a189be8754
### What changes were proposed in this pull request? Avoid some python docs where first sentence has "e.g." or similar as the period causes the docs to show only half of the first sentence as the summary. ### Why are the changes needed? See for example https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.ml.regression.LinearRegressionModel.html?highlight=linearregressionmodel#pyspark.ml.regression.LinearRegressionModel.summary where the method description is clearly truncated. ### Does this PR introduce _any_ user-facing change? Only changes docs. ### How was this patch tested? Manual testing of docs. Closes #32508 from srowen/TruncatedPythonDesc. Authored-by: Sean Owen <srowen@gmail.com> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
850 lines
30 KiB
Python
850 lines
30 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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import warnings
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from functools import reduce
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from threading import RLock
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from pyspark import since
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from pyspark.rdd import RDD
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from pyspark.sql.conf import RuntimeConfig
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from pyspark.sql.dataframe import DataFrame
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from pyspark.sql.pandas.conversion import SparkConversionMixin
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from pyspark.sql.readwriter import DataFrameReader
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from pyspark.sql.streaming import DataStreamReader
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from pyspark.sql.types import DataType, StructType, \
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_make_type_verifier, _infer_schema, _has_nulltype, _merge_type, _create_converter, \
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_parse_datatype_string
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from pyspark.sql.utils import install_exception_handler
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__all__ = ["SparkSession"]
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def _monkey_patch_RDD(sparkSession):
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def toDF(self, schema=None, sampleRatio=None):
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"""
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Converts current :class:`RDD` into a :class:`DataFrame`
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This is a shorthand for ``spark.createDataFrame(rdd, schema, sampleRatio)``
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Parameters
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----------
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schema : :class:`pyspark.sql.types.DataType`, str or list, optional
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a :class:`pyspark.sql.types.DataType` or a datatype string or a list of
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column names, default is None. The data type string format equals to
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:class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
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omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
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``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`.
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We can also use ``int`` as a short name for :class:`pyspark.sql.types.IntegerType`.
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sampleRatio : float, optional
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the sample ratio of rows used for inferring
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Returns
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-------
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:class:`DataFrame`
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Examples
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--------
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>>> rdd.toDF().collect()
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[Row(name='Alice', age=1)]
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"""
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return sparkSession.createDataFrame(self, schema, sampleRatio)
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RDD.toDF = toDF
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class SparkSession(SparkConversionMixin):
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"""The entry point to programming Spark with the Dataset and DataFrame API.
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A SparkSession can be used create :class:`DataFrame`, register :class:`DataFrame` as
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tables, execute SQL over tables, cache tables, and read parquet files.
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To create a SparkSession, use the following builder pattern:
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.. autoattribute:: builder
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:annotation:
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Examples
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--------
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>>> spark = SparkSession.builder \\
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... .master("local") \\
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... .appName("Word Count") \\
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... .config("spark.some.config.option", "some-value") \\
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... .getOrCreate()
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>>> from datetime import datetime
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>>> from pyspark.sql import Row
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>>> spark = SparkSession(sc)
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>>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1,
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... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1),
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... time=datetime(2014, 8, 1, 14, 1, 5))])
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>>> df = allTypes.toDF()
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>>> df.createOrReplaceTempView("allTypes")
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>>> spark.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a '
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... 'from allTypes where b and i > 0').collect()
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[Row((i + 1)=2, (d + 1)=2.0, (NOT b)=False, list[1]=2, \
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dict[s]=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)]
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>>> df.rdd.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect()
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[(1, 'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])]
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"""
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class Builder(object):
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"""Builder for :class:`SparkSession`.
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"""
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_lock = RLock()
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_options = {}
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_sc = None
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def config(self, key=None, value=None, conf=None):
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"""Sets a config option. Options set using this method are automatically propagated to
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both :class:`SparkConf` and :class:`SparkSession`'s own configuration.
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.. versionadded:: 2.0.0
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Parameters
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----------
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key : str, optional
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a key name string for configuration property
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value : str, optional
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a value for configuration property
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conf : :class:`SparkConf`, optional
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an instance of :class:`SparkConf`
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Examples
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--------
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For an existing SparkConf, use `conf` parameter.
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>>> from pyspark.conf import SparkConf
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>>> SparkSession.builder.config(conf=SparkConf())
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<pyspark.sql.session...
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For a (key, value) pair, you can omit parameter names.
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>>> SparkSession.builder.config("spark.some.config.option", "some-value")
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<pyspark.sql.session...
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"""
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with self._lock:
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if conf is None:
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self._options[key] = str(value)
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else:
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for (k, v) in conf.getAll():
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self._options[k] = v
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return self
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def master(self, master):
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"""Sets the Spark master URL to connect to, such as "local" to run locally, "local[4]"
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to run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone
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cluster.
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.. versionadded:: 2.0.0
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Parameters
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----------
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master : str
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a url for spark master
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"""
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return self.config("spark.master", master)
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def appName(self, name):
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"""Sets a name for the application, which will be shown in the Spark web UI.
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If no application name is set, a randomly generated name will be used.
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.. versionadded:: 2.0.0
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Parameters
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----------
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name : str
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an application name
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"""
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return self.config("spark.app.name", name)
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@since(2.0)
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def enableHiveSupport(self):
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"""Enables Hive support, including connectivity to a persistent Hive metastore, support
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for Hive SerDes, and Hive user-defined functions.
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"""
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return self.config("spark.sql.catalogImplementation", "hive")
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def _sparkContext(self, sc):
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with self._lock:
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self._sc = sc
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return self
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def getOrCreate(self):
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"""Gets an existing :class:`SparkSession` or, if there is no existing one, creates a
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new one based on the options set in this builder.
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.. versionadded:: 2.0.0
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Examples
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--------
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This method first checks whether there is a valid global default SparkSession, and if
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yes, return that one. If no valid global default SparkSession exists, the method
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creates a new SparkSession and assigns the newly created SparkSession as the global
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default.
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>>> s1 = SparkSession.builder.config("k1", "v1").getOrCreate()
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>>> s1.conf.get("k1") == "v1"
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True
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In case an existing SparkSession is returned, the config options specified
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in this builder will be applied to the existing SparkSession.
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>>> s2 = SparkSession.builder.config("k2", "v2").getOrCreate()
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>>> s1.conf.get("k1") == s2.conf.get("k1")
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True
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>>> s1.conf.get("k2") == s2.conf.get("k2")
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True
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"""
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with self._lock:
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from pyspark.context import SparkContext
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from pyspark.conf import SparkConf
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session = SparkSession._instantiatedSession
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if session is None or session._sc._jsc is None:
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if self._sc is not None:
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sc = self._sc
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else:
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sparkConf = SparkConf()
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for key, value in self._options.items():
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sparkConf.set(key, value)
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# This SparkContext may be an existing one.
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sc = SparkContext.getOrCreate(sparkConf)
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# Do not update `SparkConf` for existing `SparkContext`, as it's shared
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# by all sessions.
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session = SparkSession(sc)
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for key, value in self._options.items():
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session._jsparkSession.sessionState().conf().setConfString(key, value)
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return session
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builder = Builder()
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"""A class attribute having a :class:`Builder` to construct :class:`SparkSession` instances."""
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_instantiatedSession = None
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_activeSession = None
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def __init__(self, sparkContext, jsparkSession=None):
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from pyspark.sql.context import SQLContext
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self._sc = sparkContext
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self._jsc = self._sc._jsc
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self._jvm = self._sc._jvm
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if jsparkSession is None:
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if self._jvm.SparkSession.getDefaultSession().isDefined() \
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and not self._jvm.SparkSession.getDefaultSession().get() \
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.sparkContext().isStopped():
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jsparkSession = self._jvm.SparkSession.getDefaultSession().get()
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else:
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jsparkSession = self._jvm.SparkSession(self._jsc.sc())
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self._jsparkSession = jsparkSession
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self._jwrapped = self._jsparkSession.sqlContext()
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self._wrapped = SQLContext(self._sc, self, self._jwrapped)
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_monkey_patch_RDD(self)
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install_exception_handler()
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# If we had an instantiated SparkSession attached with a SparkContext
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# which is stopped now, we need to renew the instantiated SparkSession.
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# Otherwise, we will use invalid SparkSession when we call Builder.getOrCreate.
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if SparkSession._instantiatedSession is None \
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or SparkSession._instantiatedSession._sc._jsc is None:
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SparkSession._instantiatedSession = self
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SparkSession._activeSession = self
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self._jvm.SparkSession.setDefaultSession(self._jsparkSession)
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self._jvm.SparkSession.setActiveSession(self._jsparkSession)
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def _repr_html_(self):
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return """
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<div>
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<p><b>SparkSession - {catalogImplementation}</b></p>
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{sc_HTML}
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</div>
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""".format(
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catalogImplementation=self.conf.get("spark.sql.catalogImplementation"),
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sc_HTML=self.sparkContext._repr_html_()
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)
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@since(2.0)
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def newSession(self):
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"""
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Returns a new SparkSession as new session, that has separate SQLConf,
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registered temporary views and UDFs, but shared SparkContext and
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table cache.
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"""
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return self.__class__(self._sc, self._jsparkSession.newSession())
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@classmethod
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def getActiveSession(cls):
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"""
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Returns the active SparkSession for the current thread, returned by the builder
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.. versionadded:: 3.0.0
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Returns
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-------
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:class:`SparkSession`
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Spark session if an active session exists for the current thread
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Examples
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--------
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>>> s = SparkSession.getActiveSession()
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>>> l = [('Alice', 1)]
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>>> rdd = s.sparkContext.parallelize(l)
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>>> df = s.createDataFrame(rdd, ['name', 'age'])
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>>> df.select("age").collect()
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[Row(age=1)]
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"""
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from pyspark import SparkContext
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sc = SparkContext._active_spark_context
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if sc is None:
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return None
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else:
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if sc._jvm.SparkSession.getActiveSession().isDefined():
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SparkSession(sc, sc._jvm.SparkSession.getActiveSession().get())
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return SparkSession._activeSession
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else:
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return None
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@property
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@since(2.0)
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def sparkContext(self):
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"""Returns the underlying :class:`SparkContext`."""
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return self._sc
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@property
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@since(2.0)
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def version(self):
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"""The version of Spark on which this application is running."""
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return self._jsparkSession.version()
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@property
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@since(2.0)
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def conf(self):
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"""Runtime configuration interface for Spark.
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This is the interface through which the user can get and set all Spark and Hadoop
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configurations that are relevant to Spark SQL. When getting the value of a config,
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this defaults to the value set in the underlying :class:`SparkContext`, if any.
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Returns
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-------
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:class:`pyspark.sql.conf.RuntimeConfig`
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"""
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if not hasattr(self, "_conf"):
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self._conf = RuntimeConfig(self._jsparkSession.conf())
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return self._conf
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@property
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def catalog(self):
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"""Interface through which the user may create, drop, alter or query underlying
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databases, tables, functions, etc.
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.. versionadded:: 2.0.0
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Returns
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-------
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:class:`Catalog`
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"""
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from pyspark.sql.catalog import Catalog
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if not hasattr(self, "_catalog"):
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self._catalog = Catalog(self)
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return self._catalog
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@property
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def udf(self):
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"""Returns a :class:`UDFRegistration` for UDF registration.
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.. versionadded:: 2.0.0
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Returns
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-------
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:class:`UDFRegistration`
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"""
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from pyspark.sql.udf import UDFRegistration
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return UDFRegistration(self)
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def range(self, start, end=None, step=1, numPartitions=None):
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"""
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Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
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``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
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step value ``step``.
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.. versionadded:: 2.0.0
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Parameters
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----------
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start : int
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the start value
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end : int, optional
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the end value (exclusive)
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step : int, optional
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the incremental step (default: 1)
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numPartitions : int, optional
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the number of partitions of the DataFrame
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Returns
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-------
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:class:`DataFrame`
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Examples
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--------
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>>> spark.range(1, 7, 2).collect()
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[Row(id=1), Row(id=3), Row(id=5)]
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If only one argument is specified, it will be used as the end value.
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>>> spark.range(3).collect()
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[Row(id=0), Row(id=1), Row(id=2)]
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"""
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if numPartitions is None:
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numPartitions = self._sc.defaultParallelism
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if end is None:
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jdf = self._jsparkSession.range(0, int(start), int(step), int(numPartitions))
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else:
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jdf = self._jsparkSession.range(int(start), int(end), int(step), int(numPartitions))
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return DataFrame(jdf, self._wrapped)
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def _inferSchemaFromList(self, data, names=None):
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"""
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Infer schema from list of Row, dict, or tuple.
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Parameters
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----------
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data : iterable
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list of Row, dict, or tuple
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names : list, optional
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list of column names
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Returns
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-------
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:class:`pyspark.sql.types.StructType`
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"""
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if not data:
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raise ValueError("can not infer schema from empty dataset")
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schema = reduce(_merge_type, (_infer_schema(row, names) for row in data))
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if _has_nulltype(schema):
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raise ValueError("Some of types cannot be determined after inferring")
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return schema
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def _inferSchema(self, rdd, samplingRatio=None, names=None):
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"""
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Infer schema from an RDD of Row, dict, or tuple.
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Parameters
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----------
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rdd : :class:`RDD`
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an RDD of Row, dict, or tuple
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samplingRatio : float, optional
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sampling ratio, or no sampling (default)
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names : list, optional
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Returns
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-------
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:class:`pyspark.sql.types.StructType`
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"""
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first = rdd.first()
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if not first:
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raise ValueError("The first row in RDD is empty, "
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"can not infer schema")
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if samplingRatio is None:
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schema = _infer_schema(first, names=names)
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if _has_nulltype(schema):
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for row in rdd.take(100)[1:]:
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schema = _merge_type(schema, _infer_schema(row, names=names))
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if not _has_nulltype(schema):
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break
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else:
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raise ValueError("Some of types cannot be determined by the "
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"first 100 rows, please try again with sampling")
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else:
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if samplingRatio < 0.99:
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rdd = rdd.sample(False, float(samplingRatio))
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schema = rdd.map(lambda row: _infer_schema(row, names)).reduce(_merge_type)
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return schema
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def _createFromRDD(self, rdd, schema, samplingRatio):
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"""
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Create an RDD for DataFrame from an existing RDD, returns the RDD and schema.
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"""
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if schema is None or isinstance(schema, (list, tuple)):
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struct = self._inferSchema(rdd, samplingRatio, names=schema)
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converter = _create_converter(struct)
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rdd = rdd.map(converter)
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if isinstance(schema, (list, tuple)):
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for i, name in enumerate(schema):
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struct.fields[i].name = name
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struct.names[i] = name
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schema = struct
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elif not isinstance(schema, StructType):
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raise TypeError("schema should be StructType or list or None, but got: %s" % schema)
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# convert python objects to sql data
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rdd = rdd.map(schema.toInternal)
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return rdd, schema
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def _createFromLocal(self, data, schema):
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"""
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Create an RDD for DataFrame from a list or pandas.DataFrame, returns
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the RDD and schema.
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"""
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# make sure data could consumed multiple times
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if not isinstance(data, list):
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data = list(data)
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if schema is None or isinstance(schema, (list, tuple)):
|
|
struct = self._inferSchemaFromList(data, names=schema)
|
|
converter = _create_converter(struct)
|
|
data = map(converter, data)
|
|
if isinstance(schema, (list, tuple)):
|
|
for i, name in enumerate(schema):
|
|
struct.fields[i].name = name
|
|
struct.names[i] = name
|
|
schema = struct
|
|
|
|
elif not isinstance(schema, StructType):
|
|
raise TypeError("schema should be StructType or list or None, but got: %s" % schema)
|
|
|
|
# convert python objects to sql data
|
|
data = [schema.toInternal(row) for row in data]
|
|
return self._sc.parallelize(data), schema
|
|
|
|
@staticmethod
|
|
def _create_shell_session():
|
|
"""
|
|
Initialize a SparkSession for a pyspark shell session. This is called from shell.py
|
|
to make error handling simpler without needing to declare local variables in that
|
|
script, which would expose those to users.
|
|
"""
|
|
import py4j
|
|
from pyspark.conf import SparkConf
|
|
from pyspark.context import SparkContext
|
|
try:
|
|
# Try to access HiveConf, it will raise exception if Hive is not added
|
|
conf = SparkConf()
|
|
if conf.get('spark.sql.catalogImplementation', 'hive').lower() == 'hive':
|
|
SparkContext._jvm.org.apache.hadoop.hive.conf.HiveConf()
|
|
return SparkSession.builder\
|
|
.enableHiveSupport()\
|
|
.getOrCreate()
|
|
else:
|
|
return SparkSession.builder.getOrCreate()
|
|
except (py4j.protocol.Py4JError, TypeError):
|
|
if conf.get('spark.sql.catalogImplementation', '').lower() == 'hive':
|
|
warnings.warn("Fall back to non-hive support because failing to access HiveConf, "
|
|
"please make sure you build spark with hive")
|
|
|
|
return SparkSession.builder.getOrCreate()
|
|
|
|
def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=True):
|
|
"""
|
|
Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
|
|
|
|
When ``schema`` is a list of column names, the type of each column
|
|
will be inferred from ``data``.
|
|
|
|
When ``schema`` is ``None``, it will try to infer the schema (column names and types)
|
|
from ``data``, which should be an RDD of either :class:`Row`,
|
|
:class:`namedtuple`, or :class:`dict`.
|
|
|
|
When ``schema`` is :class:`pyspark.sql.types.DataType` or a datatype string, it must match
|
|
the real data, or an exception will be thrown at runtime. If the given schema is not
|
|
:class:`pyspark.sql.types.StructType`, it will be wrapped into a
|
|
:class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value".
|
|
Each record will also be wrapped into a tuple, which can be converted to row later.
|
|
|
|
If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
|
|
rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
|
|
|
|
.. versionadded:: 2.0.0
|
|
|
|
.. versionchanged:: 2.1.0
|
|
Added verifySchema.
|
|
|
|
Parameters
|
|
----------
|
|
data : :class:`RDD` or iterable
|
|
an RDD of any kind of SQL data representation (:class:`Row`,
|
|
:class:`tuple`, ``int``, ``boolean``, etc.), or :class:`list`, or
|
|
:class:`pandas.DataFrame`.
|
|
schema : :class:`pyspark.sql.types.DataType`, str or list, optional
|
|
a :class:`pyspark.sql.types.DataType` or a datatype string or a list of
|
|
column names, default is None. The data type string format equals to
|
|
:class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
|
|
omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
|
|
``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`.
|
|
We can also use ``int`` as a short name for :class:`pyspark.sql.types.IntegerType`.
|
|
samplingRatio : float, optional
|
|
the sample ratio of rows used for inferring
|
|
verifySchema : bool, optional
|
|
verify data types of every row against schema. Enabled by default.
|
|
|
|
Returns
|
|
-------
|
|
:class:`DataFrame`
|
|
|
|
Notes
|
|
-----
|
|
Usage with spark.sql.execution.arrow.pyspark.enabled=True is experimental.
|
|
|
|
Examples
|
|
--------
|
|
>>> l = [('Alice', 1)]
|
|
>>> spark.createDataFrame(l).collect()
|
|
[Row(_1='Alice', _2=1)]
|
|
>>> spark.createDataFrame(l, ['name', 'age']).collect()
|
|
[Row(name='Alice', age=1)]
|
|
|
|
>>> d = [{'name': 'Alice', 'age': 1}]
|
|
>>> spark.createDataFrame(d).collect()
|
|
[Row(age=1, name='Alice')]
|
|
|
|
>>> rdd = sc.parallelize(l)
|
|
>>> spark.createDataFrame(rdd).collect()
|
|
[Row(_1='Alice', _2=1)]
|
|
>>> df = spark.createDataFrame(rdd, ['name', 'age'])
|
|
>>> df.collect()
|
|
[Row(name='Alice', age=1)]
|
|
|
|
>>> from pyspark.sql import Row
|
|
>>> Person = Row('name', 'age')
|
|
>>> person = rdd.map(lambda r: Person(*r))
|
|
>>> df2 = spark.createDataFrame(person)
|
|
>>> df2.collect()
|
|
[Row(name='Alice', age=1)]
|
|
|
|
>>> from pyspark.sql.types import *
|
|
>>> schema = StructType([
|
|
... StructField("name", StringType(), True),
|
|
... StructField("age", IntegerType(), True)])
|
|
>>> df3 = spark.createDataFrame(rdd, schema)
|
|
>>> df3.collect()
|
|
[Row(name='Alice', age=1)]
|
|
|
|
>>> spark.createDataFrame(df.toPandas()).collect() # doctest: +SKIP
|
|
[Row(name='Alice', age=1)]
|
|
>>> spark.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP
|
|
[Row(0=1, 1=2)]
|
|
|
|
>>> spark.createDataFrame(rdd, "a: string, b: int").collect()
|
|
[Row(a='Alice', b=1)]
|
|
>>> rdd = rdd.map(lambda row: row[1])
|
|
>>> spark.createDataFrame(rdd, "int").collect()
|
|
[Row(value=1)]
|
|
>>> spark.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL
|
|
Traceback (most recent call last):
|
|
...
|
|
Py4JJavaError: ...
|
|
"""
|
|
SparkSession._activeSession = self
|
|
self._jvm.SparkSession.setActiveSession(self._jsparkSession)
|
|
if isinstance(data, DataFrame):
|
|
raise TypeError("data is already a DataFrame")
|
|
|
|
if isinstance(schema, str):
|
|
schema = _parse_datatype_string(schema)
|
|
elif isinstance(schema, (list, tuple)):
|
|
# Must re-encode any unicode strings to be consistent with StructField names
|
|
schema = [x.encode('utf-8') if not isinstance(x, str) else x for x in schema]
|
|
|
|
try:
|
|
import pandas
|
|
has_pandas = True
|
|
except Exception:
|
|
has_pandas = False
|
|
if has_pandas and isinstance(data, pandas.DataFrame):
|
|
# Create a DataFrame from pandas DataFrame.
|
|
return super(SparkSession, self).createDataFrame(
|
|
data, schema, samplingRatio, verifySchema)
|
|
return self._create_dataframe(data, schema, samplingRatio, verifySchema)
|
|
|
|
def _create_dataframe(self, data, schema, samplingRatio, verifySchema):
|
|
if isinstance(schema, StructType):
|
|
verify_func = _make_type_verifier(schema) if verifySchema else lambda _: True
|
|
|
|
def prepare(obj):
|
|
verify_func(obj)
|
|
return obj
|
|
elif isinstance(schema, DataType):
|
|
dataType = schema
|
|
schema = StructType().add("value", schema)
|
|
|
|
verify_func = _make_type_verifier(
|
|
dataType, name="field value") if verifySchema else lambda _: True
|
|
|
|
def prepare(obj):
|
|
verify_func(obj)
|
|
return obj,
|
|
else:
|
|
prepare = lambda obj: obj
|
|
|
|
if isinstance(data, RDD):
|
|
rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
|
|
else:
|
|
rdd, schema = self._createFromLocal(map(prepare, data), schema)
|
|
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
|
|
jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
|
|
df = DataFrame(jdf, self._wrapped)
|
|
df._schema = schema
|
|
return df
|
|
|
|
def sql(self, sqlQuery):
|
|
"""Returns a :class:`DataFrame` representing the result of the given query.
|
|
|
|
.. versionadded:: 2.0.0
|
|
|
|
Returns
|
|
-------
|
|
:class:`DataFrame`
|
|
|
|
Examples
|
|
--------
|
|
>>> df.createOrReplaceTempView("table1")
|
|
>>> df2 = spark.sql("SELECT field1 AS f1, field2 as f2 from table1")
|
|
>>> df2.collect()
|
|
[Row(f1=1, f2='row1'), Row(f1=2, f2='row2'), Row(f1=3, f2='row3')]
|
|
"""
|
|
return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
|
|
|
|
def table(self, tableName):
|
|
"""Returns the specified table as a :class:`DataFrame`.
|
|
|
|
.. versionadded:: 2.0.0
|
|
|
|
Returns
|
|
-------
|
|
:class:`DataFrame`
|
|
|
|
Examples
|
|
--------
|
|
>>> df.createOrReplaceTempView("table1")
|
|
>>> df2 = spark.table("table1")
|
|
>>> sorted(df.collect()) == sorted(df2.collect())
|
|
True
|
|
"""
|
|
return DataFrame(self._jsparkSession.table(tableName), self._wrapped)
|
|
|
|
@property
|
|
def read(self):
|
|
"""
|
|
Returns a :class:`DataFrameReader` that can be used to read data
|
|
in as a :class:`DataFrame`.
|
|
|
|
.. versionadded:: 2.0.0
|
|
|
|
Returns
|
|
-------
|
|
:class:`DataFrameReader`
|
|
"""
|
|
return DataFrameReader(self._wrapped)
|
|
|
|
@property
|
|
def readStream(self):
|
|
"""
|
|
Returns a :class:`DataStreamReader` that can be used to read data streams
|
|
as a streaming :class:`DataFrame`.
|
|
|
|
.. versionadded:: 2.0.0
|
|
|
|
Notes
|
|
-----
|
|
This API is evolving.
|
|
|
|
Returns
|
|
-------
|
|
:class:`DataStreamReader`
|
|
"""
|
|
return DataStreamReader(self._wrapped)
|
|
|
|
@property
|
|
def streams(self):
|
|
"""Returns a :class:`StreamingQueryManager` that allows managing all the
|
|
:class:`StreamingQuery` instances active on `this` context.
|
|
|
|
.. versionadded:: 2.0.0
|
|
|
|
Notes
|
|
-----
|
|
This API is evolving.
|
|
|
|
Returns
|
|
-------
|
|
:class:`StreamingQueryManager`
|
|
"""
|
|
from pyspark.sql.streaming import StreamingQueryManager
|
|
return StreamingQueryManager(self._jsparkSession.streams())
|
|
|
|
@since(2.0)
|
|
def stop(self):
|
|
"""Stop the underlying :class:`SparkContext`.
|
|
"""
|
|
from pyspark.sql.context import SQLContext
|
|
self._sc.stop()
|
|
# We should clean the default session up. See SPARK-23228.
|
|
self._jvm.SparkSession.clearDefaultSession()
|
|
self._jvm.SparkSession.clearActiveSession()
|
|
SparkSession._instantiatedSession = None
|
|
SparkSession._activeSession = None
|
|
SQLContext._instantiatedContext = None
|
|
|
|
@since(2.0)
|
|
def __enter__(self):
|
|
"""
|
|
Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax.
|
|
"""
|
|
return self
|
|
|
|
@since(2.0)
|
|
def __exit__(self, exc_type, exc_val, exc_tb):
|
|
"""
|
|
Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax.
|
|
|
|
Specifically stop the SparkSession on exit of the with block.
|
|
"""
|
|
self.stop()
|
|
|
|
|
|
def _test():
|
|
import os
|
|
import doctest
|
|
from pyspark.context import SparkContext
|
|
from pyspark.sql import Row
|
|
import pyspark.sql.session
|
|
|
|
os.chdir(os.environ["SPARK_HOME"])
|
|
|
|
globs = pyspark.sql.session.__dict__.copy()
|
|
sc = SparkContext('local[4]', 'PythonTest')
|
|
globs['sc'] = sc
|
|
globs['spark'] = SparkSession(sc)
|
|
globs['rdd'] = rdd = sc.parallelize(
|
|
[Row(field1=1, field2="row1"),
|
|
Row(field1=2, field2="row2"),
|
|
Row(field1=3, field2="row3")])
|
|
globs['df'] = rdd.toDF()
|
|
(failure_count, test_count) = doctest.testmod(
|
|
pyspark.sql.session, globs=globs,
|
|
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
|
|
globs['sc'].stop()
|
|
if failure_count:
|
|
sys.exit(-1)
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|