#
# 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 os
import sys
import warnings
from collections.abc import Sized
from functools import reduce
from threading import RLock
from types import TracebackType
from typing import (
Any,
ClassVar,
Dict,
Iterable,
List,
Optional,
Tuple,
Type,
Union,
Set,
cast,
no_type_check,
overload,
TYPE_CHECKING,
)
from pyspark.conf import SparkConf
from pyspark.util import is_remote_only
from pyspark.sql.conf import RuntimeConfig
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.functions import lit
from pyspark.sql.pandas.conversion import SparkConversionMixin
from pyspark.sql.profiler import AccumulatorProfilerCollector, Profile
from pyspark.sql.readwriter import DataFrameReader
from pyspark.sql.sql_formatter import SQLStringFormatter
from pyspark.sql.streaming import DataStreamReader
from pyspark.sql.types import (
AtomicType,
DataType,
StructField,
StructType,
_make_type_verifier,
_infer_schema,
_has_nulltype,
_merge_type,
_create_converter,
_parse_datatype_string,
_from_numpy_type,
)
from pyspark.errors.exceptions.captured import install_exception_handler
from pyspark.sql.utils import (
is_timestamp_ntz_preferred,
to_str,
try_remote_session_classmethod,
remote_only,
)
from pyspark.errors import PySparkValueError, PySparkTypeError, PySparkRuntimeError
if TYPE_CHECKING:
from py4j.java_gateway import JavaObject
import pyarrow as pa
from pyspark.core.context import SparkContext
from pyspark.core.rdd import RDD
from pyspark.sql._typing import AtomicValue, RowLike, OptionalPrimitiveType
from pyspark.sql.catalog import Catalog
from pyspark.sql.pandas._typing import ArrayLike, DataFrameLike as PandasDataFrameLike
from pyspark.sql.streaming import StreamingQueryManager
from pyspark.sql.tvf import TableValuedFunction
from pyspark.sql.udf import UDFRegistration
from pyspark.sql.udtf import UDTFRegistration
from pyspark.sql.datasource import DataSourceRegistration
from pyspark.sql.dataframe import DataFrame as ParentDataFrame
# Running MyPy type checks will always require pandas and
# other dependencies so importing here is fine.
from pyspark.sql.connect.client import SparkConnectClient
from pyspark.sql.connect.shell.progress import ProgressHandler
__all__ = ["SparkSession"]
def _monkey_patch_RDD(sparkSession: "SparkSession") -> None:
@no_type_check
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 = spark.range(1).rdd.map(lambda x: tuple(x))
>>> rdd.collect()
[(0,)]
>>> rdd.toDF().show()
+---+
| _1|
+---+
| 0|
+---+
"""
return sparkSession.createDataFrame(self, schema, sampleRatio)
if not is_remote_only():
from pyspark import RDD
RDD.toDF = toDF # type: ignore[method-assign]
class classproperty(property):
"""Same as Python's @property decorator, but for class attributes.
Examples
--------
>>> class Builder:
... def build(self):
... return MyClass()
...
>>> class MyClass:
... @classproperty
... def builder(cls):
... print("instantiating new builder")
... return Builder()
...
>>> c1 = MyClass.builder
instantiating new builder
>>> c2 = MyClass.builder
instantiating new builder
>>> c1 == c2
False
>>> isinstance(c1.build(), MyClass)
True
"""
def __get__(self, instance: Any, owner: Any = None) -> "SparkSession.Builder":
# The "type: ignore" below silences the following error from mypy:
# error: Argument 1 to "classmethod" has incompatible
# type "Optional[Callable[[Any], Any]]";
# expected "Callable[..., Any]" [arg-type]
return classmethod(self.fget).__get__(None, owner)() # type: ignore
[docs]class SparkSession(SparkConversionMixin):
"""The entry point to programming Spark with the Dataset and DataFrame API.
A SparkSession can be used to 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:
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. autoattribute:: builder
:annotation:
Examples
--------
Create a Spark session.
>>> spark = (
... SparkSession.builder
... .master("local")
... .appName("Word Count")
... .config("spark.some.config.option", "some-value")
... .getOrCreate()
... )
Create a Spark session with Spark Connect.
>>> spark = (
... SparkSession.builder
... .remote("sc://localhost")
... .appName("Word Count")
... .config("spark.some.config.option", "some-value")
... .getOrCreate()
... ) # doctest: +SKIP
"""
class Builder:
"""Builder for :class:`SparkSession`."""
_lock = RLock()
def __init__(self) -> None:
self._options: Dict[str, Any] = {}
@overload
def config(self, *, conf: SparkConf) -> "SparkSession.Builder":
...
@overload
def config(self, key: str, value: Any) -> "SparkSession.Builder":
...
@overload
def config(self, *, map: Dict[str, "OptionalPrimitiveType"]) -> "SparkSession.Builder":
...
def config(
self,
key: Optional[str] = None,
value: Optional[Any] = None,
conf: Optional[SparkConf] = None,
*,
map: Optional[Dict[str, "OptionalPrimitiveType"]] = None,
) -> "SparkSession.Builder":
"""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
.. versionchanged:: 3.4.0
Supports Spark Connect.
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`
map: dictionary, optional
a dictionary of configurations to set
.. versionadded:: 3.4.0
Returns
-------
:class:`SparkSession.Builder`
See Also
--------
:class:`SparkConf`
Examples
--------
For an existing :class:`SparkConf`, use `conf` parameter.
>>> from pyspark.conf import SparkConf
>>> conf = SparkConf().setAppName("example").setMaster("local")
>>> SparkSession.builder.config(conf=conf)
<pyspark.sql.session.SparkSession.Builder...
For a (key, value) pair, you can omit parameter names.
>>> SparkSession.builder.config("spark.some.config.option", "some-value")
<pyspark.sql.session.SparkSession.Builder...
Set multiple configurations.
>>> SparkSession.builder.config(
... "spark.some.config.number", 123).config("spark.some.config.float", 0.123)
<pyspark.sql.session.SparkSession.Builder...
Set multiple configurations using a dictionary.
>>> SparkSession.builder.config(
... map={"spark.some.config.number": 123, "spark.some.config.float": 0.123})
<pyspark.sql.session.SparkSession.Builder...
"""
with self._lock:
if conf is not None:
for k, v in conf.getAll():
self._options[k] = v
self._validate_startup_urls()
elif map is not None:
for k, v in map.items(): # type: ignore[assignment]
v = to_str(v) # type: ignore[assignment]
self._options[k] = v
self._validate_startup_urls()
else:
value = to_str(value)
self._options[cast(str, key)] = value
self._validate_startup_urls()
return self
def _validate_startup_urls(
self,
) -> None:
"""
Helper function that validates the combination of startup URLs and raises an exception
if incompatible options are selected.
"""
if ("spark.master" in self._options or "MASTER" in os.environ) and (
"spark.remote" in self._options or "SPARK_REMOTE" in os.environ
):
raise PySparkRuntimeError(
errorClass="CANNOT_CONFIGURE_SPARK_CONNECT_MASTER",
messageParameters={
"master_url": self._options.get("spark.master", os.environ.get("MASTER")),
"connect_url": self._options.get(
"spark.remote", os.environ.get("SPARK_REMOTE")
),
},
)
if "spark.remote" in self._options:
remote = cast(str, self._options.get("spark.remote"))
if ("SPARK_REMOTE" in os.environ and os.environ["SPARK_REMOTE"] != remote) and (
"SPARK_LOCAL_REMOTE" in os.environ and not remote.startswith("local")
):
raise PySparkRuntimeError(
errorClass="CANNOT_CONFIGURE_SPARK_CONNECT",
messageParameters={
"existing_url": os.environ["SPARK_REMOTE"],
"new_url": remote,
},
)
def master(self, master: str) -> "SparkSession.Builder":
"""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
Returns
-------
:class:`SparkSession.Builder`
Examples
--------
>>> SparkSession.builder.master("local")
<pyspark.sql.session.SparkSession.Builder...
"""
return self.config("spark.master", master)
def remote(self, url: str) -> "SparkSession.Builder":
"""Sets the Spark remote URL to connect to, such as "sc://host:port" to run
it via Spark Connect server.
.. versionadded:: 3.4.0
Parameters
----------
url : str
URL to Spark Connect server
Returns
-------
:class:`SparkSession.Builder`
Examples
--------
>>> SparkSession.builder.remote("sc://localhost") # doctest: +SKIP
<pyspark.sql.session.SparkSession.Builder...
"""
return self.config("spark.remote", url)
def appName(self, name: str) -> "SparkSession.Builder":
"""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
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
name : str
an application name
Returns
-------
:class:`SparkSession.Builder`
Examples
--------
>>> SparkSession.builder.appName("My app")
<pyspark.sql.session.SparkSession.Builder...
"""
return self.config("spark.app.name", name)
def enableHiveSupport(self) -> "SparkSession.Builder":
"""Enables Hive support, including connectivity to a persistent Hive metastore, support
for Hive SerDes, and Hive user-defined functions.
.. versionadded:: 2.0.0
Returns
-------
:class:`SparkSession.Builder`
Examples
--------
>>> SparkSession.builder.enableHiveSupport()
<pyspark.sql.session.SparkSession.Builder...
"""
return self.config("spark.sql.catalogImplementation", "hive")
def getOrCreate(self) -> "SparkSession":
"""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
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`SparkSession`
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
The configuration of the SparkSession can be changed afterwards
>>> s1.conf.set("k1", "v1_new")
>>> s1.conf.get("k1") == "v1_new"
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") == "v1_new"
True
>>> s1.conf.get("k2") == s2.conf.get("k2") == "v2"
True
"""
opts = dict(self._options)
if is_remote_only():
from pyspark.sql.connect.session import SparkSession as RemoteSparkSession
url = opts.get("spark.remote", os.environ.get("SPARK_REMOTE"))
if url is None:
raise PySparkRuntimeError(
errorClass="CONNECT_URL_NOT_SET",
messageParameters={},
)
os.environ["SPARK_CONNECT_MODE_ENABLED"] = "1"
opts["spark.remote"] = url
return RemoteSparkSession.builder.config(map=opts).getOrCreate() # type: ignore
from pyspark.core.context import SparkContext
with self._lock:
if (
"SPARK_CONNECT_MODE_ENABLED" in os.environ
or "SPARK_REMOTE" in os.environ
or "spark.remote" in opts
):
with SparkContext._lock:
from pyspark.sql.connect.session import SparkSession as RemoteSparkSession
if (
SparkContext._active_spark_context is None
and SparkSession._instantiatedSession is None
):
url = opts.get("spark.remote", os.environ.get("SPARK_REMOTE"))
if url is None:
raise PySparkRuntimeError(
errorClass="CONNECT_URL_NOT_SET",
messageParameters={},
)
if url.startswith("local"):
os.environ["SPARK_LOCAL_REMOTE"] = "1"
RemoteSparkSession._start_connect_server(url, opts)
url = "sc://localhost"
os.environ["SPARK_CONNECT_MODE_ENABLED"] = "1"
opts["spark.remote"] = url
return cast(
SparkSession,
RemoteSparkSession.builder.config(map=opts).getOrCreate(),
)
elif "SPARK_LOCAL_REMOTE" in os.environ:
url = "sc://localhost"
os.environ["SPARK_CONNECT_MODE_ENABLED"] = "1"
opts["spark.remote"] = url
return cast(
SparkSession,
RemoteSparkSession.builder.config(map=opts).getOrCreate(),
)
else:
raise PySparkRuntimeError(
errorClass="SESSION_ALREADY_EXIST",
messageParameters={},
)
session = SparkSession._instantiatedSession
if session is None or session._sc._jsc is None:
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, options=self._options)
else:
getattr(
getattr(session._jvm, "SparkSession$"), "MODULE$"
).applyModifiableSettings(session._jsparkSession, self._options)
return session
# Spark Connect-specific API
@remote_only
def create(self) -> "SparkSession":
"""Creates a new SparkSession. Can only be used in the context of Spark Connect
and will throw an exception otherwise.
.. versionadded:: 3.5.0
Returns
-------
:class:`SparkSession`
Notes
-----
This method will update the default and/or active session if they are not set.
"""
opts = dict(self._options)
if "SPARK_REMOTE" in os.environ or "spark.remote" in opts:
from pyspark.sql.connect.session import SparkSession as RemoteSparkSession
# Validate that no incompatible configuration options are selected.
self._validate_startup_urls()
url = opts.get("spark.remote", os.environ.get("SPARK_REMOTE"))
if url.startswith("local"):
raise PySparkRuntimeError(
errorClass="UNSUPPORTED_LOCAL_CONNECTION_STRING",
messageParameters={},
)
# Mark this Spark Session as Spark Connect. This prevents that local PySpark is
# used in conjunction with Spark Connect mode.
os.environ["SPARK_CONNECT_MODE_ENABLED"] = "1"
opts["spark.remote"] = url
return cast(SparkSession, RemoteSparkSession.builder.config(map=opts).create())
else:
raise PySparkRuntimeError(
errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
messageParameters={"feature": "SparkSession.builder.create"},
)
# SPARK-47544: Explicitly declaring this as an identifier instead of a method.
# If changing, make sure this bug is not reintroduced.
builder: Builder = classproperty(lambda cls: cls.Builder()) # type: ignore
"""Creates a :class:`Builder` for constructing a :class:`SparkSession`.
.. versionchanged:: 3.4.0
Supports Spark Connect.
"""
_instantiatedSession: ClassVar[Optional["SparkSession"]] = None
_activeSession: ClassVar[Optional["SparkSession"]] = None
def __init__(
self,
sparkContext: "SparkContext",
jsparkSession: Optional["JavaObject"] = None,
options: Dict[str, Any] = {},
):
self._sc = sparkContext
self._jsc = self._sc._jsc
self._jvm = self._sc._jvm
assert self._jvm is not None
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()
getattr(getattr(self._jvm, "SparkSession$"), "MODULE$").applyModifiableSettings(
jsparkSession, options
)
else:
jsparkSession = self._jvm.SparkSession(self._jsc.sc(), options)
else:
getattr(getattr(self._jvm, "SparkSession$"), "MODULE$").applyModifiableSettings(
jsparkSession, options
)
self._jsparkSession = jsparkSession
_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
assert self._jvm is not None
self._jvm.SparkSession.setDefaultSession(self._jsparkSession)
self._jvm.SparkSession.setActiveSession(self._jsparkSession)
self._profiler_collector = AccumulatorProfilerCollector()
def _repr_html_(self) -> str:
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_(),
)
@property
def _jconf(self) -> "JavaObject":
"""Accessor for the JVM SQL-specific configurations"""
return self._jsparkSession.sessionState().conf()
if not is_remote_only():
[docs] def newSession(self) -> "SparkSession":
"""
Returns a new :class:`SparkSession` as new session, that has separate SQLConf,
registered temporary views and UDFs, but shared :class:`SparkContext` and
table cache.
.. versionadded:: 2.0.0
Returns
-------
:class:`SparkSession`
Spark session if an active session exists for the current thread
Examples
--------
>>> spark.newSession()
<...SparkSession object ...>
"""
return self.__class__(self._sc, self._jsparkSession.newSession())
[docs] @classmethod
@try_remote_session_classmethod
def getActiveSession(cls) -> Optional["SparkSession"]:
"""
Returns the active :class:`SparkSession` for the current thread, returned by the builder
.. versionadded:: 3.0.0
.. versionchanged:: 3.5.0
Supports Spark Connect.
Returns
-------
:class:`SparkSession`
Spark session if an active session exists for the current thread
Examples
--------
>>> s = SparkSession.getActiveSession()
>>> df = s.createDataFrame([('Alice', 1)], ['name', 'age'])
>>> df.select("age").show()
+---+
|age|
+---+
| 1|
+---+
"""
from pyspark import SparkContext
sc = SparkContext._active_spark_context
if sc is None:
return None
else:
assert sc._jvm is not None
if sc._jvm.SparkSession.getActiveSession().isDefined():
SparkSession(sc, sc._jvm.SparkSession.getActiveSession().get())
return SparkSession._activeSession
else:
return None
[docs] @classmethod
@try_remote_session_classmethod
def active(cls) -> "SparkSession":
"""
Returns the active or default :class:`SparkSession` for the current thread, returned by
the builder.
.. versionadded:: 3.5.0
Returns
-------
:class:`SparkSession`
Spark session if an active or default session exists for the current thread.
"""
session = cls.getActiveSession()
if session is None:
session = cls._instantiatedSession
if session is None:
raise PySparkRuntimeError(
errorClass="NO_ACTIVE_OR_DEFAULT_SESSION",
messageParameters={},
)
return session
if not is_remote_only():
@property
def sparkContext(self) -> "SparkContext":
"""
Returns the underlying :class:`SparkContext`.
.. versionadded:: 2.0.0
Returns
-------
:class:`SparkContext`
Examples
--------
>>> spark.sparkContext
<SparkContext master=... appName=...>
Create an RDD from the Spark context
>>> rdd = spark.sparkContext.parallelize([1, 2, 3])
>>> rdd.collect()
[1, 2, 3]
"""
return self._sc
@property
def version(self) -> str:
"""
The version of Spark on which this application is running.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
str
the version of Spark in string.
Examples
--------
>>> _ = spark.version
"""
return self._jsparkSession.version()
@property
def conf(self) -> RuntimeConfig:
"""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.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`pyspark.sql.conf.RuntimeConfig`
Examples
--------
>>> spark.conf
<pyspark...RuntimeConf...>
Set a runtime configuration for the session
>>> spark.conf.set("key", "value")
>>> spark.conf.get("key")
'value'
"""
if not hasattr(self, "_conf"):
self._conf = RuntimeConfig(self._jsparkSession.conf())
return self._conf
@property
def catalog(self) -> "Catalog":
"""Interface through which the user may create, drop, alter or query underlying
databases, tables, functions, etc.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`Catalog`
Examples
--------
>>> spark.catalog
<...Catalog object ...>
Create a temp view, show the list, and drop it.
>>> spark.range(1).createTempView("test_view")
>>> spark.catalog.listTables() # doctest: +SKIP
[Table(name='test_view', catalog=None, namespace=[], description=None, ...
>>> _ = spark.catalog.dropTempView("test_view")
"""
from pyspark.sql.catalog import Catalog
if not hasattr(self, "_catalog"):
self._catalog = Catalog(self)
return self._catalog
@property
def udf(self) -> "UDFRegistration":
"""Returns a :class:`UDFRegistration` for UDF registration.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`UDFRegistration`
Examples
--------
Register a Python UDF, and use it in SQL.
>>> strlen = spark.udf.register("strlen", lambda x: len(x))
>>> spark.sql("SELECT strlen('test')").show()
+------------+
|strlen(test)|
+------------+
| 4|
+------------+
"""
from pyspark.sql.udf import UDFRegistration
return UDFRegistration(self)
@property
def udtf(self) -> "UDTFRegistration":
"""Returns a :class:`UDTFRegistration` for UDTF registration.
.. versionadded:: 3.5.0
Returns
-------
:class:`UDTFRegistration`
Notes
-----
Supports Spark Connect.
"""
from pyspark.sql.udtf import UDTFRegistration
return UDTFRegistration(self)
@property
def dataSource(self) -> "DataSourceRegistration":
"""Returns a :class:`DataSourceRegistration` for data source registration.
.. versionadded:: 4.0.0
Returns
-------
:class:`DataSourceRegistration`
Notes
-----
This feature is experimental and unstable.
"""
from pyspark.sql.datasource import DataSourceRegistration
return DataSourceRegistration(self)
@property
def profile(self) -> Profile:
"""Returns a :class:`Profile` for performance/memory profiling.
.. versionadded:: 4.0.0
Returns
-------
:class:`Profile`
Notes
-----
Supports Spark Connect.
"""
return Profile(self._profiler_collector)
[docs] def range(
self,
start: int,
end: Optional[int] = None,
step: int = 1,
numPartitions: Optional[int] = None,
) -> DataFrame:
"""
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
.. versionchanged:: 3.4.0
Supports Spark Connect.
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).show()
+---+
| id|
+---+
| 1|
| 3|
| 5|
+---+
If only one argument is specified, it will be used as the end value.
>>> spark.range(3).show()
+---+
| id|
+---+
| 0|
| 1|
| 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)
def _inferSchemaFromList(
self, data: Iterable[Any], names: Optional[List[str]] = None
) -> StructType:
"""
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 PySparkValueError(
errorClass="CANNOT_INFER_EMPTY_SCHEMA",
messageParameters={},
)
infer_dict_as_struct = self._jconf.inferDictAsStruct()
infer_array_from_first_element = self._jconf.legacyInferArrayTypeFromFirstElement()
infer_map_from_first_pair = self._jconf.legacyInferMapStructTypeFromFirstItem()
prefer_timestamp_ntz = is_timestamp_ntz_preferred()
schema = reduce(
_merge_type,
(
_infer_schema(
row,
names,
infer_dict_as_struct=infer_dict_as_struct,
infer_array_from_first_element=infer_array_from_first_element,
infer_map_from_first_pair=infer_map_from_first_pair,
prefer_timestamp_ntz=prefer_timestamp_ntz,
)
for row in data
),
)
if _has_nulltype(schema):
raise PySparkValueError(
errorClass="CANNOT_DETERMINE_TYPE",
messageParameters={},
)
return schema
def _inferSchema(
self,
rdd: "RDD[Any]",
samplingRatio: Optional[float] = None,
names: Optional[List[str]] = None,
) -> StructType:
"""
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 isinstance(first, Sized) and len(first) == 0:
raise PySparkValueError(
errorClass="CANNOT_INFER_EMPTY_SCHEMA",
messageParameters={},
)
infer_dict_as_struct = self._jconf.inferDictAsStruct()
infer_array_from_first_element = self._jconf.legacyInferArrayTypeFromFirstElement()
infer_map_from_first_pair = self._jconf.legacyInferMapStructTypeFromFirstItem()
prefer_timestamp_ntz = is_timestamp_ntz_preferred()
if samplingRatio is None:
schema = _infer_schema(
first,
names=names,
infer_dict_as_struct=infer_dict_as_struct,
prefer_timestamp_ntz=prefer_timestamp_ntz,
)
if _has_nulltype(schema):
for row in rdd.take(100)[1:]:
schema = _merge_type(
schema,
_infer_schema(
row,
names=names,
infer_dict_as_struct=infer_dict_as_struct,
infer_array_from_first_element=infer_array_from_first_element,
infer_map_from_first_pair=infer_map_from_first_pair,
prefer_timestamp_ntz=prefer_timestamp_ntz,
),
)
if not _has_nulltype(schema):
break
else:
raise PySparkValueError(
errorClass="CANNOT_DETERMINE_TYPE",
messageParameters={},
)
else:
if samplingRatio < 0.99:
rdd = rdd.sample(False, float(samplingRatio))
schema = rdd.map(
lambda row: _infer_schema(
row,
names,
infer_dict_as_struct=infer_dict_as_struct,
infer_array_from_first_element=infer_array_from_first_element,
infer_map_from_first_pair=infer_map_from_first_pair,
prefer_timestamp_ntz=prefer_timestamp_ntz,
)
).reduce(_merge_type)
return schema
def _createFromRDD(
self,
rdd: "RDD[Any]",
schema: Optional[Union[DataType, List[str]]],
samplingRatio: Optional[float],
) -> Tuple["RDD[Tuple]", StructType]:
"""
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)
tupled_rdd = rdd.map(converter)
if isinstance(schema, (list, tuple)):
for i, name in enumerate(schema):
struct.fields[i].name = name
struct.names[i] = name
elif isinstance(schema, StructType):
struct = schema
tupled_rdd = rdd
else:
raise PySparkTypeError(
errorClass="NOT_LIST_OR_NONE_OR_STRUCT",
messageParameters={
"arg_name": "schema",
"arg_type": type(schema).__name__,
},
)
# convert python objects to sql data
internal_rdd = tupled_rdd.map(struct.toInternal)
return internal_rdd, struct
def _createFromLocal(
self, data: Iterable[Any], schema: Optional[Union[DataType, List[str]]]
) -> Tuple["RDD[Tuple]", StructType]:
"""
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)
tupled_data: Iterable[Tuple] = map(converter, data)
if isinstance(schema, (list, tuple)):
for i, name in enumerate(schema):
struct.fields[i].name = name
struct.names[i] = name
elif isinstance(schema, StructType):
struct = schema
tupled_data = data
else:
raise PySparkTypeError(
errorClass="NOT_LIST_OR_NONE_OR_STRUCT",
messageParameters={
"arg_name": "schema",
"arg_type": type(schema).__name__,
},
)
# convert python objects to sql data
internal_data = [struct.toInternal(row) for row in tupled_data]
return self._sc.parallelize(internal_data), struct
@staticmethod
def _create_shell_session() -> "SparkSession":
"""
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.core.context import SparkContext
try:
# Try to access HiveConf, it will raise exception if Hive is not added
conf = SparkConf()
assert SparkContext._jvm is not None
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._getActiveSessionOrCreate()
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._getActiveSessionOrCreate()
@staticmethod
def _getActiveSessionOrCreate(**static_conf: Any) -> "SparkSession":
"""
Returns the active :class:`SparkSession` for the current thread, returned by the builder,
or if there is no existing one, creates a new one based on the options set in the builder.
NOTE that 'static_conf' might not be set if there's an active or default Spark session
running.
"""
spark = SparkSession.getActiveSession()
if spark is None:
builder = SparkSession.builder
for k, v in static_conf.items():
builder = builder.config(k, v)
spark = builder.getOrCreate()
return spark
@overload # type: ignore[override]
def createDataFrame(
self,
data: Iterable["RowLike"],
schema: Union[List[str], Tuple[str, ...]] = ...,
samplingRatio: Optional[float] = ...,
) -> DataFrame:
...
@overload
def createDataFrame(
self,
data: "RDD[RowLike]",
schema: Union[List[str], Tuple[str, ...]] = ...,
samplingRatio: Optional[float] = ...,
) -> DataFrame:
...
@overload
def createDataFrame(
self,
data: Iterable["RowLike"],
schema: Union[StructType, str],
*,
verifySchema: bool = ...,
) -> DataFrame:
...
@overload
def createDataFrame(
self,
data: "RDD[RowLike]",
schema: Union[StructType, str],
*,
verifySchema: bool = ...,
) -> DataFrame:
...
@overload
def createDataFrame(
self,
data: "RDD[AtomicValue]",
schema: Union[AtomicType, str],
verifySchema: bool = ...,
) -> DataFrame:
...
@overload
def createDataFrame(
self,
data: Iterable["AtomicValue"],
schema: Union[AtomicType, str],
verifySchema: bool = ...,
) -> DataFrame:
...
@overload
def createDataFrame(
self, data: "PandasDataFrameLike", samplingRatio: Optional[float] = ...
) -> DataFrame:
...
@overload
def createDataFrame(self, data: "pa.Table", samplingRatio: Optional[float] = ...) -> DataFrame:
...
@overload
def createDataFrame(
self,
data: "PandasDataFrameLike",
schema: Union[StructType, str],
verifySchema: bool = ...,
) -> DataFrame:
...
@overload
def createDataFrame(
self,
data: "pa.Table",
schema: Union[StructType, str],
verifySchema: bool = ...,
) -> DataFrame:
...
[docs] def createDataFrame( # type: ignore[misc]
self,
data: Union["RDD[Any]", Iterable[Any], "PandasDataFrameLike", "ArrayLike", "pa.Table"],
schema: Optional[Union[AtomicType, StructType, str]] = None,
samplingRatio: Optional[float] = None,
verifySchema: bool = True,
) -> DataFrame:
"""
Creates a :class:`DataFrame` from an :class:`RDD`, a list, a :class:`pandas.DataFrame`,
a :class:`numpy.ndarray`, or a :class:`pyarrow.Table`.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
.. versionchanged:: 4.0.0
Supports :class:`pyarrow.Table`.
Parameters
----------
data : :class:`RDD` or iterable
an RDD of any kind of SQL data representation (:class:`Row`,
:class:`tuple`, ``int``, ``boolean``, ``dict``, etc.), or :class:`list`,
:class:`pandas.DataFrame`, :class:`numpy.ndarray`, or :class:`pyarrow.Table`.
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<>``.
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.
samplingRatio : float, optional
the sample ratio of rows used for inferring. The first few rows will be used
if ``samplingRatio`` is ``None``. This option is effective only when the input is
:class:`RDD`.
verifySchema : bool, optional
verify data types of every row against schema. Enabled by default.
When the input is :class:`pyarrow.Table` or when the input class is
:class:`pandas.DataFrame` and `spark.sql.execution.arrow.pyspark.enabled` is enabled,
this option is not effective. It follows Arrow type coercion. This option is not
supported with Spark Connect.
.. versionadded:: 2.1.0
Returns
-------
:class:`DataFrame`
Notes
-----
Usage with `spark.sql.execution.arrow.pyspark.enabled=True` is experimental.
Examples
--------
Create a DataFrame from a list of tuples.
>>> spark.createDataFrame([('Alice', 1)]).show()
+-----+---+
| _1| _2|
+-----+---+
|Alice| 1|
+-----+---+
Create a DataFrame from a list of dictionaries.
>>> d = [{'name': 'Alice', 'age': 1}]
>>> spark.createDataFrame(d).show()
+---+-----+
|age| name|
+---+-----+
| 1|Alice|
+---+-----+
Create a DataFrame with column names specified.
>>> spark.createDataFrame([('Alice', 1)], ['name', 'age']).show()
+-----+---+
| name|age|
+-----+---+
|Alice| 1|
+-----+---+
Create a DataFrame with the explicit schema specified.
>>> from pyspark.sql.types import *
>>> schema = StructType([
... StructField("name", StringType(), True),
... StructField("age", IntegerType(), True)])
>>> spark.createDataFrame([('Alice', 1)], schema).show()
+-----+---+
| name|age|
+-----+---+
|Alice| 1|
+-----+---+
Create a DataFrame with the schema in DDL formatted string.
>>> spark.createDataFrame([('Alice', 1)], "name: string, age: int").show()
+-----+---+
| name|age|
+-----+---+
|Alice| 1|
+-----+---+
Create an empty DataFrame.
When initializing an empty DataFrame in PySpark, it's mandatory to specify its schema,
as the DataFrame lacks data from which the schema can be inferred.
>>> spark.createDataFrame([], "name: string, age: int").show()
+----+---+
|name|age|
+----+---+
+----+---+
Create a DataFrame from Row objects.
>>> from pyspark.sql import Row
>>> Person = Row('name', 'age')
>>> df = spark.createDataFrame([Person("Alice", 1)])
>>> df.show()
+-----+---+
| name|age|
+-----+---+
|Alice| 1|
+-----+---+
Create a DataFrame from a pandas DataFrame.
>>> spark.createDataFrame(df.toPandas()).show() # doctest: +SKIP
+-----+---+
| name|age|
+-----+---+
|Alice| 1|
+-----+---+
>>> spark.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP
+---+---+
| 0| 1|
+---+---+
| 1| 2|
+---+---+
Create a DataFrame from a PyArrow Table.
>>> spark.createDataFrame(df.toArrow()).show() # doctest: +SKIP
+-----+---+
| name|age|
+-----+---+
|Alice| 1|
+-----+---+
>>> table = pyarrow.table({'0': [1], '1': [2]}) # doctest: +SKIP
>>> spark.createDataFrame(table).collect() # doctest: +SKIP
+---+---+
| 0| 1|
+---+---+
| 1| 2|
+---+---+
"""
SparkSession._activeSession = self
assert self._jvm is not None
self._jvm.SparkSession.setActiveSession(self._jsparkSession)
if isinstance(data, DataFrame):
raise PySparkTypeError(
errorClass="INVALID_TYPE",
messageParameters={"arg_name": "data", "arg_type": "DataFrame"},
)
if isinstance(schema, str):
schema = cast(Union[AtomicType, StructType, str], _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 as pd
has_pandas = True
except Exception:
has_pandas = False
try:
import numpy as np
has_numpy = True
except Exception:
has_numpy = False
try:
import pyarrow as pa
has_pyarrow = True
except Exception:
has_pyarrow = False
if has_numpy and isinstance(data, np.ndarray):
# `data` of numpy.ndarray type will be converted to a pandas DataFrame,
# so pandas is required.
from pyspark.sql.pandas.utils import require_minimum_pandas_version
require_minimum_pandas_version()
if data.ndim not in [1, 2]:
raise PySparkValueError(
errorClass="INVALID_NDARRAY_DIMENSION",
messageParameters={"dimensions": "1 or 2"},
)
if data.ndim == 1 or data.shape[1] == 1:
column_names = ["value"]
else:
column_names = ["_%s" % i for i in range(1, data.shape[1] + 1)]
if schema is None and not self._jconf.arrowPySparkEnabled():
# Construct `schema` from `np.dtype` of the input NumPy array
# TODO: Apply the logic below when self._jconf.arrowPySparkEnabled() is True
spark_type = _from_numpy_type(data.dtype)
if spark_type is not None:
schema = StructType(
[StructField(name, spark_type, nullable=True) for name in column_names]
)
data = pd.DataFrame(data, columns=column_names)
if has_pandas and isinstance(data, pd.DataFrame):
# Create a DataFrame from pandas DataFrame.
return super(SparkSession, self).createDataFrame( # type: ignore[call-overload]
data, schema, samplingRatio, verifySchema
)
if has_pyarrow and isinstance(data, pa.Table):
# Create a DataFrame from PyArrow Table.
return super(SparkSession, self).createDataFrame( # type: ignore[call-overload]
data, schema, samplingRatio, verifySchema
)
return self._create_dataframe(
data, schema, samplingRatio, verifySchema # type: ignore[arg-type]
)
def _create_dataframe(
self,
data: Union["RDD[Any]", Iterable[Any]],
schema: Optional[Union[DataType, List[str]]],
samplingRatio: Optional[float],
verifySchema: bool,
) -> DataFrame:
if isinstance(schema, StructType):
verify_func = _make_type_verifier(schema) if verifySchema else lambda _: True
@no_type_check
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
)
@no_type_check
def prepare(obj):
verify_func(obj)
return (obj,)
else:
def prepare(obj: Any) -> Any:
return obj
if not is_remote_only():
from pyspark.core.rdd import RDD
if not is_remote_only() and isinstance(data, RDD):
rdd, struct = self._createFromRDD(data.map(prepare), schema, samplingRatio)
else:
rdd, struct = self._createFromLocal(
map(prepare, data), schema # type: ignore[arg-type]
)
assert self._jvm is not None
jrdd = self._jvm.SerDeUtil.toJavaArray(rdd._to_java_object_rdd())
jdf = self._jsparkSession.applySchemaToPythonRDD(jrdd.rdd(), struct.json())
df = DataFrame(jdf, self)
df._schema = struct
return df
[docs] def sql(
self, sqlQuery: str, args: Optional[Union[Dict[str, Any], List]] = None, **kwargs: Any
) -> "ParentDataFrame":
"""Returns a :class:`DataFrame` representing the result of the given query.
When ``kwargs`` is specified, this method formats the given string by using the Python
standard formatter. The method binds named parameters to SQL literals or
positional parameters from `args`. It doesn't support named and positional parameters
in the same SQL query.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect and parameterized SQL.
.. versionchanged:: 3.5.0
Added positional parameters.
Parameters
----------
sqlQuery : str
SQL query string.
args : dict or list
A dictionary of parameter names to Python objects or a list of Python objects
that can be converted to SQL literal expressions. See
`Supported Data Types`_ for supported value types in Python.
For example, dictionary keys: "rank", "name", "birthdate";
dictionary or list values: 1, "Steven", datetime.date(2023, 4, 2).
A value can be also a `Column` of a literal or collection constructor functions such
as `map()`, `array()`, `struct()`, in that case it is taken as is.
.. _Supported Data Types: https://spark.apache.org/docs/latest/sql-ref-datatypes.html
.. versionadded:: 3.4.0
kwargs : dict
Other variables that the user wants to set that can be referenced in the query
.. versionchanged:: 3.3.0
Added optional argument ``kwargs`` to specify the mapping of variables in the query.
This feature is experimental and unstable.
Returns
-------
:class:`DataFrame`
Notes
-----
In Spark Classic, a temporary view referenced in `spark.sql` is resolved immediately,
while in Spark Connect it is lazily analyzed.
So in Spark Connect if a view is dropped, modified or replaced after `spark.sql`, the
execution may fail or generate different results.
Examples
--------
Executing a SQL query.
>>> spark.sql("SELECT * FROM range(10) where id > 7").show()
+---+
| id|
+---+
| 8|
| 9|
+---+
Executing a SQL query with variables as Python formatter standard.
>>> spark.sql(
... "SELECT * FROM range(10) WHERE id > {bound1} AND id < {bound2}", bound1=7, bound2=9
... ).show()
+---+
| id|
+---+
| 8|
+---+
>>> mydf = spark.range(10)
>>> spark.sql(
... "SELECT {col} FROM {mydf} WHERE id IN {x}",
... col=mydf.id, mydf=mydf, x=tuple(range(4))).show()
+---+
| id|
+---+
| 0|
| 1|
| 2|
| 3|
+---+
>>> spark.sql('''
... SELECT m1.a, m2.b
... FROM {table1} m1 INNER JOIN {table2} m2
... ON m1.key = m2.key
... ORDER BY m1.a, m2.b''',
... table1=spark.createDataFrame([(1, "a"), (2, "b")], ["a", "key"]),
... table2=spark.createDataFrame([(3, "a"), (4, "b"), (5, "b")], ["b", "key"])).show()
+---+---+
| a| b|
+---+---+
| 1| 3|
| 2| 4|
| 2| 5|
+---+---+
Also, it is possible to query using class:`Column` from :class:`DataFrame`.
>>> mydf = spark.createDataFrame([(1, 4), (2, 4), (3, 6)], ["A", "B"])
>>> spark.sql("SELECT {df.A}, {df[B]} FROM {df}", df=mydf).show()
+---+---+
| A| B|
+---+---+
| 1| 4|
| 2| 4|
| 3| 6|
+---+---+
And substitute named parameters with the `:` prefix by SQL literals.
>>> from pyspark.sql.functions import create_map, lit
>>> spark.sql(
... "SELECT *, element_at(:m, 'a') AS C FROM {df} WHERE {df[B]} > :minB",
... {"minB" : 5, "m" : create_map(lit('a'), lit(1))}, df=mydf).show()
+---+---+---+
| A| B| C|
+---+---+---+
| 3| 6| 1|
+---+---+---+
Or positional parameters marked by `?` in the SQL query by SQL literals.
>>> from pyspark.sql.functions import array, lit
>>> spark.sql(
... "SELECT *, element_at(?, 1) AS C FROM {df} WHERE {df[B]} > ? and ? < {df[A]}",
... args=[array(lit(1), lit(2), lit(3)), 5, 2], df=mydf).show()
+---+---+---+
| A| B| C|
+---+---+---+
| 3| 6| 1|
+---+---+---+
"""
from pyspark.sql.classic.column import _to_java_column
formatter = SQLStringFormatter(self)
if len(kwargs) > 0:
sqlQuery = formatter.format(sqlQuery, **kwargs)
try:
if isinstance(args, Dict):
litArgs = {k: _to_java_column(lit(v)) for k, v in (args or {}).items()}
elif args is None or isinstance(args, List):
assert self._jvm is not None
litArgs = self._jvm.PythonUtils.toArray(
[_to_java_column(lit(v)) for v in (args or [])]
)
else:
raise PySparkTypeError(
errorClass="INVALID_TYPE",
messageParameters={"arg_name": "args", "arg_type": type(args).__name__},
)
return DataFrame(self._jsparkSession.sql(sqlQuery, litArgs), self)
finally:
if len(kwargs) > 0:
formatter.clear()
[docs] def table(self, tableName: str) -> DataFrame:
"""Returns the specified table as a :class:`DataFrame`.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Parameters
----------
tableName : str
the table name to retrieve.
Returns
-------
:class:`DataFrame`
Notes
-----
In Spark Classic, a temporary view referenced in `spark.table` is resolved immediately,
while in Spark Connect it is lazily analyzed.
So in Spark Connect if a view is dropped, modified or replaced after `spark.table`, the
execution may fail or generate different results.
Examples
--------
>>> spark.range(5).createOrReplaceTempView("table1")
>>> spark.table("table1").sort("id").show()
+---+
| id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
+---+
"""
if not isinstance(tableName, str):
raise PySparkTypeError(
errorClass="NOT_STR",
messageParameters={"arg_name": "tableName", "arg_type": type(tableName).__name__},
)
return DataFrame(self._jsparkSession.table(tableName), self)
@property
def read(self) -> DataFrameReader:
"""
Returns a :class:`DataFrameReader` that can be used to read data
in as a :class:`DataFrame`.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Returns
-------
:class:`DataFrameReader`
Examples
--------
>>> spark.read
<...DataFrameReader object ...>
Write a DataFrame into a JSON file and read it back.
>>> import tempfile
>>> with tempfile.TemporaryDirectory(prefix="read") as d:
... # Write a DataFrame into a JSON file
... spark.createDataFrame(
... [{"age": 100, "name": "Hyukjin Kwon"}]
... ).write.mode("overwrite").format("json").save(d)
...
... # Read the JSON file as a DataFrame.
... spark.read.format('json').load(d).show()
+---+------------+
|age| name|
+---+------------+
|100|Hyukjin Kwon|
+---+------------+
"""
return DataFrameReader(self)
@property
def readStream(self) -> DataStreamReader:
"""
Returns a :class:`DataStreamReader` that can be used to read data streams
as a streaming :class:`DataFrame`.
.. versionadded:: 2.0.0
.. versionchanged:: 3.5.0
Supports Spark Connect.
Notes
-----
This API is evolving.
Returns
-------
:class:`DataStreamReader`
Examples
--------
>>> spark.readStream
<pyspark...DataStreamReader object ...>
The example below uses Rate source that generates rows continuously.
After that, we operate a modulo by 3, and then write the stream out to the console.
The streaming query stops in 3 seconds.
>>> import time
>>> df = spark.readStream.format("rate").load()
>>> df = df.selectExpr("value % 3 as v")
>>> q = df.writeStream.format("console").start()
>>> time.sleep(3)
>>> q.stop()
"""
return DataStreamReader(self)
@property
def streams(self) -> "StreamingQueryManager":
"""Returns a :class:`StreamingQueryManager` that allows managing all the
:class:`StreamingQuery` instances active on `this` context.
.. versionadded:: 2.0.0
.. versionchanged:: 3.5.0
Supports Spark Connect.
Notes
-----
This API is evolving.
Returns
-------
:class:`StreamingQueryManager`
Examples
--------
>>> spark.streams
<pyspark...StreamingQueryManager object ...>
Get the list of active streaming queries
>>> sq = spark.readStream.format(
... "rate").load().writeStream.format('memory').queryName('this_query').start()
>>> sqm = spark.streams
>>> [q.name for q in sqm.active]
['this_query']
>>> sq.stop()
"""
from pyspark.sql.streaming import StreamingQueryManager
if hasattr(self, "_sqm"):
return self._sqm
self._sqm: StreamingQueryManager = StreamingQueryManager(self._jsparkSession.streams())
return self._sqm
@property
def tvf(self) -> "TableValuedFunction":
"""
Returns a :class:`TableValuedFunction` that can be used to call a table-valued function
(TVF).
.. versionadded:: 4.0.0
Notes
-----
This API is evolving.
Returns
-------
:class:`TableValuedFunction`
Examples
--------
>>> spark.tvf
<pyspark...TableValuedFunction object ...>
>>> import pyspark.sql.functions as sf
>>> spark.tvf.explode(sf.array(sf.lit(1), sf.lit(2), sf.lit(3))).show()
+---+
|col|
+---+
| 1|
| 2|
| 3|
+---+
"""
from pyspark.sql.tvf import TableValuedFunction
return TableValuedFunction(self)
[docs] def stop(self) -> None:
"""
Stop the underlying :class:`SparkContext`.
.. versionadded:: 2.0.0
.. versionchanged:: 3.4.0
Supports Spark Connect.
Examples
--------
>>> spark.stop() # doctest: +SKIP
"""
from pyspark.sql.context import SQLContext
self._sc.stop()
# We should clean the default session up. See SPARK-23228.
assert self._jvm is not None
self._jvm.SparkSession.clearDefaultSession()
self._jvm.SparkSession.clearActiveSession()
SparkSession._instantiatedSession = None
SparkSession._activeSession = None
SQLContext._instantiatedContext = None
def __enter__(self) -> "SparkSession":
"""
Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax.
.. versionadded:: 2.0.0
Examples
--------
>>> with SparkSession.builder.master("local").getOrCreate() as session:
... session.range(5).show() # doctest: +SKIP
+---+
| id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
+---+
"""
return self
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc_val: Optional[BaseException],
exc_tb: Optional[TracebackType],
) -> None:
"""
Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax.
Specifically stop the SparkSession on exit of the with block.
.. versionadded:: 2.0.0
Examples
--------
>>> with SparkSession.builder.master("local").getOrCreate() as session:
... session.range(5).show() # doctest: +SKIP
+---+
| id|
+---+
| 0|
| 1|
| 2|
| 3|
| 4|
+---+
"""
self.stop()
# SparkConnect-specific API
@property
@remote_only
def client(self) -> "SparkConnectClient":
"""
Gives access to the Spark Connect client. In normal cases this is not necessary to be used
and only relevant for testing.
.. versionadded:: 3.4.0
Returns
-------
:class:`SparkConnectClient`
Notes
-----
This API is unstable, and a developer API. It returns non-API instance
:class:`SparkConnectClient`.
This is an API dedicated to Spark Connect client only. With regular Spark Session, it throws
an exception.
"""
raise PySparkRuntimeError(
errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
messageParameters={"feature": "SparkSession.client"},
)
[docs] @remote_only
def addArtifacts(
self, *path: str, pyfile: bool = False, archive: bool = False, file: bool = False
) -> None:
"""
Add artifact(s) to the client session. Currently only local files are supported.
.. versionadded:: 3.5.0
Parameters
----------
*path : tuple of str
Artifact's URIs to add.
pyfile : bool
Whether to add them as Python dependencies such as .py, .egg, .zip or .jar files.
The pyfiles are directly inserted into the path when executing Python functions
in executors.
archive : bool
Whether to add them as archives such as .zip, .jar, .tar.gz, .tgz, or .tar files.
The archives are unpacked on the executor side automatically.
file : bool
Add a file to be downloaded with this Spark job on every node.
The ``path`` passed can only be a local file for now.
Notes
-----
This is an API dedicated to Spark Connect client only. With regular Spark Session, it throws
an exception.
"""
raise PySparkRuntimeError(
errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
messageParameters={"feature": "SparkSession.addArtifact(s)"},
)
addArtifact = addArtifacts
[docs] @remote_only
def registerProgressHandler(self, handler: "ProgressHandler") -> None:
"""
Register a progress handler to be called when a progress update is received from the server.
.. versionadded:: 4.0
Parameters
----------
handler : ProgressHandler
A callable that follows the ProgressHandler interface. This handler will be called
on every progress update.
Examples
--------
>>> def progress_handler(stages, inflight_tasks, done):
... print(f"{len(stages)} Stages known, Done: {done}")
>>> spark.registerProgressHandler(progress_handler)
>>> res = spark.range(10).repartition(1).collect() # doctest: +SKIP
3 Stages known, Done: False
3 Stages known, Done: True
>>> spark.clearProgressHandlers()
"""
raise PySparkRuntimeError(
errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
messageParameters={"feature": "SparkSession.registerProgressHandler"},
)
[docs] @remote_only
def removeProgressHandler(self, handler: "ProgressHandler") -> None:
"""
Remove a progress handler that was previously registered.
.. versionadded:: 4.0
Parameters
----------
handler : ProgressHandler
The handler to remove if present in the list of progress handlers.
"""
raise PySparkRuntimeError(
errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
messageParameters={"feature": "SparkSession.removeProgressHandler"},
)
[docs] @remote_only
def clearProgressHandlers(self) -> None:
"""
Clear all registered progress handlers.
.. versionadded:: 4.0
"""
raise PySparkRuntimeError(
errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
messageParameters={"feature": "SparkSession.clearProgressHandlers"},
)
[docs] @remote_only
def copyFromLocalToFs(self, local_path: str, dest_path: str) -> None:
"""
Copy file from local to cloud storage file system.
If the file already exits in destination path, old file is overwritten.
.. versionadded:: 3.5.0
Parameters
----------
local_path: str
Path to a local file. Directories are not supported.
The path can be either an absolute path or a relative path.
dest_path: str
The cloud storage path to the destination the file will
be copied to.
The path must be an an absolute path.
Notes
-----
This API is a developer API.
Also, this is an API dedicated to Spark Connect client only. With regular
Spark Session, it throws an exception.
"""
raise PySparkRuntimeError(
errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
messageParameters={"feature": "SparkSession.copyFromLocalToFs"},
)
[docs] @remote_only
def interruptAll(self) -> List[str]:
"""
Interrupt all operations of this session currently running on the connected server.
.. versionadded:: 3.5.0
Returns
-------
list of str
List of operationIds of interrupted operations.
Notes
-----
There is still a possibility of operation finishing just as it is interrupted.
"""
raise PySparkRuntimeError(
errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
messageParameters={"feature": "SparkSession.interruptAll"},
)
[docs] @remote_only
def interruptTag(self, tag: str) -> List[str]:
"""
Interrupt all operations of this session with the given operation tag.
.. versionadded:: 3.5.0
Returns
-------
list of str
List of operationIds of interrupted operations.
Notes
-----
There is still a possibility of operation finishing just as it is interrupted.
"""
raise PySparkRuntimeError(
errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
messageParameters={"feature": "SparkSession.interruptTag"},
)
[docs] @remote_only
def interruptOperation(self, op_id: str) -> List[str]:
"""
Interrupt an operation of this session with the given operationId.
.. versionadded:: 3.5.0
Returns
-------
list of str
List of operationIds of interrupted operations.
Notes
-----
There is still a possibility of operation finishing just as it is interrupted.
"""
raise PySparkRuntimeError(
errorClass="ONLY_SUPPORTED_WITH_SPARK_CONNECT",
messageParameters={"feature": "SparkSession.interruptOperation"},
)
[docs] def addTag(self, tag: str) -> None:
"""
Add a tag to be assigned to all the operations started by this thread in this session.
Often, a unit of execution in an application consists of multiple Spark executions.
Application programmers can use this method to group all those jobs together and give a
group tag. The application can use :meth:`SparkSession.interruptTag` to cancel all running
executions with this tag.
There may be multiple tags present at the same time, so different parts of application may
use different tags to perform cancellation at different levels of granularity.
.. versionadded:: 3.5.0
.. versionchanged:: 4.0.0
Supports Spark Classic.
Parameters
----------
tag : str
The tag to be added. Cannot contain ',' (comma) character or be an empty string.
"""
self._jsparkSession.addTag(tag)
[docs] def removeTag(self, tag: str) -> None:
"""
Remove a tag previously added to be assigned to all the operations started by this thread in
this session. Noop if such a tag was not added earlier.
.. versionadded:: 3.5.0
.. versionchanged:: 4.0.0
Supports Spark Classic.
Parameters
----------
tag : list of str
The tag to be removed. Cannot contain ',' (comma) character or be an empty string.
"""
self._jsparkSession.removeTag(tag)
def _test() -> None:
import os
import doctest
import pyspark.sql.session
os.chdir(os.environ["SPARK_HOME"])
# Disable Doc Tests for Spark Connect only functions:
pyspark.sql.session.SparkSession.registerProgressHandler.__doc__ = None
pyspark.sql.session.SparkSession.removeProgressHandler.__doc__ = None
pyspark.sql.session.SparkSession.clearProgressHandlers.__doc__ = None
globs = pyspark.sql.session.__dict__.copy()
globs["spark"] = (
SparkSession.builder.master("local[4]").appName("sql.session tests").getOrCreate()
)
(failure_count, test_count) = doctest.testmod(
pyspark.sql.session,
globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
)
globs["spark"].stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()