在成对的RDDs上按密钥和组对rdd进行Spark streaming分组，并从每个组中选取最新的

from pyspark import SparkContext
from pyspark.streaming import StreamingContext

# 创建SparkContext和StreamingContext
sc = SparkContext("local[2]", "SparkStreamingExample")
ssc = StreamingContext(sc, 1)

# 创建输入DStream
inputDStream = ssc.socketTextStream("localhost", 9999)

# 转换输入DStream为RDDs
rdd1 = inputDStream.map(lambda line: line.split(" ")).map(lambda words: (words[0], words[1]))
rdd2 = inputDStream.map(lambda line: line.split(" ")).map(lambda words: (words[0], words[2]))

# 定义转换函数
def transformFunc(rdd1, rdd2):
    # 对成对的RDDs按密钥进行分组
    groupedRDD = rdd1.groupByKey().join(rdd2.groupByKey())

    # 从每个组中选取最新的值
    latestValuesRDD = groupedRDD.mapValues(lambda values: max(values))

    return latestValuesRDD

# 应用转换函数
transformedDStream = inputDStream.transformWith(transformFunc, rdd1, rdd2)

# 输出结果
transformedDStream.pprint()

# 启动StreamingContext
ssc.start()
ssc.awaitTermination()