# # 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 types import TracebackType from typing import ( Any, ClassVar, Dict, Iterable, List, Optional, Tuple, Type, Union, cast, no_type_check, overload, TYPE_CHECKING, ) from py4j.java_gateway import JavaObject from pyspark import SparkConf, SparkContext, 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.sql_formatter import SQLStringFormatter from pyspark.sql.streaming import DataStreamReader from pyspark.sql.types import ( AtomicType, DataType, StructType, _make_type_verifier, _infer_schema, _has_nulltype, _merge_type, _create_converter, _parse_datatype_string, ) from pyspark.sql.utils import install_exception_handler, is_timestamp_ntz_preferred if TYPE_CHECKING: from pyspark.sql._typing import AtomicValue, RowLike from pyspark.sql.catalog import Catalog from pyspark.sql.pandas._typing import DataFrameLike as PandasDataFrameLike from pyspark.sql.streaming import StreamingQueryManager from pyspark.sql.udf import UDFRegistration __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.toDF().collect() [Row(name='Alice', age=1)] """ return sparkSession.createDataFrame(self, schema, sampleRatio) RDD.toDF = toDF # type: ignore[assignment] [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: """Builder for :class:`SparkSession`.""" _lock = RLock() _options: Dict[str, Any] = {} _sc: Optional[SparkContext] = None @overload def config(self, *, conf: SparkConf) -> "SparkSession.Builder": ... @overload def config(self, key: str, value: Any) -> "SparkSession.Builder": ... def config( self, key: Optional[str] = None, value: Optional[Any] = None, conf: Optional[SparkConf] = 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 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[cast(str, key)] = str(value) else: for (k, v) in conf.getAll(): self._options[k] = v return self 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 """ return self.config("spark.master", master) 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 Parameters ---------- name : str an application name """ return self.config("spark.app.name", name) @since(2.0) def enableHiveSupport(self) -> "SparkSession.Builder": """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 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 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: 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 builder = Builder() """A class attribute having a :class:`Builder` to construct :class:`SparkSession` instances.""" _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) 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() [docs] @since(2.0) 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. """ return self.__class__(self._sc, self._jsparkSession.newSession()) [docs] @classmethod def getActiveSession(cls) -> Optional["SparkSession"]: """ 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: 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 @property # type: ignore[misc] @since(2.0) def sparkContext(self) -> SparkContext: """Returns the underlying :class:`SparkContext`.""" return self._sc @property # type: ignore[misc] @since(2.0) def version(self) -> str: """The version of Spark on which this application is running.""" return self._jsparkSession.version() @property # type: ignore[misc] @since(2.0) 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. Returns ------- :class:`pyspark.sql.conf.RuntimeConfig` """ 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 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) -> "UDFRegistration": """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: 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 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) 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 ValueError("can not infer schema from empty dataset") infer_dict_as_struct = self._jconf.inferDictAsStruct() prefer_timestamp_ntz = is_timestamp_ntz_preferred() schema = reduce( _merge_type, (_infer_schema(row, names, infer_dict_as_struct, prefer_timestamp_ntz) for row in data), ) if _has_nulltype(schema): raise ValueError("Some of types cannot be determined after inferring") 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 not first: raise ValueError("The first row in RDD is empty, " "can not infer schema") infer_dict_as_struct = self._jconf.inferDictAsStruct() 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, prefer_timestamp_ntz=prefer_timestamp_ntz, ), ) 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, infer_dict_as_struct=infer_dict_as_struct, 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 TypeError("schema should be StructType or list or None, but got: %s" % schema) # 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 TypeError("schema should be StructType or list or None, but got: %s" % schema) # 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.conf import SparkConf from pyspark.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 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: "PandasDataFrameLike", schema: Union[StructType, str], verifySchema: bool = ..., ) -> DataFrame: ... [docs] def createDataFrame( # type: ignore[misc] self, data: Union[RDD[Any], Iterable[Any], "PandasDataFrameLike"], 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 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<>``. 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 assert self._jvm is not None self._jvm.SparkSession.setActiveSession(self._jsparkSession) if isinstance(data, DataFrame): raise TypeError("data is already a 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 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( # 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 isinstance(data, RDD): rdd, struct = self._createFromRDD(data.map(prepare), schema, samplingRatio) else: rdd, struct = self._createFromLocal(map(prepare, data), schema) 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, **kwargs: Any) -> DataFrame: """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. .. versionadded:: 2.0.0 Parameters ---------- sqlQuery : str SQL query string. 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` 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| +---+---+ """ formatter = SQLStringFormatter(self) if len(kwargs) > 0: sqlQuery = formatter.format(sqlQuery, **kwargs) try: return DataFrame(self._jsparkSession.sql(sqlQuery), 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 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) @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 Returns ------- :class:`DataFrameReader` """ 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 Notes ----- This API is evolving. Returns ------- :class:`DataStreamReader` """ 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 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) -> None: """Stop the underlying :class:`SparkContext`.""" 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 @since(2.0) def __enter__(self) -> "SparkSession": """ Enable 'with SparkSession.builder.(...).getOrCreate() as session: app' syntax. """ return self @since(2.0) 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. """ self.stop() def _test() -> None: 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()