在本地加载Spark数据不完整的HDFS URI

时间:2015-03-16 14:31:43

标签: scala sbt apache-spark

我在本地CSV文件中遇到SBT加载问题。基本上,我在Scala Eclipse中编写了一个Spark程序,它读取以下文件:

val searches = sc.textFile("hdfs:///data/searches")

这在hdfs上工作正常,但出于de-bug原因,我希望从本地目录加载此文件,我已将其设置为项目目录。

所以我厌倦了以下事情:

val searches = sc.textFile("file:///data/searches")
val searches = sc.textFile("./data/searches")
val searches = sc.textFile("/data/searches")

其中任何一个都不允许我从本地读取文件,并且所有这些都在SBT上返回此错误:

Exception in thread "main" java.io.IOException: Incomplete HDFS URI, no host: hdfs:/data/pages
at org.apache.hadoop.hdfs.DistributedFileSystem.initialize(DistributedFileSystem.java:143)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2397)
at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:89)
at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2431)
at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2413)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:368)
at org.apache.hadoop.fs.Path.getFileSystem(Path.java:296)
at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:256)
at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:228)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:304)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:179)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
at org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
at org.apache.spark.rdd.FlatMappedRDD.getPartitions(FlatMappedRDD.scala:30)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
at org.apache.spark.rdd.MappedRDD.getPartitions(MappedRDD.scala:28)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:204)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:202)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:202)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1135)
at org.apache.spark.rdd.RDD.count(RDD.scala:904)
at com.user.Result$.get(SparkData.scala:200)
at com.user.StreamingApp$.main(SprayHerokuExample.scala:35)
at com.user.StreamingApp.main(SprayHerokuExample.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.spark.deploy.SparkSubmit$.launch(SparkSubmit.scala:328)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:75)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

在错误报告中,com.user.Result $ .get(SparkData.scala:200)是调用sc.textFile的行。它似乎默认在Hadoop环境中运行。有什么办法可以在本地读取这个文件吗?

编辑:在本地时,我已将build.sb重新配置为:

submit <<= inputTask{(argTask:TaskKey[Seq[String]]) => {
(argTask,mainClass in Compile,assemblyOutputPath in assembly,sparkHome) map { 
(args,main,jar,sparkHome) => {
  args match {
    case List(output) => {
      val sparkCmd = sparkHome+"/bin/spark-submit"
      Process(
        sparkCmd :: "--class" :: main.get :: "--master" :: "local[4]" ::
        jar.getPath :: "local[4]" :: output :: Nil)!
    } 
    case _ => Process("echo" :: "Usage" :: Nil) !
  }
}

}}}

submit命令是我用来运行代码的。

找到解决方案:事实证明file:/// path /是正确的方法,但在我的情况下,完整的路径是有效的:即主页/项目/数据/搜索。虽然只是放数据/搜索没有(尽管在home / projects目录下工作)。

1 个答案:

答案 0 :(得分:3)

这应该有效:

sc.textFile("file:///data/searches")

从你的错误看起来火花正在加载Hadoop配置,当你有一个Hadoop配置文件或Hadoop环境变量集(如HADOOP_CONF_DIR)时,这可以确定