Apache Spark 如何使用sc.textFile(“s3 n：//bucket/*.csv”)将文件名Map到RDD？

8mmmxcuj 于 2022-12-23 发布在 Apache

关注(0)|答案(3)|浏览(199)

请注意，我必须使用sc.textFile，但我可以接受任何其他答案。
我想做的是简单地将正在处理的文件名添加到RDD中...类似于：
var rdd = sc.文本文件（"s3n：//bucket/*. csv"）.Map（行=〉文件名+"，"+行）
非常感谢!
EDIT2：EDIT1的解决方案是使用Hadoop 2.4或以上版本，但我没有使用slaves ......等进行过测试，但提到的一些解决方案只适用于小数据集，如果要使用大数据，则必须使用Hadoop RDD
编辑：我试过以下方法，但没有成功：

: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：它正在连接到S3，这不是问题（因为我已经测试了很多次）。
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)

apache-spark

来源：https://stackoverflow.com/questions/29521665/how-to-map-filenames-to-rdd-using-sc-textfiles3n-bucket-csv

3条答案

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bqf10yzr1#

唯一包含文件名的文本方法是wholeTextFiles。

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

赞(0）回复(0）举报 2022-12-23

bvjxkvbb2#

只需在Map过程中将其作为变量传入（或设置为对象属性）。

sc.textFile("s3n://bucket/"+fn+".csv").map(line=>set_filepath(line,fn))

赞(0）回复(0）举报 2022-12-23

iugsix8n3#

如果你正在处理大数据，那么Hadoop RDD就是答案，否则，再加上其他建议，它就不会起作用。
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) }
}

赞(0）回复(0）举报 2022-12-23

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Apache Spark 如何使用sc.textFile(“s3 n：//bucket/*.csv”)将文件名Map到RDD？

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