Error- Overloaded method value createDirectStream in error Spark Streaming打包报错

谁借莪１个温暖的怀抱￠ 2023-08-17 16:03 138阅读 0赞

直接上代码

StreamingExamples.setStreamingLogLevels()
        val Array(brokers, topics) = args
    
        // Create context with 2 second batch interval
        // 创建conf,spark streaming至少要启动两个线程，一个负责接受数据，一个负责处理数据
        val conf = new SparkConf().setMaster("local[4]").setAppName("NetworkWordCount")
    
        // 创建StreamingContext，每隔2秒产生一个批次
        val ssc = new StreamingContext(conf, Seconds(2));
    
        val topicsSet = topics.split(",").toSet
    
        // 配置Kafka参数
        val kafkaParams = Map[String, String]("metadata.broker.list" -> brokers)
    
        // 用直连方式读取Kafka数据，在Kafka中读取偏移量
        val messages = KafkaUtils.createDirectStream[String, String](
          ssc,
          LocationStrategies.PreferConsistent,// 位置策略（如果Kafka和spark程序在同一台机器，会从最优位置读取数据【当前位置】）
          ConsumerStrategies.Subscribe[String, String](topicsSet, kafkaParams))// 订阅策略（可以指定用正则的方式读取topic【topic-*】）
    
        //====================在下面写业务逻辑============================
        val lines = messages.map(_.value())
        val words = lines.flatMap(_.split(" "))
        val wordCounts = words.map(x=>(x, 1L)).reduceByKey(_+_)
        wordCounts.print()
        //====================在上面写业务逻辑============================
    
        ssc.start()
        ssc.awaitTermination()

打包报错

Error:(44, 49) overloaded method value createDirectStream with alternatives:
      (jssc: org.apache.spark.streaming.api.java.JavaStreamingContext,locationStrategy: org.apache.spark.streaming.kafka010.LocationStrategy,consumerStrategy: org.apache.spark.streaming.kafka010.ConsumerStrategy[String,String],perPartitionConfig: org.apache.spark.streaming.kafka010.PerPartitionConfig)org.apache.spark.streaming.api.java.JavaInputDStream[org.apache.kafka.clients.consumer.ConsumerRecord[String,String]] <and>
      (jssc: org.apache.spark.streaming.api.java.JavaStreamingContext,locationStrategy: org.apache.spark.streaming.kafka010.LocationStrategy,consumerStrategy: org.apache.spark.streaming.kafka010.ConsumerStrategy[String,String])org.apache.spark.streaming.api.java.JavaInputDStream[org.apache.kafka.clients.consumer.ConsumerRecord[String,String]] <and>
      (ssc: org.apache.spark.streaming.StreamingContext,locationStrategy: org.apache.spark.streaming.kafka010.LocationStrategy,consumerStrategy: org.apache.spark.streaming.kafka010.ConsumerStrategy[String,String],perPartitionConfig: org.apache.spark.streaming.kafka010.PerPartitionConfig)org.apache.spark.streaming.dstream.InputDStream[org.apache.kafka.clients.consumer.ConsumerRecord[String,String]] <and>
      (ssc: org.apache.spark.streaming.StreamingContext,locationStrategy: org.apache.spark.streaming.kafka010.LocationStrategy,consumerStrategy: org.apache.spark.streaming.kafka010.ConsumerStrategy[String,String])org.apache.spark.streaming.dstream.InputDStream[org.apache.kafka.clients.consumer.ConsumerRecord[String,String]]
     cannot be applied to (org.apache.spark.streaming.StreamingContext, org.apache.spark.streaming.kafka010.LocationStrategy, org.apache.spark.streaming.kafka010.ConsumerStrategy[Nothing,Nothing])
        val messages = KafkaUtils.createDirectStream[String, String](

这是一个很长的信息，说主题需要设置\[字符串\]，而不是设置\[字符\]。

我能看到解决这个问题的最佳方法是：

val topicsSet = topics.toString.split(",").toSet

但是，如果你真的只有一个主题，那么只需按照上面的Set(topics)将字符串拆分成一组单个字符。

转载于:https://www.cnblogs.com/RzCong/p/11400749.html