编写Scala工程代码，将MySQL的shtd_store库中表CUSTOMER、NATION、PART、PARTSUPP、REGION、SUPPLIER的数据全量抽取到Hive的ods库中对应表customer，nation，part，partsupp，region，supplier中，将表ORDERS、LINEITEM的数据增量抽取到Hive的ods库中对应表ORDERS，LINEITEM中。 

        本题直白一点将就是使用sparksql抽取mysql库中数据到hive中,拆开来看分为三步,第一步为连接hive,第二步为连接mysql并将数据抽取出来,第三步为将抽取出来的数据传到hive中所对应的表

首先第一步:连接hive

val spark = SparkSession.builder()
      .master("local[*]")
      .appName("抽取数据")
      .enableHiveSupport()
      .config("spark.sql.warehouse.dir", "hdfs://master:50070/usr/hive/warehouse")
      .config("hive.metastore.uris", "thrift://master:9083")
      .getOrCreate()

第二步:连接mysql并将数据拿到

spark.read
      .format("jdbc")
      .option("driver", "com.mysql.jdbc.Driver")
      .option("url", "jdbc:mysql://localhost:3306/spark-sql")
      .option("user", "root")
      .option("password", "123456")
      .option("dbtable", "user")
      .load()
      .createTempView("data")
    spark.sql("select * from data").show()

最好show一下看看数据是否抽取成功

第三部:将数据传给hive中对应的表

spark.sql("use study")
    //静态分区
    spark.sql(
      """
        |create table if not exists customer(
        |id int,
        |name string,
        |age int
        |)
        |partitioned by(time string)
        |row format delimited fields terminated by '\t'
        |;
        |""".stripMargin)
    println("*************")
    spark.sql(
      """
        |insert overwrite table customer partition (time='1001')
        |select id,name,age
        |from data;
        |""".stripMargin)
    spark.sql("select * from customer").show()

完整代码:

def main(args: Array[String]): Unit = {
    System.setProperty("HADOOP_USER_NAME", "root")
    val spark = SparkSession.builder()
      .master("local[*]")
      .appName("抽取数据")
      .enableHiveSupport()
      .config("spark.sql.warehouse.dir", "hdfs://master:50070/usr/hive/warehouse")
      .config("hive.metastore.uris", "thrift://master:9083")
      .getOrCreate()

    spark.read
      .format("jdbc")
      .option("driver", "com.mysql.jdbc.Driver")
      .option("url", "jdbc:mysql://localhost:3306/spark-sql")
      .option("user", "root")
      .option("password", "123456")
      .option("dbtable", "user")
      .load()
      .createTempView("data")
    spark.sql("select * from data").show()
    println("***************")
    spark.sql("use study")
    //静态分区
    spark.sql(
      """
        |create table if not exists customer(
        |id int,
        |name string,
        |age int
        |)
        |partitioned by(time string)
        |row format delimited fields terminated by '\t'
        |;
        |""".stripMargin)
    println("*************")
    spark.sql(
      """
        |insert overwrite table customer partition (time='1001')
        |select id,name,age
        |from data;
        |""".stripMargin)
    spark.sql("select * from customer").show()
    println("-------------------------------------------------------------")



    spark.stop()
  }

本题不难,首先要把hive和spark环境配好

所需环境配置:

pom文件

<?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">
    <parent>
        <artifactId>atguigu-classes</artifactId>
        <groupId>com.atguigu.bigdata</groupId>
        <version>1.0-SNAPSHOT</version>
    </parent>
    <modelVersion>4.0.0</modelVersion>

    <artifactId>spark-core</artifactId>
    <dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>3.0.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.12</artifactId>
            <version>3.0.3</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.12</artifactId>
            <version>3.0.3</version>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.27</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_2.12</artifactId>
            <version>3.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.hive</groupId>
            <artifactId>hive-exec</artifactId>
            <version>1.2.1</version>
        </dependency>
        <dependency>
            <groupId>log4j</groupId>
            <artifactId>log4j</artifactId>
            <version>1.2.17</version>
        </dependency>

    </dependencies>
    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
    </properties>


</project>

然后将hive中的hive-site.xml文件移到resources下

运行代码前一定要先把hive进程启动,然后hive的服务端也要启动

启动命令:hive --service metastore &