基于IDEA环境下的Spark2.X程序开发

我们选择在线安装

这个是windows下的scala,直接双击安装就可以了

安装好之后可以验证一下

这个是我本地的jdk1.8安装包，直接双击安装

安装完成后可以验证一下

https://archive.apache.org/dist/maven/maven-3/3.3.9/binaries/

解压

我的本地是win10系统

配置好环境变量我们可以验证一下

修改这个文件

这个是默认的

改成这样子

把本地的maven配置进来

接下来就是等待自动把相应的架包下载下来

把scala添加进来了

接下来我们创建目录

在scala目录下建包

在这个包里面创建一个scala的类

输入以下代码

配置maven的 pom.xml文件

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>com.spark</groupId>
  <artifactId>sparkStu</artifactId>
  <packaging>war</packaging>
  <version>1.0-SNAPSHOT</version>
  <name>sparkStu Maven Webapp</name>
  <url>http://maven.apache.org</url>

  <properties>
    <hadoop.version>2.6.0</hadoop.version>
    <scala.binary.version>2.11</scala.binary.version>
    <spark.version>2.2.0</spark.version>
  </properties>

  <dependencies>
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_${scala.binary.version}</artifactId>
      <version>${spark.version}</version>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_${scala.binary.version}</artifactId>
      <version>${spark.version}</version>
    </dependency>

<!--
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.11</artifactId>
      <version>2.2.0</version>
    </dependency>
-->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming_${scala.binary.version}</artifactId>
      <version>${spark.version}</version>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-hive_${scala.binary.version}</artifactId>
      <version>${spark.version}</version>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-streaming-kafka-0-10_${scala.binary.version}</artifactId>
      <version>${spark.version}</version>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql-kafka-0-10_${scala.binary.version}</artifactId>
      <version>${spark.version}</version>
    </dependency>

    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>${hadoop.version}</version>
    </dependency>

    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>3.8.1</version>
      <scope>test</scope>
    </dependency>



  </dependencies>
  <build>
    <finalName>sparkStu</finalName>
  </build>
</project>

在Test.scala里加上这段内容

我们编写一个简单的代码

package com.spark.test

import org.apache.spark.sql.SparkSession

object Test {

  def main(args: Array[String]): Unit = {
   val spark= SparkSession
       .builder
         .appName("HdfsTest")
           .getOrCreate()
     val filePart = "E://Mycode/datas/stu.txt"
     val rdd= spark.sparkContext.textFile(filePart)

     val lines= rdd.flatMap(x => x.split(" ")).collect().toList
     println(lines)
  }
}

运行一下

结果报错了

E:\software\jdk1.8\bin\java "-javaagent:E:\software\IDEA\IntelliJ IDEA 2017.2.6\lib\idea_rt.jar=59010:E:\software\IDEA\IntelliJ IDEA 2017.2.6\bin" -Dfile.encoding=UTF-8 -classpath 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com.spark.test.Test
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
18/03/14 17:01:07 INFO SparkContext: Running Spark version 2.2.0
18/03/14 17:01:07 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
18/03/14 17:01:08 ERROR Shell: Failed to locate the winutils binary in the hadoop binary path
java.io.IOException: Could not locate executable null\bin\winutils.exe in the Hadoop binaries.
    at org.apache.hadoop.util.Shell.getQualifiedBinPath(Shell.java:355)
    at org.apache.hadoop.util.Shell.getWinUtilsPath(Shell.java:370)
    at org.apache.hadoop.util.Shell.<clinit>(Shell.java:363)
    at org.apache.hadoop.util.StringUtils.<clinit>(StringUtils.java:79)
    at org.apache.hadoop.security.Groups.parseStaticMapping(Groups.java:104)
    at org.apache.hadoop.security.Groups.<init>(Groups.java:86)
    at org.apache.hadoop.security.Groups.<init>(Groups.java:66)
    at org.apache.hadoop.security.Groups.getUserToGroupsMappingService(Groups.java:280)
    at org.apache.hadoop.security.UserGroupInformation.initialize(UserGroupInformation.java:271)
    at org.apache.hadoop.security.UserGroupInformation.ensureInitialized(UserGroupInformation.java:248)
    at org.apache.hadoop.security.UserGroupInformation.loginUserFromSubject(UserGroupInformation.java:763)
    at org.apache.hadoop.security.UserGroupInformation.getLoginUser(UserGroupInformation.java:748)
    at org.apache.hadoop.security.UserGroupInformation.getCurrentUser(UserGroupInformation.java:621)
    at org.apache.spark.util.Utils$$anonfun$getCurrentUserName$1.apply(Utils.scala:2430)
    at org.apache.spark.util.Utils$$anonfun$getCurrentUserName$1.apply(Utils.scala:2430)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.util.Utils$.getCurrentUserName(Utils.scala:2430)
    at org.apache.spark.SparkContext.<init>(SparkContext.scala:295)
    at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2509)
    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:909)
    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:901)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:901)
    at com.spark.test.Test$.main(Test.scala:11)
    at com.spark.test.Test.main(Test.scala)
18/03/14 17:01:08 ERROR SparkContext: Error initializing SparkContext.
org.apache.spark.SparkException: A master URL must be set in your configuration
    at org.apache.spark.SparkContext.<init>(SparkContext.scala:376)
    at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2509)
    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:909)
    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:901)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:901)
    at com.spark.test.Test$.main(Test.scala:11)
    at com.spark.test.Test.main(Test.scala)
18/03/14 17:01:08 INFO SparkContext: Successfully stopped SparkContext
Exception in thread "main" org.apache.spark.SparkException: A master URL must be set in your configuration
    at org.apache.spark.SparkContext.<init>(SparkContext.scala:376)
    at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2509)
    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:909)
    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:901)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:901)
    at com.spark.test.Test$.main(Test.scala:11)
    at com.spark.test.Test.main(Test.scala)

Process finished with exit code 1

这是因为我本地没有配置好hadoop,现在我们配一个

这个是我本地的hadoop/bin

下面把本地win10的环境变量配置一下

再重启一下idea,再运行一下程序

报了另外一个错误，但是可以确定的是前面的错误我们解决了

E:\software\jdk1.8\bin\java "-javaagent:E:\software\IDEA\IntelliJ IDEA 2017.2.6\lib\idea_rt.jar=60011:E:\software\IDEA\IntelliJ IDEA 2017.2.6\bin" -Dfile.encoding=UTF-8 -classpath 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com.spark.test.Test
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
18/03/14 17:34:56 INFO SparkContext: Running Spark version 2.2.0
18/03/14 17:34:57 ERROR SparkContext: Error initializing SparkContext.
org.apache.spark.SparkException: A master URL must be set in your configuration
    at org.apache.spark.SparkContext.<init>(SparkContext.scala:376)
    at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2509)
    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:909)
    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:901)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:901)
    at com.spark.test.Test$.main(Test.scala:11)
    at com.spark.test.Test.main(Test.scala)
18/03/14 17:34:57 INFO SparkContext: Successfully stopped SparkContext
Exception in thread "main" org.apache.spark.SparkException: A master URL must be set in your configuration
    at org.apache.spark.SparkContext.<init>(SparkContext.scala:376)
    at org.apache.spark.SparkContext$.getOrCreate(SparkContext.scala:2509)
    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:909)
    at org.apache.spark.sql.SparkSession$Builder$$anonfun$6.apply(SparkSession.scala:901)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.sql.SparkSession$Builder.getOrCreate(SparkSession.scala:901)
    at com.spark.test.Test$.main(Test.scala:11)
    at com.spark.test.Test.main(Test.scala)

Process finished with exit code 1

这里的错误是说要指明你的程序运行在什么地方

在程序里加上这一句，指明我们现在在本地运行

我们再运行一次，可以看到没问题了

我们继续修改程序，加上这一句

再次运行看看结果

把相同的单词进行累加

我们看看运行结果

刚刚我们使用的是rdd的方式，接下来我们使用dataSet的方式

dataSet可以近似的理解为数据库的一张张表

我们运行的结果

用空格切分单词

运行结果

E:\software\jdk1.8\bin\java "-javaagent:E:\software\IDEA\IntelliJ IDEA 2017.2.6\lib\idea_rt.jar=62232:E:\software\IDEA\IntelliJ IDEA 2017.2.6\bin" -Dfile.encoding=UTF-8 -classpath 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com.spark.test.Test
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
18/03/14 20:41:05 INFO SparkContext: Running Spark version 2.2.0
18/03/14 20:41:06 INFO SparkContext: Submitted application: HdfsTest
18/03/14 20:41:06 INFO SecurityManager: Changing view acls to: Brave
18/03/14 20:41:06 INFO SecurityManager: Changing modify acls to: Brave
18/03/14 20:41:06 INFO SecurityManager: Changing view acls groups to: 
18/03/14 20:41:06 INFO SecurityManager: Changing modify acls groups to: 
18/03/14 20:41:06 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users  with view permissions: Set(Brave); groups with view permissions: Set(); users  with modify permissions: Set(Brave); groups with modify permissions: Set()
18/03/14 20:41:07 INFO Utils: Successfully started service 'sparkDriver' on port 62269.
18/03/14 20:41:07 INFO SparkEnv: Registering MapOutputTracker
18/03/14 20:41:07 INFO SparkEnv: Registering BlockManagerMaster
18/03/14 20:41:07 INFO BlockManagerMasterEndpoint: Using org.apache.spark.storage.DefaultTopologyMapper for getting topology information
18/03/14 20:41:07 INFO BlockManagerMasterEndpoint: BlockManagerMasterEndpoint up
18/03/14 20:41:07 INFO DiskBlockManager: Created local directory at C:\Users\Brave\AppData\Local\Temp\blockmgr-2ad95228-3532-4a24-b6b6-b09973c4a4ff
18/03/14 20:41:07 INFO MemoryStore: MemoryStore started with capacity 1998.3 MB
18/03/14 20:41:07 INFO SparkEnv: Registering OutputCommitCoordinator
18/03/14 20:41:07 INFO Utils: Successfully started service 'SparkUI' on port 4040.
18/03/14 20:41:07 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://192.168.56.1:4040
18/03/14 20:41:07 INFO Executor: Starting executor ID driver on host localhost
18/03/14 20:41:07 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 62282.
18/03/14 20:41:07 INFO NettyBlockTransferService: Server created on 192.168.56.1:62282
18/03/14 20:41:07 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy
18/03/14 20:41:07 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, 192.168.56.1, 62282, None)
18/03/14 20:41:07 INFO BlockManagerMasterEndpoint: Registering block manager 192.168.56.1:62282 with 1998.3 MB RAM, BlockManagerId(driver, 192.168.56.1, 62282, None)
18/03/14 20:41:07 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 192.168.56.1, 62282, None)
18/03/14 20:41:07 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, 192.168.56.1, 62282, None)
18/03/14 20:41:08 INFO SharedState: Setting hive.metastore.warehouse.dir ('null') to the value of spark.sql.warehouse.dir ('file:/E:/Mycode/SparkStu/spark-warehouse/').
18/03/14 20:41:08 INFO SharedState: Warehouse path is 'file:/E:/Mycode/SparkStu/spark-warehouse/'.
18/03/14 20:41:09 INFO StateStoreCoordinatorRef: Registered StateStoreCoordinator endpoint
18/03/14 20:41:11 INFO FileSourceStrategy: Pruning directories with: 
18/03/14 20:41:11 INFO FileSourceStrategy: Post-Scan Filters: 
18/03/14 20:41:11 INFO FileSourceStrategy: Output Data Schema: struct<value: string>
18/03/14 20:41:11 INFO FileSourceScanExec: Pushed Filters: 
18/03/14 20:41:12 INFO CodeGenerator: Code generated in 321.911944 ms
18/03/14 20:41:12 INFO CodeGenerator: Code generated in 9.798824 ms
18/03/14 20:41:12 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 213.6 KB, free 1998.1 MB)
18/03/14 20:41:12 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 20.2 KB, free 1998.1 MB)
18/03/14 20:41:12 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on 192.168.56.1:62282 (size: 20.2 KB, free: 1998.3 MB)
18/03/14 20:41:12 INFO SparkContext: Created broadcast 0 from show at Test.scala:17
18/03/14 20:41:13 INFO FileSourceScanExec: Planning scan with bin packing, max size: 4194417 bytes, open cost is considered as scanning 4194304 bytes.
18/03/14 20:41:13 INFO SparkContext: Starting job: show at Test.scala:17
18/03/14 20:41:13 INFO DAGScheduler: Got job 0 (show at Test.scala:17) with 1 output partitions
18/03/14 20:41:13 INFO DAGScheduler: Final stage: ResultStage 0 (show at Test.scala:17)
18/03/14 20:41:13 INFO DAGScheduler: Parents of final stage: List()
18/03/14 20:41:13 INFO DAGScheduler: Missing parents: List()
18/03/14 20:41:13 INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[5] at show at Test.scala:17), which has no missing parents
18/03/14 20:41:13 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 13.0 KB, free 1998.1 MB)
18/03/14 20:41:13 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 6.1 KB, free 1998.1 MB)
18/03/14 20:41:13 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on 192.168.56.1:62282 (size: 6.1 KB, free: 1998.3 MB)
18/03/14 20:41:13 INFO SparkContext: Created broadcast 1 from broadcast at DAGScheduler.scala:1006
18/03/14 20:41:13 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 0 (MapPartitionsRDD[5] at show at Test.scala:17) (first 15 tasks are for partitions Vector(0))
18/03/14 20:41:13 INFO TaskSchedulerImpl: Adding task set 0.0 with 1 tasks
18/03/14 20:41:13 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, localhost, executor driver, partition 0, PROCESS_LOCAL, 5268 bytes)
18/03/14 20:41:13 INFO Executor: Running task 0.0 in stage 0.0 (TID 0)
18/03/14 20:41:13 INFO CodeGenerator: Code generated in 13.617205 ms
18/03/14 20:41:13 INFO FileScanRDD: Reading File path: file:///E:/Mycode/datas/stu.txt, range: 0-113, partition values: [empty row]
18/03/14 20:41:13 INFO CodeGenerator: Code generated in 11.971125 ms
18/03/14 20:41:13 INFO Executor: Finished task 0.0 in stage 0.0 (TID 0). 1745 bytes result sent to driver
18/03/14 20:41:13 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 258 ms on localhost (executor driver) (1/1)
18/03/14 20:41:13 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool 
18/03/14 20:41:13 INFO DAGScheduler: ResultStage 0 (show at Test.scala:17) finished in 0.284 s
18/03/14 20:41:13 INFO DAGScheduler: Job 0 finished: show at Test.scala:17, took 0.483521 s
18/03/14 20:41:13 INFO CodeGenerator: Code generated in 23.334109 ms
+------+
| value|
+------+
|hadoop|
|hadoop|
|  java|
|  java|
| spark|
| spark|
|  hive|
| hbase|
| sqoop|
| sqoop|
| mysql|
| redit|
| flume|
| flume|
|  join|
|   hue|
| scala|
|python|
+------+

18/03/14 20:41:13 INFO SparkContext: Invoking stop() from shutdown hook
18/03/14 20:41:13 INFO SparkUI: Stopped Spark web UI at http://192.168.56.1:4040
18/03/14 20:41:13 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
18/03/14 20:41:13 INFO MemoryStore: MemoryStore cleared
18/03/14 20:41:13 INFO BlockManager: BlockManager stopped
18/03/14 20:41:14 INFO BlockManagerMaster: BlockManagerMaster stopped
18/03/14 20:41:14 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
18/03/14 20:41:14 INFO SparkContext: Successfully stopped SparkContext
18/03/14 20:41:14 INFO ShutdownHookManager: Shutdown hook called
18/03/14 20:41:14 INFO ShutdownHookManager: Deleting directory C:\Users\Brave\AppData\Local\Temp\spark-2c920b38-6a7f-4914-a2ef-9ee345492414

Process finished with exit code 0

增加这个字段

运行结果

分组统计

运行结果

把最后的代码放上来

package com.spark.test

import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}
object Test {

  def main(args: Array[String]): Unit = {
   val spark= SparkSession
       .builder
         .master("local")
         .appName("HdfsTest")
           .getOrCreate()
     val filePart = "E://Mycode/datas/stu.txt"
//     val rdd= spark.sparkContext.textFile(filePart)
//     val lines= rdd.flatMap(x => x.split(" ")).map(x=>(x,1)).reduceByKey((a,b)=>(a+b)).collect().toList
    //     println(lines)
    import spark.implicits._
    val dataSet= spark.read.textFile(filePart)
      .flatMap(x => x.split(" "))
      .map(x=>(x,1)).groupBy("_1").count()
      .show()
    
  }
}

现在我们把程序打包

我们把代码稍微改一下

package com.spark.test

import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}
object Test {

  def main(args: Array[String]): Unit = {
   val spark= SparkSession
       .builder
         .master("local")
         .appName("HdfsTest")
           .getOrCreate()

    val filePart = args(0)
    //  val filePart = "E://Mycode/datas/stu.txt"
//     val rdd= spark.sparkContext.textFile(filePart)
//     val lines= rdd.flatMap(x => x.split(" ")).map(x=>(x,1)).reduceByKey((a,b)=>(a+b)).collect().toList
    //     println(lines)
    import spark.implicits._
    val dataSet= spark.read.textFile(filePart)
      .flatMap(x => x.split(" "))
      .map(x=>(x,1)).groupBy("_1").count()
      .show()

  }
}

把这些都剔除掉

剩下这两个

打包完成了

把这个包上传到我们的集群上

这个是我们的数据文件

我们把数据文件上传的hdfs上面去，先启动hdfs

同时记得把zookeeper也启动了，不然会出问题的

现在hdfs上创建一个目录

把本地的文件上传

我们在集群上跑一下

bin/spark-submit --master local[2] /opt/jars/sparkStu.jar hdfs://bigdata-pro01.kfk.com:9000/user/datas/stu.txt

可以看到跑下来的结果

posted on 2018-03-14 22:17 百里登峰阅读(1850) 评论(0) 编辑收藏举报

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