Spark+IDEA单机版环境搭建+IDEA快捷键

1. IDEA中配置Spark运行环境

    请参考博文：http://www.cnblogs.com/jackchen-Net/p/6867838.html

3.1.Project Struct查看项目的配置信息

 

3.2.IDEA中如果没有默认安装Scala，可在本地安装即可

   如果需要安装多版本的scala请注意：

   如果您在本地已经安装了msi结尾的scala，还需要安装第二个版本，建议下载zip包，优点是直接解压在IDEA中配置即可。如第3步所示。

   注意：scala下载地址：http://www.scala-lang.org/download/2.10.4.html

3.3.查看scala环境配置，可以通过下图绿色的”+”添加本地已经下载的scala安装包

   

   

3.4.特别注意，如果在执行spark代码遇到如下问题，请更改scala版本

Exception in thread "main" java.lang.NoSuchMethodError:
scala.collection.immutable.HashSet$.empty()Lscala/collection/immutable/HashSet;
at akka.actor.ActorCell$.<init>(ActorCell.scala:336)
at akka.actor.ActorCell$.<clinit>(ActorCell.scala)
at akka.actor.RootActorPath.$div(ActorPath.scala:159)
at akka.actor.LocalActorRefProvider.<init>(ActorRefProvider.scala:464)
at akka.actor.LocalActorRefProvider.<init>(ActorRefProvider.scala:452)
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:39)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:27)
at java.lang.reflect.Constructor.newInstance(Constructor.java:513)
at akka.actor.ReflectiveDynamicAccess$$anonfun$createInstanceFor$2.apply(DynamicAccess.scala:78)
at scala.util.Try$.apply(Try.scala:191)
at akka.actor.ReflectiveDynamicAccess.createInstanceFor(DynamicAccess.scala:73)
at akka.actor.ReflectiveDynamicAccess$$anonfun$createInstanceFor$3.apply(DynamicAccess.scala:84)
at akka.actor.ReflectiveDynamicAccess$$anonfun$createInstanceFor$3.apply(DynamicAccess.scala:84)
at scala.util.Success.flatMap(Try.scala:230)
at akka.actor.ReflectiveDynamicAccess.createInstanceFor(DynamicAccess.scala:84)
at akka.actor.ActorSystemImpl.liftedTree1$1(ActorSystem.scala:584)
at akka.actor.ActorSystemImpl.<init>(ActorSystem.scala:577)
at akka.actor.ActorSystem$.apply(ActorSystem.scala:141)
at akka.actor.ActorSystem$.apply(ActorSystem.scala:108)
at akka.Akka$.delayedEndpoint$akka$Akka$1(Akka.scala:11)
at akka.Akka$delayedInit$body.apply(Akka.scala:9)
at scala.Function0$class.apply$mcV$sp(Function0.scala:40)
at scala.runtime.AbstractFunction0.apply$mcV$sp(AbstractFunction0.scala:12)
at scala.App$$anonfun$main$1.apply(App.scala:76)
at scala.App$$anonfun$main$1.apply(App.scala:76)
at scala.collection.immutable.List.foreach(List.scala:383)
at scala.collection.generic.TraversableForwarder$class.foreach(TraversableForwarder.scala:35)
at scala.App$class.main(App.scala:76)
at akka.Akka$.main(Akka.scala:9)
at akka.Akka.main(Akka.scala)

　　解决方法是将scala2.11版本改为2.10版本即可。（注意:spark版本为1.6.0）

3.5.导入程序运行所需要的jar包

  •  通过libary，点击”+”将spark-assembly-1.6.0-hadoop2.6.0.jar导入Classes位置
  •  通过spark官网下载spark1.6.0的源码文件(spark1.6.0-src.tgz)解压在windows本地后，通过点击最右侧的”+”导入所有的源码包，从而可以查看源代码。

 

 3.6.建立一个scala文件并写代码

package com.bigdata.demo
import org.apache.spark.{SparkConf, SparkContext}
/**
  * Created by SimonsZhao on 3/25/2017.
  */
object  wordCount {
  def main(args: Array[String]) {
    val conf =new SparkConf().setMaster("local").setAppName("wordCount")
    val sc =new SparkContext(conf)
    val data=sc.textFile("E://scala//spark//testdata//word.txt")
    data.flatMap(_.split("	")).map((_,1)).reduceByKey(_+_).collect().foreach(println)
  }
}

3.7.运行结果

Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
17/03/25 17:25:49 INFO SparkContext: Running Spark version 1.6.0
17/03/25 17:25:50 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/03/25 17:25:50 INFO SecurityManager: Changing view acls to: SimonsZhao
17/03/25 17:25:50 INFO SecurityManager: Changing modify acls to: SimonsZhao
17/03/25 17:25:50 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(SimonsZhao); users with modify permissions: Set(SimonsZhao)
17/03/25 17:25:51 INFO Utils: Successfully started service 'sparkDriver' on port 53279.
17/03/25 17:25:51 INFO Slf4jLogger: Slf4jLogger started
17/03/25 17:25:51 INFO Remoting: Starting remoting
17/03/25 17:25:51 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@192.168.191.1:53292]
17/03/25 17:25:51 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 53292.
17/03/25 17:25:51 INFO SparkEnv: Registering MapOutputTracker
17/03/25 17:25:51 INFO SparkEnv: Registering BlockManagerMaster
17/03/25 17:25:51 INFO DiskBlockManager: Created local directory at C:UsersSimonsZhaoAppDataLocalTemplockmgr-7e548732-b1db-4e3c-acdb-37e686b10dff
17/03/25 17:25:51 INFO MemoryStore: MemoryStore started with capacity 2.4 GB
17/03/25 17:25:51 INFO SparkEnv: Registering OutputCommitCoordinator
17/03/25 17:25:51 INFO Utils: Successfully started service 'SparkUI' on port 4040.
17/03/25 17:25:51 INFO SparkUI: Started SparkUI at http://192.168.191.1:4040
17/03/25 17:25:52 INFO Executor: Starting executor ID driver on host localhost
17/03/25 17:25:52 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 53299.
17/03/25 17:25:52 INFO NettyBlockTransferService: Server created on 53299
17/03/25 17:25:52 INFO BlockManagerMaster: Trying to register BlockManager
17/03/25 17:25:52 INFO BlockManagerMasterEndpoint: Registering block manager localhost:53299 with 2.4 GB RAM, BlockManagerId(driver, localhost, 53299)
17/03/25 17:25:52 INFO BlockManagerMaster: Registered BlockManager
17/03/25 17:25:52 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 153.6 KB, free 153.6 KB)
17/03/25 17:25:52 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 13.9 KB, free 167.5 KB)
17/03/25 17:25:52 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on localhost:53299 (size: 13.9 KB, free: 2.4 GB)
17/03/25 17:25:52 INFO SparkContext: Created broadcast 0 from textFile at wordCount.scala:11
17/03/25 17:25:54 WARN : Your hostname, SimonsCJ resolves to a loopback/non-reachable address: fe80:0:0:0:0:5efe:c0a8:bf01%30, but we couldn't find any external IP address!
17/03/25 17:25:55 INFO FileInputFormat: Total input paths to process : 1
17/03/25 17:25:55 INFO SparkContext: Starting job: collect at wordCount.scala:12
17/03/25 17:25:55 INFO DAGScheduler: Registering RDD 3 (map at wordCount.scala:12)
17/03/25 17:25:55 INFO DAGScheduler: Got job 0 (collect at wordCount.scala:12) with 1 output partitions
17/03/25 17:25:55 INFO DAGScheduler: Final stage: ResultStage 1 (collect at wordCount.scala:12)
17/03/25 17:25:55 INFO DAGScheduler: Parents of final stage: List(ShuffleMapStage 0)
17/03/25 17:25:55 INFO DAGScheduler: Missing parents: List(ShuffleMapStage 0)
17/03/25 17:25:55 INFO DAGScheduler: Submitting ShuffleMapStage 0 (MapPartitionsRDD[3] at map at wordCount.scala:12), which has no missing parents
17/03/25 17:25:55 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 4.1 KB, free 171.6 KB)
17/03/25 17:25:55 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 2.3 KB, free 173.9 KB)
17/03/25 17:25:55 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on localhost:53299 (size: 2.3 KB, free: 2.4 GB)
17/03/25 17:25:55 INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1006
17/03/25 17:25:55 INFO DAGScheduler: Submitting 1 missing tasks from ShuffleMapStage 0 (MapPartitionsRDD[3] at map at wordCount.scala:12)
17/03/25 17:25:55 INFO TaskSchedulerImpl: Adding task set 0.0 with 1 tasks
17/03/25 17:25:55 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, localhost, partition 0,PROCESS_LOCAL, 2129 bytes)
17/03/25 17:25:55 INFO Executor: Running task 0.0 in stage 0.0 (TID 0)
17/03/25 17:25:55 INFO HadoopRDD: Input split: file:/E:/scala/spark/testdata/word.txt:0+19
17/03/25 17:25:55 INFO deprecation: mapred.tip.id is deprecated. Instead, use mapreduce.task.id
17/03/25 17:25:55 INFO deprecation: mapred.task.id is deprecated. Instead, use mapreduce.task.attempt.id
17/03/25 17:25:55 INFO deprecation: mapred.task.is.map is deprecated. Instead, use mapreduce.task.ismap
17/03/25 17:25:55 INFO deprecation: mapred.task.partition is deprecated. Instead, use mapreduce.task.partition
17/03/25 17:25:55 INFO deprecation: mapred.job.id is deprecated. Instead, use mapreduce.job.id
17/03/25 17:25:55 INFO Executor: Finished task 0.0 in stage 0.0 (TID 0). 2253 bytes result sent to driver
17/03/25 17:25:55 INFO DAGScheduler: ShuffleMapStage 0 (map at wordCount.scala:12) finished in 0.228 s
17/03/25 17:25:55 INFO DAGScheduler: looking for newly runnable stages
17/03/25 17:25:55 INFO DAGScheduler: running: Set()
17/03/25 17:25:55 INFO DAGScheduler: waiting: Set(ResultStage 1)
17/03/25 17:25:55 INFO DAGScheduler: failed: Set()
17/03/25 17:25:55 INFO DAGScheduler: Submitting ResultStage 1 (ShuffledRDD[4] at reduceByKey at wordCount.scala:12), which has no missing parents
17/03/25 17:25:55 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 194 ms on localhost (1/1)
17/03/25 17:25:55 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
17/03/25 17:25:55 INFO MemoryStore: Block broadcast_2 stored as values in memory (estimated size 2.6 KB, free 176.4 KB)
17/03/25 17:25:55 INFO MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 1600.0 B, free 178.0 KB)
17/03/25 17:25:55 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on localhost:53299 (size: 1600.0 B, free: 2.4 GB)
17/03/25 17:25:55 INFO SparkContext: Created broadcast 2 from broadcast at DAGScheduler.scala:1006
17/03/25 17:25:55 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 1 (ShuffledRDD[4] at reduceByKey at wordCount.scala:12)
17/03/25 17:25:55 INFO TaskSchedulerImpl: Adding task set 1.0 with 1 tasks
17/03/25 17:25:55 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 1, localhost, partition 0,NODE_LOCAL, 1894 bytes)
17/03/25 17:25:55 INFO Executor: Running task 0.0 in stage 1.0 (TID 1)
17/03/25 17:25:55 INFO ShuffleBlockFetcherIterator: Getting 1 non-empty blocks out of 1 blocks
17/03/25 17:25:55 INFO ShuffleBlockFetcherIterator: Started 0 remote fetches in 6 ms
17/03/25 17:25:55 INFO Executor: Finished task 0.0 in stage 1.0 (TID 1). 1349 bytes result sent to driver
17/03/25 17:25:55 INFO DAGScheduler: ResultStage 1 (collect at wordCount.scala:12) finished in 0.059 s
17/03/25 17:25:55 INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 1) in 59 ms on localhost (1/1)
17/03/25 17:25:55 INFO TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks have all completed, from pool
17/03/25 17:25:55 INFO DAGScheduler: Job 0 finished: collect at wordCount.scala:12, took 0.532461 s
(you,1)
(hello,2)
(me,1)
17/03/25 17:25:55 INFO SparkContext: Invoking stop() from shutdown hook
17/03/25 17:25:56 INFO SparkUI: Stopped Spark web UI at http://192.168.191.1:4040
17/03/25 17:25:56 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
17/03/25 17:25:56 INFO MemoryStore: MemoryStore cleared
1/03/25 17:25:56 INFO BlockManager: BlockManager stopped
17/03/25 17:25:56 INFO BlockManagerMaster: BlockManagerMaster stopped
17/03/25 17:25:56 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
17/03/25 17:25:56 INFO SparkContext: Successfully stopped SparkContext
17/03/25 17:25:56 INFO ShutdownHookManager: Shutdown hook called
17/03/25 17:25:56 INFO ShutdownHookManager: Deleting directory C:UsersSimonsZhaoAppDataLocalTempspark-220c67fe-f2c3-400b-bfe1-fe833e33e74f

2.Spark在windows环境搭建

 2.1.进入spark官网下载对应hadoop版本的spark安装文件

     http://spark.apache.org/docs/latest/

 2.2.在windows下面配置环境变量(新建SPARK_HOME系统变量，输入spark安装文件路径，在PATH中加入%SPARK_HOME%in;变量即可。)

       进入windows控制台中，直接输入spark-shell即可显示如下图。

     否则，需要进入下载的spark1.6.0的下载安装目录中，执行spark-shell

       执行结果如下：

 

3.测试是否正确：

    3.1.准备数据

　　E:scalaspark estdata中的work.txt文件中写入以下文件

  　　  Hello you

    　　Hello me

    3.2.输入并查看结果输出

3.其他可能碰到的问题   

java.lang.RuntimeException: java.lang.RuntimeException: The root scratch dir: /tmp/hive on HDFS should be writable. Curr

　　正常情况下是可以运行成功并进入到Spark的命令行环境下的，但是对于有些用户可能会遇到空指针的错误。这个时候，主要是因为Hadoop的bin目录下没有winutils.exe文件的原因造成的。这里的解决办法是： 

  • 前往 https://github.com/steveloughran/winutils 下载该项目的zip包在你的系统中，然后选择你安装的Hadoop版本号，然后进入到bin目录下，将找到的winutils.exe这个文件放入到Hadoop的bin目录下，我这里是F:hadoopin。 
  • 在打开的cmd中输入F:hadoopinwinutils.exe chmod 777 /tmp/Hive     这个操作是用来修改权限的。注意前面的F:hadoopin部分要对应的替换成实际你所安装的bin目录所在位置。
  • 经过这几个步骤之后，然后再次开启一个新的cmd窗口，如果正常的话，应该就可以通过直接输入spark-shell来运行Spark了。 

END~

4.IDEA 快捷键常用集锦

/**
 *  IDEA快捷键
 *  Alt+enter 导入包,自动修正
 *  ctrl+alt+L  自动格式化代码
 *  alt+insert 自动生成构造器、getter/setter等等常用方法
 *  ctrl+d  复制当前行到下一行
 *  shift+enter 另起一行
 *  ctrl+N 查找类
 *  双击shift 在项目的所有目录查找，就是你想看到你不想看到的和你没想过你能看到的都给你找出来
 *  Ctrl+Alt+O 优化导入的类和包
 *  Ctrl+J  自动代码
 *  按Ctrl-J组合键来执行一些你记不起来的Live Template缩写。比如，键“it”然后按Ctrl-J看看有什么发生。
 *  Ctrl＋O可以选择父类的方法进行重写
 *  Ctrl＋Q可以看JavaDoc
 *  Ctrl+Alt+M  抽取方法
 *  Ctrl+Alt+V   抽取局部变量
 *  Ctrl+Alt+C   抽取常量
 *  Ctrl+Alt+F   抽取实例变量
 *  Ctrl+Alt+t 使用if-else trycatch方法
 *  shift+alt+下箭头
 */
【推广】 免费学中医，健康全家人
原文地址：https://www.cnblogs.com/jackchen-Net/p/6623829.html