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.sparkimport java.sql.DriverManagerimport org.apache.spark.rdd.JdbcRDDimport 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.sparkimport java.util.Propertiesimport 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