Source code for pyspark.streaming.context

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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

__all__ = ["StreamingContext"]

T = TypeVar("T")

[docs]class StreamingContext: """ 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 """ _transformerSerializer = None # Reference to a currently active StreamingContext _activeContext = None def __init__( self, sparkContext: SparkContext, batchDuration: Optional[int] = None, jssc: Optional[JavaObject] = None, ): warnings.warn( "DStream is deprecated as of Spark 3.4.0. Migrate to Structured Streaming.", FutureWarning, ) self._sc = sparkContext self._jvm = self._sc._jvm self._jssc = jssc or self._initialize_context(self._sc, batchDuration) def _initialize_context(self, sc: SparkContext, duration: Optional[int]) -> JavaObject: self._ensure_initialized() assert self._jvm is not None and duration is not None return self._jvm.JavaStreamingContext(sc._jsc, self._jduration(duration)) def _jduration(self, seconds: int) -> JavaObject: """ Create Duration object given number of seconds """ assert self._jvm is not None return self._jvm.Duration(int(seconds * 1000)) @classmethod def _ensure_initialized(cls) -> None: SparkContext._ensure_initialized() gw = SparkContext._gateway assert gw is not None java_import(gw.jvm, "org.apache.spark.streaming.*") java_import(gw.jvm, "*") java_import(gw.jvm, "org.apache.spark.streaming.api.python.*") from pyspark.java_gateway import ensure_callback_server_started ensure_callback_server_started(gw) # register serializer for TransformFunction # it happens before creating SparkContext when loading from checkpointing cls._transformerSerializer = TransformFunctionSerializer( SparkContext._active_spark_context, CloudPickleSerializer(), gw, )
[docs] @classmethod def getOrCreate( cls, checkpointPath: str, setupFunc: Callable[[], "StreamingContext"] ) -> "StreamingContext": """ Either recreate a StreamingContext from checkpoint data or create a new StreamingContext. If checkpoint data exists in the provided `checkpointPath`, then StreamingContext will be recreated from the checkpoint data. If the data does not exist, then the provided setupFunc will be used to create a new context. Parameters ---------- checkpointPath : str Checkpoint directory used in an earlier streaming program setupFunc : function Function to create a new context and setup DStreams """ cls._ensure_initialized() gw = SparkContext._gateway assert gw is not None # Check whether valid checkpoint information exists in the given path ssc_option = gw.jvm.StreamingContextPythonHelper().tryRecoverFromCheckpoint(checkpointPath) if ssc_option.isEmpty(): ssc = setupFunc() ssc.checkpoint(checkpointPath) return ssc jssc = gw.jvm.JavaStreamingContext(ssc_option.get()) # If there is already an active instance of Python SparkContext use it, or create a new one if not SparkContext._active_spark_context: jsc = jssc.sparkContext() conf = SparkConf(_jconf=jsc.getConf()) SparkContext(conf=conf, gateway=gw, jsc=jsc) sc = SparkContext._active_spark_context assert sc is not None # update ctx in serializer assert cls._transformerSerializer is not None cls._transformerSerializer.ctx = sc return StreamingContext(sc, None, jssc)
[docs] @classmethod def getActive(cls) -> Optional["StreamingContext"]: """ Return either the currently active StreamingContext (i.e., if there is a context started but not stopped) or None. """ activePythonContext = cls._activeContext if activePythonContext is not None: # Verify that the current running Java StreamingContext is active and is the same one # backing the supposedly active Python context activePythonContextJavaId = activePythonContext._jssc.ssc().hashCode() activeJvmContextOption = activePythonContext._jvm.StreamingContext.getActive() if activeJvmContextOption.isEmpty(): cls._activeContext = None elif activeJvmContextOption.get().hashCode() != activePythonContextJavaId: cls._activeContext = None raise RuntimeError( "JVM's active JavaStreamingContext is not the JavaStreamingContext " "backing the action Python StreamingContext. This is unexpected." ) return cls._activeContext
[docs] @classmethod def getActiveOrCreate( cls, checkpointPath: str, setupFunc: Callable[[], "StreamingContext"] ) -> "StreamingContext": """ Either return the active StreamingContext (i.e. currently started but not stopped), or recreate a StreamingContext from checkpoint data or create a new StreamingContext using the provided setupFunc function. If the checkpointPath is None or does not contain valid checkpoint data, then setupFunc will be called to create a new context and setup DStreams. Parameters ---------- checkpointPath : str Checkpoint directory used in an earlier streaming program. Can be None if the intention is to always create a new context when there is no active context. setupFunc : function Function to create a new JavaStreamingContext and setup DStreams """ if not callable(setupFunc): raise TypeError("setupFunc should be callable.") activeContext = cls.getActive() if activeContext is not None: return activeContext elif checkpointPath is not None: return cls.getOrCreate(checkpointPath, setupFunc) else: return setupFunc()
@property def sparkContext(self) -> SparkContext: """ Return SparkContext which is associated with this StreamingContext. """ return self._sc
[docs] def start(self) -> None: """ Start the execution of the streams. """ self._jssc.start() StreamingContext._activeContext = self
[docs] def awaitTermination(self, timeout: Optional[int] = None) -> None: """ Wait for the execution to stop. Parameters ---------- timeout : int, optional time to wait in seconds """ if timeout is None: self._jssc.awaitTermination() else: self._jssc.awaitTerminationOrTimeout(int(timeout * 1000))
[docs] def awaitTerminationOrTimeout(self, timeout: int) -> None: """ Wait for the execution to stop. Return `true` if it's stopped; or throw the reported error during the execution; or `false` if the waiting time elapsed before returning from the method. Parameters ---------- timeout : int time to wait in seconds """ return self._jssc.awaitTerminationOrTimeout(int(timeout * 1000))
[docs] def stop(self, stopSparkContext: bool = True, stopGraceFully: bool = False) -> None: """ Stop the execution of the streams, with option of ensuring all received data has been processed. Parameters ---------- stopSparkContext : bool, optional Stop the associated SparkContext or not stopGracefully : bool, optional Stop gracefully by waiting for the processing of all received data to be completed """ self._jssc.stop(stopSparkContext, stopGraceFully) StreamingContext._activeContext = None if stopSparkContext: self._sc.stop()
[docs] def remember(self, duration: int) -> None: """ Set each DStreams in this context to remember RDDs it generated in the last given duration. DStreams remember RDDs only for a limited duration of time and releases them for garbage collection. This method allows the developer to specify how long to remember the RDDs (if the developer wishes to query old data outside the DStream computation). Parameters ---------- duration : int Minimum duration (in seconds) that each DStream should remember its RDDs """ self._jssc.remember(self._jduration(duration))
[docs] 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)
[docs] def socketTextStream( self, hostname: str, port: int, storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_2 ) -> "DStream[str]": """ Create an input from TCP source hostname:port. Data is received using a TCP socket and receive byte is interpreted as UTF8 encoded ``\\n`` delimited lines. Parameters ---------- hostname : str Hostname to connect to for receiving data port : int Port to connect to for receiving data storageLevel : :class:`pyspark.StorageLevel`, optional Storage level to use for storing the received objects """ jlevel = self._sc._getJavaStorageLevel(storageLevel) return DStream( self._jssc.socketTextStream(hostname, port, jlevel), self, UTF8Deserializer() )
[docs] def textFileStream(self, directory: str) -> "DStream[str]": """ Create an input stream that monitors a Hadoop-compatible file system for new files and reads them as text files. Files must be written to the monitored directory by "moving" them from another location within the same file system. File names starting with . are ignored. The text files must be encoded as UTF-8. """ return DStream(self._jssc.textFileStream(directory), self, UTF8Deserializer())
[docs] def binaryRecordsStream(self, directory: str, recordLength: int) -> "DStream[bytes]": """ Create an input stream that monitors a Hadoop-compatible file system for new files and reads them as flat binary files with records of fixed length. Files must be written to the monitored directory by "moving" them from another location within the same file system. File names starting with . are ignored. Parameters ---------- directory : str Directory to load data from recordLength : int Length of each record in bytes """ return DStream( self._jssc.binaryRecordsStream(directory, recordLength), self, NoOpSerializer() )
def _check_serializers(self, rdds: List[RDD[T]]) -> None: # make sure they have same serializer if len(set(rdd._jrdd_deserializer for rdd in rdds)) > 1: for i in range(len(rdds)): # reset them to sc.serializer rdds[i] = rdds[i]._reserialize()
[docs] def queueStream( self, rdds: List[RDD[T]], oneAtATime: bool = True, default: Optional[RDD[T]] = None, ) -> "DStream[T]": """ Create an input stream from a queue of RDDs or list. In each batch, it will process either one or all of the RDDs returned by the queue. Parameters ---------- rdds : list Queue of RDDs oneAtATime : bool, optional pick one rdd each time or pick all of them once. default : :class:`pyspark.RDD`, optional The default rdd if no more in rdds Notes ----- Changes to the queue after the stream is created will not be recognized. """ if default and not isinstance(default, RDD): default = self._sc.parallelize(default) # type: ignore[arg-type] if not rdds and default: rdds = [rdds] # type: ignore[list-item] if rdds and not isinstance(rdds[0], RDD): rdds = [self._sc.parallelize(input) for input in rdds] # type: ignore[arg-type] self._check_serializers(rdds) assert self._jvm is not None queue = self._jvm.PythonDStream.toRDDQueue([r._jrdd for r in rdds]) if default: default = default._reserialize(rdds[0]._jrdd_deserializer) assert default is not None jdstream = self._jssc.queueStream(queue, oneAtATime, default._jrdd) else: jdstream = self._jssc.queueStream(queue, oneAtATime) return DStream(jdstream, self, rdds[0]._jrdd_deserializer)
[docs] def transform( self, dstreams: List["DStream[Any]"], transformFunc: Callable[..., RDD[T]] ) -> "DStream[T]": """ Create a new DStream in which each RDD is generated by applying a function on RDDs of the DStreams. The order of the JavaRDDs in the transform function parameter will be the same as the order of corresponding DStreams in the list. """ jdstreams = [d._jdstream for d in dstreams] # change the final serializer to sc.serializer func = TransformFunction( self._sc, lambda t, *rdds: transformFunc(rdds), *[d._jrdd_deserializer for d in dstreams], ) assert self._jvm is not None jfunc = self._jvm.TransformFunction(func) jdstream = self._jssc.transform(jdstreams, jfunc) return DStream(jdstream, self, self._sc.serializer)
[docs] def union(self, *dstreams: "DStream[T]") -> "DStream[T]": """ Create a unified DStream from multiple DStreams of the same type and same slide duration. """ if not dstreams: raise ValueError("should have at least one DStream to union") if len(dstreams) == 1: return dstreams[0] if len(set(s._jrdd_deserializer for s in dstreams)) > 1: raise ValueError("All DStreams should have same serializer") if len(set(s._slideDuration for s in dstreams)) > 1: raise ValueError("All DStreams should have same slide duration") assert SparkContext._jvm is not None jdstream_cls = jpair_dstream_cls = gw = SparkContext._gateway if is_instance_of(gw, dstreams[0]._jdstream, jdstream_cls): cls = jdstream_cls elif is_instance_of(gw, dstreams[0]._jdstream, jpair_dstream_cls): cls = jpair_dstream_cls else: cls_name = dstreams[0]._jdstream.getClass().getCanonicalName() raise TypeError("Unsupported Java DStream class %s" % cls_name) assert gw is not None jdstreams = gw.new_array(cls, len(dstreams)) for i in range(0, len(dstreams)): jdstreams[i] = dstreams[i]._jdstream return DStream( self._jssc.union(jdstreams), self, dstreams[0]._jrdd_deserializer, )
[docs] def addStreamingListener(self, streamingListener: StreamingListener) -> None: """ Add a [[org.apache.spark.streaming.scheduler.StreamingListener]] object for receiving system events related to streaming. """ assert self._jvm is not None self._jssc.addStreamingListener( self._jvm.JavaStreamingListenerWrapper( self._jvm.PythonStreamingListenerWrapper(streamingListener) ) )