Source code for pyspark.sql.session

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# To disallow implicit relative import. Remove this once we drop Python 2.
from __future__ import absolute_import
from __future__ import print_function
import sys
import warnings
from functools import reduce
from threading import RLock

if sys.version >= '3':
    basestring = unicode = str
    xrange = range
    from itertools import imap as map

from pyspark import since
from pyspark.rdd import RDD, ignore_unicode_prefix
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 Row, DataType, StringType, StructType, \
    _make_type_verifier, _infer_schema, _has_nulltype, _merge_type, _create_converter, \
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)``

        :param schema: a :class:`pyspark.sql.types.StructType` or list of names of columns
        :param samplingRatio: the sample ratio of rows used for inferring
        :return: a DataFrame

        >>> rdd.toDF().collect()
        [Row(name=u'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 SparkSession, use the following builder pattern: >>> spark = SparkSession.builder \\ ... .master("local") \\ ... .appName("Word Count") \\ ... .config("spark.some.config.option", "some-value") \\ ... .getOrCreate() .. autoattribute:: builder :annotation: """
[docs] class Builder(object): """Builder for :class:`SparkSession`. """ _lock = RLock() _options = {} _sc = None
[docs] @since(2.0) 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. 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... :param key: a key name string for configuration property :param value: a value for configuration property :param conf: an instance of :class:`SparkConf` """ 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
[docs] @since(2.0) 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. :param master: a url for spark master """ return self.config("spark.master", master)
[docs] @since(2.0) 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. :param name: an application name """ return self.config("", name)
[docs] @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
[docs] @since(2.0) 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. 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 @ignore_unicode_prefix def __init__(self, sparkContext, jsparkSession=None): """Creates a new SparkSession. >>> from datetime import datetime >>> 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 + CAST(1 AS BIGINT))=2, (d + CAST(1 AS DOUBLE))=2.0, (NOT b)=False, list[1]=2, \ dict[s]=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)] >>> x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect() [(1, u'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])] """ 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._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 SparkSession as new session, that has separate SQLConf, registered temporary views and UDFs, but shared SparkContext and table cache. """ return self.__class__(self._sc, self._jsparkSession.newSession())
[docs] @classmethod @since(3.0) def getActiveSession(cls): """ Returns the active SparkSession for the current thread, returned by the builder. >>> s = SparkSession.getActiveSession() >>> l = [('Alice', 1)] >>> rdd = s.sparkContext.parallelize(l) >>> df = s.createDataFrame(rdd, ['name', 'age']) >>>"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. """ if not hasattr(self, "_conf"): self._conf = RuntimeConfig(self._jsparkSession.conf()) return self._conf @property @since(2.0) def catalog(self): """Interface through which the user may create, drop, alter or query underlying databases, tables, functions, etc. :return: :class:`Catalog` """ from pyspark.sql.catalog import Catalog if not hasattr(self, "_catalog"): self._catalog = Catalog(self) return self._catalog @property @since(2.0) def udf(self): """Returns a :class:`UDFRegistration` for UDF registration. :return: :class:`UDFRegistration` """ from pyspark.sql.udf import UDFRegistration return UDFRegistration(self)
[docs] @since(2.0) 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``. :param start: the start value :param end: the end value (exclusive) :param step: the incremental step (default: 1) :param numPartitions: the number of partitions of the DataFrame :return: :class:`DataFrame` >>> 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 or tuple. :param data: list of Row or tuple :param names: list of column names :return: :class:`pyspark.sql.types.StructType` """ if not data: raise ValueError("can not infer schema from empty dataset") first = data[0] if type(first) is dict: warnings.warn("inferring schema from dict is deprecated," "please use pyspark.sql.Row instead") 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 or tuple. :param rdd: an RDD of Row or tuple :param samplingRatio: sampling ratio, or no sampling (default) :return: :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 type(first) is dict: warnings.warn("Using RDD of dict to inferSchema is deprecated. " "Use pyspark.sql.Row instead") 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 = 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 = 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 = 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 SparkSession for a pyspark shell session. This is called from 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': 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] @since(2.0) @ignore_unicode_prefix 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``. :param data: an RDD of any kind of SQL data representation (e.g. row, tuple, int, boolean, etc.), :class:`list`, or :class:`pandas.DataFrame`. :param schema: 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 ``IntegerType``. :param samplingRatio: the sample ratio of rows used for inferring :param verifySchema: verify data types of every row against schema. :return: :class:`DataFrame` .. versionchanged:: 2.1 Added verifySchema. .. note:: Usage with spark.sql.execution.arrow.pyspark.enabled=True is experimental. .. note:: When Arrow optimization is enabled, strings inside Pandas DataFrame in Python 2 are converted into bytes as they are bytes in Python 2 whereas regular strings are left as strings. When using strings in Python 2, use unicode `u""` as Python standard practice. >>> l = [('Alice', 1)] >>> spark.createDataFrame(l).collect() [Row(_1=u'Alice', _2=1)] >>> spark.createDataFrame(l, ['name', 'age']).collect() [Row(name=u'Alice', age=1)] >>> d = [{'name': 'Alice', 'age': 1}] >>> spark.createDataFrame(d).collect() [Row(age=1, name=u'Alice')] >>> rdd = sc.parallelize(l) >>> spark.createDataFrame(rdd).collect() [Row(_1=u'Alice', _2=1)] >>> df = spark.createDataFrame(rdd, ['name', 'age']) >>> df.collect() [Row(name=u'Alice', age=1)] >>> from pyspark.sql import Row >>> Person = Row('name', 'age') >>> person = r: Person(*r)) >>> df2 = spark.createDataFrame(person) >>> df2.collect() [Row(name=u'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=u'Alice', age=1)] >>> spark.createDataFrame(df.toPandas()).collect() # doctest: +SKIP [Row(name=u'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=u'Alice', b=1)] >>> rdd = 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, basestring): 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(, 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] @ignore_unicode_prefix @since(2.0) def sql(self, sqlQuery): """Returns a :class:`DataFrame` representing the result of the given query. :return: :class:`DataFrame` >>> df.createOrReplaceTempView("table1") >>> df2 = spark.sql("SELECT field1 AS f1, field2 as f2 from table1") >>> df2.collect() [Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')] """ return DataFrame(self._jsparkSession.sql(sqlQuery), self._wrapped)
[docs] @since(2.0) def table(self, tableName): """Returns the specified table as a :class:`DataFrame`. :return: :class:`DataFrame` >>> df.createOrReplaceTempView("table1") >>> df2 = spark.table("table1") >>> sorted(df.collect()) == sorted(df2.collect()) True """ return DataFrame(self._jsparkSession.table(tableName), self._wrapped)
@property @since(2.0) def read(self): """ Returns a :class:`DataFrameReader` that can be used to read data in as a :class:`DataFrame`. :return: :class:`DataFrameReader` """ return DataFrameReader(self._wrapped) @property @since(2.0) def readStream(self): """ Returns a :class:`DataStreamReader` that can be used to read data streams as a streaming :class:`DataFrame`. .. note:: Evolving. :return: :class:`DataStreamReader` """ return DataStreamReader(self._wrapped) @property @since(2.0) def streams(self): """Returns a :class:`StreamingQueryManager` that allows managing all the :class:`StreamingQuery` instances active on `this` context. .. note:: Evolving. :return: :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`. """ 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
@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()