acab558e55
This commit is published into the public domain. ### What changes were proposed in this pull request? Some syntax issues in docstrings have been fixed. ### Why are the changes needed? In some places, the documentation did not render as intended, e.g. parameter documentations were not formatted as such. ### Does this PR introduce any user-facing change? Slight improvements in documentation. ### How was this patch tested? Manual testing and `dev/lint-python` run. No new Sphinx warnings arise due to this change. Closes #28559 from DavidToneian/SPARK-31739. Authored-by: David Toneian <david@toneian.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
1118 lines
60 KiB
Python
1118 lines
60 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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if sys.version >= '3':
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basestring = unicode = str
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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.rdd import ignore_unicode_prefix
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from pyspark.sql.column import _to_seq
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from pyspark.sql.types import *
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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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@since(1.4)
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def format(self, source):
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"""Specifies the input data source format.
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:param source: string, name of the data source, e.g. 'json', 'parquet'.
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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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@since(1.4)
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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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:param schema: 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, basestring):
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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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You can set the following option(s) for reading files:
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* ``timeZone``: sets the string that indicates a time zone ID to be used to parse
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timestamps in the JSON/CSV datasources or partition values. The following
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formats of `timeZone` are supported:
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* Region-based zone ID: It should have the form 'area/city', such as \
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'America/Los_Angeles'.
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* Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or \
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'+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
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Other short names like 'CST' are not recommended to use because they can be
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ambiguous. If it isn't set, the current value of the SQL config
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``spark.sql.session.timeZone`` is used by default.
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* ``pathGlobFilter``: an optional glob pattern to only include files with paths matching
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the pattern. The syntax follows org.apache.hadoop.fs.GlobFilter.
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It does not change the behavior of partition discovery.
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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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You can set the following option(s) for reading files:
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* ``timeZone``: sets the string that indicates a time zone ID to be used to parse
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timestamps in the JSON/CSV datasources or partition values. The following
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formats of `timeZone` are supported:
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* Region-based zone ID: It should have the form 'area/city', such as \
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'America/Los_Angeles'.
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* Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or \
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'+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
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Other short names like 'CST' are not recommended to use because they can be
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ambiguous. If it isn't set, the current value of the SQL config
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``spark.sql.session.timeZone`` is used by default.
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* ``pathGlobFilter``: an optional glob pattern to only include files with paths matching
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the pattern. The syntax follows org.apache.hadoop.fs.GlobFilter.
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It does not change the behavior of partition discovery.
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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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@since(1.4)
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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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:param path: optional string or a list of string for file-system backed data sources.
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:param format: optional string for format of the data source. Default to 'parquet'.
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:param schema: 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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:param options: all other string options
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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, basestring):
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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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@since(1.4)
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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):
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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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:param path: 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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:param schema: 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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:param primitivesAsString: infers all primitive values as a string type. If None is set,
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it uses the default value, ``false``.
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:param prefersDecimal: infers all floating-point values as a decimal type. If the values
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do not fit in decimal, then it infers them as doubles. If None is
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set, it uses the default value, ``false``.
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:param allowComments: ignores Java/C++ style comment in JSON records. If None is set,
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it uses the default value, ``false``.
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:param allowUnquotedFieldNames: allows unquoted JSON field names. If None is set,
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it uses the default value, ``false``.
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:param allowSingleQuotes: allows single quotes in addition to double quotes. If None is
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set, it uses the default value, ``true``.
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:param allowNumericLeadingZero: allows leading zeros in numbers (e.g. 00012). If None is
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set, it uses the default value, ``false``.
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:param allowBackslashEscapingAnyCharacter: allows accepting quoting of all character
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using backslash quoting mechanism. If None is
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set, it uses the default value, ``false``.
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:param mode: allows a mode for dealing with corrupt records during parsing. If None is
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set, it uses the default value, ``PERMISSIVE``.
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* ``PERMISSIVE``: when it meets a corrupted record, puts the malformed string \
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into a field configured by ``columnNameOfCorruptRecord``, and sets malformed \
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fields to ``null``. To keep corrupt records, an user can set a string type \
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field named ``columnNameOfCorruptRecord`` in an user-defined schema. If a \
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schema does not have the field, it drops corrupt records during parsing. \
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When inferring a schema, it implicitly adds a ``columnNameOfCorruptRecord`` \
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field in an output schema.
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* ``DROPMALFORMED``: ignores the whole corrupted records.
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* ``FAILFAST``: throws an exception when it meets corrupted records.
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:param columnNameOfCorruptRecord: allows renaming the new field having malformed string
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created by ``PERMISSIVE`` mode. This overrides
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``spark.sql.columnNameOfCorruptRecord``. If None is set,
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it uses the value specified in
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``spark.sql.columnNameOfCorruptRecord``.
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:param dateFormat: sets the string that indicates a date format. Custom date formats
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follow the formats at `datetime pattern`_.
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This applies to date type. If None is set, it uses the
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default value, ``yyyy-MM-dd``.
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:param timestampFormat: sets the string that indicates a timestamp format.
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Custom date formats follow the formats at `datetime pattern`_.
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This applies to timestamp type. If None is set, it uses the
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default value, ``yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]``.
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:param multiLine: parse one record, which may span multiple lines, per file. If None is
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set, it uses the default value, ``false``.
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:param allowUnquotedControlChars: allows JSON Strings to contain unquoted control
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characters (ASCII characters with value less than 32,
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including tab and line feed characters) or not.
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:param encoding: allows to forcibly set one of standard basic or extended encoding for
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the JSON files. For example UTF-16BE, UTF-32LE. If None is set,
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the encoding of input JSON will be detected automatically
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when the multiLine option is set to ``true``.
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:param lineSep: defines the line separator that should be used for parsing. If None is
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set, it covers all ``\\r``, ``\\r\\n`` and ``\\n``.
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:param samplingRatio: defines fraction of input JSON objects used for schema inferring.
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If None is set, it uses the default value, ``1.0``.
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:param dropFieldIfAllNull: whether to ignore column of all null values or empty
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array/struct during schema inference. If None is set, it
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uses the default value, ``false``.
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:param locale: sets a locale as language tag in IETF BCP 47 format. If None is set,
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it uses the default value, ``en-US``. For instance, ``locale`` is used while
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parsing dates and timestamps.
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:param pathGlobFilter: an optional glob pattern to only include files with paths matching
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the pattern. The syntax follows `org.apache.hadoop.fs.GlobFilter`.
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It does not change the behavior of `partition discovery`_.
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:param recursiveFileLookup: recursively scan a directory for files. Using this option
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disables `partition discovery`_.
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.. _partition discovery:
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https://spark.apache.org/docs/latest/sql-data-sources-parquet.html#partition-discovery
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.. _datetime pattern: https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html
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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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if isinstance(path, basestring):
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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, basestring):
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x = unicode(x)
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if isinstance(x, unicode):
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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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@since(1.4)
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def table(self, tableName):
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"""Returns the specified table as a :class:`DataFrame`.
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:param tableName: string, name of the table.
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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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@since(1.4)
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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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:param mergeSchema: sets whether we should merge schemas collected from all
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Parquet part-files. This will override
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``spark.sql.parquet.mergeSchema``. The default value is specified in
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``spark.sql.parquet.mergeSchema``.
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:param pathGlobFilter: an optional glob pattern to only include files with paths matching
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the pattern. The syntax follows `org.apache.hadoop.fs.GlobFilter`.
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It does not change the behavior of `partition discovery`_.
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:param recursiveFileLookup: recursively scan a directory for files. Using this option
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disables `partition discovery`_.
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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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recursiveFileLookup = options.get('recursiveFileLookup', None)
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self._set_opts(mergeSchema=mergeSchema, pathGlobFilter=pathGlobFilter,
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recursiveFileLookup=recursiveFileLookup)
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return self._df(self._jreader.parquet(_to_seq(self._spark._sc, paths)))
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@ignore_unicode_prefix
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@since(1.6)
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def text(self, paths, wholetext=False, lineSep=None, pathGlobFilter=None,
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recursiveFileLookup=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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:param paths: string, or list of strings, for input path(s).
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:param wholetext: if true, read each file from input path(s) as a single row.
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:param lineSep: defines the line separator that should be used for parsing. If None is
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set, it covers all ``\\r``, ``\\r\\n`` and ``\\n``.
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:param pathGlobFilter: an optional glob pattern to only include files with paths matching
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the pattern. The syntax follows `org.apache.hadoop.fs.GlobFilter`.
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It does not change the behavior of `partition discovery`_.
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:param recursiveFileLookup: recursively scan a directory for files. Using this option
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disables `partition discovery`_.
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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=u'hello'), Row(value=u'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=u'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)
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if isinstance(paths, basestring):
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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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@since(2.0)
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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):
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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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:param path: string, or list of strings, for input path(s),
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or RDD of Strings storing CSV rows.
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:param schema: 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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:param sep: sets a separator (one or more characters) for each field and value. If None is
|
|
set, it uses the default value, ``,``.
|
|
:param encoding: decodes the CSV files by the given encoding type. If None is set,
|
|
it uses the default value, ``UTF-8``.
|
|
:param quote: sets a single character used for escaping quoted values where the
|
|
separator can be part of the value. If None is set, it uses the default
|
|
value, ``"``. If you would like to turn off quotations, you need to set an
|
|
empty string.
|
|
:param escape: sets a single character used for escaping quotes inside an already
|
|
quoted value. If None is set, it uses the default value, ``\``.
|
|
:param comment: sets a single character used for skipping lines beginning with this
|
|
character. By default (None), it is disabled.
|
|
:param header: uses the first line as names of columns. If None is set, it uses the
|
|
default value, ``false``.
|
|
:param inferSchema: infers the input schema automatically from data. It requires one extra
|
|
pass over the data. If None is set, it uses the default value, ``false``.
|
|
:param enforceSchema: If it is set to ``true``, the specified or inferred schema will be
|
|
forcibly applied to datasource files, and headers in CSV files will be
|
|
ignored. If the option is set to ``false``, the schema will be
|
|
validated against all headers in CSV files or the first header in RDD
|
|
if the ``header`` option is set to ``true``. Field names in the schema
|
|
and column names in CSV headers are checked by their positions
|
|
taking into account ``spark.sql.caseSensitive``. If None is set,
|
|
``true`` is used by default. Though the default value is ``true``,
|
|
it is recommended to disable the ``enforceSchema`` option
|
|
to avoid incorrect results.
|
|
:param ignoreLeadingWhiteSpace: A flag indicating whether or not leading whitespaces from
|
|
values being read should be skipped. If None is set, it
|
|
uses the default value, ``false``.
|
|
:param ignoreTrailingWhiteSpace: A flag indicating whether or not trailing whitespaces from
|
|
values being read should be skipped. If None is set, it
|
|
uses the default value, ``false``.
|
|
:param nullValue: sets the string representation of a null value. If None is set, it uses
|
|
the default value, empty string. Since 2.0.1, this ``nullValue`` param
|
|
applies to all supported types including the string type.
|
|
:param nanValue: sets the string representation of a non-number value. If None is set, it
|
|
uses the default value, ``NaN``.
|
|
:param positiveInf: sets the string representation of a positive infinity value. If None
|
|
is set, it uses the default value, ``Inf``.
|
|
:param negativeInf: sets the string representation of a negative infinity value. If None
|
|
is set, it uses the default value, ``Inf``.
|
|
:param dateFormat: sets the string that indicates a date format. Custom date formats
|
|
follow the formats at `datetime pattern`_.
|
|
This applies to date type. If None is set, it uses the
|
|
default value, ``yyyy-MM-dd``.
|
|
:param timestampFormat: sets the string that indicates a timestamp format.
|
|
Custom date formats follow the formats at `datetime pattern`_.
|
|
This applies to timestamp type. If None is set, it uses the
|
|
default value, ``yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]``.
|
|
:param maxColumns: defines a hard limit of how many columns a record can have. If None is
|
|
set, it uses the default value, ``20480``.
|
|
:param maxCharsPerColumn: defines the maximum number of characters allowed for any given
|
|
value being read. If None is set, it uses the default value,
|
|
``-1`` meaning unlimited length.
|
|
:param maxMalformedLogPerPartition: this parameter is no longer used since Spark 2.2.0.
|
|
If specified, it is ignored.
|
|
:param mode: allows a mode for dealing with corrupt records during parsing. If None is
|
|
set, it uses the default value, ``PERMISSIVE``. Note that Spark tries to
|
|
parse only required columns in CSV under column pruning. Therefore, corrupt
|
|
records can be different based on required set of fields. This behavior can
|
|
be controlled by ``spark.sql.csv.parser.columnPruning.enabled``
|
|
(enabled by default).
|
|
|
|
* ``PERMISSIVE``: when it meets a corrupted record, puts the malformed string \
|
|
into a field configured by ``columnNameOfCorruptRecord``, and sets malformed \
|
|
fields to ``null``. To keep corrupt records, an user can set a string type \
|
|
field named ``columnNameOfCorruptRecord`` in an user-defined schema. If a \
|
|
schema does not have the field, it drops corrupt records during parsing. \
|
|
A record with less/more tokens than schema is not a corrupted record to CSV. \
|
|
When it meets a record having fewer tokens than the length of the schema, \
|
|
sets ``null`` to extra fields. When the record has more tokens than the \
|
|
length of the schema, it drops extra tokens.
|
|
* ``DROPMALFORMED``: ignores the whole corrupted records.
|
|
* ``FAILFAST``: throws an exception when it meets corrupted records.
|
|
|
|
:param columnNameOfCorruptRecord: allows renaming the new field having malformed string
|
|
created by ``PERMISSIVE`` mode. This overrides
|
|
``spark.sql.columnNameOfCorruptRecord``. If None is set,
|
|
it uses the value specified in
|
|
``spark.sql.columnNameOfCorruptRecord``.
|
|
:param multiLine: parse records, which may span multiple lines. If None is
|
|
set, it uses the default value, ``false``.
|
|
:param charToEscapeQuoteEscaping: sets a single character used for escaping the escape for
|
|
the quote character. If None is set, the default value is
|
|
escape character when escape and quote characters are
|
|
different, ``\0`` otherwise.
|
|
:param samplingRatio: defines fraction of rows used for schema inferring.
|
|
If None is set, it uses the default value, ``1.0``.
|
|
:param emptyValue: sets the string representation of an empty value. If None is set, it uses
|
|
the default value, empty string.
|
|
:param locale: sets a locale as language tag in IETF BCP 47 format. If None is set,
|
|
it uses the default value, ``en-US``. For instance, ``locale`` is used while
|
|
parsing dates and timestamps.
|
|
:param lineSep: defines the line separator that should be used for parsing. If None is
|
|
set, it covers all ``\\r``, ``\\r\\n`` and ``\\n``.
|
|
Maximum length is 1 character.
|
|
:param pathGlobFilter: an optional glob pattern to only include files with paths matching
|
|
the pattern. The syntax follows `org.apache.hadoop.fs.GlobFilter`.
|
|
It does not change the behavior of `partition discovery`_.
|
|
:param recursiveFileLookup: recursively scan a directory for files. Using this option
|
|
disables `partition discovery`_.
|
|
|
|
>>> df = spark.read.csv('python/test_support/sql/ages.csv')
|
|
>>> df.dtypes
|
|
[('_c0', 'string'), ('_c1', 'string')]
|
|
>>> rdd = sc.textFile('python/test_support/sql/ages.csv')
|
|
>>> df2 = spark.read.csv(rdd)
|
|
>>> df2.dtypes
|
|
[('_c0', 'string'), ('_c1', 'string')]
|
|
"""
|
|
self._set_opts(
|
|
schema=schema, sep=sep, encoding=encoding, quote=quote, escape=escape, comment=comment,
|
|
header=header, inferSchema=inferSchema, ignoreLeadingWhiteSpace=ignoreLeadingWhiteSpace,
|
|
ignoreTrailingWhiteSpace=ignoreTrailingWhiteSpace, nullValue=nullValue,
|
|
nanValue=nanValue, positiveInf=positiveInf, negativeInf=negativeInf,
|
|
dateFormat=dateFormat, timestampFormat=timestampFormat, maxColumns=maxColumns,
|
|
maxCharsPerColumn=maxCharsPerColumn,
|
|
maxMalformedLogPerPartition=maxMalformedLogPerPartition, mode=mode,
|
|
columnNameOfCorruptRecord=columnNameOfCorruptRecord, multiLine=multiLine,
|
|
charToEscapeQuoteEscaping=charToEscapeQuoteEscaping, samplingRatio=samplingRatio,
|
|
enforceSchema=enforceSchema, emptyValue=emptyValue, locale=locale, lineSep=lineSep,
|
|
pathGlobFilter=pathGlobFilter, recursiveFileLookup=recursiveFileLookup)
|
|
if isinstance(path, basestring):
|
|
path = [path]
|
|
if type(path) == list:
|
|
return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path)))
|
|
elif isinstance(path, RDD):
|
|
def func(iterator):
|
|
for x in iterator:
|
|
if not isinstance(x, basestring):
|
|
x = unicode(x)
|
|
if isinstance(x, unicode):
|
|
x = x.encode("utf-8")
|
|
yield x
|
|
keyed = path.mapPartitions(func)
|
|
keyed._bypass_serializer = True
|
|
jrdd = keyed._jrdd.map(self._spark._jvm.BytesToString())
|
|
# see SPARK-22112
|
|
# There aren't any jvm api for creating a dataframe from rdd storing csv.
|
|
# We can do it through creating a jvm dataset firstly and using the jvm api
|
|
# for creating a dataframe from dataset storing csv.
|
|
jdataset = self._spark._ssql_ctx.createDataset(
|
|
jrdd.rdd(),
|
|
self._spark._jvm.Encoders.STRING())
|
|
return self._df(self._jreader.csv(jdataset))
|
|
else:
|
|
raise TypeError("path can be only string, list or RDD")
|
|
|
|
@since(1.5)
|
|
def orc(self, path, mergeSchema=None, pathGlobFilter=None, recursiveFileLookup=None):
|
|
"""Loads ORC files, returning the result as a :class:`DataFrame`.
|
|
|
|
:param mergeSchema: sets whether we should merge schemas collected from all
|
|
ORC part-files. This will override ``spark.sql.orc.mergeSchema``.
|
|
The default value is specified in ``spark.sql.orc.mergeSchema``.
|
|
:param pathGlobFilter: an optional glob pattern to only include files with paths matching
|
|
the pattern. The syntax follows `org.apache.hadoop.fs.GlobFilter`.
|
|
It does not change the behavior of `partition discovery`_.
|
|
:param recursiveFileLookup: recursively scan a directory for files. Using this option
|
|
disables `partition discovery`_.
|
|
|
|
>>> df = spark.read.orc('python/test_support/sql/orc_partitioned')
|
|
>>> df.dtypes
|
|
[('a', 'bigint'), ('b', 'int'), ('c', 'int')]
|
|
"""
|
|
self._set_opts(mergeSchema=mergeSchema, pathGlobFilter=pathGlobFilter,
|
|
recursiveFileLookup=recursiveFileLookup)
|
|
if isinstance(path, basestring):
|
|
path = [path]
|
|
return self._df(self._jreader.orc(_to_seq(self._spark._sc, path)))
|
|
|
|
@since(1.4)
|
|
def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPartitions=None,
|
|
predicates=None, properties=None):
|
|
"""
|
|
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.
|
|
|
|
.. note:: Don't create too many partitions in parallel on a large cluster;
|
|
otherwise Spark might crash your external database systems.
|
|
|
|
:param url: a JDBC URL of the form ``jdbc:subprotocol:subname``
|
|
:param table: the name of the table
|
|
:param column: the name of a column of numeric, date, or timestamp type
|
|
that will be used for partitioning;
|
|
if this parameter is specified, then ``numPartitions``, ``lowerBound``
|
|
(inclusive), and ``upperBound`` (exclusive) will form partition strides
|
|
for generated WHERE clause expressions used to split the column
|
|
``column`` evenly
|
|
:param lowerBound: the minimum value of ``column`` used to decide partition stride
|
|
:param upperBound: the maximum value of ``column`` used to decide partition stride
|
|
:param numPartitions: the number of partitions
|
|
:param predicates: a list of expressions suitable for inclusion in WHERE clauses;
|
|
each one defines one partition of the :class:`DataFrame`
|
|
:param properties: a dictionary of JDBC database connection arguments. Normally at
|
|
least properties "user" and "password" with their corresponding values.
|
|
For example { 'user' : 'SYSTEM', 'password' : 'mypassword' }
|
|
:return: a 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)
|
|
|
|
@since(1.4)
|
|
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.
|
|
|
|
>>> 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
|
|
|
|
@since(1.4)
|
|
def format(self, source):
|
|
"""Specifies the underlying output data source.
|
|
|
|
:param source: string, name of the data source, e.g. 'json', 'parquet'.
|
|
|
|
>>> 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.
|
|
|
|
You can set the following option(s) for writing files:
|
|
* ``timeZone``: sets the string that indicates a time zone ID to be used to format
|
|
timestamps in the JSON/CSV datasources or partition values. The following
|
|
formats of `timeZone` are supported:
|
|
|
|
* Region-based zone ID: It should have the form 'area/city', such as \
|
|
'America/Los_Angeles'.
|
|
* Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or \
|
|
'+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
|
|
|
|
Other short names like 'CST' are not recommended to use because they can be
|
|
ambiguous. If it isn't set, the current value of the SQL config
|
|
``spark.sql.session.timeZone`` is used by default.
|
|
"""
|
|
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.
|
|
|
|
You can set the following option(s) for writing files:
|
|
* ``timeZone``: sets the string that indicates a time zone ID to be used to format
|
|
timestamps in the JSON/CSV datasources or partition values. The following
|
|
formats of `timeZone` are supported:
|
|
|
|
* Region-based zone ID: It should have the form 'area/city', such as \
|
|
'America/Los_Angeles'.
|
|
* Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00' or \
|
|
'+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.
|
|
|
|
Other short names like 'CST' are not recommended to use because they can be
|
|
ambiguous. If it isn't set, the current value of the SQL config
|
|
``spark.sql.session.timeZone`` is used by default.
|
|
"""
|
|
for k in options:
|
|
self._jwrite = self._jwrite.option(k, to_str(options[k]))
|
|
return self
|
|
|
|
@since(1.4)
|
|
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.
|
|
|
|
:param cols: name of columns
|
|
|
|
>>> 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
|
|
|
|
@since(2.3)
|
|
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.
|
|
|
|
:param numBuckets: the number of buckets to save
|
|
:param col: a name of a column, or a list of names.
|
|
:param cols: additional names (optional). If `col` is a list it should be empty.
|
|
|
|
.. note:: Applicable for file-based data sources in combination with
|
|
:py:meth:`DataFrameWriter.saveAsTable`.
|
|
|
|
>>> (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, basestring) for c in cols) or not(isinstance(col, basestring)):
|
|
raise TypeError("all names should be `str`")
|
|
|
|
self._jwrite = self._jwrite.bucketBy(numBuckets, col, _to_seq(self._spark._sc, cols))
|
|
return self
|
|
|
|
@since(2.3)
|
|
def sortBy(self, col, *cols):
|
|
"""Sorts the output in each bucket by the given columns on the file system.
|
|
|
|
:param col: a name of a column, or a list of names.
|
|
:param cols: additional names (optional). If `col` is a list it should be empty.
|
|
|
|
>>> (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, basestring) for c in cols) or not(isinstance(col, basestring)):
|
|
raise TypeError("all names should be `str`")
|
|
|
|
self._jwrite = self._jwrite.sortBy(col, _to_seq(self._spark._sc, cols))
|
|
return self
|
|
|
|
@since(1.4)
|
|
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.
|
|
|
|
:param path: the path in a Hadoop supported file system
|
|
:param format: the format used to save
|
|
:param mode: 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.
|
|
:param partitionBy: names of partitioning columns
|
|
:param options: all other string options
|
|
|
|
>>> 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.
|
|
|
|
Optionally overwriting any existing data.
|
|
"""
|
|
if overwrite is not None:
|
|
self.mode("overwrite" if overwrite else "append")
|
|
self._jwrite.insertInto(tableName)
|
|
|
|
@since(1.4)
|
|
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.
|
|
|
|
:param name: the table name
|
|
:param format: the format used to save
|
|
:param mode: one of `append`, `overwrite`, `error`, `errorifexists`, `ignore` \
|
|
(default: error)
|
|
:param partitionBy: names of partitioning columns
|
|
:param options: 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)
|
|
|
|
@since(1.4)
|
|
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.
|
|
|
|
:param path: the path in any Hadoop supported file system
|
|
:param mode: 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.
|
|
:param compression: compression codec to use when saving to file. This can be one of the
|
|
known case-insensitive shorten names (none, bzip2, gzip, lz4,
|
|
snappy and deflate).
|
|
:param dateFormat: sets the string that indicates a date format. Custom date formats
|
|
follow the formats at `datetime pattern`_.
|
|
This applies to date type. If None is set, it uses the
|
|
default value, ``yyyy-MM-dd``.
|
|
:param timestampFormat: sets the string that indicates a timestamp format.
|
|
Custom date formats follow the formats at `datetime pattern`_.
|
|
This applies to timestamp type. If None is set, it uses the
|
|
default value, ``yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]``.
|
|
:param encoding: specifies encoding (charset) of saved json files. If None is set,
|
|
the default UTF-8 charset will be used.
|
|
:param lineSep: defines the line separator that should be used for writing. If None is
|
|
set, it uses the default value, ``\\n``.
|
|
:param ignoreNullFields: Whether to ignore null fields when generating JSON objects.
|
|
If None is set, it uses the default value, ``true``.
|
|
|
|
>>> 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)
|
|
|
|
@since(1.4)
|
|
def parquet(self, path, mode=None, partitionBy=None, compression=None):
|
|
"""Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
|
|
|
|
:param path: the path in any Hadoop supported file system
|
|
:param mode: 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.
|
|
:param partitionBy: names of partitioning columns
|
|
:param compression: compression codec to use when saving to file. This can be one of the
|
|
known case-insensitive shorten names (none, uncompressed, snappy, gzip,
|
|
lzo, brotli, lz4, and zstd). This will override
|
|
``spark.sql.parquet.compression.codec``. If None is set, it uses the
|
|
value specified in ``spark.sql.parquet.compression.codec``.
|
|
|
|
>>> 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)
|
|
|
|
@since(1.6)
|
|
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.
|
|
|
|
:param path: the path in any Hadoop supported file system
|
|
:param compression: compression codec to use when saving to file. This can be one of the
|
|
known case-insensitive shorten names (none, bzip2, gzip, lz4,
|
|
snappy and deflate).
|
|
:param lineSep: defines the line separator that should be used for writing. If None is
|
|
set, it uses the default value, ``\\n``.
|
|
|
|
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)
|
|
|
|
@since(2.0)
|
|
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.
|
|
|
|
:param path: the path in any Hadoop supported file system
|
|
:param mode: 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.
|
|
|
|
:param compression: compression codec to use when saving to file. This can be one of the
|
|
known case-insensitive shorten names (none, bzip2, gzip, lz4,
|
|
snappy and deflate).
|
|
:param sep: sets a separator (one or more characters) for each field and value. If None is
|
|
set, it uses the default value, ``,``.
|
|
:param quote: sets a single character used for escaping quoted values where the
|
|
separator can be part of the value. If None is set, it uses the default
|
|
value, ``"``. If an empty string is set, it uses ``u0000`` (null character).
|
|
:param escape: sets a single character used for escaping quotes inside an already
|
|
quoted value. If None is set, it uses the default value, ``\``
|
|
:param escapeQuotes: a flag indicating whether values containing quotes should always
|
|
be enclosed in quotes. If None is set, it uses the default value
|
|
``true``, escaping all values containing a quote character.
|
|
:param quoteAll: a flag indicating whether all values should always be enclosed in
|
|
quotes. If None is set, it uses the default value ``false``,
|
|
only escaping values containing a quote character.
|
|
:param header: writes the names of columns as the first line. If None is set, it uses
|
|
the default value, ``false``.
|
|
:param nullValue: sets the string representation of a null value. If None is set, it uses
|
|
the default value, empty string.
|
|
:param dateFormat: sets the string that indicates a date format. Custom date formats follow
|
|
the formats at `datetime pattern`_.
|
|
This applies to date type. If None is set, it uses the
|
|
default value, ``yyyy-MM-dd``.
|
|
:param timestampFormat: sets the string that indicates a timestamp format.
|
|
Custom date formats follow the formats at `datetime pattern`_.
|
|
This applies to timestamp type. If None is set, it uses the
|
|
default value, ``yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]``.
|
|
:param ignoreLeadingWhiteSpace: a flag indicating whether or not leading whitespaces from
|
|
values being written should be skipped. If None is set, it
|
|
uses the default value, ``true``.
|
|
:param ignoreTrailingWhiteSpace: a flag indicating whether or not trailing whitespaces from
|
|
values being written should be skipped. If None is set, it
|
|
uses the default value, ``true``.
|
|
:param charToEscapeQuoteEscaping: sets a single character used for escaping the escape for
|
|
the quote character. If None is set, the default value is
|
|
escape character when escape and quote characters are
|
|
different, ``\0`` otherwise..
|
|
:param encoding: sets the encoding (charset) of saved csv files. If None is set,
|
|
the default UTF-8 charset will be used.
|
|
:param emptyValue: sets the string representation of an empty value. If None is set, it uses
|
|
the default value, ``""``.
|
|
:param lineSep: defines the line separator that should be used for writing. If None is
|
|
set, it uses the default value, ``\\n``. Maximum length is 1 character.
|
|
|
|
>>> 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)
|
|
|
|
@since(1.5)
|
|
def orc(self, path, mode=None, partitionBy=None, compression=None):
|
|
"""Saves the content of the :class:`DataFrame` in ORC format at the specified path.
|
|
|
|
:param path: the path in any Hadoop supported file system
|
|
:param mode: 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.
|
|
:param partitionBy: names of partitioning columns
|
|
:param compression: compression codec to use when saving to file. This can be one of the
|
|
known case-insensitive shorten names (none, snappy, zlib, and lzo).
|
|
This will override ``orc.compress`` and
|
|
``spark.sql.orc.compression.codec``. If None is set, it uses the value
|
|
specified in ``spark.sql.orc.compression.codec``.
|
|
|
|
>>> 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)
|
|
|
|
@since(1.4)
|
|
def jdbc(self, url, table, mode=None, properties=None):
|
|
"""Saves the content of the :class:`DataFrame` to an external database table via JDBC.
|
|
|
|
.. note:: Don't create too many partitions in parallel on a large cluster;
|
|
otherwise Spark might crash your external database systems.
|
|
|
|
:param url: a JDBC URL of the form ``jdbc:subprotocol:subname``
|
|
:param table: Name of the table in the external database.
|
|
:param mode: 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.
|
|
:param properties: a dictionary of JDBC database connection arguments. Normally at
|
|
least properties "user" and "password" with their corresponding values.
|
|
For example { 'user' : 'SYSTEM', 'password' : 'mypassword' }
|
|
"""
|
|
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)
|
|
|
|
|
|
def _test():
|
|
import doctest
|
|
import os
|
|
import tempfile
|
|
import py4j
|
|
from pyspark.context import SparkContext
|
|
from pyspark.sql import SparkSession, Row
|
|
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()
|