class_docstr: Main entry point for Spark Streaming functionality. A StreamingContext
  represents the connection to a Spark cluster, and can be used to create
  :class:`DStream` various input sources. It can be from an existing :class:`SparkContext`.
  After creating and transforming DStreams, the streaming computation can
  be started and stopped using `context.start()` and `context.stop()`,
  respectively. `context.awaitTermination()` allows the current thread
  to wait for the termination of the context by `stop()` or by an exception.
  
  .. deprecated:: Spark 3.4.0
     This is deprecated as of Spark 3.4.0.
     There are no longer updates to DStream and it's a legacy project.
     There is a newer and easier to use streaming engine in Spark called Structured Streaming.
     You should use Spark Structured Streaming for your streaming applications.
  
  Parameters
  ----------
  sparkContext : :class:`SparkContext`
      SparkContext object.
  batchDuration : int, optional
      the time interval (in seconds) at which streaming
      data will be divided into batches
class_docstr_tok: ["Main","entry","point","for","Spark","Streaming","functionality",".","A","StreamingContext","represents","the","connection","to","a","Spark","cluster",",","and","can","be","used","to","create",":","class",":","`","DStream","`","various","input","sources",".","It","can","be","from","an","existing",":","class",":","`","SparkContext","`",".","After","creating","and","transforming","DStreams",",","the","streaming","computation","can","be","started","and","stopped","using","`","context.start","(",")","`","and","`","context.stop","(",")","`",",","respectively",".","`","context.awaitTermination","(",")","`","allows","the","current","thread","to","wait","for","the","termination","of","the","context","by","`","stop","(",")","`","or","by","an","exception",".","..","deprecated",":",":","Spark","3.4.0","This","is","deprecated","as","of","Spark","3.4.0",".","There","are","no","longer","updates","to","DStream","and","it","'s","a","legacy","project",".","There","is","a","newer","and","easier","to","use","streaming","engine","in","Spark","called","Structured","Streaming",".","You","should","use","Spark","Structured","Streaming","for","your","streaming","applications",".","Parameters","--","--","--","--","--","sparkContext",":",":","class",":","`","SparkContext","`","SparkContext","object",".","batchDuration",":","int",",","optional","the","time","interval","(","in","seconds",")","at","which","streaming","data","will","be","divided","into","batches"]
docstr: Sets the context to periodically checkpoint the DStream operations for master
  fault-tolerance. The graph will be checkpointed every batch interval.
  
  Parameters
  ----------
  directory : str
      HDFS-compatible directory where the checkpoint data will be reliably stored
docstr_tok: ["Sets","the","context","to","periodically","checkpoint","the","DStream","operations","for","master","fault-tolerance",".","The","graph","will","be","checkpointed","every","batch","interval",".","Parameters","--","--","--","--","--","directory",":","str","HDFS-compatible","directory","where","the","checkpoint","data","will","be","reliably","stored"]
code: def checkpoint(self, directory: str) -> None:
      """
      Sets the context to periodically checkpoint the DStream operations for master
      fault-tolerance. The graph will be checkpointed every batch interval.
  
      Parameters
      ----------
      directory : str
          HDFS-compatible directory where the checkpoint data will be reliably stored
      """
      self._jssc.checkpoint(directory)
  
imports: from typing import Any, Callable, List, Optional, TypeVar
  from py4j.java_gateway import java_import, is_instance_of, JavaObject
  from pyspark import RDD, SparkConf
  from pyspark.serializers import NoOpSerializer, UTF8Deserializer, CloudPickleSerializer
  from pyspark.context import SparkContext
  from pyspark.storagelevel import StorageLevel
  from pyspark.streaming.dstream import DStream
  from pyspark.streaming.listener import StreamingListener
  from pyspark.streaming.util import TransformFunction, TransformFunctionSerializer
  import warnings
label_desc: Use image node_id 14 for calling the StreamingContext obj's underlying member method code with example usage: obj.checkpoint(directory) without return types