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: Returns the value of Spark SQL configuration property for the given key.
  
  If the key is not set and defaultValue is set, return
  defaultValue. If the key is not set and defaultValue is not set, return
  the system default value.
  
  .. versionadded:: 1.3.0
  
  Examples
  --------
  >>> sqlContext.getConf("spark.sql.shuffle.partitions")
  '200'
  >>> sqlContext.getConf("spark.sql.shuffle.partitions", "10")
  '10'
  >>> sqlContext.setConf("spark.sql.shuffle.partitions", "50")
  >>> sqlContext.getConf("spark.sql.shuffle.partitions", "10")
  '50'
code: def getConf(
      self,
      key: str,
      defaultValue: Union[Optional[str], _NoValueType] = _NoValue,
  ) -> Optional[str]:
      """Returns the value of Spark SQL configuration property for the given key.
  
      If the key is not set and defaultValue is set, return
      defaultValue. If the key is not set and defaultValue is not set, return
      the system default value.
  
      .. versionadded:: 1.3.0
  
      Examples
      --------
      >>> sqlContext.getConf("spark.sql.shuffle.partitions")
      '200'
      >>> sqlContext.getConf("spark.sql.shuffle.partitions", "10")
      '10'
      >>> sqlContext.setConf("spark.sql.shuffle.partitions", "50")
      >>> sqlContext.getConf("spark.sql.shuffle.partitions", "10")
      '50'
      """
      return self.sparkSession.conf.get(key, defaultValue)
  
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 7 for calling the SQLContext obj's underlying member method code with example usage: obj.getConf(key, defaultValue) and returns: self