基于Intellij IDEA搭建Spark开发环境搭建
基于Intellij IDEA搭建Spark开发环境搭——参考文档
● 参考文档http://spark.apache.org/docs/latest/programming-guide.html
● 操作步骤
·a)创建maven 项目
·b)引入依赖（Spark 依赖、打包插件等等）
基于Intellij IDEA搭建Spark开发环境—maven vs sbt
● 哪个熟悉用哪个
● Maven也可以构建scala项目
基于Intellij IDEA搭建Spark开发环境搭—maven构建scala项目
● 参考文档http://docs.scala-lang.org/tutorials/scala-with-maven.html
● 操作步骤
a)用maven构建scala项目(基于net.alchim31.maven:scala-archetype-simple)
b)pom.xml引入依赖(spark依赖、打包插件等等)
在pom.xml文件中的合适位置添加以下内容：
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>2.2.0</version>
<scope>provided</scope> //设置作用域，不将所有依赖文件打包到最终的项目中
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.4.1</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer"></transformer>
</transformers>
<createDependencyReducedPom>false</createDependencyReducedPom>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
进行一次打包操作以测试是否工作正常。
在Terminal中输入指令：
mvn clean package 运行结果如下： D:\Code\JavaCode\sparkMaven>mvn clean package [INFO] Scanning for projects... [INFO] [INFO] ---------------------< com.zimo.spark:scala-spark >--------------------- [INFO] Building scala-spark 1.0-SNAPSHOT [INFO] --------------------------------[ jar ]--------------------------------- [INFO] [INFO] --- maven-clean-plugin:2.5:clean (default-clean) @ scala-spark --- [INFO] [INFO] --- maven-resources-plugin:2.6:resources (default-resources) @ scala-spark --- [WARNING] Using platform encoding (GBK actually) to copy filtered resources, i.e. build is platform dependent! [INFO] skip non existing resourceDirectory D:\Code\JavaCode\sparkMaven\src\main\resources [INFO] [INFO] --- maven-compiler-plugin:3.1:compile (default-compile) @ scala-spark --- [INFO] No sources to compile [INFO] [INFO] --- maven-resources-plugin:2.6:testResources (default-testResources) @ scala-spark --- [WARNING] Using platform encoding (GBK actually) to copy filtered resources, i.e. build is platform dependent! [INFO] skip non existing resourceDirectory D:\Code\JavaCode\sparkMaven\src\test\resources [INFO] [INFO] --- maven-compiler-plugin:3.1:testCompile (default-testCompile) @ scala-spark --- [INFO] No sources to compile [INFO] [INFO] --- maven-surefire-plugin:2.12.4:test (default-test) @ scala-spark --- [INFO] No tests to run. [INFO] [INFO] --- maven-jar-plugin:2.4:jar (default-jar) @ scala-spark --- [WARNING] JAR will be empty - no content was marked for inclusion! [INFO] Building jar: D:\Code\JavaCode\sparkMaven\target\scala-spark-1.0-SNAPSHOT.jar [INFO] [INFO] --- maven-shade-plugin:2.4.1:shade (default) @ scala-spark --- [INFO] Replacing original artifact with shaded artifact. [INFO] Replacing D:\Code\JavaCode\sparkMaven\target\scala-spark-1.0-SNAPSHOT.jar with D:\Code\JavaCode\sparkMaven\target\scala-spark-1.0-SNAPSHOT-shaded.jar [INFO] ------------------------------------------------------------------------ [INFO] BUILD SUCCESS [INFO] ------------------------------------------------------------------------ [INFO] Total time: 9.675 s [INFO] Finished at: 2018-09-11T15:33:53+08:00 [INFO] ------------------------------------------------------------------------
出现了BUILD SUCCESS，表明一切正常。下面给大家演示以下Scala编程的大致流程，以及在该框架下同样用Java进行实现应该如何操作。
Scala编程实现WordCount
注意：此处必须选为Object，否则没有main方法！
然后输入以下代码，执行打包操作
def main(args: Array[String]): Unit = { println("hello spark") }
完成后可以看到项目目录下多出来了一个target目录。这就是使用Scala编程的一个大致流程，下面我们来写一个WordCount程序。（后面也会有Java编程的版本提供给大家）
首先在集群中创建以下目录和测试文件：
[hadoop@masternode ~]$ cd /home/hadoop/ [hadoop@masternode ~]$ ll total 68 drwxr-xr-x. 9 hadoop hadoop 4096 Sep 10 22:15 app drwxrwxr-x. 6 hadoop hadoop 4096 Aug 17 10:42 data drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Desktop drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Documents drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Downloads drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Music drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Pictures drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Public drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Templates drwxrwxr-x. 3 hadoop hadoop 4096 Apr 18 10:11 tools drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Videos -rw-rw-r--. 1 hadoop hadoop 20876 Apr 20 18:03 zookeeper.out [hadoop@masternode ~]$ mkdir testSpark/ [hadoop@masternode ~]$ ll total 72 drwxr-xr-x. 9 hadoop hadoop 4096 Sep 10 22:15 app drwxrwxr-x. 6 hadoop hadoop 4096 Aug 17 10:42 data drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Desktop drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Documents drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Downloads drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Music drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Pictures drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Public drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Templates drwxrwxr-x. 2 hadoop hadoop 4096 Sep 12 10:23 testSpark drwxrwxr-x. 3 hadoop hadoop 4096 Apr 18 10:11 tools drwxr-xr-x. 2 hadoop hadoop 4096 Apr 17 10:03 Videos -rw-rw-r--. 1 hadoop hadoop 20876 Apr 20 18:03 zookeeper.out [hadoop@masternode ~]$ cd testSpark/ [hadoop@masternode testSpark]$ vi word.txt apache hadoop spark scala apache hadoop spark scala apache hadoop spark scala apache hadoop spark scala
WordCount.scala代码如下：（如果右键New下面没有“Scala Class“”选项，请检查IDEA是否添加了scala插件）
package com.zimo.spark import org.apache.spark.{SparkConf, SparkContext} /** * Created by Zimo on 2018/9/11 */ object MyWordCount { def main(args: Array[String]): Unit = { //参数检查 if (args.length < 2) { System.err.println("Usage: myWordCount <input> <output>") System.exit(1) } //获取参数 val input = args(0) val output = args(1) //创建Scala版本的SparkContext val conf = new SparkConf().setAppName("myWordCount") val sc = new SparkContext(conf) //读取数据 val lines = sc.textFile(input) //进行相关计算 lines.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).collect().foreach(println) //保存结果 sc.stop() } }
从代码可以看出scala的优势就是简洁，但是可读性较差。所以，学习可以与后面的java代码进行对比。
然后打包
打包完成后把上图中的文件上传到spark集群上去，然后执行。
[hadoop@masternode testSpark]$ rz [hadoop@masternode testSpark]$ ll total 8 -rw-r--r--. 1 hadoop hadoop 1936 Sep 12 10:59 scala-spark-1.0-SNAPSHOT.jar -rw-rw-r--. 1 hadoop hadoop 104 Sep 12 10:26 word.txt [hadoop@masternode testSpark]$ cd ../app/spark-2.2.0/ [hadoop@masternode spark-2.2.0]$ cd bin/ [hadoop@masternode bin]$ ll total 92 -rwxr-xr-x. 1 hadoop hadoop 1089 Jul 1 2017 beeline -rw-r--r--. 1 hadoop hadoop 899 Jul 1 2017 beeline.cmd -rwxr-xr-x. 1 hadoop hadoop 1933 Jul 1 2017 find-spark-home -rw-r--r--. 1 hadoop hadoop 1909 Jul 1 2017 load-spark-env.cmd -rw-r--r--. 1 hadoop hadoop 2133 Jul 1 2017 load-spark-env.sh -rwxr-xr-x. 1 hadoop hadoop 2989 Jul 1 2017 pyspark -rw-r--r--. 1 hadoop hadoop 1493 Jul 1 2017 pyspark2.cmd -rw-r--r--. 1 hadoop hadoop 1002 Jul 1 2017 pyspark.cmd -rwxr-xr-x. 1 hadoop hadoop 1030 Jul 1 2017 run-example -rw-r--r--. 1 hadoop hadoop 988 Jul 1 2017 run-example.cmd -rwxr-xr-x. 1 hadoop hadoop 3196 Jul 1 2017 spark-class -rw-r--r--. 1 hadoop hadoop 2467 Jul 1 2017 spark-class2.cmd -rw-r--r--. 1 hadoop hadoop 1012 Jul 1 2017 spark-class.cmd -rwxr-xr-x. 1 hadoop hadoop 1039 Jul 1 2017 sparkR -rw-r--r--. 1 hadoop hadoop 1014 Jul 1 2017 sparkR2.cmd -rw-r--r--. 1 hadoop hadoop 1000 Jul 1 2017 sparkR.cmd -rwxr-xr-x. 1 hadoop hadoop 3017 Jul 1 2017 spark-shell -rw-r--r--. 1 hadoop hadoop 1530 Jul 1 2017 spark-shell2.cmd -rw-r--r--. 1 hadoop hadoop 1010 Jul 1 2017 spark-shell.cmd -rwxr-xr-x. 1 hadoop hadoop 1065 Jul 1 2017 spark-sql -rwxr-xr-x. 1 hadoop hadoop 1040 Jul 1 2017 spark-submit -rw-r--r--. 1 hadoop hadoop 1128 Jul 1 2017 spark-submit2.cmd -rw-r--r--. 1 hadoop hadoop 1012 Jul 1 2017 spark-submit.cmd
[hadoop@masternode testSpark]$ ./spark-submit --class com.zimo.spark.MyWordCount ~/testSpark/scala-spark-1.0-SNAPSHOT.jar ~/testSpark/word.txt ~/testSpark/
运行结果如下图所示：
以上操作是把结果直接打印出来，下面我们尝试一下将结果保存到文本当中去。修改以下代码：
//进行相关计算 //lines.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_).collect().foreach(println) val resultRDD = lines.flatMap(_.split(" ")).map((_,1)).reduceByKey(_+_) //保存结果 resultRDD.saveAsTextFile(output)
再次执行：
./spark-submit --class com.zimo.spark.MyWordCount ~/testSpark/scala-spark-1.0-SNAPSHOT.jar ~/testSpark/word.txt ~/testSpark/result
//输出目录一定要为不存在的目录！
结果如下：
[hadoop@masternode testSpark]$ ll total 5460 drwxrwxr-x. 2 hadoop hadoop 4096 Sep 12 16:02 result -rw-r--r--. 1 hadoop hadoop 5582827 Sep 12 16:00 scala-spark-1.0-SNAPSHOT.jar -rw-rw-r--. 1 hadoop hadoop 104 Sep 12 15:52 word.txt [hadoop@masternode testSpark]$ cd result/ [hadoop@masternode result]$ ll total 4 -rw-r--r--. 1 hadoop hadoop 42 Sep 12 16:02 part-00000 -rw-r--r--. 1 hadoop hadoop 0 Sep 12 16:02 _SUCCESS [hadoop@masternode result]$ cat part-00000 (scala,4) (spark,4) (hadoop,4) (apache,4)
Java编程实现WordCount
在同样目录新建一个java目录，并设置为”Sources Root”。
单元测试目录”test”同样需要建一个java文件夹。
同理设置为”Test Sources Root”。然后分别再创建resources目录（用于存放配置文件），并分别设置为“Resources Root”和“Test Resources Root”。
最后，创建一个“com.zimo.spark”包，并在下面新建一个MyJavaWordCount.Class类（如果右键New下面没有“Java Class”选项请参看博文https://www.cnblogs.com/zimo-jing/p/9628784.html下的详细讲解），其中的代码为如下：
package com.zimo.spark; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.api.java.function.FlatMapFunction; import org.apache.spark.api.java.function.Function2; import org.apache.spark.api.java.function.PairFunction; import scala.Tuple2; import java.util.Arrays; import java.util.Iterator; /** * Created by Zimo on 2018/9/12 */ public class MyJavaWordCount { public static void main(String[] args) { //参数检查 if (args.length < 2) { System.err.println("Usage: MyJavaWordCount <input> <output>"); System.exit(1); } //获取参数 String input = args[0]; String output = args[1]; //创建Java版本的SparkContext SparkConf conf = new SparkConf().setAppName("MyJavaWordCount"); JavaSparkContext sc = new JavaSparkContext(conf); //读取数据 JavaRDD<String> inputRDD = sc.textFile(input); //进行相关计算 JavaRDD<String> words = inputRDD.flatMap(new FlatMapFunction<String, String>() { @Override public Iterator<String> call(String line) throws Exception { return Arrays.asList(line.split(" ")); } }); JavaPairRDD<String, Integer> result = words.mapToPair(new PairFunction<String, String, Integer>() { @Override public Tuple2<String, Integer> call(String word) throws Exception { return new Tuple2<String, Integer>(word, 1); } }).reduceByKey(new Function2<Integer, Integer, Integer>() { @Override public Integer call(Integer x, Integer y) throws Exception { return x+y; } }); //保存结果 result.saveAsTextFile(output); //关闭sc sc.stop(); } }
注意：此处要做一点点修改。注释掉pom.xml文件下的此处内容
此处是默认Source ROOT的路径，所以打包时就只能打包Scala下的代码，而我们新建的Java目录则不会被打包，注释之后则会以我们之前的目录配置为主。
然后就可以执行打包和集群上的运行操作了。运行和Scala编程一模一样，我在这里就不赘述了，大家参见上面即可！只是需要注意一点：output目录必须为不存在的目录，请记得每次运行前进行修改！