Spark 4/5

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

在shell中编写第一个spark程序

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

<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>
    
    <dependency>
        <groupId>org.scala-lang</groupId>
        <artifactId>scala-library</artifactId>
        <version>${scala.version}</version>
    </dependency>

    
    <dependency>
        <groupId>org.apache.spark</groupId>
        <artifactId>spark-core_2.12</artifactId>
        <version>${spark.version}</version>
    </dependency>

</dependencies>



<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>


<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>
            
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.2.2</version>
            </plugin>
            
            <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>

        
        <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>

注意：将粘贴的内容拷贝到指定的位置

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：

使用maven命令打包（两种方式任选其一）

Scala
mvn clean package

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

输入运行参数

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())