Spark写入hdfs不能使用saveAsNewAPIHadoopFile方法

我在CDH 5.2.0上使用Spark 1.1.0，并试图确保我可以读取和写入hdfs。Spark写入hdfs不能使用saveAsNewAPIHadoopFile方法

我很快意识到.textFile和.saveAsTextFile调用旧的API，似乎与我们的hdfs版本不兼容。

def testHDFSReadOld(sc: SparkContext, readFile: String){ 
    //THIS WILL FAIL WITH 
    //(TID 0, dl1rhd416.internal.edmunds.com): java.lang.IllegalStateException: unread block data 
    //java.io.ObjectInputStream$BlockDataInputStream.setBlockDataMode(ObjectInputStream.java:2420) 

    sc.textFile(readFile).take(2).foreach(println) 
    } 

    def testHDFSWriteOld(sc: SparkContext, writeFile: String){ 
    //THIS WILL FAIL WITH 
    //(TID 0, dl1rhd416.internal.edmunds.com): java.lang.IllegalStateException: unread block data 
    //java.io.ObjectInputStream$BlockDataInputStream.setBlockDataMode(ObjectInputStream.java:2420) 

    sc.parallelize(List("THIS","ISCOOL")).saveAsTextFile(writeFile) 
    }

移动到新的API方法固定从hdfs读取！

def testHDFSReadNew(sc: SparkContext, readFile: String){ 
    //THIS WORKS 
    sc.newAPIHadoopFile(readFile, classOf[TextInputFormat], classOf[LongWritable], 
     classOf[Text],sc.hadoopConfiguration).map{ 
     case (x:LongWritable, y: Text) => y.toString 
    }.take(2).foreach(println) 
    }

所以看来我正在取得进展。写作不再像上面那样出现严重错误，而是似乎正在工作。唯一的问题是，除了目录中孤独的SUCCESS标志文件之外，什么都没有。更令人困惑的是，日志显示数据正在写入_temporary目录。看起来好像文件提交者从未意识到需要将文件从_temporary目录移动到输出目录。

def testHDFSWriteNew(sc: SparkContext, writeFile: String){ 
    /*This will have an error message of: 
    INFO ConnectionManager: Removing SendingConnection to ConnectionManagerId(dl1rhd400.internal.edmunds.com,35927) 
    14/11/21 02:02:27 INFO ConnectionManager: Key not valid ? [email protected] 
     14/11/21 02:02:27 INFO ConnectionManager: key already cancelled ? [email protected] 
     java.nio.channels.CancelledKeyException 
    at org.apache.spark.network.ConnectionManager.run(ConnectionManager.scala:386) 
    at org.apache.spark.network.ConnectionManager$$anon$4.run(ConnectionManager.scala:139) 

    However lately it hasn't even had errors, symptoms are no part files in the directory but a success flag is there 
    */ 
    val conf = sc.hadoopConfiguration 
    conf.set("mapreduce.task.files.preserve.failedtasks", "true") 
    conf.set("mapred.output.dir", writeFile) 
    sc.parallelize(List("THIS","ISCOOL")).map(x => (NullWritable.get, new Text(x))) 
     .saveAsNewAPIHadoopFile(writeFile, classOf[NullWritable], classOf[Text], classOf[TextOutputFormat[NullWritable, Text]], conf) 

    }

当我在本地运行并指定hdfs路径时，文件在hdfs中显示正常。只有当我在我们的火花独立群集上运行时才会发生这种情况。

我提交作业如下：火花提交--deploy模式客户端--master火花：// sparkmaster --class driverclass driverjar

来源

2014-11-22 sashu2012

你可以尝试用下面的代码？

import org.apache.hadoop.io._ 
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat 
val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x))) 
nums.saveAsNewAPIHadoopFile[TextOutputFormat[IntWritable, Text]]("/data/newAPIHadoopFile")

以下代码也适用于我。

val x = sc.parallelize(List("THIS","ISCOOL")).map(x => (NullWritable.get, new Text(x))) 
x.saveAsNewAPIHadoopFile("/data/nullwritable", classOf[NullWritable], classOf[Text], classOf[TextOutputFormat[NullWritable, Text]], sc.hadoopConfiguration)

[根@ sparkmaster〜]＃hadoop的FS -cat /数据/ nullwritable/*

15/08/20 02:09:19 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

来源

2015-08-20 06:06:50

Spark写入hdfs不能使用saveAsNewAPIHadoopFile方法

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