如何使用 Spark 从目录中递归读取 Hadoop 文件? [英] How to recursively read Hadoop files from directory using Spark?

问题描述

在给定的目录中，我有许多不同的文件夹，每个文件夹中都有 Hadoop 文件(part_001 等).

Inside the given directory I have many different folders and inside each folder I have Hadoop files (part_001, etc.).

directory
   -> folder1
      -> part_001...
      -> part_002...
   -> folder2
      -> part_001...
   ...

给定目录，如何使用Scala递归读取该目录中所有文件夹的内容并将这些内容加载到Spark中的单个RDD中?

Given the directory, how can I recursively read the content of all folders inside this directory and load this content into a single RDD in Spark using Scala?

我发现了这个，但它不会递归进入子文件夹(我使用的是import org.apache.hadoop.mapreduce.lib.input):

I found this, but it does not recursively enters into sub-folders (I am using import org.apache.hadoop.mapreduce.lib.input):

  var job: Job = null
  try {
    job = Job.getInstance()
    FileInputFormat.setInputPaths(job, new Path("s3n://" + bucketNameData + "/" + directoryS3))
    FileInputFormat.setInputDirRecursive(job, true)
  } catch {
    case ioe: IOException => ioe.printStackTrace(); System.exit(1);
  }
  val sourceData = sc.newAPIHadoopRDD(job.getConfiguration(), classOf[TextInputFormat], classOf[LongWritable], classOf[Text]).values

我也发现了这个网页 使用 SequenceFile，但我还是不明白如何将它应用到我的案例中?

I also found this web-page that uses SequenceFile, but again I don't understand how to apply it to my case?

推荐答案

如果你使用的是 Spark，你可以使用通配符来做到这一点，如下所示:

If you are using Spark, you can do this using wilcards as follow:

scala>sc.textFile("path/*/*")

sc 是 SparkContext，如果您使用 spark-shell 是默认初始化的，或者如果您正在创建自己的程序，则必须自己实例化 SparkContext.

sc is the SparkContext which if you are using spark-shell is initialized by default or if you are creating your own program should will have to instance a SparkContext by yourself.

注意以下标志:

scala> sc.hadoopConfiguration.get("mapreduce.input.fileinputformat.input.dir.recursive") 
> res6: String = null

您应该将此标志设置为 true:

Yo should set this flag to true:

sc.hadoopConfiguration.set("mapreduce.input.fileinputformat.input.dir.recursive","true")

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