目录
一、编写WordCount(Spark_scala)提交到spark高可用集群
1.项目结构
2.导入依赖
3.编写scala版的WordCount
4.maven打包
5.运行jar包
6.查询hdfs的输出结果
二、本地编写WordCount(Spark_scala)读取本地文件
1.项目结构
2.编写scala版的WordCount
3.编辑Edit Configurations配置文件
4.直接本地运行LocalWordCount.scala文件
三、本地编写WordCount(Spark_java版)读取本地文件，并输出到本地
1.项目结构
2.编写java版的WordCount
3.编辑Edit Configurations配置文件
4.运行后查看结果
四、本地编写WordCount(Spark_java_lambda版)读取本地文件，并输出到本地
1.项目结构
2.编写java-lambda版的WordCount
3.编辑Edit Configurations配置文件
4.运行后查看结果
搞了一个晚上加一个白天，总算搞出来了，呼~~
本地编写IDEA之前需要在windows下安装scala、hadoop和spark环境，参考文章如下：
《Scala安装》 《Windows环境部署Hadoop-3.3.2和Spark3.3.2》
一、编写WordCount(Spark_scala)提交到spark高可用集群
首先安装好scala，然后在IDEA创建一个maven项目，开始编写代码
1.项目结构
2.导入依赖
<name>spark-in-action</name>
<url>http://maven.apache.org</url>
<!-- 定义的一些常量 -->
<properties>
<maven.compiler.source>8</maven.compiler.source>
<maven.compiler.target>8</maven.compiler.target>
<encoding>UTF-8</encoding>
<spark.version>3.3.2</spark.version>
<scala.version>2.12.15</scala.version>
</properties>
<dependencies>
<!-- scala的依赖 -->
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<!-- spark core 即为spark内核 ，其他高级组件都要依赖spark core -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.12</artifactId>
<version>${spark.version}</version>
</dependency>
</dependencies>
<!-- 配置Maven的镜像库 -->
<!-- 依赖下载国内镜像库 -->
<repositories>
<repository>
<id>nexus-aliyun</id>
<name>Nexus aliyun</name>
<layout>default</layout>
<url>http://maven.aliyun.com/nexus/content/groups/public</url>
<snapshots>
<enabled>false</enabled>
<updatePolicy>never</updatePolicy>
</snapshots>
<releases>
<enabled>true</enabled>
<updatePolicy>never</updatePolicy>
</releases>
</repository>
</repositories>
<!-- maven插件下载国内镜像库 -->
<pluginRepositories>
<pluginRepository>
<id>ali-plugin</id>
<url>http://maven.aliyun.com/nexus/content/groups/public/</url>
<snapshots>
<enabled>false</enabled>
<updatePolicy>never</updatePolicy>
</snapshots>
<releases>
<enabled>true</enabled>
<updatePolicy>never</updatePolicy>
</releases>
</pluginRepository>
</pluginRepositories>
<build>
<pluginManagement>
<plugins>
<!-- 编译scala的插件 -->
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.2</version>
</plugin>
<!-- 编译java的插件 -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.5.1</version>
</plugin>
</plugins>
</pluginManagement>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<executions>
<execution>
<id>scala-compile-first</id>
<phase>process-resources</phase>
<goals>
<goal>add-source</goal>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>scala-test-compile</id>
<phase>process-test-resources</phase>
<goals>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<executions>
<execution>
<phase>compile</phase>
<goals>
<goal>compile</goal>
</goals>
</execution>
</executions>
</plugin>
<!-- 打jar插件 -->
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>2.4.3</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>3.编写scala版的WordCount
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
object WordCount {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("WordCount")
// 1.创建SparkContext
val sc = new SparkContext(conf)
// 2.创建RDD(神奇的大集合，该集合中不装真正要计算的数据，而是装描述信息)
val lines: RDD[String] = sc.textFile(args(0))
// 3.对RDD进行操作，调用RDD的方法
// -------------Transformation (转换算子开始) --------------
// 切分压平
val words: RDD[String] = lines.flatMap(_.split(" "))
// 将单词和1组合放入到元组中
val wordAndOne: RDD[(String, Int)] = words.map((_, 1))
// 将key相同的数据进行分组聚合
val reduced: RDD[(String, Int)] = wordAndOne.reduceByKey(_ + _)
// 按照次数排序
val sorted: RDD[(String, Int)] = reduced.sortBy(_._2, false) // 降序
// -------------Transformation (转换算子结束) --------------
// 4.调用Action
// 将数据写入到外部的存储系统中
sorted.saveAsTextFile(args(1))
// 5.释放资源
sc.stop()
}
}4.maven打包
这个是胖包，除了项目本身的依赖，还有其他依赖，上面的original是瘦包，只有项目本身的依赖
5.运行jar包
首先启动zk、hdfs和spark高可用集群，这里我搭建的是standalone模式的高可用集群，不是on Yarn的
创建/opt/soft/spark-3.2.3/submit目录，将jar包上传到该目录下
提交命令
[root@node141 submit]# ../bin/spark-submit --master spark://node141:7077 --class cn.doitedu.day01.WordCount --executor-memory 1g --total-executor-cores 4 ./spark-in-action-1.0.jar hdfs://node141:9000/words.txt hdfs://node141:9000/out-1
--master spark://node141:7077 spark的master节点
--class cn.doitedu.day01.WordCount 运行的类名
--executor-memory 1g 占用的内存
--total-executor-cores 4 占用的核数
./spark-in-action-1.0.jar 运行的jar包地址
hdfs://node141:9000/words.txt 代码中args[0]对应的参数
hdfs://node141:9000/out-1 代码中args[1]对应的参数
6.查询hdfs的输出结果
二、本地编写WordCount(Spark_scala)读取本地文件
1.项目结构
2.编写scala版的WordCount
package cn.doitedu.day01
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
/**
* 使用本地模式运行Spark程序(开发调试的时候使用)
*/
object LocalWordCount {
def main(args: Array[String]): Unit = {
// 指定当前用户为root
System.setProperty("HADOOP_USER_NAME", "root")
val conf = new SparkConf()
.setAppName("WordCount")
.setMaster("local[*]") // 本地模式，*表示根据当前机器的核数开多个线程
// 1.创建SparkContext
val sc = new SparkContext(conf)
// 2.创建RDD(神奇的大集合，该集合中不装真正要计算的数据，而是装描述信息)
val lines: RDD[String] = sc.textFile(args(0))
// 3.对RDD进行操作，调用RDD的方法
// -------------Transformation (转换算子开始) --------------
// 切分压平
val words: RDD[String] = lines.flatMap(line => {
val words = line.split(" ")
println(words) // debug
words
})
// 将单词和1组合放入到元祖中
val wordAndOne: RDD[(String, Int)] = words.map((_, 1))
// 将key相同的数据进行分组聚合
val reduced: RDD[(String, Int)] = wordAndOne.reduceByKey(_ + _)
// 按照次数排序
val sorted: RDD[(String, Int)] = reduced.sortBy(_._2, false) // 降序
// -------------Transformation (转换算子结束) --------------
// 4.调用Action
// 将数据写入到外部的存储系统中
sorted.saveAsTextFile(args(1))
// 5.释放资源
sc.stop()
}
}3.编辑Edit Configurations配置文件
4.直接本地运行LocalWordCount.scala文件
查看运行结果
三、本地编写WordCount(Spark_java版)读取本地文件，并输出到本地
1.项目结构
2.编写java版的WordCount
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;
public class JavaWordCount {
public static void main(String[] args) {
SparkConf conf = new SparkConf()
.setAppName("JavaWordCount")
.setMaster("local[*]");
JavaSparkContext jsc = new JavaSparkContext(conf);
JavaRDD<String> lines = jsc.textFile(args[0]);
JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
@Override
public Iterator<String> call(String lines) throws Exception {
String[] words = lines.split("\\s+");
return Arrays.asList(words).iterator();
}
});
JavaPairRDD<String, Integer> wordAndOne = words.mapToPair(new PairFunction<String, String, Integer>() {
@Override
public Tuple2<String, Integer> call(String word) throws Exception {
return Tuple2.apply(word, 1);
}
});
JavaPairRDD<String, Integer> reduced = wordAndOne.reduceByKey(new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
});
// 将原来的kv顺序颠倒 (flink,3) ----> (3,flink)
JavaPairRDD<Integer, String> swapped = reduced.mapToPair(new PairFunction<Tuple2<String, Integer>, Integer, String>() {
@Override
public Tuple2<Integer, String> call(Tuple2<String, Integer> tp) throws Exception {
return tp.swap(); // 交换
}
});
JavaPairRDD<Integer, String> sorted = swapped.sortByKey(false);
JavaPairRDD<String, Integer> result = sorted.mapToPair(new PairFunction<Tuple2<Integer, String>, String, Integer>() {
@Override
public Tuple2<String, Integer> call(Tuple2<Integer, String> tp) throws Exception {
return tp.swap();
}
});
result.saveAsTextFile(args[1]);
jsc.stop();
}
}3.编辑Edit Configurations配置文件
4.运行后查看结果
四、本地编写WordCount(Spark_java_lambda版)读取本地文件，并输出到本地
1.项目结构
2.编写java-lambda版的WordCount
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 scala.Tuple2;
import java.util.Arrays;
public class JavaLambdaWordCount {
public static void main(String[] args) {
SparkConf conf = new SparkConf()
.setAppName("JavaLambdaWordCount")
.setMaster("local[*]");
JavaSparkContext jsc = new JavaSparkContext(conf);
JavaRDD<String> lines = jsc.textFile(args[0]);
JavaRDD<String> words = lines.flatMap(line -> Arrays.asList(line.split(" ")).iterator());
JavaPairRDD<String, Integer> wordAndOne = words.mapToPair(w -> Tuple2.apply(w, 1));
// JavaPairRDD<String, Integer> reduced = wordAndOne.reduceByKey((a, b) -> a + b);
JavaPairRDD<String, Integer> reduced = wordAndOne.reduceByKey(Integer::sum);
JavaPairRDD<Integer, String> swapped = reduced.mapToPair(Tuple2::swap);
// 排序
JavaPairRDD<Integer, String> sorted = swapped.sortByKey(false);
// 调回来
JavaPairRDD<String, Integer> result = sorted.mapToPair(Tuple2::swap);
result.saveAsTextFile(args[1]);
jsc.stop();
}
}3.编辑Edit Configurations配置文件
4.运行后查看结果
好啦~~
