Source code for pyspark.sql.session

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from __future__ import print_function
import sys
import warnings
from functools import reduce
from threading import RLock

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

from pyspark import since
from pyspark.rdd import RDD, ignore_unicode_prefix
from pyspark.sql.catalog import Catalog
from pyspark.sql.conf import RuntimeConfig
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.readwriter import DataFrameReader
from pyspark.sql.types import Row, DataType, StringType, StructType, _verify_type, \
    _infer_schema, _has_nulltype, _merge_type, _create_converter, _parse_datatype_string
from pyspark.sql.utils import install_exception_handler

__all__ = ["SparkSession"]

def _monkey_patch_RDD(sparkSession):
    def toDF(self, schema=None, sampleRatio=None):
        Converts current :class:`RDD` into a :class:`DataFrame`

        This is a shorthand for ``spark.createDataFrame(rdd, schema, sampleRatio)``

        :param schema: a 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(object): """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() """
[docs] class Builder(object): """Builder for :class:`SparkSession`. """ _lock = RLock() _options = {} @since(2.0)
[docs] 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] 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] def appName(self, name): """Sets a name for the application, which will be shown in the Spark web UI. :param name: an application name """ return self.config("", name)
[docs] 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")
[docs] 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. """ with self._lock: from pyspark.conf import SparkConf from pyspark.context import SparkContext from pyspark.sql.context import SQLContext sparkConf = SparkConf() for key, value in self._options.items(): sparkConf.set(key, value) sparkContext = SparkContext.getOrCreate(sparkConf) return SQLContext.getOrCreate(sparkContext).sparkSession
builder = Builder() _instantiatedContext = 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: jsparkSession = self._jvm.SparkSession( self._jsparkSession = jsparkSession self._jwrapped = self._jsparkSession.wrapped() self._wrapped = SQLContext(self._sc, self, self._jwrapped) _monkey_patch_RDD(self) install_exception_handler() if SparkSession._instantiatedContext is None: SparkSession._instantiatedContext = self @since(2.0)
[docs] 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())
@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. """ 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.context import UDFRegistration return UDFRegistration(self._wrapped) @since(2.0)
[docs] def range(self, start, end=None, step=1, numPartitions=None): """ Create a :class:`DataFrame` with single 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): """ Infer schema from list of Row or tuple. :param data: list of Row or tuple :return: 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, map(_infer_schema, data)) if _has_nulltype(schema): raise ValueError("Some of types cannot be determined after inferring") return schema def _inferSchema(self, rdd, samplingRatio=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: 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) if _has_nulltype(schema): for row in rdd.take(100)[1:]: schema = _merge_type(schema, _infer_schema(row)) 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 = 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) 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 an 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) if isinstance(schema, (list, tuple)): for i, name in enumerate(schema): struct.fields[i].name = name struct.names[i] = name schema = struct elif isinstance(schema, StructType): for row in data: _verify_type(row, schema) else: 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 @since(2.0) @ignore_unicode_prefix
[docs] def createDataFrame(self, data, schema=None, samplingRatio=None): """ 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 :class:`Row`, or :class:`namedtuple`, or :class:`dict`. When ``schema`` is :class:`DataType` or datatype string, it must match the real data, or exception will be thrown at runtime. If the given schema is not StructType, it will be wrapped into a 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.), or :class:`list`, or :class:`pandas.DataFrame`. :param schema: a :class:`DataType` or a datatype string or a list of column names, default is None. The data type string format equals to `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 ByteType. We can also use `int` as a short name for IntegerType. :param samplingRatio: the sample ratio of rows used for inferring :return: :class:`DataFrame` .. versionchanged:: 2.0 The schema parameter can be a DataType or a datatype string after 2.0. If it's not a StructType, it will be wrapped into a StructType and each record will also be wrapped into a tuple. >>> 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: ... """ if isinstance(data, DataFrame): raise TypeError("data is already a DataFrame") if isinstance(schema, basestring): schema = _parse_datatype_string(schema) try: import pandas has_pandas = True except Exception: has_pandas = False if has_pandas and isinstance(data, pandas.DataFrame): if schema is None: schema = [str(x) for x in data.columns] data = [r.tolist() for r in data.to_records(index=False)] if isinstance(schema, StructType): def prepare(obj): _verify_type(obj, schema) return obj elif isinstance(schema, DataType): datatype = schema def prepare(obj): _verify_type(obj, datatype) return (obj, ) schema = StructType().add("value", datatype) else: if isinstance(schema, list): schema = [x.encode('utf-8') if not isinstance(x, str) else x for x in schema] 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
@ignore_unicode_prefix @since(2.0)
[docs] 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] 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) @since(2.0)
[docs] def stop(self): """Stop the underlying :class:`SparkContext`. """ self._sc.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: exit(-1) if __name__ == "__main__": _test()