编写Scala代码，使用Spark讲Mysql数据表中的数据抽取到Hive的ODS层

抽取MySQL的metast库中Production表的全量数据进入Hive的ods库中表production，字段排序、类型不变，同时添加静态分区，分区字段类型为String，且值为当前日期的前一天日期（分区字段格式为yyyyMMdd）。

使用IDEA创建maven项目

配置pom文件

<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/maven-v4_0_0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>com.tledu</groupId>
  <artifactId>llll</artifactId>
  <version>1.0-SNAPSHOT</version>
  <name>${project.artifactId}</name>
  <description>My wonderfull scala app</description>
  <inceptionYear>2018</inceptionYear>
  <licenses>
    <license>
      <name>My License</name>
      <url>http://....</url>
      <distribution>repo</distribution>
    </license>
  </licenses>
 
  <properties>
    <maven.compiler.source>1.8</maven.compiler.source>
    <maven.compiler.target>1.8</maven.compiler.target>
    <encoding>UTF-8</encoding>
    <scala.version>2.11.11</scala.version>
    <scala.compat.version>2.11</scala.compat.version>
    <spec2.version>4.2.0</spec2.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_${scala.compat.version}</artifactId>
      <version>2.3.2</version>
      <scope>provided</scope>
    </dependency>
 
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_${scala.compat.version}</artifactId>
      <version>2.3.2</version>
      <scope>provided</scope>
    </dependency>
 
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-hive_2.11</artifactId>
      <version>2.0.2</version>
      <scope>provided</scope>
    </dependency>
 
    <dependency>
      <groupId>mysql</groupId>
      <artifactId>mysql-connector-java</artifactId>
      <version>8.0.23</version>
    </dependency>
 
 
 
    <!-- Test -->
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.12</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.scalatest</groupId>
      <artifactId>scalatest_${scala.compat.version}</artifactId>
      <version>3.0.5</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.specs2</groupId>
      <artifactId>specs2-core_${scala.compat.version}</artifactId>
      <version>${spec2.version}</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.specs2</groupId>
      <artifactId>specs2-junit_${scala.compat.version}</artifactId>
      <version>${spec2.version}</version>
      <scope>test</scope>
    </dependency>
  </dependencies>
 
  <build>
    <sourceDirectory>src/main/scala</sourceDirectory>
    <testSourceDirectory>src/test/scala</testSourceDirectory>
    <plugins>
      <plugin>
        <!-- see http://davidb.github.com/scala-maven-plugin -->
        <groupId>net.alchim31.maven</groupId>
        <artifactId>scala-maven-plugin</artifactId>
        <version>3.3.2</version>
        <executions>
          <execution>
            <goals>
              <goal>compile</goal>
              <goal>testCompile</goal>
            </goals>
            <configuration>
              <args>
                <arg>-dependencyfile</arg>
                <arg>${project.build.directory}/.scala_dependencies</arg>
              </args>
            </configuration>
          </execution>
        </executions>
      </plugin>
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-surefire-plugin</artifactId>
        <version>2.21.0</version>
        <configuration>
          <!-- Tests will be run with scalatest-maven-plugin instead -->
          <skipTests>true</skipTests>
        </configuration>
      </plugin>
      <plugin>
        <groupId>org.scalatest</groupId>
        <artifactId>scalatest-maven-plugin</artifactId>
        <version>2.0.0</version>
        <configuration>
          <reportsDirectory>${project.build.directory}/surefire-reports</reportsDirectory>
          <junitxml>.</junitxml>
          <filereports>TestSuiteReport.txt</filereports>
          <!-- Comma separated list of JUnit test class names to execute -->
          <jUnitClasses>samples.AppTest</jUnitClasses>
        </configuration>
        <executions>
          <execution>
            <id>test</id>
            <goals>
              <goal>test</goal>
            </goals>
          </execution>
        </executions>
      </plugin>
 
      <plugin>
        <artifactId>maven-assembly-plugin</artifactId>
        <configuration>
          <descriptorRefs>
            <descriptorRef>jar-with-dependencies</descriptorRef>
          </descriptorRefs>
        </configuration>
        <executions>
          <execution>
            <id>make-assembly</id>
            <phase>package</phase>
            <goals>
              <goal>assembly</goal>
            </goals>
          </execution>
        </executions>
      </plugin>
    </plugins>
  </build>
</project>

导入scala
我这里演示用的是Unbanto，操作步骤一样
​​
​​点击＋号去添加，这里注意scala版本号要与pom配置文件中的一致

创建一个scala目录并将它标记为根目录，在scala里新建一个object

编程过程如下

object demo01 {

  def getYesterday(): String = {
    val dateFormat: SimpleDateFormat = new SimpleDateFormat("yyyyMMdd")
    val cal: Calendar = Calendar.getInstance()
    cal.add(Calendar.DATE, -1)
    dateFormat.format(cal.getTime())
  }



  def main(args: Array[String]): Unit = {
    //source start
    val spark = SparkSession.builder()
      .master("local[1]")
      .config("spark.sql.parquet.writeLegacyFormat", true)
      //100个分区，执行完之后只有一个分区；
      .config("spark.sql.sources.partitionOverwriteMode", "dynamic")//动态分区
      .config("spark.sql.legacy.parquet.int96RebaseModeInWrite","LEGACY")
      .config("hive.exec.dynamic.partition.mode", "nonstrict")
      .enableHiveSupport().getOrCreate()

    //spark连接mysql
    val url = s"jdbc:mysql://IP地址:3306/shtd_industry?useUnicode=true&characterEncoding=utf8&useSSL=false"
   

    val readerCustomerInf = spark.read.format("jdbc")

      .option("url", url)

      .option("driver", "com.mysql.jdbc.Driver")

      .option("user", "root")

      .option("password", "123456")

      .option("dbtable", "数据库表名")
      .load() //转换为DataFrame
     //source end
    //增加分区字段   etl
    val addPtDF = readerCustomerInf.withColumn("etl_date", lit(getYesterday()))

    val tableName = "hive表名"
    //切换hive的数据库
    import spark.sql
    sql("use ods")
    //sink
    addPtDF.write.mode("overwrite").partitionBy("etl_date").saveAsTable(tableName).formatted("orc")

    spark.table(tableName).show()
  }
}

将编写好的代码打包发送到linux中

在集群上上传你打好的包

通常使用rz指令上传

可以写一个脚本运行你的包

vi spark.sh

/opt/module/spark-3.1.1-yarn/bin/spark-submit \
--class 要运行的类名 \
--master yarn \
--deploy-mode client \
--driver-memory 2g \
--executor-memory 1g \
--executor-cores 2 \
/jar包的地址/这里是你的jar包

保存退出

sh spark.sh 运行脚本
Mysql数据就导入HIVE数据库的ods层中了