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【SPARK】使用Spark(jdbc)从MySQL读取和保存数据

2016-01-07 21:32 推荐: 赛克通浏览: 320字号: 大中小

摘要: 本文最初思路来自 http://www.sparkexpert.com ，但是，发现 https://github.com/sujee81/SparkApps 提供的源码太老了，Spark官方从1.4.0已经放弃原来的方法（包含：createJDBCTable...

本文最初思路来自 http://www.sparkexpert.com ，但是，发现 https://github.com/sujee81/SparkApps 提供的源码太老了，Spark官方从1.4.0已经放弃原来的方法（包含：createJDBCTable，insertIntoJDBC等），取而代之的是 sqlContext.read().jdbc()和sqlContext.write().jdbc()方法。

一、源码下载

git clone https://github.com/jiekechoo/spark-jdbc-apps.git

源代码目录如下，今天主要说明前面两个：

spark-load-from-db：从数据库读取
spark-save-to-db：保存到数据库
spark-stats：下一篇文章介绍
spark-jdbcrdd：下一篇文章介绍

二、源码分析

依赖包分析

父项目pom，定义了共用组件slf4j，spark版本1.5.1，mysql5.1.32等

<?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>com.sectong</groupId>
    <artifactId>spark-apps-parent</artifactId>
    <version>1.0-SNAPSHOT</version>

    <name>spark-apps-parent</name>
    <packaging>pom</packaging>

    <modules>
        <module>spark-jdbcrdd</module>
        <module>spark-load-from-db</module>
        <module>spark-save-to-db</module>
        <module>spark-stats</module>
    </modules>
    <dependencies>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-api</artifactId>
            <version>1.7.13</version>
        </dependency>
    </dependencies>
    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <spark.version>1.5.1</spark.version>
        <mysql.version>5.1.32</mysql.version>
    </properties>

</project>

保存到数据库spark-save-to-db

依赖包，主要是spark-core和spark-sql，还有mysql驱动：

<?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>
    <parent>
        <groupId>com.sectong</groupId>
        <artifactId>spark-apps-parent</artifactId>
        <version>1.0-SNAPSHOT</version>
    </parent>
    <artifactId>spark-save-to-db</artifactId>

    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.10</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.10</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>${mysql.version}</version>
        </dependency>
    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>3.2</version>
                <configuration>
                    <source>1.8</source>
                    <target>1.8</target>
                    <compilerArgument>-Xlint:all</compilerArgument>
                    <showWarnings>true</showWarnings>
                    <showDeprecation>true</showDeprecation>
                </configuration>
            </plugin>
        </plugins>
    </build>

</project>

看源码：

package com.sectong;

import java.io.Serializable;
import java.util.Properties;

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 org.apache.spark.sql.SaveMode;

public class Main implements Serializable {

    /**
     * 
     */
    private static final long serialVersionUID = -8513279306224995844L;
    private static final String MYSQL_USERNAME = "demo";
    private static final String MYSQL_PWD = "demo";
    private static final String MYSQL_CONNECTION_URL = "jdbc:mysql://192.168.1.91:3306/demo";

    private static final JavaSparkContext sc = new JavaSparkContext(new SparkConf().setAppName("SparkSaveToDb").setMaster("local[*]"));

    private static final SQLContext sqlContext = new SQLContext(sc);

    public static void main(String[] args) {
        // Sample data-frame loaded from a JSON file
        DataFrame usersDf = sqlContext.read().json("users.json");

        // Save data-frame to MySQL (or any other JDBC supported databases)
        Properties connectionProperties = new Properties();
        connectionProperties.put("user", MYSQL_USERNAME);
        connectionProperties.put("password", MYSQL_PWD);

        // write dataframe to jdbc mysql
        usersDf.write().mode(SaveMode.Append).jdbc(MYSQL_CONNECTION_URL, "users", connectionProperties);
    }
}

我们为了写入数据方便测试，需要一个json文件，类似下方：

{"id":994,"name":"Betty","email":"bsmithrl@simplemachines.org","city":"Eláteia","country":"Greece","ip":"9.19.204.44"},
{"id":995,"name":"Anna","email":"alewisrm@canalblog.com","city":"Shangjing","country":"China","ip":"14.207.119.126"},
{"id":996,"name":"David","email":"dgarrettrn@japanpost.jp","city":"Tsarychanka","country":"Ukraine","ip":"111.252.63.159"},
{"id":997,"name":"Heather","email":"hgilbertro@skype.com","city":"Koilás","country":"Greece","ip":"29.57.181.250"},
{"id":998,"name":"Diane","email":"ddanielsrp@statcounter.com","city":"Mapiripán","country":"Colombia","ip":"19.205.181.99"},
{"id":999,"name":"Philip","email":"pfullerrq@reuters.com","city":"El Cairo","country":"Colombia","ip":"210.248.121.194"},
{"id":1000,"name":"Maria","email":"mfordrr@shop-pro.jp","city":"Karabash","country":"Russia","ip":"224.21.41.52"}

读取文件时，users.json需要与jar包在同一目录下，测试采用本地运行方式：

DataFrame usersDf = sqlContext.read().json("users.json");

其中，代码中的这行mode(SaveMode.Append)要特别注意，这个使得每次写入的数据是增加到数据表中。否则会一直提示：Exception in thread "main" java.lang.RuntimeException: Table users already exists.

usersDf.write().mode(SaveMode.Append).jdbc(MYSQL_CONNECTION_URL, "users", connectionProperties);

打包，上传spark运行：

/opt/spark/bin/spark-submit --class com.sectong.Main --driver-class-path mysql-connector-java-5.1.32.jar spark-save-to-db-1.0-SNAPSHOT.jar

结果就是下面这样了：保存数据后结果

从数据库读取spark-load-from-db

依赖包与保存数据基本一致，不再说明。

看源码：

package com.sectong;

import java.io.Serializable;
import java.util.List;
import java.util.Properties;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class Main implements Serializable {

    /**
     * 
     */
    private static final long serialVersionUID = -8513279306224995844L;

    private static final Logger LOGGER = LoggerFactory.getLogger(Main.class);

    private static final String MYSQL_USERNAME = "demo";
    private static final String MYSQL_PWD = "demo";
    private static final String MYSQL_CONNECTION_URL = "jdbc:mysql://192.168.1.91:3306/demo";

    private static final JavaSparkContext sc = new JavaSparkContext(new SparkConf().setAppName("SparkJdbcFromDb").setMaster("local[*]"));

    private static final SQLContext sqlContext = new SQLContext(sc);

    public static void main(String[] args) {

        Properties properties = new Properties();
        properties.put("user", MYSQL_USERNAME);
        properties.put("password", MYSQL_PWD);
        // Load MySQL query result as DataFrame
        DataFrame jdbcDF = sqlContext.read().jdbc(MYSQL_CONNECTION_URL, "users", properties);

        List<Row> employeeFullNameRows = jdbcDF.collectAsList();

        for (Row employeeFullNameRow : employeeFullNameRows) {
            LOGGER.info(employeeFullNameRow.toString());
        }
    }
}

读取MySQL数据，这行最关键：

DataFrame jdbcDF = sqlContext.read().jdbc(MYSQL_CONNECTION_URL, "users", properties);

再打印出来：

List<Row> employeeFullNameRows = jdbcDF.collectAsList();
for (Row employeeFullNameRow : employeeFullNameRows) {
            LOGGER.info(employeeFullNameRow.toString());
        }

Spark运行程序，注意--driver-class-path mysql-connector-java-5.1.32.jar参数，需要把mysql-connector挂载上：

/opt/spark/bin/spark-submit --class com.sectong.Main --driver-class-path mysql-connector-java-5.1.32.jar spark-load-from-db-1.0-SNAPSHOT.jar

中间运行省略了，把结果输出：

2016-01-06 08:14:01[main] INFO  Main:43 - [Matriz de Camaragibe,Brazil,sgarciadp@nifty.com,494,39.244.171.48,Steven]
2016-01-06 08:14:01[main] INFO  Main:43 - [Huarancante,Peru,njacksondq@si.edu,495,67.123.78.80,Nicholas]
2016-01-06 08:14:01[main] INFO  Main:43 - [Zandak,Russia,sjonesdr@nbcnews.com,496,167.69.237.11,Sarah]
2016-01-06 08:14:01[main] INFO  Main:43 - [Somovo,Russia,jgardnerds@nsw.gov.au,497,112.190.104.80,Judy]
2016-01-06 08:14:01[main] INFO  Main:43 - [Huaping,China,calexanderdt@blinklist.com,498,79.242.142.206,Christine]
2016-01-06 08:14:01[main] INFO  Main:43 - [Isulan,Philippines,wgomezdu@imdb.com,499,26.220.121.74,Wanda]
2016-01-06 08:14:01[main] INFO  Main:43 - [Wujiayao,China,wleedv@latimes.com,500,26.104.219.178,Walter]
2016-01-06 08:14:01[main] INFO  Main:43 - [Dongtou,China,hriveradw@skype.com,501,82.13.121.35,Henry]
2016-01-06 08:14:01[Thread-3] INFO  SparkContext:59 - Invoking stop() from shutdown hook
2016-01-06 08:14:01[Thread-3] INFO  ContextHandler:843 - stopped o.s.j.s.ServletContextHandler{/static/sql,null}
2016-01-06 08:14:01[Thread-3] INFO  ContextHandler:843 - stopped o.s.j.s.ServletContextHandler{/SQL/execution/json,null}

微信公众服务号：sectong

原文地址： http://blog.sectong.com/blog/spark_jdbc_load_save.html

热点推荐

【SPARK】使用Spark(jdbc)从MySQL读取和保存数据

一、源码下载

二、源码分析

依赖包分析

保存到数据库spark-save-to-db

从数据库读取spark-load-from-db

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