spark访问mysql、spark访问hive

spark访问mysql:

导入依赖

 <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.11</artifactId>
      <version>2.3.4</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-sql -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_2.11</artifactId>
      <version>2.3.4</version>
    </dependency>
    <!-- https://mvnrepository.com/artifact/mysql/mysql-connector-java -->
    <dependency>
      <groupId>mysql</groupId>
      <artifactId>mysql-connector-java</artifactId>
      <version>5.1.38</version>
    </dependency>

利用SparkSession编写spark SQL

val spark = SparkSession.builder().master("local[*]").appName("job").getOrCreate()
    val prop = new Properties()
    prop.setProperty("driver","com.mysql.jdbc.Driver")
    prop.setProperty("user","root")
    prop.setProperty("password","1234")
    
    //读表操作
    val jdbcDF = spark.read.jdbc("jdbc:mysql://192.168.56.192/word","keyword",prop)
        jdbcDF.show(false)

    //写入操作
    import spark.implicits._
    val df = spark.createDataFrame(spark.sparkContext.parallelize(Seq((13,"衣服",300.0),(14,"裤子",299.00))))
      .toDF("id","name","type")
    df.write.mode("append")
      .jdbc("jdbc:mysql://192.168.56.192/word","keyword",prop)

spark访问hive:

导入依赖：

<!--hive-spark-->
    <dependency>
      <groupId>org.apache.spark </groupId>
      <artifactId>spark-hive_2.11</artifactId>
      <version>2.3.4</version>
    </dependency>

resource资源文件配置：

将hadoop集群五个配置项中的core-site.xml、hdfs-site.xml和hive配置项中的hive-site.xml复制一份到resource内

编写spark SQL

def main(args: Array[String]): Unit = {
    val spark =SparkSession.builder().master("local[2]").appName("job").enableHiveSupport().getOrCreate()
    val df = spark.sql("select * from study.shop")
    df.show(false)
  }

注：在正常的sparkSession语句内添加hiveSupport即可