我在尝试将一个文件从hdfs读入spark时遇到错误。hdfs中存在readme.md文件
spark@osboxes hadoop]$ hdfs dfs -ls README.md
16/02/26 00:29:14 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
-rw-r--r-- 1 spark supergroup 4811 2016-02-25 23:38 README.md在Spark壳里，我给了
scala> val readme = sc.textFile("hdfs://localhost:9000/README.md")
readme: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[1] at textFile at <console>:27
scala> readme.count
16/02/26 00:25:26 DEBUG BlockManager: Getting local block broadcast_4
16/02/26 00:25:26 DEBUG BlockManager: Level for block broadcast_4 is StorageLevel(true, true, false, true, 1)
16/02/26 00:25:26 DEBUG BlockManager: Getting block broadcast_4 from memory
16/02/26 00:25:26 DEBUG HadoopRDD: Creating new JobConf and caching it for later re-use
16/02/26 00:25:26 DEBUG Client: The ping interval is 60000 ms.
16/02/26 00:25:26 DEBUG Client: Connecting to localhost/127.0.0.1:9000
16/02/26 00:25:26 DEBUG Client: IPC Client (648679508) connection to localhost/127.0.0.1:9000 from spark: starting, having connections 1
16/02/26 00:25:26 DEBUG Client: IPC Client (648679508) connection to localhost/127.0.0.1:9000 from spark sending #4
16/02/26 00:25:26 DEBUG Client: IPC Client (648679508) connection to localhost/127.0.0.1:9000 from spark got value #4
16/02/26 00:25:26 DEBUG ProtobufRpcEngine: Call: getFileInfo took 6ms
org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: hdfs://localhost:9000/README.md
at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:285)
at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:228)
at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:313)
at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1929)
at org.apache.spark.rdd.RDD.count(RDD.scala:1143)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:30)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:35)
at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:37)
at $iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:39)
at $iwC$$iwC$$iwC$$iwC.<init>(<console>:41)
at $iwC$$iwC$$iwC.<init>(<console>:43)
at $iwC$$iwC.<init>(<console>:45)
at $iwC.<init>(<console>:47)
at <init>(<console>:49)
at .<init>(<console>:53)
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:1346)
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:857)
at org.apache.spark.repl.SparkILoop.interpretStartingWith(SparkILoop.scala:902)
at org.apache.spark.repl.SparkILoop.command(SparkILoop.scala:814)
at org.apache.spark.repl.SparkILoop.processLine$1(SparkILoop.scala:657)
at org.apache.spark.repl.SparkILoop.innerLoop$1(SparkILoop.scala:665)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$loop(SparkILoop.scala:670)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply$mcZ$sp(SparkILoop.scala:997)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
at org.apache.spark.repl.SparkILoop$$anonfun$org$apache$spark$repl$SparkILoop$$process$1.apply(SparkILoop.scala:945)
at scala.tools.nsc.util.ScalaClassLoader$.savingContextLoader(ScalaClassLoader.scala:135)
at org.apache.spark.repl.SparkILoop.org$apache$spark$repl$SparkILoop$$process(SparkILoop.scala:945)
at org.apache.spark.repl.SparkILoop.process(SparkILoop.scala:1059)
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:731)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
scala> 16/02/26 00:25:36 DEBUG Client: IPC Client (648679508) connection to localhost/127.0.0.1:9000 from spark: closed
16/02/26 00:25:36 DEBUG Client: IPC Client (648679508) connection to localhost/127.0.0.1:9000 from spark: stopped, remaining connections 0在core-site.xml中，我有以下条目：
<configuration>
<property>
<name>fs.defaultFS</name>
<value>hdfs://localhost:9000</value>
</property>hdfs-site.xml包含以下详细信息：
<configuration>
<property>
<name>dfs.replication</name>
<value>1</value>
</property>我是不是漏了什么？我的操作系统是centos linux 7.2.1511版（核心），hadoop是2.7.2，spark是1.6.0-bin-hadoop2.6
5条答案
按热度按时间mgdq6dx11#
在我的例子中，readme.md文件位于我的主目录的spark文件夹（spark-2.4.3-bin-hadoop2.7）中。
这样，完整路径就是“/home/sdayneko/spark-2.4.3-bin-hadoop2.7/readme.md”
我将此路径放入输入变量：
在那之后，它成功了：）
vm0i2vca2#
这是由于目录之间的内部Map造成的。首先转到保存文件（readme.md）的目录。运行命令：
df -k .. 您将获得目录的实际装入点。例如：/xyz现在，尝试在这个挂载点中查找您的文件（readme.md）。例如：/xyz/home/omi/myDir/README.md在代码中使用此路径。val readme = sc.textfile("/xyz/home/omi/myDir/README.md");l5tcr1uw3#
你能试着把你的命令改成如下然后运行吗
b0zn9rqh4#
我面对过这个问题，发现如果表被破坏了，你就可以得到这个问题。
show partitions myschema.mytable;结果：partitionkey= partitionkey=xyz如果你在hdfs上为table folder做ls
ls -ltr hdfs://servername/data/fid/work/hive/myschema/mytable partitionkey=你将得到只是分区文件夹不匹配。在阅读Spark的时候。。。你会得到这个问题
org.apache.hadoop.mapred.invalidinputexception input path does not您必须执行drop partition或msck repair table来解决此问题。感谢和问候，kamleshkumar Gujaratihvvq6cgz5#
默认情况下，
hdfs dfs -ls将把你的用户主文件夹放在hdfs上，而不是hdfs的根目录。您可以通过比较hdfs dfs -ls以及hdfs dfs -ls /. 当您使用完整的hdfs url时，您使用的是一个绝对路径，它找不到您的文件（因为它位于您的用户主文件夹中）。当您使用相对路径时，问题就消失了：）你可能想知道
hdfs dfs -put还将使用hdfs主文件夹作为文件的默认目标，而不是hdfs的根目录。