一. 官方参考链接

         Spark Streaming - Spark 3.2.1 Documentationhttp://spark.apache.org/docs/latest/streaming-programming-guide.html

二. Spark Streaming 核心抽象 DStream

• 简介

is an extension of the core Spark API

tenables scalable, high-throughput, fault-tolerant

stream processing of live data streams.

• 流

输入：  Kafka, Flume, Kinesis, or TCP sockets

//To do 业务逻辑处理

输出： filesystems, databases, and live dashboards

常用流处理操作框架

        Storm: 真正的实时流处理 Tuple

        Spark Streaming: 并不是真正实时流处理，而是mini batch操作（将数据流按时间间隔拆分成很小的批次）

        使用spark一栈式解决问题，批处理出发

        receives live input data streams

        divides the data into batches

        batches are then processed by the Spark engine to generate

        the final stream of results in batches

Flink: 流处理出发

Kafka Stream：

        DStreams can be created either from input data streams from sources such as Kafka, Flume, and Kinesis, or by applying high-level operations on other DStreams.

Internally, a DStream is represented as a sequence of RDDs.

三. 范例

from pyspark import SparkContext, SparkConf
from pyspark.streaming import StreamingContext,kafka

if __name__ == '__main__':
    # nc -lk 9999 做测试
    conf = SparkConf().setAppName('spark_streaming').setMaster('local[2]')
    sc = SparkContext(conf=conf)
    scc = StreamingContext(sc, 10)
    lines = scc.socketTextStream('localhost', 9999)
    words = lines.flatMap(lambda line: line.split(" ")).map(lambda word: (word, 1)).reduceByKey(lambda x, y: x+y)
    words.pprint()
    scc.start()
    scc.awaitTermination()
    # sc.stop()