使用kafka的spark流时，无法迭代从转换数据流到列表检索到的密钥列表

下面是与KafkaSpark流的代码。在这里，我试图获得密钥批作为dstream，然后把它转换成一个列表。以便对其进行迭代，并将与每个键相关的数据放入以该键命名的hdfs文件夹中。
键基本上是-schema.table\u name

val ssc = new StreamingContext(sparkConf, Seconds(args{7}.toLong)) // configured to run for every 60 seconds
val warehouseLocation="Spark-warehouse"
val spark = SparkSession.builder.config(sparkConf).getOrCreate() 
import spark.implicits._

val kafkaParams = Map[String, Object](
  "bootstrap.servers" -> conf.getString("kafka.brokers"),
  "zookeeper.connect" -> conf.getString("kafka.zookeeper"),
  "group.id" -> conf.getString("kafka.consumergroups"),
  "auto.offset.reset" -> args { 1 },
  "enable.auto.commit" -> (conf.getString("kafka.autoCommit").toBoolean: java.lang.Boolean),
  "key.deserializer" -> classOf[StringDeserializer],
  "value.deserializer" -> classOf[StringDeserializer],
  "security.protocol" -> "SASL_PLAINTEXT",
  "session.timeout.ms" -> args { 2 },
  "max.poll.records" -> args { 3 },
  "request.timeout.ms" -> args { 4 },
  "fetch.max.wait.ms" -> args { 5 })

val messages = KafkaUtils.createDirectStream[String, String](
  ssc,
  LocationStrategies.PreferConsistent,
  ConsumerStrategies.
  Subscribe[String, String](topicsSet, kafkaParams))

正在提取密钥，但其类型为dstream[string]

val keys = messages.map(x=>(x.key()))

var final_list_of_keys = List[String]()

将其转换为一个列表并更新var final \u list \u of \u键

keys.foreachRDD( rdd => {

val  df_keys = spark.read.json(rdd).distinct().toDF().persist(StorageLevel.MEMORY_ONLY)
df_keys.show()
val comma_separated_keys= df_keys.distinct().collect().mkString("").replace("[","").replace("]",",")

final_list_of_keys= comma_separated_keys.split(",").toList

现在尝试遍历列表。

for ( i <- final_list_of_keys)
 {
  println(i)

val message1 = messages.filter(x =>  x.key().toString().equals(i)).map(x=>x.value()).persist(StorageLevel.MEMORY_ONLY) //.toString())

 message1.foreachRDD((rdd, batchTime) => {

 if (!rdd.isEmpty())
 {

   val df1 = spark.read.json(rdd).persist(StorageLevel.MEMORY_ONLY)  //.withColumn("pharmacy_location",lit(args{6}))

   val df2=df1.withColumn("message",struct( struct($"message.data.*",lit(args{6}).as("pharmacy_location")).alias("data"), struct($"message.headers.*").as("headers"))).persist(StorageLevel.MEMORY_ONLY)

   val df3= df2.drop("headers").drop("messageSchema").drop("messageSchemaId").persist(StorageLevel.MEMORY_ONLY)

   df3.coalesce(1).write.json(conf.getString("hdfs.streamoutpath1")+ PATH_SEPERATOR + i + PATH_SEPERATOR + args{6}+ PATH_SEPERATOR+ date_today.format(System.currentTimeMillis())
        + PATH_SEPERATOR + date_today_hour.format(System.currentTimeMillis()) + PATH_SEPERATOR + System.currentTimeMillis())

   df1.unpersist
   df2.unpersist()
   df3.unpersist()

 }

})

try
{
messages.foreachRDD { rdd =>
  val offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
  messages.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)                            // push it back 
}
}
catch
{
  case e: BlockMissingException => e.printStackTrace()
 case e: IOException => e.printStackTrace()
 case e:Throwable => e.printStackTrace()
}

}
 ssc.start()
 ssc.awaitTermination()

但是我得到了一个错误-在启动上下文之后添加新的输入、转换和输出操作是不受支持的
当我试图将for循环保持在keys.foreachrdd之外时，列表不会得到更新并且保持为空。
有人可以建议我如何实际重做这段代码有一个列表中的键，然后去他们把数据在正确的目录。
从我的研究中我看到了这篇文章-
类似的帖子，但无法从中获得任何解决方案
另外，当我使用map时，foreachrdd中的过滤器和foreachrdd中的另一个过滤器可能会导致问题。参考邮政编码相似的邮政编码

以下是问题代码-

val messages = KafkaUtils.createDirectStream[String, String](
  ssc,
  LocationStrategies.PreferConsistent,
  ConsumerStrategies.
  Subscribe[String, String](topicsSet, kafkaParams)).persist(StorageLevel.MEMORY_ONLY)

 messages.foreachRDD((rdd,batchTime) =>          ///foreachRDD means go over each rdd parallelly , it gives the rdd and we will put the batch time also
{ 
  val table_list=rdd.map(x => x.key()).distinct().collect()  ////kafka sends data in key value pairs,
                                                           ///here rdd means key and values(key is tablename) and first we need to get all the distinct keys(this batch had 5 tables)

 val rddList = table_list.map(x=>(x,(rdd.filter(y=>y.key().equals(x)))))
 ///here x means table name and we are filtering data in the rdd which is equalent to current_table_name
  ///Now this table_list will contains the key(table) and values corresponding to each key
rddList.foreach(tuple =>  //here foreach not in parallal, we want to go one by one , touple is nothing but collection of key and multiple
   {

   val tableName= tuple._1.toString()   //tuple._1 will be the table name
  val tableRdd= tuple._2.map(x=>(x.value())).persist(StorageLevel.MEMORY_ONLY) // .toDF()

  ///tuple._2  will be the complete key value pair,we are putting the value in the hdfs

//   val tableRdd= messages.filter(x => x.key().toString().equals(tableName)).map(x=>x.value()).persist(StorageLevel.MEMORY_ONLY)
   println(tableName)

/* Your logic */

使用kafka的spark流时，无法迭代从转换数据流到列表检索到的密钥列表

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