spark报警 java.io.FileNotFoundException: /root/spark/mydatavipday2.ipdb (Permission denied)

报错代码为
val address = IpParse.evaluate(dataMapIp)
调用工具类的如下代码，new 对象的时候找不到文件
val url = “/root/spark/mydatavipday2.ipdb”
db = new City(url);

主代码

ssc.foreachRDD(rdd => {
//      对每个RDD的分区的数据进行操作，主要是建立连接，不要每条数据都建立连接
      val newrdd: Unit = rdd.foreachPartition(it => {
//      根据RedisUtil工具类获取redis客户端
        val client: JedisCluster = RedisClusterUtil.getJedis

        //val start = System.currentTimeMillis
        println("程序正在运行。。。")
        val client_md: Jedis = RedisUtil.getJedisClient
        // 建立mysql的连接
        val conn: Connection = MysqlUtil.getConnection

        it.foreach(line => {
                      //将省市县解析后添加的dataMap里面
              println("dataMapIp",dataMapIp)
              if(dataMapIp != "null" && dataMapIp != ""){
                val address = IpParse.evaluate(dataMapIp)
                println("address",address)
                if (address != null){
                  dataMap.put("$country",address(0))
                  dataMap.put("$province",address(1))
                  dataMap.put("$city",address(2))
                }
              }

将IP解析为国家、省份、市区 的工具类

package com.tcl.realtime.util

import java.sql.SQLException

import net.ipip.ipdb.City
import net.ipip.ipdb.IPFormatException
import java.util.Objects


object IpParse { //只读一次ipip库文
//sfsdf
  def main(args: Array[String]): Unit = {
    val address = IpParse.evaluate("14.29.77.234")
    println(address(0),address(1),address(2))
  }

  def evaluate(ip: String): Array[String] = {
    var db: City = null
    var cns: Array[String] = null
    //try {
      //val url = this.getClass().getClassLoader().getResource("mydatavipday2.ipdb").getPath()
      val url = "/root/spark/mydatavipday2.ipdb"
      //val url = "/origin_data/ip/mydatavipday2vim.ipdb"
      //println(url)
      db = new City(url);
      println(db)
      //db = new City("D:\code\sparkstream_kudu\src\main\resources\mydatavipday2.ipdb");
      if (Objects.isNull(ip) || "" == ip.trim) return null
      //todo 1.174.142.24, 52.46.57.139  ip字段中存在脏数据 需要过滤
      val split = ip.split(",")

      cns = db.find(split(0), "CN")

    //} catch {
    //  case e: Exception =>
    //                e.printStackTrace();
    //}
    cns
  }
}

执行命令为local模式的时候，正常运行，不报错

spark-submit 
--class com.tcl.realtime.app.Events 
--master local[4] 
--executor-memory 2g 
--driver-memory 2g 
--num-executors 3 
--executor-cores 1 
/root/spark/sparkstream_kudu-1.0-SNAPSHOT-jar-with-dependencies.jar

执行命令为yarn时候就报错。 new City这段逻辑实际为各个excutor执行的，因此将mydatavipday2.ipdb，放在了各个机器的 /root/spark/ 目录下，并且将spark目录和mydatavipday2.ipdb的权限都设置为了 777。 还是报权限拒绝。 执行jar的用户为root 用户，只有/root 的权限为dr-xr-xr-x 。 没有写权限。

spark-submit 
--class com.tcl.realtime.app.Events `在这里插入代码片`
--master yarn 
--deploy-mode client 
--executor-memory 2g 
--driver-memory 2g 
--num-executors 3 
--executor-cores 1 
/root/spark/sparkstream_kudu-1.0-SNAPSHOT-jar-with-dependencies.jar

将各个节点的/root 目录的权限改为777 程序能够正常运行

 java.io.FileNotFoundException: /root/spark/mydatavipday2.ipdb (Permission denied)
	at java.io.FileInputStream.open0(Native Method)
	at java.io.FileInputStream.open(FileInputStream.java:195)
	at java.io.FileInputStream.(FileInputStream.java:138)
	at net.ipip.ipdb.Reader.(Reader.java:21)
	at net.ipip.ipdb.City.(City.java:17)
	at com.tcl.realtime.util.IpParse$.evaluate(IpParse.scala:25)
	at com.tcl.realtime.app.Events$$anonfun$doSomething$1$$anonfun$1$$anonfun$apply$3.apply(Events.scala:206)
	at com.tcl.realtime.app.Events$$anonfun$doSomething$1$$anonfun$1$$anonfun$apply$3.apply(Events.scala:46)
	at scala.collection.Iterator$class.foreach(Iterator.scala:891)
	at scala.collection.AbstractIterator.foreach(Iterator.scala:1334)
	at com.tcl.realtime.app.Events$$anonfun$doSomething$1$$anonfun$1.apply(Events.scala:46)
	at com.tcl.realtime.app.Events$$anonfun$doSomething$1$$anonfun$1.apply(Events.scala:36)
	at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:935)
	at org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$28.apply(RDD.scala:935)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2121)
	at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2121)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
	at org.apache.spark.scheduler.Task.run(Task.scala:121)
	at org.apache.spark.executor.Executor$TaskRunner$$anonfun$11.apply(Executor.scala:407)
	at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1408)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:413)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)