldNames=None, allowSingleQuotes=None,
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allowNumericLeadingZero=None, allowBackslashEscapingAnyCharacter=None,
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mode=None, columnNameOfCorruptRecord=None, dateFormat=None, timestampFormat=None,
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multiLine=None, allowUnquotedControlChars=None, lineSep=None, samplingRatio=None,
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dropFieldIfAllNull=None, encoding=None, locale=None, pathGlobFilter=None,
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recursiveFileLookup=None, allowNonNumericNumbers=None,
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modifiedBefore=None, modifiedAfter=None):
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"""
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Loads JSON files and returns the results as a :class:`DataFrame`.
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`JSON Lines <http://jsonlines.org/>`_ (newline-delimited JSON) is supported by default.
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For JSON (one record per file), set the ``multiLine`` parameter to ``true``.
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If the ``schema`` parameter is not specified, this function goes
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through the input once to determine the input schema.
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.. versionadded:: 1.4.0
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Parameters
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----------
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path : str, list or :class:`RDD`
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string represents path to the JSON dataset, or a list of paths,
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or RDD of Strings storing JSON objects.
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schema : :class:`pyspark.sql.types.StructType` or str, optional
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an optional :class:`pyspark.sql.types.StructType` for the input schema or
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a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).
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Other Parameters
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----------------
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Extra options
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For the extra options, refer to
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`Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-json.html#data-source-option>`_
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in the version you use.
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.. # noqa
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Examples
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--------
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>>> df1 = spark.read.json('python/test_support/sql/people.json')
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>>> df1.dtypes
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[('age', 'bigint'), ('name', 'string')]
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>>> rdd = sc.textFile('python/test_support/sql/people.json')
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>>> df2 = spark.read.json(rdd)
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>>> df2.dtypes
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[('age', 'bigint'), ('name', 'string')]
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"""
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self._set_opts(
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schema=schema, primitivesAsString=primitivesAsString, prefersDecimal=prefersDecimal,
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allowComments=allowComments, allowUnquotedFieldNames=allowUnquotedFieldNames,
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allowSingleQuotes=allowSingleQuotes, allowNumericLeadingZero=allowNumericLeadingZero,
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allowBackslashEscapingAnyCharacter=allowBackslashEscapingAnyCharacter,
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mode=mode, columnNameOfCorruptRecord=columnNameOfCorruptRecord, dateFormat=dateFormat,
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timestampFormat=timestampFormat, multiLine=multiLine,
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allowUnquotedControlChars=allowUnquotedControlChars, lineSep=lineSep,
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samplingRatio=samplingRatio, dropFieldIfAllNull=dropFieldIfAllNull, encoding=encoding,
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locale=locale, pathGlobFilter=pathGlobFilter, recursiveFileLookup=recursiveFileLookup,
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modifiedBefore=modifiedBefore, modifiedAfter=modifiedAfter,
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allowNonNumericNumbers=allowNonNumericNumbers)
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if isinstance(path, str):
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path = [path]
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if type(path) == list:
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return self._df(self._jreader.json(self._spark._sc._jvm.PythonUtils.toSeq(path)))
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elif isinstance(path, RDD):
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def func(iterator):
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for x in iterator:
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if not isinstance(x, str):
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x = str(x)
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if isinstance(x, str):
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x = x.encode("utf-8")
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yield x
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keyed = path.mapPartitions(func)
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keyed._bypass_serializer = True
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jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
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return self._df(self._jreader.json(jrdd))
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else:
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raise TypeError("path can be only string, list or RDD")
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def table(self, tableName):
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"""Returns the specified table as a :class:`DataFrame`.
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.. versionadded:: 1.4.0
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Parameters
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----------
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tableName : str
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string, name of the table.
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Examples
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--------
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>>> df = spark.read.parquet('python/test_support/sql/parquet_partitioned')
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>>> df.createOrReplaceTempView('tmpTable')
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>>> spark.read.table('tmpTable').dtypes
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[('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
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"""
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return self._df(self._jreader.table(tableName))
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def parquet(self, *paths, **options):
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"""
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Loads Parquet files, returning the result as a :class:`DataFrame`.
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.. versionadded:: 1.4.0
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Parameters
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----------
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paths : str
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Other Parameters
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----------------
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**options
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For the extra options, refer to
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`Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#data-source-option>`_
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in the version you use.
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.. # noqa
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Examples
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--------
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>>> df = spark.read.parquet('python/test_support/sql/parquet_partitioned')
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>>> df.dtypes
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[('name', 'string'), ('year', 'int'), ('month', 'int'), ('day', 'int')]
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"""
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mergeSchema = options.get('mergeSchema', None)
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pathGlobFilter = options.get('pathGlobFilter', None)
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modifiedBefore = options.get('modifiedBefore', None)
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modifiedAfter = options.get('modifiedAfter', None)
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recursiveFileLookup = options.get('recursiveFileLookup', None)
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datetimeRebaseMode = options.get('datetimeRebaseMode', None)
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int96RebaseMode = options.get('int96RebaseMode', None)
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self._set_opts(mergeSchema=mergeSchema, pathGlobFilter=pathGlobFilter,
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recursiveFileLookup=recursiveFileLookup, modifiedBefore=modifiedBefore,
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modifiedAfter=modifiedAfter, datetimeRebaseMode=datetimeRebaseMode,
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int96RebaseMode=int96RebaseMode)
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return self._df(self._jreader.parquet(_to_seq(self._spark._sc, paths)))
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def text(self, paths, wholetext=False, lineSep=None, pathGlobFilter=None,
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recursiveFileLookup=None, modifiedBefore=None,
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modifiedAfter=None):
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"""
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Loads text files and returns a :class:`DataFrame` whose schema starts with a
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string column named "value", and followed by partitioned columns if there
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are any.
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The text files must be encoded as UTF-8.
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By default, each line in the text file is a new row in the resulting DataFrame.
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.. versionadded:: 1.6.0
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Parameters
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----------
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paths : str or list
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string, or list of strings, for input path(s).
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Other Parameters
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----------------
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Extra options
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For the extra options, refer to
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`Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-text.html#data-source-option>`_
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in the version you use.
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.. # noqa
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Examples
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--------
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>>> df = spark.read.text('python/test_support/sql/text-test.txt')
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>>> df.collect()
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[Row(value='hello'), Row(value='this')]
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>>> df = spark.read.text('python/test_support/sql/text-test.txt', wholetext=True)
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>>> df.collect()
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[Row(value='hello\\nthis')]
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"""
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self._set_opts(
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wholetext=wholetext, lineSep=lineSep, pathGlobFilter=pathGlobFilter,
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recursiveFileLookup=recursiveFileLookup, modifiedBefore=modifiedBefore,
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modifiedAfter=modifiedAfter)
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if isinstance(paths, str):
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paths = [paths]
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return self._df(self._jreader.text(self._spark._sc._jvm.PythonUtils.toSeq(paths)))
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def csv(self, path, schema=None, sep=None, encoding=None, quote=None, escape=None,
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comment=None, header=None, inferSchema=None, ignoreLeadingWhiteSpace=None,
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ignoreTrailingWhiteSpace=None, nullValue=None, nanValue=None, positiveInf=None,
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negativeInf=None, dateFormat=None, timestampFormat=None, maxColumns=None,
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maxCharsPerColumn=None, maxMalformedLogPerPartition=None, mode=None,
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columnNameOfCorruptRecord=None, multiLine=None, charToEscapeQuoteEscaping=None,
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samplingRatio=None, enforceSchema=None, emptyValue=None, locale=None, lineSep=None,
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pathGlobFilter=None, recursiveFileLookup=None, modifiedBefore=None, modifiedAfter=None,
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unescapedQuoteHandling=None):
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r"""Loads a CSV file and returns the result as a :class:`DataFrame`.
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This function will go through the input once to determine the input schema if
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``inferSchema`` is enabled. To avoid going through the entire data once, disable
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``inferSchema`` option or specify the schema explicitly using ``schema``.
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.. versionadded:: 2.0.0
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Parameters
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----------
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path : str or list
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string, or list of strings, for input path(s),
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or RDD of Strings storing CSV rows.
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schema : :class:`pyspark.sql.types.StructType` or str, optional
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an optional :class:`pyspark.sql.types.StructType` for the input schema
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or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).
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Other Parameters
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----------------
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Extra options
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For the extra options, refer to
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`Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option>`_
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in the version you use.
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.. # noqa
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Examples
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--------
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>>> df = spark.read.csv('python/test_support/sql/ages.csv')
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>>> df.dtypes
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[('_c0', 'string'), ('_c1', 'string')]
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>>> rdd = sc.textFile('python/test_support/sql/ages.csv')
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>>> df2 = spark.read.csv(rdd)
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>>> df2.dtypes
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[('_c0', 'string'), ('_c1', 'string')]
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"""
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self._set_opts(
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schema=schema, sep=sep, encoding=encoding, quote=quote, escape=escape, comment=comment,
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header=header, inferSchema=inferSchema, ignoreLeadingWhiteSpace=ignoreLeadingWhiteSpace,
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ignoreTrailingWhiteSpace=ignoreTrailingWhiteSpace, nullValue=nullValue,
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nanValue=nanValue, positiveInf=positiveInf, negativeInf=negativeInf,
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dateFormat=dateFormat, timestampFormat=timestampFormat, maxColumns=maxColumns,
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maxCharsPerColumn=maxCharsPerColumn,
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maxMalformedLogPerPartition=maxMalformedLogPerPartition, mode=mode,
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columnNameOfCorruptRecord=columnNameOfCorruptRecord, multiLine=multiLine,
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charToEscapeQuoteEscaping=charToEscapeQuoteEscaping, samplingRatio=samplingRatio,
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enforceSchema=enforceSchema, emptyValue=emptyValue, locale=locale, lineSep=lineSep,
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pathGlobFilter=pathGlobFilter, recursiveFileLookup=recursiveFileLookup,
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modifiedBefore=modifiedBefore, modifiedAfter=modifiedAfter,
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unescapedQuoteHandling=unescapedQuoteHandling)
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if isinstance(path, str):
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path = [path]
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if type(path) == list:
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return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path)))
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elif isinstance(path, RDD):
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def func(iterator):
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for x in iterator:
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if not isinstance(x, str):
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x = str(x)
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if isinstance(x, str):
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x = x.encode("utf-8")
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yield x
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keyed = path.mapPartitions(func)
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keyed._bypass_serializer = True
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jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
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# see SPARK-22112
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# There aren't any jvm api for creating a dataframe from rdd storing csv.
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# We can do it through creating a jvm dataset firstly and using the jvm api
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# for creating a dataframe from dataset storing csv.
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jdataset = self._spark._ssql_ctx.createDataset(
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jrdd.rdd(),
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self._spark._jvm.Encoders.STRING())
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return self._df(self._jreader.csv(jdataset))
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else:
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raise TypeError("path can be only string, list or RDD")
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def orc(self, path, mergeSchema=None, pathGlobFilter=None, recursiveFileLookup=None,
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modifiedBefore=None, modifiedAfter=None):
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"""Loads ORC files, returning the result as a :class:`DataFrame`.
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.. versionadded:: 1.5.0
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Parameters
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----------
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path : str or list
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Other Parameters
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----------------
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Extra options
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For the extra options, refer to
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`Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-orc.html#data-source-option>`_
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in the version you use.
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|
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.. # noqa
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Examples
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--------
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>>> df = spark.read.orc('python/test_support/sql/orc_partitioned')
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>>> df.dtypes
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[('a', 'bigint'), ('b', 'int'), ('c', 'int')]
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"""
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self._set_opts(mergeSchema=mergeSchema, pathGlobFilter=pathGlobFilter,
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modifiedBefore=modifiedBefore, modifiedAfter=modifiedAfter,
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recursiveFileLookup=recursiveFileLookup)
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if isinstance(path, str):
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path = [path]
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return self._df(self._jreader.orc(_to_seq(self._spark._sc, path)))
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def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPartitions=None,
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predicates=None, properties=None):
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"""
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Construct a :class:`DataFrame` representing the database table named ``table``
|
|
accessible via JDBC URL ``url`` and connection ``properties``.
|
|
|
|
Partitions of the table will be retrieved in parallel if either ``column`` or
|
|
``predicates`` is specified. ``lowerBound``, ``upperBound`` and ``numPartitions``
|
|
is needed when ``column`` is specified.
|
|
|
|
If both ``column`` and ``predicates`` are specified, ``column`` will be used.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
Parameters
|
|
----------
|
|
table : str
|
|
the name of the table
|
|
column : str, optional
|
|
alias of ``partitionColumn`` option. Refer to ``partitionColumn`` in
|
|
`Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html#data-source-option>`_
|
|
in the version you use.
|
|
predicates : list, optional
|
|
a list of expressions suitable for inclusion in WHERE clauses;
|
|
each one defines one partition of the :class:`DataFrame`
|
|
properties : dict, optional
|
|
a dictionary of JDBC database connection arguments. Normally at
|
|
least properties "user" and "password" with their corresponding values.
|
|
For example { 'user' : 'SYSTEM', 'password' : 'mypassword' }
|
|
|
|
Other Parameters
|
|
----------------
|
|
Extra options
|
|
For the extra options, refer to
|
|
`Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html#data-source-option>`_
|
|
in the version you use.
|
|
|
|
.. # noqa
|
|
|
|
Notes
|
|
-----
|
|
Don't create too many partitions in parallel on a large cluster;
|
|
otherwise Spark might crash your external database systems.
|
|
|
|
Returns
|
|
-------
|
|
:class:`DataFrame`
|
|
"""
|
|
if properties is None:
|
|
properties = dict()
|
|
jprop = JavaClass("java.util.Properties", self._spark._sc._gateway._gateway_client)()
|
|
for k in properties:
|
|
jprop.setProperty(k, properties[k])
|
|
if column is not None:
|
|
assert lowerBound is not None, "lowerBound can not be None when ``column`` is specified"
|
|
assert upperBound is not None, "upperBound can not be None when ``column`` is specified"
|
|
assert numPartitions is not None, \
|
|
"numPartitions can not be None when ``column`` is specified"
|
|
return self._df(self._jreader.jdbc(url, table, column, int(lowerBound), int(upperBound),
|
|
int(numPartitions), jprop))
|
|
if predicates is not None:
|
|
gateway = self._spark._sc._gateway
|
|
jpredicates = utils.toJArray(gateway, gateway.jvm.java.lang.String, predicates)
|
|
return self._df(self._jreader.jdbc(url, table, jpredicates, jprop))
|
|
return self._df(self._jreader.jdbc(url, table, jprop))
|
|
|
|
|
|
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:: 1.4
|
|
"""
|
|
def __init__(self, df):
|
|
self._df = df
|
|
self._spark = df.sql_ctx
|
|
self._jwrite = df._jdf.write()
|
|
|
|
def _sq(self, jsq):
|
|
from pyspark.sql.streaming import StreamingQuery
|
|
return StreamingQuery(jsq)
|
|
|
|
def mode(self, saveMode):
|
|
"""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:: 1.4.0
|
|
|
|
Examples
|
|
--------
|
|
>>> df.write.mode('append').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
|
|
"""
|
|
# 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._jwrite = self._jwrite.mode(saveMode)
|
|
return self
|
|
|
|
def format(self, source):
|
|
"""Specifies the underlying output data source.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
Parameters
|
|
----------
|
|
source : str
|
|
string, name of the data source, e.g. 'json', 'parquet'.
|
|
|
|
Examples
|
|
--------
|
|
>>> df.write.format('json').save(os.path.join(tempfile.mkdtemp(), 'data'))
|
|
"""
|
|
self._jwrite = self._jwrite.format(source)
|
|
return self
|
|
|
|
@since(1.5)
|
|
def option(self, key, value):
|
|
"""Adds an output option for the underlying data source.
|
|
"""
|
|
self._jwrite = self._jwrite.option(key, to_str(value))
|
|
return self
|
|
|
|
@since(1.4)
|
|
def options(self, **options):
|
|
"""Adds output options for the underlying data source.
|
|
"""
|
|
for k in options:
|
|
self._jwrite = self._jwrite.option(k, to_str(options[k]))
|
|
return self
|
|
|
|
def partitionBy(self, *cols):
|
|
"""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:: 1.4.0
|
|
|
|
Parameters
|
|
----------
|
|
cols : str or list
|
|
name of columns
|
|
|
|
Examples
|
|
--------
|
|
>>> df.write.partitionBy('year', 'month').parquet(os.path.join(tempfile.mkdtemp(), 'data'))
|
|
"""
|
|
if len(cols) == 1 and isinstance(cols[0], (list, tuple)):
|
|
cols = cols[0]
|
|
self._jwrite = self._jwrite.partitionBy(_to_seq(self._spark._sc, cols))
|
|
return self
|
|
|
|
def bucketBy(self, numBuckets, col, *cols):
|
|
"""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
|
|
--------
|
|
>>> (df.write.format('parquet') # doctest: +SKIP
|
|
... .bucketBy(100, 'year', 'month')
|
|
... .mode("overwrite")
|
|
... .saveAsTable('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:]
|
|
|
|
if not all(isinstance(c, str) for c in cols) or not(isinstance(col, str)):
|
|
raise TypeError("all names should be `str`")
|
|
|
|
self._jwrite = self._jwrite.bucketBy(numBuckets, col, _to_seq(self._spark._sc, cols))
|
|
return self
|
|
|
|
def sortBy(self, col, *cols):
|
|
"""Sorts the output in each bucket by the given columns on the file system.
|
|
|
|
.. versionadded:: 2.3.0
|
|
|
|
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
|
|
--------
|
|
>>> (df.write.format('parquet') # doctest: +SKIP
|
|
... .bucketBy(100, 'year', 'month')
|
|
... .sortBy('day')
|
|
... .mode("overwrite")
|
|
... .saveAsTable('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:]
|
|
|
|
if not all(isinstance(c, str) for c in cols) or not(isinstance(col, str)):
|
|
raise TypeError("all names should be `str`")
|
|
|
|
self._jwrite = self._jwrite.sortBy(col, _to_seq(self._spark._sc, cols))
|
|
return self
|
|
|
|
def save(self, path=None, format=None, mode=None, partitionBy=None, **options):
|
|
"""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:: 1.4.0
|
|
|
|
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
|
|
--------
|
|
>>> df.write.mode("append").save(os.path.join(tempfile.mkdtemp(), 'data'))
|
|
"""
|
|
self.mode(mode).options(**options)
|
|
if partitionBy is not None:
|
|
self.partitionBy(partitionBy)
|
|
if format is not None:
|
|
self.format(format)
|
|
if path is None:
|
|
self._jwrite.save()
|
|
else:
|
|
self._jwrite.save(path)
|
|
|
|
@since(1.4)
|
|
def insertInto(self, tableName, overwrite=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.
|
|
|
|
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.
|
|
|
|
"""
|
|
if overwrite is not None:
|
|
self.mode("overwrite" if overwrite else "append")
|
|
self._jwrite.insertInto(tableName)
|
|
|
|
def saveAsTable(self, name, format=None, mode=None, partitionBy=None, **options):
|
|
"""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:: 1.4.0
|
|
|
|
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
|
|
"""
|
|
self.mode(mode).options(**options)
|
|
if partitionBy is not None:
|
|
self.partitionBy(partitionBy)
|
|
if format is not None:
|
|
self.format(format)
|
|
self._jwrite.saveAsTable(name)
|
|
|
|
def json(self, path, mode=None, compression=None, dateFormat=None, timestampFormat=None,
|
|
lineSep=None, encoding=None, ignoreNullFields=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:: 1.4.0
|
|
|
|
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
|
|
--------
|
|
>>> df.write.json(os.path.join(tempfile.mkdtemp(), 'data'))
|
|
"""
|
|
self.mode(mode)
|
|
self._set_opts(
|
|
compression=compression, dateFormat=dateFormat, timestampFormat=timestampFormat,
|
|
lineSep=lineSep, encoding=encoding, ignoreNullFields=ignoreNullFields)
|
|
self._jwrite.json(path)
|
|
|
|
def parquet(self, path, mode=None, partitionBy=None, compression=None):
|
|
"""Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
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
|
|
--------
|
|
>>> df.write.parquet(os.path.join(tempfile.mkdtemp(), 'data'))
|
|
"""
|
|
self.mode(mode)
|
|
if partitionBy is not None:
|
|
self.partitionBy(partitionBy)
|
|
self._set_opts(compression=compression)
|
|
self._jwrite.parquet(path)
|
|
|
|
def text(self, path, compression=None, lineSep=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:: 1.6.0
|
|
|
|
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
|
|
|
|
The DataFrame must have only one column that is of string type.
|
|
Each row becomes a new line in the output file.
|
|
"""
|
|
self._set_opts(compression=compression, lineSep=lineSep)
|
|
self._jwrite.text(path)
|
|
|
|
def csv(self, path, mode=None, compression=None, sep=None, quote=None, escape=None,
|
|
header=None, nullValue=None, escapeQuotes=None, quoteAll=None, dateFormat=None,
|
|
timestampFormat=None, ignoreLeadingWhiteSpace=None, ignoreTrailingWhiteSpace=None,
|
|
charToEscapeQuoteEscaping=None, encoding=None, emptyValue=None, lineSep=None):
|
|
r"""Saves the content of the :class:`DataFrame` in CSV format at the specified path.
|
|
|
|
.. versionadded:: 2.0.0
|
|
|
|
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
|
|
--------
|
|
>>> df.write.csv(os.path.join(tempfile.mkdtemp(), 'data'))
|
|
"""
|
|
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._jwrite.csv(path)
|
|
|
|
def orc(self, path, mode=None, partitionBy=None, compression=None):
|
|
"""Saves the content of the :class:`DataFrame` in ORC format at the specified path.
|
|
|
|
.. versionadded:: 1.5.0
|
|
|
|
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
|
|
--------
|
|
>>> orc_df = spark.read.orc('python/test_support/sql/orc_partitioned')
|
|
>>> orc_df.write.orc(os.path.join(tempfile.mkdtemp(), 'data'))
|
|
"""
|
|
self.mode(mode)
|
|
if partitionBy is not None:
|
|
self.partitionBy(partitionBy)
|
|
self._set_opts(compression=compression)
|
|
self._jwrite.orc(path)
|
|
|
|
def jdbc(self, url, table, mode=None, properties=None):
|
|
"""Saves the content of the :class:`DataFrame` to an external database table via JDBC.
|
|
|
|
.. versionadded:: 1.4.0
|
|
|
|
Parameters
|
|
----------
|
|
table : str
|
|
Name of the table in the external database.
|
|
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.
|
|
properties : dict
|
|
a dictionary of JDBC database connection arguments. Normally at
|
|
least properties "user" and "password" with their corresponding values.
|
|
For example { 'user' : 'SYSTEM', 'password' : 'mypassword' }
|
|
|
|
Other Parameters
|
|
----------------
|
|
Extra options
|
|
For the extra options, refer to
|
|
`Data Source Option <https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html#data-source-option>`_
|
|
in the version you use.
|
|
|
|
.. # noqa
|
|
|
|
Notes
|
|
-----
|
|
Don't create too many partitions in parallel on a large cluster;
|
|
otherwise Spark might crash your external database systems.
|
|
"""
|
|
if properties is None:
|
|
properties = dict()
|
|
jprop = JavaClass("java.util.Properties", self._spark._sc._gateway._gateway_client)()
|
|
for k in properties:
|
|
jprop.setProperty(k, properties[k])
|
|
self.mode(mode)._jwrite.jdbc(url, table, jprop)
|
|
|
|
|
|
class DataFrameWriterV2(object):
|
|
"""
|
|
Interface used to write a class:`pyspark.sql.dataframe.DataFrame`
|
|
to external storage using the v2 API.
|
|
|
|
.. versionadded:: 3.1.0
|
|
"""
|
|
|
|
def __init__(self, df, table):
|
|
self._df = df
|
|
self._spark = df.sql_ctx
|
|
self._jwriter = df._jdf.writeTo(table)
|
|
|
|
@since(3.1)
|
|
def using(self, provider):
|
|
"""
|
|
Specifies a provider for the underlying output data source.
|
|
Spark's default catalog supports "parquet", "json", etc.
|
|
"""
|
|
self._jwriter.using(provider)
|
|
return self
|
|
|
|
@since(3.1)
|
|
def option(self, key, value):
|
|
"""
|
|
Add a write option.
|
|
"""
|
|
self._jwriter.option(key, to_str(value))
|
|
return self
|
|
|
|
@since(3.1)
|
|
def options(self, **options):
|
|
"""
|
|
Add write options.
|
|
"""
|
|
options = {k: to_str(v) for k, v in options.items()}
|
|
self._jwriter.options(options)
|
|
return self
|
|
|
|
@since(3.1)
|
|
def tableProperty(self, property, value):
|
|
"""
|
|
Add table property.
|
|
"""
|
|
self._jwriter.tableProperty(property, value)
|
|
return self
|
|
|
|
@since(3.1)
|
|
def partitionedBy(self, col, *cols):
|
|
"""
|
|
Partition the output table created by `create`, `createOrReplace`, or `replace` using
|
|
the given columns or transforms.
|
|
|
|
When specified, the table data will be stored by these values for efficient reads.
|
|
|
|
For example, when a table is partitioned by day, it may be stored
|
|
in a directory layout like:
|
|
|
|
* `table/day=2019-06-01/`
|
|
* `table/day=2019-06-02/`
|
|
|
|
Partitioning is one of the most widely used techniques to optimize physical data layout.
|
|
It provides a coarse-grained index for skipping unnecessary data reads when queries have
|
|
predicates on the partitioned columns. In order for partitioning to work well, the number
|
|
of distinct values in each column should typically be less than tens of thousands.
|
|
|
|
`col` and `cols` support only the following functions:
|
|
|
|
* :py:func:`pyspark.sql.functions.years`
|
|
* :py:func:`pyspark.sql.functions.months`
|
|
* :py:func:`pyspark.sql.functions.days`
|
|
* :py:func:`pyspark.sql.functions.hours`
|
|
* :py:func:`pyspark.sql.functions.bucket`
|
|
|
|
"""
|
|
col = _to_java_column(col)
|
|
cols = _to_seq(self._spark._sc, [_to_java_column(c) for c in cols])
|
|
return self
|
|
|
|
@since(3.1)
|
|
def create(self):
|
|
"""
|
|
Create a new table from the contents of the data frame.
|
|
|
|
The new table's schema, partition layout, properties, and other configuration will be
|
|
based on the configuration set on this writer.
|
|
"""
|
|
self._jwriter.create()
|
|
|
|
@since(3.1)
|
|
def replace(self):
|
|
"""
|
|
Replace an existing table with the contents of the data frame.
|
|
|
|
The existing table's schema, partition layout, properties, and other configuration will be
|
|
replaced with the contents of the data frame and the configuration set on this writer.
|
|
"""
|
|
self._jwriter.replace()
|
|
|
|
@since(3.1)
|
|
def createOrReplace(self):
|
|
"""
|
|
Create a new table or replace an existing table with the contents of the data frame.
|
|
|
|
The output table's schema, partition layout, properties,
|
|
and other configuration will be based on the contents of the data frame
|
|
and the configuration set on this writer.
|
|
If the table exists, its configuration and data will be replaced.
|
|
"""
|
|
self._jwriter.createOrReplace()
|
|
|
|
@since(3.1)
|
|
def append(self):
|
|
"""
|
|
Append the contents of the data frame to the output table.
|
|
"""
|
|
self._jwriter.append()
|
|
|
|
@since(3.1)
|
|
def overwrite(self, condition):
|
|
"""
|
|
Overwrite rows matching the given filter condition with the contents of the data frame in
|
|
the output table.
|
|
"""
|
|
self._jwriter.overwrite(condition)
|
|
|
|
@since(3.1)
|
|
def overwritePartitions(self):
|
|
"""
|
|
Overwrite all partition for which the data frame contains at least one row with the contents
|
|
of the data frame in the output table.
|
|
|
|
This operation is equivalent to Hive's `INSERT OVERWRITE ... PARTITION`, which replaces
|
|
partitions dynamically depending on the contents of the data frame.
|
|
"""
|
|
self._jwriter.overwritePartitions()
|
|
|
|
|
|
def _test():
|
|
import doctest
|
|
import os
|
|
import tempfile
|
|
import py4j
|
|
from pyspark.context import SparkContext
|
|
from pyspark.sql import SparkSession
|
|
import pyspark.sql.readwriter
|
|
|
|
os.chdir(os.environ["SPARK_HOME"])
|
|
|
|
globs = pyspark.sql.readwriter.__dict__.copy()
|
|
sc = SparkContext('local[4]', 'PythonTest')
|
|
try:
|
|
spark = SparkSession.builder.getOrCreate()
|
|
except py4j.protocol.Py4JError:
|
|
spark = SparkSession(sc)
|
|
|
|
globs['tempfile'] = tempfile
|
|
globs['os'] = os
|
|
globs['sc'] = sc
|
|
globs['spark'] = spark
|
|
globs['df'] = spark.read.parquet('python/test_support/sql/parquet_partitioned')
|
|
(failure_count, test_count) = doctest.testmod(
|
|
pyspark.sql.readwriter, globs=globs,
|
|
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
|
|
sc.stop()
|
|
if failure_count:
|
|
sys.exit(-1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|