如何使用sc.textFile（“s3n：//bucket/*.csv”）将文件名映射到RDD？

时间：2015-04-08 17:42:39

标签： amazon-s3 mapping apache-spark filenames rdd

请注意，我必须使用sc.textFile，但我会接受任何其他答案。

我想要做的是简单地将正在处理的文件名添加到RDD ......有些事情如下：

var rdd = sc.textFile（＆＃34; s3n：//bucket/*.csv"）。map（line =＆gt; filename +＆＃34;，＆＃34; + line）

非常感谢！

EDIT2：EDIT1的解决方案是使用Hadoop 2.4或更高版本。但是，我没有通过使用从属设备等来测试它。但是，一些提到的解决方案仅适用于小型数据集。如果要使用大数据，则必须使用HadoopRDD

编辑：我尝试过以下操作，但没有用：

:cp symjar/aws-java-sdk-1.9.29.jar
:cp symjar/aws-java-sdk-flow-build-tools-1.9.29.jar

import com.amazonaws.services.s3.AmazonS3Client
import com.amazonaws.services.s3.model.{S3ObjectSummary, ObjectListing, GetObjectRequest}
import com.amazonaws.auth._


def awsAccessKeyId = "AKEY"
def awsSecretAccessKey = "SKEY"

val hadoopConf = sc.hadoopConfiguration;
hadoopConf.set("fs.s3n.impl", "org.apache.hadoop.fs.s3native.NativeS3FileSystem")
hadoopConf.set("fs.s3n.awsAccessKeyId", awsAccessKeyId)
hadoopConf.set("fs.s3n.awsSecretAccessKey", awsSecretAccessKey)

var rdd = sc.wholeTextFiles("s3n://bucket/dir/*.csv").map { case (filename, content) =>  filename }
rdd.count

注意：它连接到S3，这不是问题（因为我已多次测试过它）。

我得到的错误是：

INFO input.FileInputFormat: Total input paths to process : 4
java.io.FileNotFoundException: File does not exist: /RTLM-918/simple/t1-100.csv
    at org.apache.hadoop.hdfs.DistributedFileSystem.getFileStatus(DistributedFileSystem.java:517)
    at org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat$OneFileInfo.<init>(CombineFileInputFormat.java:489)
    at org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat.getMoreSplits(CombineFileInputFormat.java:280)
    at org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat.getSplits(CombineFileInputFormat.java:240)
    at org.apache.spark.rdd.WholeTextFileRDD.getPartitions(NewHadoopRDD.scala:267)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:217)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:32)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:219)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:217)
    at scala.Option.getOrElse(Option.scala:120)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:217)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1511)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:813)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:29)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:34)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:36)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:38)
    at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:40)
    at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:42)
    at $iwC$$iwC$$iwC$$iwC.<init>(<console>:44)
    at $iwC$$iwC$$iwC.<init>(<console>:46)
    at $iwC$$iwC.<init>(<console>:48)
    at $iwC.<init>(<console>:50)
    at <init>(<console>:52)
    at .<init>(<console>:56)
    at .<clinit>(<console>)
    at .<init>(<console>:7)
    at .<clinit>(<console>)
    at $print(<console>)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
    at org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1338)
    at org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
    at org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
    at org.apache.spark.repl.SparkILoop.reallyInterpret$1(SparkILoop.scala:856)
    at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:901)
    at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:813)
    at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:656)
    at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:664)
    at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:669)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:996)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:944)
    at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:944)
    at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
    at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:944)
    at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1058)
    at org.apache.spark.repl.Main$.main(Main.scala:31)
    at org.apache.spark.repl.Main.main(Main.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:110)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

3 个答案:

答案 0 :(得分：6)

包含文件名的唯一文本方法是wholeTextFiles。

sc.wholeTextFiles(path).map { case (filename, content) => ... }

答案 1 :(得分：1)

在地图中将其作为变量传递（或设置为对象属性）。

  

sc.textFile（ “S3N：//桶/” + FN + “的.csv”）.MAP（线=＆GT; set_filepath（线，FN））

答案 2 :(得分：0)

如果您正在处理大数据HadoopRDD就是答案。否则，有其他建议，它将无法正常工作。

代码：

val text = sc.hadoopFile("s3n://.../", classOf[TextInputFormat], classOf[LongWritable], classOf[Text])
// Cast to a HadoopRDD
val hadoopRdd = text.asInstanceOf[HadoopRDD[LongWritable, Text]]

val fileAndLine = hadoopRdd.mapPartitionsWithInputSplit { (inputSplit, iterator) ⇒
  val file = inputSplit.asInstanceOf[FileSplit]
  iterator.map { tpl ⇒ (file.getPath, tpl._2) }
}
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