class_docstr: The entry point for working with structured data (rows and columns) in Spark, in Spark 1.x.
  
  As of Spark 2.0, this is replaced by :class:`SparkSession`. However, we are keeping the class
  here for backward compatibility.
  
  A SQLContext can be used to create :class:`DataFrame`, register :class:`DataFrame` as
  tables, execute SQL over tables, cache tables, and read parquet files.
  
  .. deprecated:: 3.0.0
      Use :func:`SparkSession.builder.getOrCreate()` instead.
  
  Parameters
  ----------
  sparkContext : :class:`SparkContext`
      The :class:`SparkContext` backing this SQLContext.
  sparkSession : :class:`SparkSession`
      The :class:`SparkSession` around which this SQLContext wraps.
  jsqlContext : optional
      An optional JVM Scala SQLContext. If set, we do not instantiate a new
      SQLContext in the JVM, instead we make all calls to this object.
      This is only for internal.
  
  Examples
  --------
  >>> from datetime import datetime
  >>> from pyspark.sql import Row
  >>> sqlContext = SQLContext(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")
  >>> sqlContext.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_docstr_tok: ["The","entry","point","for","working","with","structured","data","(","rows","and","columns",")","in","Spark",",","in","Spark","1.x",".","As","of","Spark","2.0",",","this","is","replaced","by",":","class",":","`","SparkSession","`",".","However",",","we","are","keeping","the","class","here","for","backward","compatibility",".","A","SQLContext","can","be","used","to","create",":","class",":","`","DataFrame","`",",","register",":","class",":","`","DataFrame","`","as","tables",",","execute","SQL","over","tables",",","cache","tables",",","and","read","parquet","files",".","..","deprecated",":",":","3.0.0","Use",":","func",":","`","SparkSession.builder.getOrCreate","(",")","`","instead",".","Parameters","--","--","--","--","--","sparkContext",":",":","class",":","`","SparkContext","`","The",":","class",":","`","SparkContext","`","backing","this","SQLContext",".","sparkSession",":",":","class",":","`","SparkSession","`","The",":","class",":","`","SparkSession","`","around","which","this","SQLContext","wraps",".","jsqlContext",":","optional","An","optional","JVM","Scala","SQLContext",".","If","set",",","we","do","not","instantiate","a","new","SQLContext","in","the","JVM",",","instead","we","make","all","calls","to","this","object",".","This","is","only","for","internal",".","Examples","--","--","--","--",">",">",">","from","datetime","import","datetime",">",">",">","from","pyspark.sql","import","Row",">",">",">","sqlContext","=","SQLContext","(","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","''",")",">",">",">","sqlContext.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","]",")","]"]
docstr: Sets the given Spark SQL configuration property.
  
  .. versionadded:: 1.3.0
code: def setConf(self, key: str, value: Union[bool, int, str]) -> None:
      """Sets the given Spark SQL configuration property.
  
      .. versionadded:: 1.3.0
      """
      self.sparkSession.conf.set(key, value)
  
imports: import sys
  import warnings
  from typing import Optional, Union, Callable, Any, Iterable, List, Tuple, overload, Type, ClassVar, TYPE_CHECKING, cast
  from py4j.java_gateway import JavaObject
  from pyspark import _NoValue
  from pyspark._globals import _NoValueType
  from pyspark.sql.session import _monkey_patch_RDD, SparkSession
  from pyspark.sql.dataframe import DataFrame
  from pyspark.sql.readwriter import DataFrameReader
  from pyspark.sql.streaming import DataStreamReader
  from pyspark.sql.udf import UDFRegistration
  from pyspark.sql.udtf import UDTFRegistration
  from pyspark.errors.exceptions.captured import install_exception_handler
  from pyspark.context import SparkContext
  from pyspark.rdd import RDD
  from pyspark.sql.types import AtomicType, DataType, StructType
  from pyspark.sql.streaming import StreamingQueryManager
label_desc: Use image node_id 6 for calling the SQLContext obj's underlying member method code with example usage: obj.setConf(key, value) without return types