[大数据]项目实战——参数配置化Spark将Hive表的数据写入需要用户名密码认证的ElasticSearch（Java版本）

目 录

1. 项目实战——将Hive表的数据直接导入ElasticSearch
   ??此篇文章不用写代码，简单粗暴，但是相对没有那么灵活；底层采用MapReduce计算框架，导入速度相对较慢！

2. 项目实战——Spark将Hive表的数据写入ElasticSearch（Java版本）
   ??此篇文章需要Java代码，实现功能和篇幅类似，直接Java一站式解决Hive内用Spark取数，新建ES索引，灌入数据，并且采用ES别名机制，实现ES数据更新的无缝更新，底层采用Spark计算框架，导入速度相对文章1的做法较快的多！；

3. 项目实战——钉钉报警验证ElasticSearch和Hive数据仓库内的数据质量（Java版本）
   ??此篇文章主要选取关键性指标，数据校验数据源Hive和目标ES内的数据是否一致；

4. 项目实战——Spark将Hive表的数据写入需要用户名密码认证的ElasticSearch（Java版本）
   ??此篇文章主要讲述如何通过spark将hive数据写入带账号密码权限认证的ElasticSearch 内；

5. 项目实战(生产环境部署上线)——参数配置化Spark将Hive表的数据写入需要用户名密码认证的ElasticSearch（Java版本））
   ??此篇文章主要讲述如何通过spark将hive数据写入带账号密码权限认证的ElasticSearch 内，同时而是，spark，es建索引参数配置化，每次新增一张表同步到es只需要新增一个xml配置文件即可，也是博主生产环境运用的java代码，弥补下很多老铁吐槽方法4的不足。

??综述：
??1.如果感觉编码能力有限，又想用到Hive数据导入ElasticSearch，可以考虑文章1；
??2.如果有编码能力，个人建议采用文章2和文章3的组合情况（博主推荐），作为离线或者近线数据从数据仓库Hive导入ElasticSearch的架构方案，并且此次分享的Java代码为博主最早实现的版本1，主要在于易懂，实现功能，学者们可以二次加工，请不要抱怨代码写的烂；
??3.如果是elasticsearch是自带账号密码权限认证的,如云产品或者自己设置了账号密码认证的，那么办法，只能用文章4了；
??4.如果部署上线，还是要看文章5。

• 本人Hive版本：2.3.5

• 本人ES版本：7.7.1

• 本人Spark版本：2.3.3

  背 景

  ??将要创建的ES索引信息和ES的连接信息参数化，这样每次新增一张表时，只需要新增一个xml配置文件即可，es服务器迁移，只需要变更一个ES文件即可，因为是大数据环境嘛，博主选择把这两类配置文件放在hdfs上，当然如果没有hdfs，也可以把配置文件放到ftp，或者某共享文件夹下，只是不同文件系统在读取配置文件的IO流略有不同，读者根据自己的文件系统来选择相应的文件IO流即可。
  ??如图1，主要数据链路架构就是通过调用编译好的jar包读取hdfs上的配置文件信息，再通过spark将hive的表同步到Elasticsearch内。

  

  图1 参数化数据链路图

  ??ElasticSearch是可以配置用户名，密码认证的，特别是云产品，公司如果买的ElasticSearch的云服务，那必然是带用户名密码认证的，即当你访问你的ES时，默认一般是9200端口时会弹出如图2的提示，需要你填写用户名密码；

解决方案

ping通ES的机器

??在你要访问的源机器ping通需要目标端的es机器ip，ping不通，找网管；

telnet通ES的机器的端口

??在你要访问的源机器telnet通需要目标端的es机器ip和端口，telnet不通，找网管；

拿到用户名和密码

??既然是用户名和密码认证，当然需要向管理员拿到账号和密码，拿到用户名和密码后，先去测试下该用户名能否登陆es，并且能否读写权限，读写，创建index(非必要)，可以在kibana上验证,认证访问，最好在你跑程序的地方，跑一下RESTFul风格的代码,如下（linux环境shell命令行内直接跑）；

# 用户名密码有转移字符，记得前面加\转移，如abc!123,写成abc\!123
# 用户名密码有转移字符，记得前面加\转移，如abc!123,写成abc\!123
# 用户名密码有转移字符，记得前面加\转移，如abc!123,写成abc\!123
curl -k -u user:password -XGET http://es-ip:9200/your_index/_search

??windows cmd下：

# 注意用户名密码后面是@符号，用户名密码有转译字符可不转译，别乱搞
# 注意用户名密码后面是@符号，用户名密码有转译字符可不转译，别乱搞
# 注意用户名密码后面是@符号，用户名密码有转译字符可不转译，别乱搞
curl "http://user:password@es-ip:9200/your_index/_search"

??如果能获取到数据，说明网络，账号一切都Ok，加上kibana能读写index，说明权限Ok，否则，哪一环出了问题去找到相关的人员解决，准备工作都Ok了，再去写代码，不然代码一直报错，让你怀疑人生；

项目树

??总体项目树图谱如图1所示，编程软件：IntelliJ IDEA 2019.3 x64,采用Maven架构；
/LXWalaz1s1s/13037253)

• feign:连接ES和Spark客户端相关的Java类；
• utils:操作ES和Spark相关的Java类；
• resources:日志log的配置类；
• pom.xml:Maven配置文件；

Maven配置文件pox.xml

??该项目使用到的Maven依赖包存在pom.xml上，具体如下所示；.

<?xml version="1.0" encoding="UTF-8"?>
<?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>org.example</groupId>
  <artifactId>SparkOnHiveToEs_buildinginfo_v1</artifactId>
  <version>1.0-SNAPSHOT</version>

  <name>SparkOnHiveToEs_buildinginfo_v1</name>
  <!-- FIXME change it to the project's website -->
  <url>http://www.example.com</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
    <maven.compiler.source>1.7</maven.compiler.source>
    <maven.compiler.target>1.7</maven.compiler.target>
  </properties>

  <dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.11</version>
      <scope>test</scope>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.elasticsearch/elasticsearch -->
    <!--ES本身的依赖-->
    <dependency>
      <groupId>org.elasticsearch</groupId>
      <artifactId>elasticsearch</artifactId>
      <version>7.7.1</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.elasticsearch.client/elasticsearch-rest-high-level-client -->
    <!--ES高级API，用来连接ES的Client等操作-->
    <dependency>
      <groupId>org.elasticsearch.client</groupId>
      <artifactId>elasticsearch-rest-high-level-client</artifactId>
      <version>7.7.1</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/junit/junit -->
    <!--junit，Test测试使用-->
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.12</version>
      <scope>test</scope>
    </dependency>


    <!-- https://mvnrepository.com/artifact/org.projectlombok/lombok -->
    <!--lombok ，用来自动生成对象类的构造函数，get，set属性等-->
    <dependency>
      <groupId>org.projectlombok</groupId>
      <artifactId>lombok</artifactId>
      <version>1.18.12</version>
      <scope>provided</scope>
    </dependency>
    <dependency>
      <groupId>org.testng</groupId>
      <artifactId>testng</artifactId>
      <version>RELEASE</version>
      <scope>compile</scope>
    </dependency>

    <!-- https://mvnrepository.com/artifact/com.fasterxml.jackson.core/jackson-databind -->
    <!--jackson，用来封装json-->
    <dependency>
      <groupId>com.fasterxml.jackson.core</groupId>
      <artifactId>jackson-databind</artifactId>
      <version>2.11.0</version>
    </dependency>

    <dependency>
      <groupId>org.elasticsearch</groupId>
      <artifactId>elasticsearch-hadoop</artifactId>
      <version>7.7.1</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.elasticsearch/elasticsearch-spark-20 -->
    <dependency>
      <groupId>org.elasticsearch</groupId>
      <artifactId>elasticsearch-spark-20_2.11</artifactId>
      <version>7.7.1</version>
    </dependency>


    <!-- https://mvnrepository.com/artifact/org.apache.spark/spark-core -->
    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-core_2.11</artifactId>
      <version>2.3.3</version>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-sql_2.11</artifactId>
      <version>2.3.3</version>
    </dependency>

    <dependency>
      <groupId>org.apache.spark</groupId>
      <artifactId>spark-hive_2.11</artifactId>
      <version>2.3.3</version>
      <scope>compile</scope>
    </dependency>

    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>4.12</version>
      <scope>compile</scope>
    </dependency>

    <dependency>
      <groupId>org.apache.logging.log4j</groupId>
      <artifactId>log4j-core</artifactId>
      <version>2.9.1</version>
    </dependency>

    <dependency>
      <groupId>org.apache.logging.log4j</groupId>
      <artifactId>log4j-api</artifactId>
      <version>2.9.1</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/dom4j/dom4j -->
    <dependency>
      <groupId>dom4j</groupId>
      <artifactId>dom4j</artifactId>
      <version>1.6.1</version>
    </dependency>

    <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-common -->
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>2.8.5</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-hdfs</artifactId>
      <version>2.8.5</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>2.8.5</version>
    </dependency>

  </dependencies>


  <build>
  <plugins>
    <!-- 在maven项目中既有java又有scala代码时配置 maven-scala-plugin 插件打包时可以将两类代码一起打包 -->
    <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>

    <!-- maven 打jar包需要插件 -->
    <plugin>
      <artifactId>maven-assembly-plugin</artifactId>
      <version>2.4</version>
      <configuration>
        <!-- 设置false后是去掉 MySpark-1.0-SNAPSHOT-jar-with-dependencies.jar 后的 “-jar-with-dependencies” -->
        <!--<appendAssemblyId>false</appendAssemblyId>-->
        <descriptorRefs>
          <descriptorRef>jar-with-dependencies</descriptorRef>
        </descriptorRefs>
        <archive>
          <manifest>
            <mainClass>com.bjsxt.scalaspark.core.examples.ExecuteLinuxShell</mainClass>
          </manifest>
        </archive>
      </configuration>
      <executions>
        <execution>

          <id>make-assembly</id>
          <phase>package</phase>
          <goals>
            <goal>assembly</goal>
          </goals>
        </execution>
      </executions>
    </plugin>
  </plugins>
</build>
</project>

日志配置文件

??最终这个Job是需要给spark-submit调用的，所以希望有一些有用关键的信息可以通过日志输出，而不是采用System,out.println的形式输出到console端，所以要用到log.info("关键内容信息")方法，所以设置两个log的配置信息，如，只输出bug，不输出warn等，可以根据自己需求来配置，具体两个log配置文件内容如下；
??log4j.properties配置如下；

log4j.rootLogger=INFO, stdout, R
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%5p - %m%n
log4j.appender.R=org.apache.log4j.RollingFileAppender
log4j.appender.R.File=firestorm.log
log4j.appender.R.MaxFileSize=100KB
log4j.appender.R.MaxBackupIndex=1
log4j.appender.R.layout=org.apache.log4j.PatternLayout
log4j.appender.R.layout.ConversionPattern=%p %t %c - %m%n
log4j.logger.com.codefutures=INFO

??log4j2.xml配置如下；

<?xml version="1.0" encoding="UTF-8"?>

<Configuration status="warn">
    <Appenders>
        <Console name="Console" target="SYSTEM_OUT">
            <PatternLayout pattern="%m%n" />
        </Console>
    </Appenders>
    <Loggers>
        <Root level="INFO">
            <AppenderRef ref="Console" />
        </Root>
    </Loggers>
</Configuration>

读取hdfs配置文件

??注意配置是存在hdfs上的，当然读者也可以根据自己需求存在不同的文件系统内，因为存在hdfs文件系统，所以要遵循hdfs文件系统的IO流，具体参看一下PropertiesUtils.java

package cn.focusmedia.esapp.utils;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.dom4j.Document;
import org.dom4j.DocumentException;
import org.dom4j.Element;
import org.dom4j.io.SAXReader;

import java.io.*;
import java.util.Iterator;
import java.util.Properties;


public class PropertiesUtils {
    public static String getProperties(String filePath,String key)
    {
//        //本地文件系统
//        Properties prop =new Properties();
//        try {
//            InputStream inputStream=new BufferedInputStream(new FileInputStream(new File(filePath)));
//            prop.load(inputStream);
//
//        } catch (Exception e) {
//            e.printStackTrace();
//        }
//        return  prop.getProperty(key);

        //hdfs文件系统
        Configuration conf = new Configuration();
        FileSystem fs=null;
        Properties prop =new Properties();
        try {
            fs= FileSystem.get(conf);
        } catch (IOException e) {
            e.printStackTrace();
        }
        Path path = new Path(filePath);
        FSDataInputStream inputStream=null;
        try {
            inputStream  = fs.open(path);
            prop.load(inputStream);

        } catch (IOException e) {
            e.printStackTrace();
        }

        return  prop.getProperty(key);
    }

    //解xml
    public static String getXML(String filePath,String key)
    {
        Configuration conf = new Configuration();
        FileSystem fs=null;
        try {
             fs= FileSystem.get(conf);
        } catch (IOException e) {
            e.printStackTrace();
        }
        Path path = new Path(filePath);
        FSDataInputStream inputStream=null;
        try {
            inputStream  = fs.open(path);
        } catch (IOException e) {
            e.printStackTrace();
        }


         File file=new File(filePath);
        SAXReader reader=new SAXReader();
        String myValue = null;
        try {
            Document doc=reader.read(inputStream);
          //  Document doc=reader.read(file);
            Element root = doc.getRootElement();
            Element foo;

            for (Iterator i = root.elementIterator("VALUE"); i.hasNext();) {
                foo = (Element) i.next();
                myValue= foo.elementText(key);
            }
        } catch (DocumentException e) {
            e.printStackTrace();
        }

        return myValue;

    }


}

连接Spark的客户端

??程序最终选择在yarn上跑，所以这一块可以选择忽略。

连接ElasticSearch的客户端

??将ES的连接信息配置文件存在hdfs的/app/hive_to_es/configure/prod_es_connection.properties,内容如下，用户名密码可以配进去，但是没必要，因为毕竟hdfs文件系统，安全性不高，博主用户名密码是写死在程序内。

#ElasticSearch Connection
node_num=3
node1=10.218.10.22
node2=10.218.10.21
node3=10.218.10.20
port=9200

??要想操作ES，首先需要配置连接ES的客户端，具体代码如下的EsClient.java文件；

package cn.focusmedia.esapp.feign;

import cn.focusmedia.esapp.utils.PropertiesUtils;
import org.apache.http.HttpHost;
import lombok.extern.slf4j.Slf4j;
import org.apache.http.auth.AuthScope;
import org.apache.http.auth.UsernamePasswordCredentials;
import org.apache.http.client.CredentialsProvider;
import org.apache.http.impl.client.BasicCredentialsProvider;
import org.apache.http.impl.nio.client.HttpAsyncClientBuilder;
import org.elasticsearch.action.get.GetRequest;
import org.elasticsearch.action.get.GetResponse;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestClientBuilder;
import org.elasticsearch.client.RestHighLevelClient;
import org.junit.Test;


import java.io.IOException;

@Slf4j
public class EsClient
{
    public static RestHighLevelClient getClient()
    {
        int num=Integer.parseInt(PropertiesUtils.getProperties("/app/hive_to_es/configure/prod_es_connection.properties", "node_num"));
        int port=Integer.parseInt(PropertiesUtils.getProperties("/app/hive_to_es/configure/prod_es_connection.properties","port"));
//        int num=Integer.parseInt(PropertiesUtils.getProperties("configure/prod_es_connection.properties", "node_num"));
//        int port=Integer.parseInt(PropertiesUtils.getProperties("configure/prod_es_connection.properties","port"));

        HttpHost[] myHttpHost = new HttpHost[num];

        for(int i=1;i<=num;i++)
        {
            myHttpHost[i-1]=new HttpHost(PropertiesUtils.getProperties("/app/hive_to_es/configure/prod_es_connection.properties","node"+i),port);
          //  myHttpHost[i-1]=new HttpHost(PropertiesUtils.getProperties("configure/prod_es_connection.properties","node"+i),port);
        }

        final CredentialsProvider credentialsProvider = new BasicCredentialsProvider();
        credentialsProvider.setCredentials(AuthScope.ANY,
                new UsernamePasswordCredentials("test", "test1234"));  //es账号密码

        RestClientBuilder builder = RestClient.builder(
                myHttpHost)
                .setHttpClientConfigCallback(new RestClientBuilder.HttpClientConfigCallback() {
                    @Override
                    public HttpAsyncClientBuilder customizeHttpClient(
                            HttpAsyncClientBuilder httpClientBuilder) {
                        httpClientBuilder.disableAuthCaching();
                        return httpClientBuilder
                                .setDefaultCredentialsProvider(credentialsProvider);
                    }
                });

        //创建RestHighLevelClient对象
        RestHighLevelClient myclient=new RestHighLevelClient(builder);

        log.info("RestClientUtil intfo create rest high level client successful!");

        return myclient;

    }

}

Spark将Hive表的数据写入ElasticSearch工具类实现

??Spark将Hive表的数据写入ElasticSearch工具类实现主要在utils/EsUtils.java文件下，我这里比较偷懒，将所有的实现方法都放在这个文件下，大家觉得不爽的话可以自己按需拆分，具体设计的内容如下；

package cn.focusmedia.esapp.utils;
import cn.focusmedia.esapp.feign.EsClient;
import lombok.extern.slf4j.Slf4j;
import org.apache.spark.SparkConf;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.elasticsearch.action.admin.indices.alias.IndicesAliasesRequest;
import org.elasticsearch.action.admin.indices.alias.get.GetAliasesRequest;
import org.elasticsearch.action.admin.indices.delete.DeleteIndexRequest;
import org.elasticsearch.action.admin.indices.flush.FlushRequest;
import org.elasticsearch.action.admin.indices.flush.FlushResponse;
import org.elasticsearch.action.support.master.AcknowledgedResponse;
import org.elasticsearch.client.GetAliasesResponse;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.client.indices.CreateIndexRequest;
import org.elasticsearch.client.indices.CreateIndexResponse;
import org.elasticsearch.client.indices.DeleteAliasRequest;
import org.elasticsearch.client.indices.GetIndexRequest;
import org.elasticsearch.common.xcontent.XContentType;
import org.elasticsearch.spark.sql.api.java.JavaEsSparkSQL;
import org.junit.Test;
import java.io.IOException;


@Slf4j
public class EsUtils
{
    static RestHighLevelClient myClient= EsClient.getClient();  //获取操作ES的

    //查询索引是否存在
    @Test
    public static boolean exsitsIndex(String index) throws IOException
    {
        //准备request对象
        GetIndexRequest myrequest=new GetIndexRequest(index);
        //通过client去操作
        boolean myresult = myClient.indices().exists(myrequest, RequestOptions.DEFAULT);
        //输出结果
        log.info("The index:"+index+" is exist? :"+myresult);
        return myresult;
    }

    //创建索引
    @Test
    public static CreateIndexResponse creatIndex(String index,String index_mapping) throws IOException
    {
        log.info("The  index name will be created : "+index);

        //将准备好的setting和mapping封装到一个request对象内
        CreateIndexRequest myrequest = new CreateIndexRequest(index).source(index_mapping, XContentType.JSON);

        //通过client对象去连接ES并执行创建索引
        CreateIndexResponse myCreateIndexResponse=myClient.indices().create(myrequest, RequestOptions.DEFAULT);

        //输出结果
        log.info("The index : "+index+" was created response is "+ myCreateIndexResponse.isAcknowledged());

        return myCreateIndexResponse;
    }

    //删除索引
    @Test
    public static AcknowledgedResponse deleteIndex(String index) throws IOException {
        //准备request对象
        DeleteIndexRequest myDeleteIndexRequest = new DeleteIndexRequest();
        myDeleteIndexRequest.indices(index);

        //通过client对象执行
        AcknowledgedResponse myAcknowledgedResponse = myClient.indices().delete(myDeleteIndexRequest,RequestOptions.DEFAULT);

        //获取返回结果
        log.info("The index :"+index+"create response is "+myAcknowledgedResponse.isAcknowledged());
        return  myAcknowledgedResponse;
        //System.out.println(myAcknowledgedResponse.isAcknowledged());
    }

    //数据写入ES
    public static void tableToEs(String index,String index_auto_create,String es_mapping_id,String table_name,String es_nodes)
    {
        SparkConf conf=new SparkConf().setMaster("yarn").setAppName("SparkToES");
        conf.set("es.nodes",es_nodes);
        conf.set("es.net.http.auth.user" ,"test");
        conf.set("es.net.http.auth.pass","test1234");
        conf.set("es.nodes.wan.only","true");
        conf.set("es.nodes.discovery","false");
        conf.set("es.index.auto.create",index_auto_create);
        conf.set("es.resource",index);
        conf.set("es_mapping_id",es_mapping_id);

        SparkSession spark = SparkSession
                .builder()
                .config(conf)
                .appName("SparkToES")
                .enableHiveSupport()
                .config("spark.sql.hive.convertMetastoreParquet", false)
                .getOrCreate();

        Dataset<Row> table = spark.sql(""+table_name+"").repartition(60);
        JavaEsSparkSQL.saveToEs(table,index);

//        JavaEsSparkSQL.saveToEs(table,index, ImmutableMap.of("es.index.auto.create", index_auto_create,"es.resource", index, "es.mapping.id"
//               ,es_mapping_id,"es.nodes" ,es_nodes,"es.nodes.wan.only",wan_only));

        // "es.net.http.auth.pass" , "aib9qua!gh3Y" "es.net.http.auth.pass" , "aib9qua!gh3Y"
        log.info("Spark data from hive to ES index: "+index+" is over,go to alias index! ");
        spark.stop();
    }

    //数据写入ES,无指定的mapping_id
    public static void tableToEs(String index,String index_auto_create,String table_name,String es_nodes)
    {
        SparkConf conf=new SparkConf().setMaster("yarn").setAppName("SparkToES");
        conf.set("es.nodes",es_nodes);
        conf.set("es.net.http.auth.user" ,"test");
        conf.set("es.net.http.auth.pass","test1234");
        conf.set("es.nodes.wan.only","true");
        conf.set("es.nodes.discovery","false");
        conf.set("es.index.auto.create",index_auto_create);
        conf.set("es.resource",index);

        SparkSession spark = SparkSession
                .builder()
                .config(conf)
                .appName("SparkToES")
                .enableHiveSupport()
                .config("spark.sql.hive.convertMetastoreParquet", false)
                .getOrCreate();

        Dataset<Row> table =  spark.sql(""+table_name+"").repartition(60);
        JavaEsSparkSQL.saveToEs(table,index);

//        JavaEsSparkSQL.saveToEs(table,index, ImmutableMap.of("es.index.auto.create", index_auto_create,"es.resource", index, "es.mapping.id"
//               ,es_mapping_id,"es.nodes" ,es_nodes,"es.nodes.wan.only",wan_only));

        // "es.net.http.auth.pass" , "aib9qua!gh3Y" "es.net.http.auth.pass" , "aib9qua!gh3Y"
        log.info("Spark data from hive to ES index: "+index+" is over,go to alias index! ");
        spark.stop();
    }

    //flush下新的index数据
    public static void flushIndex(String index) throws IOException
    {
        FlushRequest myFlushRequest =new FlushRequest(index);
        FlushResponse myFlushResponse=myClient.indices().flush(myFlushRequest,RequestOptions.DEFAULT);
        int totalShards =myFlushResponse.getTotalShards();
        log.info("index: "+index+" has"+ totalShards +"flush over! ");
    }

    //别名操作，无缝连接
    //获取别名
    public static String getAlias(String alias) throws Exception
    {
        GetAliasesRequest requestWithAlias = new GetAliasesRequest(alias);
        GetAliasesResponse response = myClient.indices().getAlias(requestWithAlias, RequestOptions.DEFAULT);
        String AliasesString = response.getAliases().toString();
        String alias_index_name = null;
        try
        {
            alias_index_name = AliasesString.substring(AliasesString.indexOf("{") + 1, AliasesString.indexOf("="));
        }
        catch (Exception e)
        {
            throw new Exception("your index do not has alias,please create a alias for you index!");
        }

        return alias_index_name;
    }

    //更新别名
    public static void indexUpdateAlias(String index,String index_alias) throws Exception
    {
        String old_index_name=EsUtils.getAlias(index_alias);
        log.info(index_alias+ " old index is "+old_index_name);

        //删除别名映射的老的index
        DeleteAliasRequest myDeleteAliasRequest = new DeleteAliasRequest(old_index_name, index_alias);
        org.elasticsearch.client.core.AcknowledgedResponse myDeleteResponse=myClient.indices().deleteAlias(myDeleteAliasRequest, RequestOptions.DEFAULT);
        boolean deletealisaacknowledged = myDeleteResponse.isAcknowledged();
        log.info("delete index successfully? " + deletealisaacknowledged);

        //新建新的index别名
        IndicesAliasesRequest request = new IndicesAliasesRequest();
        IndicesAliasesRequest.AliasActions aliasAction = new IndicesAliasesRequest.AliasActions(IndicesAliasesRequest.AliasActions.Type.ADD).index(index).alias(index_alias);
        request.addAliasAction(aliasAction);
        org.elasticsearch.action.support.master.AcknowledgedResponse indicesAliasesResponse = myClient.indices().updateAliases(request, RequestOptions.DEFAULT);
        boolean createaliasacknowledged = indicesAliasesResponse.isAcknowledged();
        log.info("create index successfully? "+createaliasacknowledged);

        String now_index=EsUtils.getAlias(index_alias);
        log.info(index_alias+ " now index is "+now_index);

        if(now_index.equals(index))
        {
            log.info("index: "+index+ " alias update successfully！");
        }

    }

    //更新别名
    public static void indexUAddAlias(String index,String index_alias) throws Exception
    {
        //新建新的index别名
        IndicesAliasesRequest request = new IndicesAliasesRequest();
        IndicesAliasesRequest.AliasActions aliasAction = new IndicesAliasesRequest.AliasActions(IndicesAliasesRequest.AliasActions.Type.ADD).index(index).alias(index_alias);
        request.addAliasAction(aliasAction);
        org.elasticsearch.action.support.master.AcknowledgedResponse indicesAliasesResponse = myClient.indices().updateAliases(request, RequestOptions.DEFAULT);
        boolean createaliasacknowledged = indicesAliasesResponse.isAcknowledged();
        log.info("create index alias successfully? "+createaliasacknowledged);

        String now_index=EsUtils.getAlias(index_alias);
        log.info(index_alias+ " now index is "+now_index);

        if(now_index.equals(index))
        {
            log.info("index: "+index+ " alias create successfully！");
        }
    }

}

主函数调用工具类实现整体功能

??主函数的实现的 功能顺序下所示；

1. 创建索引
2. spark导入数据
3. flush下新的index数据
4. 获取目前的索引别名对应的索引名字，该索引名马上要失效
5. 替换最新数据别名
6. 确认别名成功切换后清除老的索引
7. 如果4步失败，说明是因为还没有建立起indexalias导致的，需要重建indexalias。

??注意，这里抽取Hive的哪张表，在ES建索引的名称，别名，index表结构要求等等做成xml存入hdfs，博主存在/app/hive_to_es/configure下，配置文件举例如dw_ads_resource_amap_city_district.xml；

index：你要建ES的索引名；
index_alias：你要建ES的索引别名；
index_auto_create：ES主键_id是否自动生成，如果写true表示自动生成，如果是false，则还需要补一个hive表内的唯一键作为es的主键_id，如下的客户表
<index_auto_create>false</index_auto_create>
<es_mapping_id>custom_id</es_mapping_id>
sql_script：spark取数语句；
index_mapping：ES的index结构，类似建表语句要求。

<configurations>
   <VALUE>
       <index>dw_sat_rs_amap_city_district</index>
       <index_alias>dw_sat_rs_amap_city_district_v0</index_alias>
       <index_auto_create>true</index_auto_create>  
       <sql_script>select * from ads.ads_resource_amap_city_district_d</sql_script>
       <index_mapping>
           {
           "settings":{
           "number_of_replicas":2,
           "number_of_shards":1,
           "max_result_window":1000000
           },
           "mappings":{
           "properties":{
           "amap_province_code":{
           "type":"keyword"
           },
           "amap_province_name":{
           "type":"keyword"
           },
           "amap_city_code":{
           "type":"keyword"
           },
           "amap_city_name":{
           "type":"keyword"
           },
           "amap_district_code":{
           "type":"keyword"
           },
           "amap_district_name":{
           "type":"keyword"
           },
           "dept_type_name":{
           "type":"keyword"
           },
           "shops":{
           "type":"integer"
           },
           "event_day":{
           "type":"keyword"
           }
           }
           }
           }
       </index_mapping>
   </VALUE>
</configurations>

??注意：以上配置文件的hdfs全路径，作为以下主函数jar包的参数，具体调用还是回到主函数内，代码如下的app.java文件；

package cn.focusmedia.esapp;

import cn.focusmedia.esapp.utils.EsUtils;
import cn.focusmedia.esapp.utils.PropertiesUtils;
import lombok.extern.slf4j.Slf4j;

import java.io.IOException;

/**
 * Hello world!
 *
 */

@Slf4j
public class App 
{

    public static void main( String[] args ) throws Exception
    {
        // 新的index名称  ，配置文件的hdfs路径作为jar调用的参数,每次采用时间戳后缀，防止重名
        String index=PropertiesUtils.getXML(args[0],"index")+ System.currentTimeMillis();

        //String index="dw_"+PropertiesUtils.getXML(args[0],"index")+"_v"+ System.currentTimeMillis();
        log.info("index:"+index);

        //es别名
        String index_alias=PropertiesUtils.getXML(args[0],"index_alias");
        log.info("index_alias:"+index_alias);

        //es index的mapping结构
        String index_mapping=PropertiesUtils.getXML(args[0],"index_mapping");
        log.info("index_mapping:"+index_mapping);

        //是否根据Hive表结构自动创建索引，一般写false，怕结构变形，可以通过根据mapping来创建规范的索引
        String index_auto_create=PropertiesUtils.getXML(args[0],"index_auto_create");
        log.info("index_auto_create:"+index_auto_create);

        //指定es index的id
        String es_mapping_id =PropertiesUtils.getXML(args[0],"es_mapping_id");
        log.info("es_mapping_id:"+es_mapping_id);

        //Hive内的表结构
        String table_name=null;
        table_name=PropertiesUtils.getXML(args[0],"sql_script");
        table_name=table_name.replaceAll("[\\t\\n\\r]"," ");
        log.info("table_name:"+table_name);

        // es集群节点集合
      //  int num=Integer.parseInt(PropertiesUtils.getProperties("configure/prod_es_connection.properties", "node_num"));
       // int port=Integer.parseInt(PropertiesUtils.getProperties("configure/prod_es_connection.properties","port"));

        int num=Integer.parseInt(PropertiesUtils.getProperties("/app/hive_to_es/configure/prod_es_connection.properties", "node_num"));
        int port=Integer.parseInt(PropertiesUtils.getProperties("/app/hive_to_es/configure/prod_es_connection.properties","port"));


        StringBuilder my_es_nodes=new StringBuilder("");

        for(int i=1;i<=num;i++)
        {
            //my_es_nodes.append(PropertiesUtils.getProperties("configure/prod_es_connection.properties","node"+i)+":"+port+",");
            my_es_nodes.append(PropertiesUtils.getProperties("/app/hive_to_es/configure/prod_es_connection.properties","node"+i)+":"+port+",");
        }

        // 去掉最后一位逗号
        String es_nodes=  my_es_nodes.substring(0,my_es_nodes.length()-1);
        log.info("es_nodes:"+es_nodes);


        // 创建索引
        if(EsUtils.exsitsIndex(index))
            EsUtils.deleteIndex(index);
        EsUtils.creatIndex(index,index_mapping);

        //spark导入数据
        //tableToEs(String index,String index_auto_create,String es_mapping_id,String table_name,String es_nodes)

        if(!Boolean.parseBoolean(index_auto_create))
        {
            EsUtils.tableToEs(index,index_auto_create,es_mapping_id,table_name,es_nodes);
        }else
        {
            EsUtils.tableToEs(index,index_auto_create,table_name,es_nodes);
        }

        //flush下新的index数据
        EsUtils.flushIndex(index);

        //获取目前的索引别名对应的索引名字，该索引名马上要失效
        try {
            String old_index=EsUtils.getAlias(index_alias);

            //替换最新数据别名
            EsUtils.indexUpdateAlias(index,index_alias);

            //确认别名成功切换后清除老的索引
            EsUtils.deleteIndex(old_index);
        }
        catch (Exception e)
        {
            e.printStackTrace();
            log.info("no old index alias,create new index alias");
            EsUtils.indexUAddAlias(index,index_alias);
        }



    }
}

打成Jar包并部署

??将调试无误的项目打成Jar包，如果还不会打Jar包，可以参考博客IntelliJ IDEA将代码打成Jar包的方式,这里我打成的Jar包名字为SparkOnHiveToEs_PROD.jar;
??将SparkOnHiveToEs_PROD.jar上传到hdfs的/app/hive_to_es/etl_jar/SparkOnHiveToEs_PROD.jar路径下，然后写一个spark-submit调用的shell脚本spark_on_hive_and_es.sh,具体如下：

#!/bin/bash

cur_dir=`pwd`


spark-submit --master yarn --deploy-mode cluster --executor-memory 8G --executor-cores 5 --num-executors 4 --queue etl --conf spark.kryoserializer.buffer.max=256m --conf spark.kryoserializer.buffer=64m  --class cn.focusmedia.esapp.App  hdfs://my-cluster/app/hive_to_es/etl_jar/SparkOnHiveToEs_PROD.jar hdfs://my-cluster/app/hive_to_es/configure/dw_ads_resource_amap_city_district.xml

dq_check_flag=$?
if [ $dq_check_flag -eq 0 ];then
    echo "city and district frome hive to es has successed！"

else
    echo "city and district frome hive to es has failed！"
   # cd ${cur_dir}/../src/ding_talk_warning_report_py/main/
   # python3 ding_talk_with_agency.py 411   此处为报错后钉钉报警，可以参考博主python栏的钉钉报警的实现
    exit 3
fi

调度shell脚本

??最后就是将这个spark_on_hive_and_es.sh脚本调度起来，如用Azkaban调度，设置自己需求的调度频率；

总 结

??采用Spark将Hive表的数据写入ElasticSearch，速度较快，可以作为离线数据从数据仓库Hive写入ElasticSearch的首席参考方案，稳定，无缝连接，且快速；至于丢失的一环，如何校验Hive的数据是否准确的通过Spark写入了ES，请参考本文的目录的文章3；
??如此一来，新增一张表，只需要填写一个xml文件，非常方便。