原文引自：http://blog.csdn.net/fengzhimohan/article/details/78471952
项目应用需要利用Spark读取mysql数据进行数据分析，然后将分析结果保存到mysql中。
开发环境：
java：1.8
IDEA
spark：1.6.2
一.读取mysql数据
1.创建一个mysql数据库
user_test表结构如下：
create table user_test (
id int(11) default null comment "id",
name varchar(64) default null comment "用户名",
password varchar(64) default null comment "密码",
age int(11) default null comment "年龄"
)engine=InnoDB default charset=utf-8;
2.插入数据
insert into user_test values(12, 'cassie', '123456', 25);
insert into user_test values(11, 'zhangs', '1234562', 26);
insert into user_test values(23, 'zhangs', '2321312', 27);
insert into user_test values(22, 'tom', 'asdfg', 28);
3.创建maven工程，命名为Test，添加java类SparkMysql
添加依赖包
pom文件内容：
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion> <groupId>SparkSQL</groupId>
<artifactId>com.sparksql.test</artifactId>
<version>1.0-SNAPSHOT</version>
<properties>
<java.version>1.8</java.version>
</properties>
<dependencies>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.24</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.6.0</version>
</dependency>
<dependency>
<groupId>net.sf.json-lib</groupId>
<artifactId>json-lib</artifactId>
<version>2.4</version>
<classifier>jdk15</classifier>
</dependency> </dependencies> </project>
4.编写spark代码
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext; import java.util.Properties; /**
* Created by Administrator on 2017/11/6.
*/
public class SparkMysql {
public static org.apache.log4j.Logger logger = org.apache.log4j.Logger.getLogger(SparkMysql.class); public static void main(String[] args) {
JavaSparkContext sparkContext = new JavaSparkContext(new SparkConf().setAppName("SparkMysql").setMaster("local[5]"));
SQLContext sqlContext = new SQLContext(sparkContext);
//读取mysql数据
readMySQL(sqlContext); //停止SparkContext
sparkContext.stop();
}
private static void readMySQL(SQLContext sqlContext){
//jdbc.url=jdbc:mysql://localhost:3306/database
String url = "jdbc:mysql://localhost:3306/test";
//查找的表名
String table = "user_test";
//增加数据库的用户名(user)密码(password),指定test数据库的驱动(driver)
Properties connectionProperties = new Properties();
connectionProperties.put("user","root");
connectionProperties.put("password","123456");
connectionProperties.put("driver","com.mysql.jdbc.Driver"); //SparkJdbc读取Postgresql的products表内容
System.out.println("读取test数据库中的user_test表内容");
// 读取表中所有数据
DataFrame jdbcDF = sqlContext.read().jdbc(url,table,connectionProperties).select("*");
//显示数据
jdbcDF.show();
}
}
运行结果：
二.写入数据到mysql中
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructType; import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Properties; /**
* Created by Administrator on 2017/11/6.
*/
public class SparkMysql {
public static org.apache.log4j.Logger logger = org.apache.log4j.Logger.getLogger(SparkMysql.class); public static void main(String[] args) {
JavaSparkContext sparkContext = new JavaSparkContext(new SparkConf().setAppName("SparkMysql").setMaster("local[5]"));
SQLContext sqlContext = new SQLContext(sparkContext);
//写入的数据内容
JavaRDD<String> personData = sparkContext.parallelize(Arrays.asList("1 tom 5","2 jack 6","3 alex 7"));
//数据库内容
String url = "jdbc:mysql://localhost:3306/test";
Properties connectionProperties = new Properties();
connectionProperties.put("user","root");
connectionProperties.put("password","123456");
connectionProperties.put("driver","com.mysql.jdbc.Driver");
/**
* 第一步：在RDD的基础上创建类型为Row的RDD
*/
//将RDD变成以Row为类型的RDD。Row可以简单理解为Table的一行数据
JavaRDD<Row> personsRDD = personData.map(new Function<String,Row>(){
public Row call(String line) throws Exception {
String[] splited = line.split(" ");
return RowFactory.create(Integer.valueOf(splited[0]),splited[1],Integer.valueOf(splited[2]));
}
}); /**
* 第二步：动态构造DataFrame的元数据。
*/
List structFields = new ArrayList();
structFields.add(DataTypes.createStructField("id",DataTypes.IntegerType,true));
structFields.add(DataTypes.createStructField("name",DataTypes.StringType,true));
structFields.add(DataTypes.createStructField("age",DataTypes.IntegerType,true)); //构建StructType，用于最后DataFrame元数据的描述
StructType structType = DataTypes.createStructType(structFields); /**
* 第三步：基于已有的元数据以及RDD<Row>来构造DataFrame
*/
DataFrame personsDF = sqlContext.createDataFrame(personsRDD,structType); /**
* 第四步：将数据写入到person表中
*/
personsDF.write().mode("append").jdbc(url,"person",connectionProperties); //停止SparkContext
sparkContext.stop();
}
}
运行结果：