流程星火RDD流和存储单一HDFS文件

问题描述

1. 我使用的卡夫卡星火流获取流数据。

1. I am using Kafka Spark Streaming to get streaming data.

val lines = KafkaUtils.createDirectStream[Array[Byte], String, DefaultDecoder, StringDecoder](ssc, kafkaConf, Set(topic)).map(_._2)

我用这DSTREAM和处理RDDS

I am using this DStream and processing RDDs

val output = lines.foreachRDD(rdd =>
        rdd.foreachPartition { partition =>
            partition.foreach { file => runConfigParser(file)}
})

runConfigParser 是分析文件和产生一个输出，我在HDFS保存Java方法。因此，多个节点将处理RDD和输出写入到一个单一的HDFS文件。因为我想加载这个FIE在HIVE。

runConfigParser is a JAVA method which parses a file and produces an output which i have to save in HDFS. So multiple nodes will process RDD and write output into one single HDFS file. As i want to load this fie in HIVE.

我应该输出 runConfigParser 的结果，并使用 sc.parallze（输出）.saveAsTextFile（路径）这样的我所有的节点会写RDD输出到单个文件的HDFS？这是设计的效率？

should I output the result of runConfigParser and use sc.parallze(output).saveAsTextFile(path) such that all my nodes will write RDD outputs to single HDFS file.? Is this design efficient ?

我将使用帕拉加载HIVE此单HDFS文件（其将不断更新作为其流数据）和查询。

I will load this single HDFS file (which will be constantly updated as its streaming data) in HIVE and query using Impala.

推荐答案

您可以使用一个函数来合并 saveAsTextFile 的结果。像这样的：

You can use a function to "merge" the result of saveAsTextFile. Like this:

import org.apache.hadoop.fs._

def saveAsTextFileAndMerge[T](hdfsServer: String, fileName: String, rdd: RDD[T]) = {
    val sourceFile = hdfsServer + "/tmp/"
    rdd.saveAsTextFile(sourceFile)
    val dstPath = hdfsServer + "/final/"
    merge(sourceFile, dstPath, fileName)
  }

  def merge(srcPath: String, dstPath: String, fileName: String): Unit = {
    val hadoopConfig = new Configuration()
    val hdfs = FileSystem.get(hadoopConfig)
    val destinationPath = new Path(dstPath)
    if (!hdfs.exists(destinationPath)) {
      hdfs.mkdirs(destinationPath)
    }
    FileUtil.copyMerge(hdfs, new Path(srcPath), hdfs, new Path(dstPath + "/" + fileName), false, hadoopConfig, null)
  }

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