idea编写spark程序

IDEA编写Spark程序

maven仓库
链接: https://pan.baidu.com/s/1mXe0zlDy4XAgdBQ6BtGkrA 提取码: 2sy4

1、 pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<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/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
 
    <groupId>cn.itcast</groupId>
    <artifactId>SparkDemo</artifactId>
    <version>1.0-SNAPSHOT</version>
 
    <!-- 指定仓库位置，依次为aliyun、cloudera和jboss仓库 -->
    <repositories>
        <repository>
            <id>aliyun</id>
            <url>http://maven.aliyun.com/nexus/content/groups/public/</url>
        </repository>
        <repository>
            <id>cloudera</id>
            <url>https://repository.cloudera.com/artifactory/cloudera-repos/</url>
        </repository>
        <repository>
            <id>jboss</id>
            <url>http://repository.jboss.com/nexus/content/groups/public</url>
        </repository>
    </repositories>
    <properties>
        <maven.compiler.source>1.8</maven.compiler.source>
        <maven.compiler.target>1.8</maven.compiler.target>
        <encoding>UTF-8</encoding>
        <scala.version>2.11.8</scala.version>
        <scala.compat.version>2.11</scala.compat.version>
        <hadoop.version>2.7.4</hadoop.version>
        <spark.version>2.2.0</spark.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive-thriftserver_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <!-- <dependency>
             <groupId>org.apache.spark</groupId>
             <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
             <version>${spark.version}</version>
         </dependency>-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql-kafka-0-10_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>
 
        <!--<dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.0-mr1-cdh5.14.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.2.0-cdh5.14.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>1.2.0-cdh5.14.0</version>
        </dependency>-->
 
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.7.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>1.3.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>1.3.1</version>
        </dependency>
        <dependency>
            <groupId>com.typesafe</groupId>
            <artifactId>config</artifactId>
            <version>1.3.3</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.38</version>
        </dependency>
    </dependencies>
 
    <build>
        <sourceDirectory>src/main/scala</sourceDirectory>
        <testSourceDirectory>src/test/scala</testSourceDirectory>
        <plugins>
            <!-- 指定编译java的插件 -->
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.5.1</version>
            </plugin>
            <!-- 指定编译scala的插件 -->
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.2.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                        <configuration>
                            <args>
                                <arg>-dependencyfile</arg>
                                <arg>${project.build.directory}/.scala_dependencies</arg>
                            </args>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-surefire-plugin</artifactId>
                <version>2.18.1</version>
                <configuration>
                    <useFile>false</useFile>
                    <disableXmlReport>true</disableXmlReport>
                    <includes>
                        <include>**/*Test.*
                        **/*Suite.*</include>
                    </includes>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <version>2.3</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <filters>
                                <filter>
                                    <artifact>*:*</artifact>
                                    <excludes>
                                        <exclude>META-INF/*.SF
                                        META-INF/*.DSA
                                        META-INF/*.RSA
                                    
                                
                            
                            
                                
                                    
                                
                            
                        
                    
                
            
        
    

前提：创建一个maven项目
编写代码
1、创建spark conf
2、实例一个sparkcontext
3、读物数据，对数据进行操作（业务逻辑）
4、保存最终的结果
Jar包执行
讲代码到导出成为jar文件，上传到集群，通过spark-submit提交任务
本地运行

object WordCount {
  def main(args: Array[String]): Unit = {
    //1.创建SparkContext
    val config = new SparkConf().setAppName("wc").setMaster("local[*]")
    val sc = new SparkContext(config)
    sc.setLogLevel("WARN")
    //2.读取文件
    val fileRDD: RDD[String] = sc.textFile("F:\\第四学期的大数据资料\\day01三月份资料\\第四周\\day04\\4.2号练习题\\word.txt")
    //3.处理数据
    val wordRDD: RDD[String] = fileRDD.flatMap(_.split(" "))
    //3.2每个单词记为1
    val wordAndOneRDD: RDD[(String, Int)] = wordRDD.map((_,1))
    //3.3根据key进行聚合,统计每个单词的数量
    val wordAndCount: RDD[(String, Int)] = wordAndOneRDD.reduceByKey(_+_)
    //4.收集结果
    val result: Array[(String, Int)] = wordAndCount.collect()
    result.foreach(println)
  }
}

集群运行

object WordCountJ {
  def main(args: Array[String]): Unit = {
    //1.创建SparkContext
    val config = new SparkConf().setAppName("wc")//.setMaster("local[*]")
    val sc = new SparkContext(config)
    sc.setLogLevel("WARN")
    //2.读取文件
    val fileRDD: RDD[String] = sc.textFile(args(0)) //文件输入路径
    //3.处理数据
    val wordRDD: RDD[String] = fileRDD.flatMap(_.split(" "))
    //3.2每个单词记为1
    val wordAndOneRDD: RDD[(String, Int)] = wordRDD.map((_,1))
    //3.3根据key进行聚合,统计每个单词的数量
    val wordAndCount: RDD[(String, Int)] = wordAndOneRDD.reduceByKey(_+_)
    wordAndCount.saveAsTextFile(args(1))//文件输出路径
  }
}

●打包

上传

执行命令提交到YARN集群

/export/servers/spark-2.2.0-bin-2.6.0-cdh5.14.0/bin/spark-submit
–class demo02.WordCountJ
–master yarn
–deploy-mode cluster
–driver-memory 1g
–executor-memory 1g
–executor-cores 2
–queue default
/export/pot/Demo06_Spark_01-1.0-SNAPSHOT.jar
hdfs://hadoop01:8020/words.txt
hdfs://hadoop01:8020/wordcount/output5

执行命令提交到Spark-HA集群

/export/servers/spark-2.2.0-bin-2.6.0-cdh5.14.0/bin/spark-submit
–class cn.itcast.sparkhello.WordCount
–master spark://hadoop01:7077,hadoop02:7077
–executor-memory 1g
–total-executor-cores 2
/export/pot/Demo06_Spark_01-1.0-SNAPSHOT.jar
hdfs://hadoop01:8020/words.txt
hdfs://hadoop01:8020/wordcount/output4