4. 启动Spark Shell编程
4.1 什么是Spark Shell
spark shell是spark中的交互式命令行客户端，可以在spark shell中使用scala编写spark程序，启动后默认已经创建了SparkContext，别名为sc
4.2 启动Spark Shell
| Shell
/opt/apps/spark-3.2.3-bin-hadoop3.2/bin/spark-shell \
--master spark://node-1.51doit.cn:7077 --executor-memory 1g \
--total-executor-cores 3 |
如果Master配置了HA高可用，需要指定两个Master（因为这两个Master任意一个都可能是Active状态）
| Shell
/bigdata/spark-3.2.3-bin-hadoop3.2/bin/spark-shell \
--master spark://node-1.51doit.cn:7077,node-2.51doit.cn:7077 \
--executor-memory 1g \
--total-executor-cores 3 |
参数说明：
--master 指定masterd地址和端口，协议为spark://，端口是RPC的通信端口
--executor-memory 指定每一个executor的使用的内存大小
--total-executor-cores指定整个application总共使用了cores
| Shell
sc.textFile("hdfs://node-1.51doit.cn:9000/words.txt").flatMap(_.split(" ")).map((_, 1)).reduceByKey(_+_).sortBy(_._2,false).saveAsTextFile("hdfs://node-1.51doit.cn:9000/out") |
5. Spark编程入门
5.1 Scala编写Spark的WorkCount
5.1.1 创建一个Maven项目
5.1.2 在pom.xml中添加依赖和插件
| XML
<!-- 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>3.2.3</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> |
5.1.3 创建一个scala目录
选择scala目录，右键，将目录转成源码包，或者点击maven的刷新按钮
5.1.4 编写Spark程序
| Scala
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkConf, SparkContext}
/**
* 1.创建SparkContext
* 2.创建RDD
* 3.调用RDD的Transformation（s）方法
* 4.调用Action
* 5.释放资源
*/
object WordCount {
def main(args: Array[String]): Unit = {
val conf: SparkConf = new SparkConf().setAppName("WordCount")
//创建SparkContext，使用SparkContext来创建RDD
val sc: SparkContext = new SparkContext(conf)
//spark写Spark程序，就是对抽象的神奇的大集合【RDD】编程，调用它高度封装的API
//使用SparkContext创建RDD
val lines: RDD[String] = sc.textFile(args(0))
//Transformation 开始 //
//切分压平
val words: RDD[String] = lines.flatMap(_.split(" "))
//将单词和一组合放在元组中
val wordAndOne: RDD[(String, Int)] = words.map((_, 1))
//分组聚合，reduceByKey可以先局部聚合再全局聚合
val reduced: RDD[(String, Int)] = wordAndOne.reduceByKey(_+_)
//排序
val sorted: RDD[(String, Int)] = reduced.sortBy(_._2, false)
//Transformation 结束 //
//调用Action将计算结果保存到HDFS中
sorted.saveAsTextFile(args(1))
//释放资源
sc.stop()
}
} |
5.1.5 使用maven打包
- 使用idea图形界面打包：
5.1.6 提交任务
- 上传jar包到服务器，然后使用sparksubmit命令提交任务
| Shell
/bigdata/spark-3.2.3-bin-hadoop3.2/bin/spark-submit \
--master spark://node-1.51doit.cn:7077 \
--executor-memory 1g --total-executor-cores 4 \
--class cn._51doit.spark.day01.WordCount \
/root/spark-in-action-1.0.jar hdfs://node-1.51doit.cn:9000/words.txt hdfs://node-1.51doit.cn:9000/out |
参数说明：
--master 指定masterd地址和端口，协议为spark://，端口是RPC的通信端口
--executor-memory 指定每一个executor的使用的内存大小
--total-executor-cores指定整个application总共使用了cores
--class 指定程序的main方法全类名
jar包路径 args0 args1
5.2 Java编写Spark的WordCount
5.2.1 使用匿名实现类方式
| Java
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 sparkConf = new SparkConf().setAppName("JavaWordCount");
//创建JavaSparkContext
JavaSparkContext jsc = new JavaSparkContext(sparkConf);
//使用JavaSparkContext创建RDD
JavaRDD<String> lines = jsc.textFile(args[0]);
//调用Transformation（s）
//切分压平
JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
@Override
public Iterator<String> call(String line) throws Exception {
return Arrays.asList(line.split(" ")).iterator();
}
});
//将单词和一组合在一起
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);
}
});
//分组聚合
JavaPairRDD<String, Integer> reduced = wordAndOne.reduceByKey(
new Function2<Integer, Integer, Integer>() {
@Override
public Integer call(Integer v1, Integer v2) throws Exception {
return v1 + v2;
}
});
//排序，先调换KV的顺序VK
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();
}
});
//再排序
JavaPairRDD<Integer, String> sorted = swapped.sortByKey(false);
//再调换顺序
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();
}
});
//触发Action，将数据保存到HDFS
result.saveAsTextFile(args[1]);
//释放资源
jsc.stop();
}
}
|
5.2.2 使用Lambda表达式方式
| Java
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");
//创建SparkContext
JavaSparkContext jsc = new JavaSparkContext(conf);
//创建RDD
JavaRDD<String> lines = jsc.textFile(args[0]);
//切分压平
JavaRDD<String> words = lines.flatMap(line -> Arrays.asList(line.split(" ")).iterator());
//将单词和一组合
JavaPairRDD<String, Integer> wordAndOne = words.mapToPair(word -> Tuple2.apply(word, 1));
//分组聚合
JavaPairRDD<String, Integer> reduced = wordAndOne.reduceByKey((a, b) -> a + b);
//调换顺序
JavaPairRDD<Integer, String> swapped = reduced.mapToPair(tp -> tp.swap());
//排序
JavaPairRDD<Integer, String> sorted = swapped.sortByKey(false);
//调换顺序
JavaPairRDD<String, Integer> result = sorted.mapToPair(tp -> tp.swap());
//将数据保存到HDFS
result.saveAsTextFile(args[1]);
//释放资源
jsc.stop();
}
} |
5.3 本地运行Spark和Debug
spark程序每次都打包上在提交到集群上比较麻烦且不方便调试，Spark还可以进行Local模式运行，方便测试和调试
5.3.1 在本地运行
| Scala
//Spark程序local模型运行,local[*]是本地运行，并开启多个线程
val conf: SparkConf = new SparkConf()
.setAppName("WordCount")
.setMaster("local[*]") //设置为local模式执行 |
5.3.2 读取HDFS中的数据
由于往HDFS中的写入数据存在权限问题，所以在代码中设置用户为HDFS目录的所属用户
| Scala
//往HDFS中写入数据，将程序的所属用户设置成更HDFS一样的用户
System.setProperty("HADOOP_USER_NAME", "root") |
5.4 使用PySpark(选学)
5.4.1 配置python环境
① 在所有节点上按照python3，版本必须是python3.6及以上版本
| Shell
yum install -y python3 |
② 修改所有节点的环境变量
| Shell
export JAVA_HOME=/usr/local/jdk1.8.0_251
export PYSPARK_PYTHON=python3
export HADOOP_HOME=/bigdata/hadoop-3.2.1
export HADOOP_CONF_DIR=/bigdata/hadoop-3.2.1/etc/hadoop
export PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin |
5.4.2 使用pyspark shell
| Shell
/bigdata/spark-3.2.3-bin-hadoop3.2/bin/pyspark \
--master spark://node-1.51doit.cn:7077 \
--executor-memory 1g --total-executor-cores 10 |
在pyspark shell使用python编写wordcount
| Python
sc.textFile("hdfs://node-1.51doit.cn:8020/data/wc").flatMap(lambda line: line.split(' ')).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a + b).sortBy(lambda t: t[1], False).saveAsTextFile('hdfs://node-1.51doit.cn:8020/out01') |
5.4.3 配置PyCharm开发环境
①配置python的环境
②配置pyspark的依赖
点击Project Structure将Spark安装包下python/lib目录的py4j-*-src.zip和pyspark.zip添加进来
③添加环境变量
点击Edit Configuration
在pycharm中使用python编写wordcount
| Python
from pyspark import SparkConf, SparkContext
if __name__ == '__main__':
conf = SparkConf().setAppName('WordCount').setMaster('local[*]')
sc = SparkContext(conf=conf)
lines = sc.textFile('file:///Users/star/Desktop/data.txt')
words = lines.flatMap(lambda line: line.split(' '))
wordAndOne = words.map(lambda word: (word, 1))
reduced = wordAndOne.reduceByKey(lambda x, y: x + y)
result = reduced.sortBy(lambda t: t[1], False)
print(result.collect()) |
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