Spark-SQL从MySQL中加载数据以及将数据写入到mysql中（Spark Shell方式，Spark SQL程序）

1． JDBC

Spark SQL可以通过JDBC从关系型数据库中读取数据的方式创建DataFrame，通过对DataFrame一系列的计算后，还可以将数据再写回关系型数据库中。

1.1． 从MySQL中加载数据（Spark Shell方式）

1.启动Spark Shell，必须指定mysql连接驱动jar包

[root@hadoop1 spark-2.1.1-bin-hadoop2.7]# bin/spark-shell --master spark://hadoop1:7077,hadoop2:7077 --jars /home/tuzq/software/spark-2.1.1-bin-hadoop2.7/jars/mysql-connector-java-5.1.38.jar --driver-class-path /home/tuzq/software/spark-2.1.1-bin-hadoop2.7/jars/mysql-connector-java-5.1.38.jar

2.从mysql中加载数据
进入bigdata中创建person表：

CREATE DATABASE bigdata CHARACTER SET utf8;
USE bigdata;
CREATE TABLE person (
    id INT(10) AUTO_INCREMENT PRIMARY KEY,
    name varchar(100),
    age INT(3)
) ENGINE=INNODB DEFAULT CHARSET=utf8;

并初始化数据：

scala> val sqlContext = new org.apache.spark.sql.SQLContext(sc)

scala> val jdbcDF = sqlContext.read.format("jdbc").options(Map("url" -> "jdbc:mysql://hadoop10:3306/bigdata", "driver" -> "com.mysql.jdbc.Driver", "dbtable" -> "person", "user" -> "root", "password" -> "123456")).load()

3.执行查询

scala> jdbcDF.show
+---+--------+---+
| id|    name|age|
+---+--------+---+
|  1|zhangsan| 19|
|  2|    lisi| 20|
|  3|  wangwu| 28|
|  4| zhaoliu| 26|
|  5|  tianqi| 55|
+---+--------+---+

1.2． 将数据写入到MySQL中（打jar包方式）

1.2.1编写Spark SQL程序

package cn.toto.spark

import java.sql.DriverManager
import org.apache.spark.rdd.JdbcRDD
import org.apache.spark.{SparkConf, SparkContext}

/**
  * Created by toto on 2017/7/11.
  */
object JdbcRDDDemo {

  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("JdbcRDDDemo").setMaster("local[2]")
    val sc = new SparkContext(conf)
    val connection = () => {
      Class.forName("com.mysql.jdbc.Driver").newInstance()
      DriverManager.getConnection("jdbc:mysql://hadoop10:3306/bigdata","root","123456")
    }
    //这个地方没有读取数据(数据库表也用的是person)
    val jdbcRDD = new JdbcRDD(
      sc,
      connection,
      "SELECT * FROM person where id >= ? AND id <= ?",
      //这里表示从取数据库中的第1、2、3、4条数据，然后分两个区
      1, 4, 2,
      r => {
        val id = r.getInt(1)
        val code = r.getString(2)
        (id, code)
      }
    )
    //这里相当于是action获取到数据
    val jrdd = jdbcRDD.collect()
    println(jrdd.toBuffer)
    sc.stop()
  }
}

注意在运行的时候使用的还是person这个表，表中的数据如下：

如果是在IDEA中运行程序，程序结果如下：

1.2.2用maven将程序打包

1.2.3.将Jar包提交到spark集群

将bigdata-1.0-SNAPSHOT.jar放到：/home/tuzq/software/sparkdata，如下：

注意在运行执行，要将mysql-connector-java-5.1.38.jar 放到：/home/tuzq/software/spark-2.1.1-bin-hadoop2.7/jars/下

bin/spark-submit --class cn.toto.spark.JdbcRDDDemo --master spark://hadoop1:7077 --jars /home/tuzq/software/spark-2.1.1-bin-hadoop2.7/jars/mysql-connector-java-5.1.38.jar --driver-class-path /home/tuzq/software/spark-2.1.1-bin-hadoop2.7/jars/mysql-connector-java-5.1.38.jar /home/tuzq/software/sparkdata/bigdata-1.0-SNAPSHOT.jar

运行结果：

2、通过Spark-sql将数据存储到数据库中

2.2.1.代码如下：

package cn.toto.spark

import java.util.Properties

import org.apache.spark.sql.{Row, SQLContext}
import org.apache.spark.sql.types.{IntegerType, StringType, StructField, StructType}
import org.apache.spark.{SparkConf, SparkContext}

/**
  * Created by toto on 2017/7/11.
  */
object JdbcRDD {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setAppName("MySQL-Demo").setMaster("local")
    val sc = new SparkContext(conf)
    val sqlContext = new SQLContext(sc)
    //通过并行化创建RDD
    val personRDD = sc.parallelize(Array("14 tom 5", "15 jerry 3", "16 kitty 6")).map(_.split(" "))
    //通过StrutType直接指定每个字段的schema
    val schema = StructType(
      List(
        StructField("id",IntegerType,true),
        StructField("name",StringType,true),
        StructField("age",IntegerType,true)
      )
    )
    //将RDD映射到rowRDD
    val rowRDD = personRDD.map(p => Row(p(0).toInt, p(1).trim, p(2).toInt))
    //将schema信息应用到rowRDD上
    val personDataFrame = sqlContext.createDataFrame(rowRDD,schema)
    //创建Properties存储数据库相关属性
    val prop = new Properties()
    prop.put("user", "root")
    prop.put("password", "123456")
    //将数据追加到数据库
    personDataFrame.write.mode("append").jdbc("jdbc:mysql://hadoop10:3306/bigdata",
      "bigdata.person",prop)
    //停止SparkContext
    sc.stop()
  }
}

运行结果：

2.2.2、用maven将程序打包

2.2.3、将Jar包提交到spark集群

bin/spark-submit --class cn.toto.spark.JdbcRDD --master spark://hadoop1:7077 --jars /home/tuzq/software/spark-2.1.1-bin-hadoop2.7/jars/mysql-connector-java-5.1.38.jar --driver-class-path /home/tuzq/software/spark-2.1.1-bin-hadoop2.7/jars/mysql-connector-java-5.1.38.jar /home/tuzq/software/sparkdata/bigdata-1.0-SNAPSHOT.jar