使用MySQL數據庫作為數據源。<br/> （1）在指定MySQL驅動包 ```shell -- 將MySQL的驅動包放到 {SPARK_HOME}/conf/mysql-connector-java-5.1.27.jar -- 或者使用spak-shell命令指定驅動包 [root@hadoop101 spark]# spark-shell --jars /opt/software/mysql-connector-java-5.1.38.jar ``` （2）scala代碼 記得啟動MySQL服務。 ```scala import java.util.Properties import org.apache.spark.sql.{DataFrame, SparkSession} object MysqlSource { def main(args: Array[String]): Unit = { val spark:SparkSession = SparkSession.builder() .master("local[4]") .appName(this.getClass.getName) .getOrCreate() // 1. MySQL相關配置 val url = "jdbc:mysql://hadoop101:3306/sqoop_db" val tableName = "orders" val props = new Properties() props.setProperty("user", "root") props.setProperty("password", "123456") props.setProperty("driver", "com.mysql.jdbc.Driver") // 2. 讀取MySQL中的數據創建DataFrame val df:DataFrame = spark.read.jdbc(url, tableName, props) df.show(5) // +--------+-------------------+-----------------+---------------+ // |order_id| order_date|order_customer_id| order_status| // +--------+-------------------+-----------------+---------------+ // | 1|2013-07-25 00:00:00| 11599| CLOSED| // | 2|2013-07-25 00:00:00| 256|PENDING_PAYMENT| // | 3|2013-07-25 00:00:00| 12111| COMPLETE| // | 4|2013-07-25 00:00:00| 8827| CLOSED| // | 5|2013-07-25 00:00:00| 11318| COMPLETE| // +--------+-------------------+-----------------+---------------+ // 3. 也可以按照mode將寫數據到MySQL df.write.mode("overwrite").jdbc(url, tableName, props) } } ```