#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import sys
import warnings
from functools import reduce
from threading import RLock
from pyspark import since
from pyspark.rdd import RDD
from pyspark.sql.conf import RuntimeConfig
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.pandas.conversion import SparkConversionMixin
from pyspark.sql.readwriter import DataFrameReader
from pyspark.sql.streaming import DataStreamReader
from pyspark.sql.types import DataType, StructType, \
_make_type_verifier, _infer_schema, _has_nulltype, _merge_type, _create_converter, \
_parse_datatype_string
from pyspark.sql.utils import install_exception_handler
__all__ = ["SparkSession"]
def _monkey_patch_RDD(sparkSession):
def toDF(self, schema=None, sampleRatio=None):
"""
Converts current :class:`RDD` into a :class:`DataFrame`
This is a shorthand for ``spark.createDataFrame(rdd, schema, sampleRatio)``
Parameters
----------
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`.
sampleRatio : float, optional
the sample ratio of rows used for inferring
Returns
-------
:class:`DataFrame`
Examples
--------
>>> rdd.toDF().collect()
[Row(name='Alice', age=1)]
"""
return sparkSession.createDataFrame(self, schema, sampleRatio)
RDD.toDF = toDF
[docs]class SparkSession(SparkConversionMixin):
"""The entry point to programming Spark with the Dataset and DataFrame API.
A SparkSession can be used create :class:`DataFrame`, register :class:`DataFrame` as
tables, execute SQL over tables, cache tables, and read parquet files.
To create a :class:`SparkSession`, use the following builder pattern:
.. autoattribute:: builder
:annotation:
Examples
--------
>>> spark = SparkSession.builder \\
... .master("local") \\
... .appName("Word Count") \\
... .config("spark.some.config.option", "some-value") \\
... .getOrCreate()
>>> from datetime import datetime
>>> from pyspark.sql import Row
>>> spark = SparkSession(sc)
>>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1,
... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1),
... time=datetime(2014, 8, 1, 14, 1, 5))])
>>> df = allTypes.toDF()
>>> df.createOrReplaceTempView("allTypes")
>>> spark.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a '
... 'from allTypes where b and i > 0').collect()
[Row((i + 1)=2, (d + 1)=2.0, (NOT b)=False, list[1]=2, \
dict[s]=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)]
>>> df.rdd.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect()
[(1, 'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])]
"""
class Builder(object):
"""Builder for :class:`SparkSession`.
"""
_lock = RLock()
_options = {}
_sc = None
def config(self, key=None, value=None, conf=None):
"""Sets a config option. Options set using this method are automatically propagated to
both :class:`SparkConf` and :class:`SparkSession`'s own configuration.
.. versionadded:: 2.0.0
Parameters
----------
key : str, optional
a key name string for configuration property
value : str, optional
a value for configuration property
conf : :class:`SparkConf`, optional
an instance of :class:`SparkConf`
Examples
--------
For an existing SparkConf, use `conf` parameter.
>>> from pyspark.conf import SparkConf
>>> SparkSession.builder.config(conf=SparkConf())
<pyspark.sql.session...
For a (key, value) pair, you can omit parameter names.
>>> SparkSession.builder.config("spark.some.config.option", "some-value")
<pyspark.sql.session...
"""
with self._lock:
if conf is None:
self._options[key] = str(value)
else:
for (k, v) in conf.getAll():
self._options[k] = v
return self
def master(self, master):
"""Sets the Spark master URL to connect to, such as "local" to run locally, "local[4]"
to run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone
cluster.
.. versionadded:: 2.0.0
Parameters
----------
master : str
a url for spark master
"""
return self.config("spark.master", master)
def appName(self, name):
"""Sets a name for the application, which will be shown in the Spark web UI.
If no application name is set, a randomly generated name will be used.
.. versionadded:: 2.0.0
Parameters
----------
name : str
an application name
"""
return self.config("spark.app.name", name)
@since(2.0)
def enableHiveSupport(self):
"""Enables Hive support, including connectivity to a persistent Hive metastore, support
for Hive SerDes, and Hive user-defined functions.
"""
return self.config("spark.sql.catalogImplementation", "hive")
def _sparkContext(self, sc):
with self._lock:
self._sc = sc
return self
def getOrCreate(self):
"""Gets an existing :class:`SparkSession` or, if there is no existing one, creates a
new one based on the options set in this builder.
.. versionadded:: 2.0.0
Examples
--------
This method first checks whether there is a valid global default SparkSession, and if
yes, return that one. If no valid global default SparkSession exists, the method
creates a new SparkSession and assigns the newly created SparkSession as the global
default.
>>> s1 = SparkSession.builder.config("k1", "v1").getOrCreate()
>>> s1.conf.get("k1") == "v1"
True
In case an existing SparkSession is returned, the config options specified
in this builder will be applied to the existing SparkSession.
>>> s2 = SparkSession.builder.config("k2", "v2").getOrCreate()
>>> s1.conf.get("k1") == s2.conf.get("k1")
True
>>> s1.conf.get("k2") == s2.conf.get("k2")
True
"""
with self._lock:
from pyspark.context import SparkContext
from pyspark.conf import SparkConf
session = SparkSession._instantiatedSession
if session is None or session._sc._jsc is None:
if self._sc is not None:
sc = self._sc
else:
sparkConf = SparkConf()
for key, value in self._options.items():
sparkConf.set(key, value)
# This SparkContext may be an existing one.
sc = SparkContext.getOrCreate(sparkConf)
# Do not update `SparkConf` for existing `SparkContext`, as it's shared
# by all sessions.
session = SparkSession(sc)
for key, value in self._options.items():
session._jsparkSession.sessionState().conf().setConfString(key, value)
return session
builder = Builder()
"""A class attribute having a :class:`Builder` to construct :class:`SparkSession` instances."""
_instantiatedSession = None
_activeSession = None
def __init__(self, sparkContext, jsparkSession=None):
from pyspark.sql.context import SQLContext
self._sc = sparkContext
self._jsc = self._sc._jsc
self._jvm = self._sc._jvm
if jsparkSession is None:
if self._jvm.SparkSession.getDefaultSession().isDefined() \
and not self._jvm.SparkSession.getDefaultSession().get() \
.sparkContext().isStopped():
jsparkSession = self._jvm.SparkSession.getDefaultSession().get()
else:
jsparkSession = self._jvm.SparkSession(self._jsc.sc())
self._jsparkSession = jsparkSession
self._jwrapped = self._jsparkSession.sqlContext()
self._wrapped = SQLContext(self._sc, self, self._jwrapped)
_monkey_patch_RDD(self)
install_exception_handler()
# If we had an instantiated SparkSession attached with a SparkContext
# which is stopped now, we need to renew the instantiated SparkSession.
# Otherwise, we will use invalid SparkSession when we call Builder.getOrCreate.
if SparkSession._instantiatedSession is None \
or SparkSession._instantiatedSession._sc._jsc is None:
SparkSession._instantiatedSession = self
SparkSession._activeSession = self
self._jvm.SparkSession.setDefaultSession(self._jsparkSession)
self._jvm.SparkSession.setActiveSession(self._jsparkSession)
def _repr_html_(self):
return """
<div>
<p><b>SparkSession - {catalogImplementation}</b></p>
{sc_HTML}
</div>
""".format(
catalogImplementation=self.conf.get("spark.sql.catalogImplementation"),
sc_HTML=self.sparkContext._repr_html_()
)
[docs] @since(2.0)
def newSession(self):
"""
Returns a new :class:`SparkSession` as new session, that has separate SQLConf,
registered temporary views and UDFs, but shared :class:`SparkContext` and
table cache.
"""
return self.__class__(self._sc, self._jsparkSession.newSession())
[docs] @classmethod
def getActiveSession(cls):
"""
Returns the active :class:`SparkSession` for the current thread, returned by the builder
.. versionadded:: 3.0.0
Returns
-------
:class:`SparkSession`
Spark session if an active session exists for the current thread
Examples
--------
>>> s = SparkSession.getActiveSession()
>>> l = [('Alice', 1)]
>>> rdd = s.sparkContext.parallelize(l)
>>> df = s.createDataFrame(rdd, ['name', 'age'])
>>> df.select("age").collect()
[Row(age=1)]
"""
from pyspark import SparkContext
sc = SparkContext._active_spark_context
if sc is None:
return None
else:
if sc._jvm.SparkSession.getActiveSession().isDefined():
SparkSession(sc, sc._jvm.SparkSession.getActiveSession().get())
return SparkSession._activeSession
else:
return None
@property
@since(2.0)
def sparkContext(self):
"""Returns the underlying :class:`SparkContext`."""
return self._sc
@property
@since(2.0)
def version(self):
"""The version of Spark on which this application is running."""
return self._jsparkSession.version()
@property
@since(2.0)
def conf(self):
"""Runtime configuration interface for Spark.
This is the interface through which the user can get and set all Spark and Hadoop
configurations that are relevant to Spark SQL. When getting the value of a config,
this defaults to the value set in the underlying :class:`SparkContext`, if any.
Returns
-------
:class:`pyspark.sql.conf.RuntimeConfig`
"""
if not hasattr(self, "_conf"):
self._conf = RuntimeConfig(self._jsparkSession.conf())
return self._conf
@property
def catalog(self):
"""Interface through which the user may create, drop, alter or query underlying
databases, tables, functions, etc.
.. versionadded:: 2.0.0
Returns
-------
:class:`Catalog`
"""
from pyspark.sql.catalog import Catalog
if not hasattr(self, "_catalog"):
self._catalog = Catalog(self)
return self._catalog
@property
def udf(self):
"""Returns a :class:`UDFRegistration` for UDF registration.
.. versionadded:: 2.0.0
Returns
-------
:class:`UDFRegistration`
"""
from pyspark.sql.udf import UDFRegistration
return UDFRegistration(self)
[docs] def range(self, start, end=None, step=1, numPartitions=None):
"""
Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
step value ``step``.
.. versionadded:: 2.0.0
Parameters
----------
start : int
the start value
end : int, optional
the end value (exclusive)
step : int, optional
the incremental step (default: 1)
numPartitions : int, optional
the number of partitions of the DataFrame
Returns
-------
:class:`DataFrame`
Examples
--------
>>> spark.range(1, 7, 2).collect()
[Row(id=1), Row(id=3), Row(id=5)]
If only one argument is specified, it will be used as the end value.
>>> spark.range(3).collect()
[Row(id=0), Row(id=1), Row(id=2)]
"""
if numPartitions is None:
numPartitions = self._sc.defaultParallelism
if end is None:
jdf = self._jsparkSession.range(0, int(start), int(step), int(numPartitions))
else:
jdf = self._jsparkSession.range(int(start), int(end), int(step), int(numPartitions))
return DataFrame(jdf, self._wrapped)
def _inferSchemaFromList(self, data, names=None):
"""
Infer schema from list of Row, dict, or tuple.
Parameters
----------
data : iterable
list of Row, dict, or tuple
names : list, optional
list of column names
Returns
-------
:class:`pyspark.sql.types.StructType`
"""
if not data:
raise ValueError("can not infer schema from empty dataset")
schema = reduce(_merge_type, (_infer_schema(row, names) for row in data))
if _has_nulltype(schema):
raise ValueError("Some of types cannot be determined after inferring")
return schema
def _inferSchema(self, rdd, samplingRatio=None, names=None):
"""
Infer schema from an RDD of Row, dict, or tuple.
Parameters
----------
rdd : :class:`RDD`
an RDD of Row, dict, or tuple
samplingRatio : float, optional
sampling ratio, or no sampling (default)
names : list, optional
Returns
-------
:class:`pyspark.sql.types.StructType`
"""
first = rdd.first()
if not first:
raise ValueError("The first row in RDD is empty, "
"can not infer schema")
if samplingRatio is None:
schema = _infer_schema(first, names=names)
if _has_nulltype(schema):
for row in rdd.take(100)[1:]:
schema = _merge_type(schema, _infer_schema(row, names=names))
if not _has_nulltype(schema):
break
else:
raise ValueError("Some of types cannot be determined by the "
"first 100 rows, please try again with sampling")
else:
if samplingRatio < 0.99:
rdd = rdd.sample(False, float(samplingRatio))
schema = rdd.map(lambda row: _infer_schema(row, names)).reduce(_merge_type)
return schema
def _createFromRDD(self, rdd, schema, samplingRatio):
"""
Create an RDD for DataFrame from an existing RDD, returns the RDD and schema.
"""
if schema is None or isinstance(schema, (list, tuple)):
struct = self._inferSchema(rdd, samplingRatio, names=schema)
converter = _create_converter(struct)
rdd = rdd.map(converter)
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
rdd = rdd.map(schema.toInternal)
return rdd, schema
def _createFromLocal(self, data, schema):
"""
Create an RDD for DataFrame from a list or pandas.DataFrame, returns
the RDD and schema.
"""
# make sure data could consumed multiple times
if not isinstance(data, list):
data = list(data)
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 :class:`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()
[docs] 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
[docs] 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)
[docs] 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())
[docs] @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()