【大数据分析】Spark的二次开发工程edata-base，介绍

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已于 2022-08-11 11:32:37 修改

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分类专栏： Spark 文章标签： spark 大数据 scala

于 2022-07-26 11:52:57 首次发布

本文链接：https://blog.youkuaiyun.com/sword_csdn/article/details/125968431

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介绍

edata-base是基于Spark的大数据二次开发库，它封装了Spark与其他常用中间的使用方法，使得基于Spark的开发更加简便。
源码仓库地址：https://gitee.com/alan-sword/edata-base

工程介绍

在这里插入图片描述

edata-base 中的POM

edata-base 规定了Spark API与其他中间件API的版本，自定义Spark工程可以自行引用，edata-base-component引用了这个父工程。

<?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.edata.bigdata</groupId>
    <artifactId>edata-base</artifactId>
    <packaging>pom</packaging>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <java.version>1.8</java.version>
        <scala.version>2.11</scala.version>
        <scala.binary.version>2.11</scala.binary.version>
        <spark.version>2.4.3</spark.version>
        <hadoop.version>3.1.2</hadoop.version>
        <posgresql.version>42.1.1</posgresql.version>
        <nebula.version>2.6.1</nebula.version>
        <flink.version>1.14.3</flink.version>
        <zookeeper.version>3.6.1</zookeeper.version>
    </properties>

    <modules>
        <module>edata-base-component</module>
        <module>edata-bigdata-test</module>
    </modules>

    <dependencies>


        <!--Postgresql-->
        <dependency>
            <groupId>org.postgresql</groupId>
            <artifactId>postgresql</artifactId>
            <version>42.3.1</version>
        </dependency>

        <!--mongodb-->
        <dependency>
            <groupId>org.mongodb.spark</groupId>
            <artifactId>mongo-spark-connector_${scala.version}</artifactId>
            <version>2.4.3</version>
        </dependency>

        <!--Spark-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-10_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-hive_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-mllib_${scala.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!--ZooKeeper-->
        <dependency>
            <groupId>org.apache.zookeeper</groupId>
            <artifactId>zookeeper</artifactId>
            <version>${zookeeper.version}</version>
        </dependency>
    </dependencies>
    <build>
<!--        <sourceDirectory>src/main/scala</sourceDirectory>-->
<!--        <testSourceDirectory>src/test/scala</testSourceDirectory>-->
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <configuration>
                    <source>${java.version}</source>
                    <target>${java.version}</target>
                    <encoding>UTF-8</encoding>
                    <showWarnings>true</showWarnings>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <version>2.15.2</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
<!--            <plugin>-->
<!--                <groupId>org.apache.maven.plugins</groupId>-->
<!--                <artifactId>maven-surefire-plugin</artifactId>-->
<!--                <version>2.19</version>-->
<!--                <configuration>-->
<!--                    <skip>true</skip>-->
<!--                </configuration>-->
<!--            </plugin>-->
        </plugins>
    </build>
</project>

edata-base-component在自定义工程中的使用

我们可以先大致看看在自定义工程edata-base-test中是如何使用edata-base-component的，首先是POM文件

<?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">

    <parent>
        <artifactId>edata-base</artifactId>
        <groupId>com.edata.bigdata</groupId>
        <version>1.0-SNAPSHOT</version>
    </parent>
    <artifactId>edata-bigdata-test</artifactId>
    <modelVersion>4.0.0</modelVersion>

    <dependencies>
        <dependency>
            <groupId>com.edata.bigdata</groupId>
            <artifactId>edata-base-component</artifactId>
            <version>1.0-SNAPSHOT</version>
        </dependency>
    </dependencies>
</project>

如上所示，edata-base-test引用了edata-base-component的包。便可以如下图般使用相关的库。

package com.edata.bigdata.viewmain
import com.edata.bigdata.bean.MyClass
import com.edata.bigdata.mongo.{SparkMongoConnector, SparkMongoImpl}
object testing {
  def main(args: Array[String]): Unit = {
    //创建连接器
    val connector:SparkMongoConnector = new SparkMongoConnector()
    connector.appname="SparkMongoTesting"
    connector.master = "local[*]"
    connector.ipport = "192.168.36.141:27017"
    connector.database = "spark"
    connector.collection = "collection"
    connector.username = "admin"
    connector.password = "123456"
    //connector.uri = "mongodb://admin:123456@192.168.36.141:27017/spark.collection?authSource=admin"
    //创建Spark-mongo数据交互实例，赋予连接器
    val smi = new SparkMongoImpl[MyClass]
    smi.connector = connector
    val data = smi.find()
    data.first()
    smi.save(data)
  }
}

自定义工程在使用edata-base-component时，只需要实现两步
（1）创建Spark与其他中间件的连接器（connector）实例。
（2）创建Spark与其他中间的接口实例，创建过程中传入自定义case class（上面代码是MyClass），并将连接器实例赋给接口实例。

edata-base-component的模块划分

如下图所示
在这里插入图片描述
edata-base-component根据Spark与主流中间件在交互上的不同划分模块，例如hdfs，mongodb，postgresql等，此外还有一些工具类，以及针对自定义case class的反射转换。使得基于edata-base-component开发的Spark程序能够自动将用户定义的case class转换成DataFrame中的Schema。

工程的使用

将edata-base-component工程打包成Jar，并在自己的自定义工程里进行引用，POM文件如下

<?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">
    <parent>
        <artifactId>edata-base</artifactId>
        <groupId>com.edata.bigdata</groupId>
        <version>1.0-SNAPSHOT</version>
    </parent>
    <artifactId>edata-bigdata-test</artifactId>
    <modelVersion>4.0.0</modelVersion>
    <dependencies>
        <dependency>
            <groupId>com.edata.bigdata</groupId>
            <artifactId>edata-base-component</artifactId>
            <version>1.0-SNAPSHOT</version>
        </dependency>
    </dependencies>
</project>

总体上，可以将基于edata-base-component的代码分成两个部分，创建连接器，以及调用API。

    val connector:KafkaComsumeConnector = new KafkaComsumeConnector()
    connector.appname = "SparkKafkaComsumerTesting"
    connector.master = "local[*]"
    connector.bootstraps = "localhost:9092"
    connector.topic = "topicA"
    connector.group_id = "direct"

    val ski = new SparkKafkaImpl[String,String]
    ski.comsumeConnector = connector
    ski.createDirectStream()

    ski.stream.foreachRDD(rdd=>{
      rdd.foreach(println)
      val kvRDD = rdd.map(record=>(record.key,record.value))
      kvRDD.foreach(println)
    })
    ski.start()