1e1b7302f4
### What changes were proposed in this pull request? I propose that we change the example code documentation to call the proper function . For example, under the `foreachBatch` function, the example code was calling the `foreach()` function by mistake. ### Why are the changes needed? I suppose it could confuse some people, and it is a typo ### Does this PR introduce any user-facing change? No, there is no "meaningful" code being change, simply the documentation ### How was this patch tested? I made the change on a fork and it still worked Closes #26299 from mstill3/patch-1. Authored-by: Matt Stillwell <18670089+mstill3@users.noreply.github.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
490 lines
18 KiB
Python
490 lines
18 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from __future__ import print_function
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import sys
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import warnings
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if sys.version >= '3':
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basestring = unicode = str
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from pyspark import since, _NoValue
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from pyspark.rdd import ignore_unicode_prefix
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from pyspark.sql.session import _monkey_patch_RDD, SparkSession
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from pyspark.sql.dataframe import DataFrame
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from pyspark.sql.readwriter import DataFrameReader
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from pyspark.sql.streaming import DataStreamReader
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from pyspark.sql.types import IntegerType, Row, StringType
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from pyspark.sql.udf import UDFRegistration
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from pyspark.sql.utils import install_exception_handler
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__all__ = ["SQLContext"]
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class SQLContext(object):
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"""The entry point for working with structured data (rows and columns) in Spark, in Spark 1.x.
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As of Spark 2.0, this is replaced by :class:`SparkSession`. However, we are keeping the class
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here for backward compatibility.
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A SQLContext can be used create :class:`DataFrame`, register :class:`DataFrame` as
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tables, execute SQL over tables, cache tables, and read parquet files.
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:param sparkContext: The :class:`SparkContext` backing this SQLContext.
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:param sparkSession: The :class:`SparkSession` around which this SQLContext wraps.
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:param jsqlContext: An optional JVM Scala SQLContext. If set, we do not instantiate a new
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SQLContext in the JVM, instead we make all calls to this object.
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"""
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_instantiatedContext = None
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@ignore_unicode_prefix
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def __init__(self, sparkContext, sparkSession=None, jsqlContext=None):
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"""Creates a new SQLContext.
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>>> from datetime import datetime
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>>> sqlContext = SQLContext(sc)
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>>> allTypes = sc.parallelize([Row(i=1, s="string", d=1.0, l=1,
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... b=True, list=[1, 2, 3], dict={"s": 0}, row=Row(a=1),
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... time=datetime(2014, 8, 1, 14, 1, 5))])
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>>> df = allTypes.toDF()
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>>> df.createOrReplaceTempView("allTypes")
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>>> sqlContext.sql('select i+1, d+1, not b, list[1], dict["s"], time, row.a '
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... 'from allTypes where b and i > 0').collect()
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[Row((i + CAST(1 AS BIGINT))=2, (d + CAST(1 AS DOUBLE))=2.0, (NOT b)=False, list[1]=2, \
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dict[s]=0, time=datetime.datetime(2014, 8, 1, 14, 1, 5), a=1)]
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>>> df.rdd.map(lambda x: (x.i, x.s, x.d, x.l, x.b, x.time, x.row.a, x.list)).collect()
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[(1, u'string', 1.0, 1, True, datetime.datetime(2014, 8, 1, 14, 1, 5), 1, [1, 2, 3])]
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"""
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self._sc = sparkContext
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self._jsc = self._sc._jsc
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self._jvm = self._sc._jvm
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if sparkSession is None:
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sparkSession = SparkSession.builder.getOrCreate()
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if jsqlContext is None:
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jsqlContext = sparkSession._jwrapped
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self.sparkSession = sparkSession
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self._jsqlContext = jsqlContext
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_monkey_patch_RDD(self.sparkSession)
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install_exception_handler()
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if SQLContext._instantiatedContext is None:
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SQLContext._instantiatedContext = self
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@property
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def _ssql_ctx(self):
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"""Accessor for the JVM Spark SQL context.
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Subclasses can override this property to provide their own
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JVM Contexts.
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"""
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return self._jsqlContext
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@property
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def _conf(self):
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"""Accessor for the JVM SQL-specific configurations"""
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return self.sparkSession._jsparkSession.sessionState().conf()
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@classmethod
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@since(1.6)
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def getOrCreate(cls, sc):
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"""
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Get the existing SQLContext or create a new one with given SparkContext.
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:param sc: SparkContext
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"""
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if cls._instantiatedContext is None:
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jsqlContext = sc._jvm.SQLContext.getOrCreate(sc._jsc.sc())
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sparkSession = SparkSession(sc, jsqlContext.sparkSession())
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cls(sc, sparkSession, jsqlContext)
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return cls._instantiatedContext
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@since(1.6)
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def newSession(self):
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"""
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Returns a new SQLContext as new session, that has separate SQLConf,
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registered temporary views and UDFs, but shared SparkContext and
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table cache.
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"""
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return self.__class__(self._sc, self.sparkSession.newSession())
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@since(1.3)
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def setConf(self, key, value):
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"""Sets the given Spark SQL configuration property.
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"""
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self.sparkSession.conf.set(key, value)
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@ignore_unicode_prefix
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@since(1.3)
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def getConf(self, key, defaultValue=_NoValue):
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"""Returns the value of Spark SQL configuration property for the given key.
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If the key is not set and defaultValue is set, return
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defaultValue. If the key is not set and defaultValue is not set, return
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the system default value.
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>>> sqlContext.getConf("spark.sql.shuffle.partitions")
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u'200'
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>>> sqlContext.getConf("spark.sql.shuffle.partitions", u"10")
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u'10'
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>>> sqlContext.setConf("spark.sql.shuffle.partitions", u"50")
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>>> sqlContext.getConf("spark.sql.shuffle.partitions", u"10")
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u'50'
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"""
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return self.sparkSession.conf.get(key, defaultValue)
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@property
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@since("1.3.1")
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def udf(self):
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"""Returns a :class:`UDFRegistration` for UDF registration.
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:return: :class:`UDFRegistration`
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"""
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return self.sparkSession.udf
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@since(1.4)
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def range(self, start, end=None, step=1, numPartitions=None):
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"""
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Create a :class:`DataFrame` with single :class:`pyspark.sql.types.LongType` column named
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``id``, containing elements in a range from ``start`` to ``end`` (exclusive) with
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step value ``step``.
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:param start: the start value
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:param end: the end value (exclusive)
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:param step: the incremental step (default: 1)
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:param numPartitions: the number of partitions of the DataFrame
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:return: :class:`DataFrame`
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>>> sqlContext.range(1, 7, 2).collect()
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[Row(id=1), Row(id=3), Row(id=5)]
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If only one argument is specified, it will be used as the end value.
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>>> sqlContext.range(3).collect()
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[Row(id=0), Row(id=1), Row(id=2)]
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"""
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return self.sparkSession.range(start, end, step, numPartitions)
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@since(1.2)
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def registerFunction(self, name, f, returnType=None):
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"""An alias for :func:`spark.udf.register`.
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See :meth:`pyspark.sql.UDFRegistration.register`.
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.. note:: Deprecated in 2.3.0. Use :func:`spark.udf.register` instead.
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"""
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warnings.warn(
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"Deprecated in 2.3.0. Use spark.udf.register instead.",
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DeprecationWarning)
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return self.sparkSession.udf.register(name, f, returnType)
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@since(2.1)
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def registerJavaFunction(self, name, javaClassName, returnType=None):
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"""An alias for :func:`spark.udf.registerJavaFunction`.
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See :meth:`pyspark.sql.UDFRegistration.registerJavaFunction`.
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.. note:: Deprecated in 2.3.0. Use :func:`spark.udf.registerJavaFunction` instead.
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"""
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warnings.warn(
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"Deprecated in 2.3.0. Use spark.udf.registerJavaFunction instead.",
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DeprecationWarning)
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return self.sparkSession.udf.registerJavaFunction(name, javaClassName, returnType)
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# TODO(andrew): delete this once we refactor things to take in SparkSession
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def _inferSchema(self, rdd, samplingRatio=None):
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"""
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Infer schema from an RDD of Row or tuple.
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:param rdd: an RDD of Row or tuple
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:param samplingRatio: sampling ratio, or no sampling (default)
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:return: :class:`pyspark.sql.types.StructType`
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"""
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return self.sparkSession._inferSchema(rdd, samplingRatio)
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@since(1.3)
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@ignore_unicode_prefix
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def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=True):
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"""
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Creates a :class:`DataFrame` from an :class:`RDD`, a list or a :class:`pandas.DataFrame`.
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When ``schema`` is a list of column names, the type of each column
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will be inferred from ``data``.
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When ``schema`` is ``None``, it will try to infer the schema (column names and types)
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from ``data``, which should be an RDD of :class:`Row`,
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or :class:`namedtuple`, or :class:`dict`.
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When ``schema`` is :class:`pyspark.sql.types.DataType` or a datatype string it must match
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the real data, or an exception will be thrown at runtime. If the given schema is not
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:class:`pyspark.sql.types.StructType`, it will be wrapped into a
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:class:`pyspark.sql.types.StructType` as its only field, and the field name will be "value",
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each record will also be wrapped into a tuple, which can be converted to row later.
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If schema inference is needed, ``samplingRatio`` is used to determined the ratio of
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rows used for schema inference. The first row will be used if ``samplingRatio`` is ``None``.
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:param data: an RDD of any kind of SQL data representation(e.g. :class:`Row`,
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:class:`tuple`, ``int``, ``boolean``, etc.), or :class:`list`, or
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:class:`pandas.DataFrame`.
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:param schema: a :class:`pyspark.sql.types.DataType` or a datatype string or a list of
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column names, default is None. The data type string format equals to
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:class:`pyspark.sql.types.DataType.simpleString`, except that top level struct type can
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omit the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use
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``byte`` instead of ``tinyint`` for :class:`pyspark.sql.types.ByteType`.
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We can also use ``int`` as a short name for :class:`pyspark.sql.types.IntegerType`.
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:param samplingRatio: the sample ratio of rows used for inferring
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:param verifySchema: verify data types of every row against schema.
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:return: :class:`DataFrame`
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.. versionchanged:: 2.0
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The ``schema`` parameter can be a :class:`pyspark.sql.types.DataType` or a
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datatype string after 2.0.
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If it's not a :class:`pyspark.sql.types.StructType`, it will be wrapped into a
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:class:`pyspark.sql.types.StructType` and each record will also be wrapped into a tuple.
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.. versionchanged:: 2.1
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Added verifySchema.
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>>> l = [('Alice', 1)]
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>>> sqlContext.createDataFrame(l).collect()
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[Row(_1=u'Alice', _2=1)]
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>>> sqlContext.createDataFrame(l, ['name', 'age']).collect()
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[Row(name=u'Alice', age=1)]
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>>> d = [{'name': 'Alice', 'age': 1}]
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>>> sqlContext.createDataFrame(d).collect()
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[Row(age=1, name=u'Alice')]
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>>> rdd = sc.parallelize(l)
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>>> sqlContext.createDataFrame(rdd).collect()
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[Row(_1=u'Alice', _2=1)]
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>>> df = sqlContext.createDataFrame(rdd, ['name', 'age'])
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>>> df.collect()
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[Row(name=u'Alice', age=1)]
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>>> from pyspark.sql import Row
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>>> Person = Row('name', 'age')
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>>> person = rdd.map(lambda r: Person(*r))
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>>> df2 = sqlContext.createDataFrame(person)
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>>> df2.collect()
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[Row(name=u'Alice', age=1)]
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>>> from pyspark.sql.types import *
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>>> schema = StructType([
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... StructField("name", StringType(), True),
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... StructField("age", IntegerType(), True)])
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>>> df3 = sqlContext.createDataFrame(rdd, schema)
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>>> df3.collect()
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[Row(name=u'Alice', age=1)]
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>>> sqlContext.createDataFrame(df.toPandas()).collect() # doctest: +SKIP
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[Row(name=u'Alice', age=1)]
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>>> sqlContext.createDataFrame(pandas.DataFrame([[1, 2]])).collect() # doctest: +SKIP
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[Row(0=1, 1=2)]
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>>> sqlContext.createDataFrame(rdd, "a: string, b: int").collect()
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[Row(a=u'Alice', b=1)]
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>>> rdd = rdd.map(lambda row: row[1])
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>>> sqlContext.createDataFrame(rdd, "int").collect()
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[Row(value=1)]
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>>> sqlContext.createDataFrame(rdd, "boolean").collect() # doctest: +IGNORE_EXCEPTION_DETAIL
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Traceback (most recent call last):
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...
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Py4JJavaError: ...
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"""
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return self.sparkSession.createDataFrame(data, schema, samplingRatio, verifySchema)
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@since(1.3)
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def registerDataFrameAsTable(self, df, tableName):
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"""Registers the given :class:`DataFrame` as a temporary table in the catalog.
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Temporary tables exist only during the lifetime of this instance of :class:`SQLContext`.
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>>> sqlContext.registerDataFrameAsTable(df, "table1")
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"""
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df.createOrReplaceTempView(tableName)
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@since(1.6)
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def dropTempTable(self, tableName):
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""" Remove the temporary table from catalog.
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>>> sqlContext.registerDataFrameAsTable(df, "table1")
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>>> sqlContext.dropTempTable("table1")
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"""
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self.sparkSession.catalog.dropTempView(tableName)
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@ignore_unicode_prefix
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@since(1.0)
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def sql(self, sqlQuery):
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"""Returns a :class:`DataFrame` representing the result of the given query.
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:return: :class:`DataFrame`
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>>> sqlContext.registerDataFrameAsTable(df, "table1")
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>>> df2 = sqlContext.sql("SELECT field1 AS f1, field2 as f2 from table1")
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>>> df2.collect()
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[Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')]
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"""
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return self.sparkSession.sql(sqlQuery)
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@since(1.0)
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def table(self, tableName):
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"""Returns the specified table or view as a :class:`DataFrame`.
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:return: :class:`DataFrame`
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>>> sqlContext.registerDataFrameAsTable(df, "table1")
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>>> df2 = sqlContext.table("table1")
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>>> sorted(df.collect()) == sorted(df2.collect())
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True
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"""
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return self.sparkSession.table(tableName)
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@ignore_unicode_prefix
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@since(1.3)
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def tables(self, dbName=None):
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"""Returns a :class:`DataFrame` containing names of tables in the given database.
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If ``dbName`` is not specified, the current database will be used.
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The returned DataFrame has two columns: ``tableName`` and ``isTemporary``
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(a column with :class:`BooleanType` indicating if a table is a temporary one or not).
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:param dbName: string, name of the database to use.
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:return: :class:`DataFrame`
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>>> sqlContext.registerDataFrameAsTable(df, "table1")
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>>> df2 = sqlContext.tables()
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>>> df2.filter("tableName = 'table1'").first()
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Row(database=u'', tableName=u'table1', isTemporary=True)
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"""
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if dbName is None:
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return DataFrame(self._ssql_ctx.tables(), self)
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else:
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return DataFrame(self._ssql_ctx.tables(dbName), self)
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@since(1.3)
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def tableNames(self, dbName=None):
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"""Returns a list of names of tables in the database ``dbName``.
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:param dbName: string, name of the database to use. Default to the current database.
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:return: list of table names, in string
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>>> sqlContext.registerDataFrameAsTable(df, "table1")
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>>> "table1" in sqlContext.tableNames()
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True
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>>> "table1" in sqlContext.tableNames("default")
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True
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"""
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if dbName is None:
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return [name for name in self._ssql_ctx.tableNames()]
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else:
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return [name for name in self._ssql_ctx.tableNames(dbName)]
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@since(1.0)
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def cacheTable(self, tableName):
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"""Caches the specified table in-memory."""
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self._ssql_ctx.cacheTable(tableName)
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@since(1.0)
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def uncacheTable(self, tableName):
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"""Removes the specified table from the in-memory cache."""
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self._ssql_ctx.uncacheTable(tableName)
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@since(1.3)
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def clearCache(self):
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"""Removes all cached tables from the in-memory cache. """
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self._ssql_ctx.clearCache()
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@property
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@since(1.4)
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def read(self):
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"""
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Returns a :class:`DataFrameReader` that can be used to read data
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in as a :class:`DataFrame`.
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:return: :class:`DataFrameReader`
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"""
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return DataFrameReader(self)
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@property
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@since(2.0)
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def readStream(self):
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"""
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Returns a :class:`DataStreamReader` that can be used to read data streams
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as a streaming :class:`DataFrame`.
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.. note:: Evolving.
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:return: :class:`DataStreamReader`
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>>> text_sdf = sqlContext.readStream.text(tempfile.mkdtemp())
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>>> text_sdf.isStreaming
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True
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"""
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return DataStreamReader(self)
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@property
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@since(2.0)
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def streams(self):
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"""Returns a :class:`StreamingQueryManager` that allows managing all the
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:class:`StreamingQuery` StreamingQueries active on `this` context.
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.. note:: Evolving.
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"""
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from pyspark.sql.streaming import StreamingQueryManager
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return StreamingQueryManager(self._ssql_ctx.streams())
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def _test():
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import os
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import doctest
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import tempfile
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from pyspark.context import SparkContext
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from pyspark.sql import Row, SQLContext
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import pyspark.sql.context
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os.chdir(os.environ["SPARK_HOME"])
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|
|
globs = pyspark.sql.context.__dict__.copy()
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sc = SparkContext('local[4]', 'PythonTest')
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|
globs['tempfile'] = tempfile
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|
globs['os'] = os
|
|
globs['sc'] = sc
|
|
globs['sqlContext'] = SQLContext(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()
|
|
jsonStrings = [
|
|
'{"field1": 1, "field2": "row1", "field3":{"field4":11}}',
|
|
'{"field1" : 2, "field3":{"field4":22, "field5": [10, 11]},"field6":[{"field7": "row2"}]}',
|
|
'{"field1" : null, "field2": "row3", "field3":{"field4":33, "field5": []}}'
|
|
]
|
|
globs['jsonStrings'] = jsonStrings
|
|
globs['json'] = sc.parallelize(jsonStrings)
|
|
(failure_count, test_count) = doctest.testmod(
|
|
pyspark.sql.context, globs=globs,
|
|
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
|
|
globs['sc'].stop()
|
|
if failure_count:
|
|
sys.exit(-1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|