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

[[email protected] 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