ElasticSearch教程入门到精通——第五部分（基于ELK技术栈elasticsearch 7.x+8.x新特性）

1. Elasticsearch集成
- 1.1 框架集成-SpringData-整体介绍
- 1.2 Spring Data Elasticsearch 介绍
- 1.3 框架集成-SpringData-代码功能集成
- - 1.3.1 创建Maven项目
  - 1.3.2 修改pom文件，增加依赖关系
  - 1.3.3 增加配置文件
  - 1.3.4 Spring Boot 主程序
  - 1.3.5 数据实体类
  - 1.3.6 配置类
  - 1.3.7 DAO 数据访问对象
- 1.4 框架集成-SpringData-集成测试-索引操作
- 1.5 框架集成-SpringData-集成测试-文档操作
- 1.6 框架集成-SpringData-集成测试-文档搜索
- 1.7 框架集成-SparkStreaming-集成
- - 1.7.1 创建Maven项目
  - 1.7.2 修改 pom 文件，增加依赖关系
  - 1.7.3 功能实现
- 1.8 框架集成-Flink-集成
- - 1.8.1 创建Maven项目
  - 1.8.2 修改 pom 文件，增加相关依赖类库
  - 1.8.3 功能实现

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1. Elasticsearch集成

1.1 框架集成-SpringData-整体介绍

Spring Data是一个用于简化数据库、非关系型数据库、索引库访问，并支持云服务的开源框架。其主要目标是使得对数据的访问变得方便快捷，并支持 map-reduce框架和云计算数据服务。Spring Data可以极大的简化JPA(Elasticsearch…)的写法，可以在几乎不用写实现的情况下，实现对数据的访问和操作。除了CRUD 外，还包括如分页、排序等一些常用的功能。

Spring Data 的官网

Spring Data 常用的功能模块如下：

Spring Data JDBC
Spring Data JPA
Spring Data LDAP
Spring Data MongoDB
Spring Data Redis
Spring Data R2DBC
Spring Data REST
Spring Data for Apache Cassandra
Spring Data for Apache Geode
Spring Data for Apache Solr
Spring Data for Pivotal GemFire
Spring Data Couchbase
Spring Data Elasticsearch
Spring Data Envers
Spring Data Neo4j
Spring Data JDBC Extensions
Spring for Apache Hadoop

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1.2 Spring Data Elasticsearch 介绍

Spring Data Elasticsearch基于Spring Data API简化 Elasticsearch 操作，将原始操作Elasticsearch 的客户端API进行封装。Spring Data为Elasticsearch 项目提供集成搜索引擎。Spring Data Elasticsearch POJO的关键功能区域为中心的模型与Elastichsearch交互文档和轻松地编写一个存储索引库数据访问层。

Spring Data Elasticsearch 官网

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1.3 框架集成-SpringData-代码功能集成

1.3.1 创建Maven项目

1.3.2 修改pom文件，增加依赖关系

<?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>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>2.3.6.RELEASE</version>
        <relativePath/>
    </parent>

    <groupId>com.atguigu</groupId>
    <artifactId>es-spring</artifactId>
    <version>0.0.1-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-elasticsearch</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-devtools</artifactId>
            <scope>runtime</scope>
            <optional>true</optional>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-test</artifactId>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework</groupId>
            <artifactId>spring-test</artifactId>
        </dependency>
    </dependencies>
</project>

1.3.3 增加配置文件

在 resources 目录中增加application.properties文件

# es 服务地址
elasticsearch.host=127.0.0.1
# es 服务端口
elasticsearch.port=9200
# 配置日志级别,开启 debug 日志
logging.level.com.atguigu.es=debug

1.3.4 Spring Boot 主程序

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

@SpringBootApplication
public class MainApplication {
    public static void main(String[] args) {
        SpringApplication.run(MainApplication.class, args);
    }
}

1.3.5 数据实体类

import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import lombok.ToString;
import org.springframework.data.annotation.Id;
import org.springframework.data.elasticsearch.annotations.Document;
import org.springframework.data.elasticsearch.annotations.Field;
import org.springframework.data.elasticsearch.annotations.FieldType;

@Data
@NoArgsConstructor
@AllArgsConstructor
@ToString
@Document(indexName = "shopping", shards = 3, replicas = 1)
public class Product {
    //必须有 id,这里的 id 是全局唯一的标识，等同于 es 中的"_id"
    @Id
    private Long id;//商品唯一标识

    /**
     * type : 字段数据类型
     * analyzer : 分词器类型
     * index : 是否索引(默认:true)
     * Keyword : 短语,不进行分词
     */
    @Field(type = FieldType.Text, analyzer = "ik_max_word")
    private String title;//商品名称

    @Field(type = FieldType.Keyword)
    private String category;//分类名称

    @Field(type = FieldType.Double)
    private Double price;//商品价格

    @Field(type = FieldType.Keyword, index = false)
    private String images;//图片地址
}

1.3.6 配置类

ElasticsearchRestTemplate是spring-data-elasticsearch项目中的一个类,和其他spring项目中的 template类似。
在新版的spring-data-elasticsearch 中，ElasticsearchRestTemplate 代替了原来的ElasticsearchTemplate。
原因是ElasticsearchTemplate基于TransportClient，TransportClient即将在8.x 以后的版本中移除。所以，我们推荐使用ElasticsearchRestTemplate。
ElasticsearchRestTemplate基于RestHighLevelClient客户端的。需要自定义配置类，继承AbstractElasticsearchConfiguration，并实现elasticsearchClient()抽象方法，创建RestHighLevelClient对象。

AbstractElasticsearchConfiguration源码：

package org.springframework.data.elasticsearch.config;

import org.elasticsearch.client.RestHighLevelClient;
import org.springframework.context.annotation.Bean;
import org.springframework.data.elasticsearch.core.ElasticsearchOperations;
import org.springframework.data.elasticsearch.core.ElasticsearchRestTemplate;
import org.springframework.data.elasticsearch.core.convert.ElasticsearchConverter;


public abstract class AbstractElasticsearchConfiguration extends ElasticsearchConfigurationSupport {

	//需重写本方法
	public abstract RestHighLevelClient elasticsearchClient();

	@Bean(name = { "elasticsearchOperations", "elasticsearchTemplate" })
	public ElasticsearchOperations elasticsearchOperations(ElasticsearchConverter elasticsearchConverter) {
		return new ElasticsearchRestTemplate(elasticsearchClient(), elasticsearchConverter);
	}
}

需要自定义配置类，继承AbstractElasticsearchConfiguration，并实现elasticsearchClient()抽象方法，创建RestHighLevelClient对象。

import lombok.Data;
import org.apache.http.HttpHost;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestClientBuilder;
import org.elasticsearch.client.RestHighLevelClient;
import org.springframework.boot.context.properties.ConfigurationProperties;
import org.springframework.context.annotation.Configuration;
import org.springframework.data.elasticsearch.config.AbstractElasticsearchConfiguration;

@ConfigurationProperties(prefix = "elasticsearch")
@Configuration
@Data
public class ElasticsearchConfig extends AbstractElasticsearchConfiguration{

    private String host ;
    private Integer port ;
    //重写父类方法
    @Override
    public RestHighLevelClient elasticsearchClient() {
        RestClientBuilder builder = RestClient.builder(new HttpHost(host, port));
        RestHighLevelClient restHighLevelClient = new
                RestHighLevelClient(builder);
        return restHighLevelClient;
    }
}

1.3.7 DAO 数据访问对象

import org.springframework.data.elasticsearch.repository.ElasticsearchRepository;
import org.springframework.stereotype.Repository;

@Repository
public interface ProductDao extends ElasticsearchRepository<Product, Long>{

}

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1.4 框架集成-SpringData-集成测试-索引操作

import com.lun.model.Product;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.data.elasticsearch.core.ElasticsearchRestTemplate;
import org.springframework.test.context.junit4.SpringRunner;

@RunWith(SpringRunner.class)
@SpringBootTest
public class SpringDataESIndexTest {
    //注入 ElasticsearchRestTemplate
    @Autowired
    private ElasticsearchRestTemplate elasticsearchRestTemplate;
    //创建索引并增加映射配置
    @Test
    public void createIndex(){
        //创建索引，系统初始化会自动创建索引
        System.out.println("创建索引");
    }

    @Test
    public void deleteIndex(){
        //创建索引，系统初始化会自动创建索引
        boolean flg = elasticsearchRestTemplate.deleteIndex(Product.class);
        System.out.println("删除索引 = " + flg);
    }
}

用Postman 检测有没有创建和删除。

#GET http://localhost:9200/_cat/indices?v

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1.5 框架集成-SpringData-集成测试-文档操作

import com.lun.dao.ProductDao;
import com.lun.model.Product;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.data.domain.Page;
import org.springframework.data.domain.PageRequest;
import org.springframework.data.domain.Sort;
import org.springframework.test.context.junit4.SpringRunner;

import java.util.ArrayList;
import java.util.List;

@RunWith(SpringRunner.class)
@SpringBootTest
public class SpringDataESProductDaoTest {

    @Autowired
    private ProductDao productDao;
    /**
     * 新增
     */
    @Test
    public void save(){
        Product product = new Product();
        product.setId(2L);
        product.setTitle("华为手机");
        product.setCategory("手机");
        product.setPrice(2999.0);
        product.setImages("http://www.atguigu/hw.jpg");
        productDao.save(product);
    }
    //POSTMAN, GET http://localhost:9200/product/_doc/2

    //修改
    @Test
    public void update(){
        Product product = new Product();
        product.setId(2L);
        product.setTitle("小米 2 手机");
        product.setCategory("手机");
        product.setPrice(9999.0);
        product.setImages("http://www.atguigu/xm.jpg");
        productDao.save(product);
    }
    //POSTMAN, GET http://localhost:9200/product/_doc/2


    //根据 id 查询
    @Test
    public void findById(){
        Product product = productDao.findById(2L).get();
        System.out.println(product);
    }

    @Test
    public void findAll(){
        Iterable<Product> products = productDao.findAll();
        for (Product product : products) {
            System.out.println(product);
        }
    }

    //删除
    @Test
    public void delete(){
        Product product = new Product();
        product.setId(2L);
        productDao.delete(product);
    }
    //POSTMAN, GET http://localhost:9200/product/_doc/2

    //批量新增
    @Test
    public void saveAll(){
        List<Product> productList = new ArrayList<>();
        for (int i = 0; i < 10; i++) {
            Product product = new Product();
            product.setId(Long.valueOf(i));
            product.setTitle("["+i+"]小米手机");
            product.setCategory("手机");
            product.setPrice(1999.0 + i);
            product.setImages("http://www.atguigu/xm.jpg");
            productList.add(product);
        }
        productDao.saveAll(productList);
    }

    //分页查询
    @Test
    public void findByPageable(){
        //设置排序(排序方式，正序还是倒序，排序的 id)
        Sort sort = Sort.by(Sort.Direction.DESC,"id");
        int currentPage=0;//当前页，第一页从 0 开始， 1 表示第二页
        int pageSize = 5;//每页显示多少条
        //设置查询分页
        PageRequest pageRequest = PageRequest.of(currentPage, pageSize,sort);
        //分页查询
        Page<Product> productPage = productDao.findAll(pageRequest);
        for (Product Product : productPage.getContent()) {
            System.out.println(Product);
        }
    }
}

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1.6 框架集成-SpringData-集成测试-文档搜索

import com.lun.dao.ProductDao;
import com.lun.model.Product;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.index.query.TermQueryBuilder;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.test.context.SpringBootTest;
import org.springframework.data.domain.PageRequest;
import org.springframework.test.context.junit4.SpringRunner;

@RunWith(SpringRunner.class)
@SpringBootTest
public class SpringDataESSearchTest {

    @Autowired
    private ProductDao productDao;
    /**
     * term 查询
     * search(termQueryBuilder) 调用搜索方法，参数查询构建器对象
     */
    @Test
    public void termQuery(){
        TermQueryBuilder termQueryBuilder = QueryBuilders.termQuery("title", "小米");
                Iterable<Product> products = productDao.search(termQueryBuilder);
        for (Product product : products) {
            System.out.println(product);
        }
    }
    /**
     * term 查询加分页
     */
    @Test
    public void termQueryByPage(){
        int currentPage= 0 ;
        int pageSize = 5;
        //设置查询分页
        PageRequest pageRequest = PageRequest.of(currentPage, pageSize);
        TermQueryBuilder termQueryBuilder = QueryBuilders.termQuery("title", "小米");
                Iterable<Product> products =
                        productDao.search(termQueryBuilder,pageRequest);
        for (Product product : products) {
            System.out.println(product);
        }
    }

}

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1.7 框架集成-SparkStreaming-集成

Spark Streaming 是Spark core API的扩展，支持实时数据流的处理，并且具有可扩展，高吞吐量，容错的特点。数据可以从许多来源获取,如Kafka， Flume，Kinesis或TCP sockets，并且可以使用复杂的算法进行处理，这些算法使用诸如 map，reduce，join和 window等高级函数表示。最后，处理后的数据可以推送到文件系统，数据库等。实际上，您可以将Spark的机器学习和图形处理算法应用于数据流。

1.7.1 创建Maven项目

1.7.2 修改 pom 文件，增加依赖关系

<?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.lun.es</groupId>
    <artifactId>sparkstreaming-elasticsearch</artifactId>
    <version>1.0</version>
    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.12</artifactId>
            <version>3.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.12</artifactId>
            <version>3.0.0</version>
        </dependency>
        <dependency>
            <groupId>org.elasticsearch</groupId>
            <artifactId>elasticsearch</artifactId>
            <version>7.8.0</version>
        </dependency>
        <!-- elasticsearch 的客户端 -->
        <dependency>
            <groupId>org.elasticsearch.client</groupId>
            <artifactId>elasticsearch-rest-high-level-client</artifactId>
            <version>7.8.0</version>
        </dependency>
        <!-- elasticsearch 依赖 2.x 的 log4j -->
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-api</artifactId>
            <version>2.8.2</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
            <version>2.8.2</version>
        </dependency>
        <!-- <dependency>-->
        <!-- <groupId>com.fasterxml.jackson.core</groupId>-->
        <!-- <artifactId>jackson-databind</artifactId>-->
        <!-- <version>2.11.1</version>-->
        <!-- </dependency>-->
        <!-- &lt;!&ndash; junit 单元测试 &ndash;&gt;-->
        <!-- <dependency>-->
        <!-- <groupId>junit</groupId>-->
        <!-- <artifactId>junit</artifactId>-->
        <!-- <version>4.12</version>-->
        <!-- </dependency>-->
    </dependencies>
</project>

1.7.3 功能实现

import org.apache.http.HttpHost
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.ReceiverInputDStream
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.elasticsearch.action.index.IndexRequest
import org.elasticsearch.client.indices.CreateIndexRequest
import org.elasticsearch.client.{RequestOptions, RestClient, RestHighLevelClient}
import org.elasticsearch.common.xcontent.XContentType
import java.util.Date

object SparkStreamingESTest {

    def main(args: Array[String]): Unit = {
        val sparkConf = new SparkConf().setMaster("local[*]").setAppName("ESTest")
        val ssc = new StreamingContext(sparkConf, Seconds(3))
        val ds: ReceiverInputDStream[String] = ssc.socketTextStream("localhost", 9999)
        ds.foreachRDD(
            rdd => {
                println("*************** " + new Date())
                rdd.foreach(
                    data => {
                        val client = new RestHighLevelClient(RestClient.builder(new HttpHost("localhost", 9200, "http")));
                        // 新增文档 - 请求对象
                        val request = new IndexRequest();
                        
                        // 设置索引及唯一性标识
                        val ss = data.split(" ")
                        println("ss = " + ss.mkString(","))
                        request.index("sparkstreaming").id(ss(0));
                        
                        val productJson =
                            s"""
                            | { "data":"${ss(1)}" }
                            |""".stripMargin;
                        
                        // 添加文档数据，数据格式为 JSON 格式
                        request.source(productJson,XContentType.JSON);
                        
                        // 客户端发送请求，获取响应对象
                        val response = client.index(request,
                        RequestOptions.DEFAULT);
                        System.out.println("_index:" + response.getIndex());
                        System.out.println("_id:" + response.getId());
                        System.out.println("_result:" + response.getResult());
                        client.close()
                    }
                )
            }
        )
        ssc.start()
        ssc.awaitTermination()
    }
}

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1.8 框架集成-Flink-集成

Apache Spark是一-种基于内存的快速、通用、可扩展的大数据分析计算引擎。Apache Spark掀开了内存计算的先河，以内存作为赌注，贏得了内存计算的飞速发展。但是在其火热的同时，开发人员发现，在Spark中，计算框架普遍存在的缺点和不足依然没有完全解决，而这些问题随着5G时代的来临以及决策者对实时数据分析结果的迫切需要而凸显的更加明显：

乱序数据，迟到数据
低延迟，高吞吐，准确性
容错性
数据精准一次性处理（Exactly-Once）

Apache Flink是一个框架和分布式处理引擎，用于对无界和有界数据流进行有状态计算。在Spark火热的同时，也默默地发展自己，并尝试着解决其他计算框架的问题。慢慢地，随着这些问题的解决，Flink 慢慢被绝大数程序员所熟知并进行大力推广，阿里公司在2015年改进Flink，并创建了内部分支Blink，目前服务于阿里集团内部搜索、推荐、广告和蚂蚁等大量核心实时业务。

1.8.1 创建Maven项目

1.8.2 修改 pom 文件，增加相关依赖类库

<?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.lun.es</groupId>
    <artifactId>flink-elasticsearch</artifactId>
    <version>1.0</version>
    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-scala_2.12</artifactId>
            <version>1.12.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-streaming-scala_2.12</artifactId>
            <version>1.12.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-clients_2.12</artifactId>
            <version>1.12.0</version>
        </dependency>
        <dependency>
            <groupId>org.apache.flink</groupId>
            <artifactId>flink-connector-elasticsearch7_2.11</artifactId>
            <version>1.12.0</version>
        </dependency>
        <!-- jackson -->
        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-core</artifactId>
            <version>2.11.1</version>
        </dependency>
    </dependencies>
</project>

1.8.3 功能实现

import org.apache.flink.api.common.functions.RuntimeContext;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.elasticsearch.ElasticsearchSinkFunction;
import org.apache.flink.streaming.connectors.elasticsearch.RequestIndexer;
import org.apache.flink.streaming.connectors.elasticsearch7.ElasticsearchSink;
import org.apache.http.HttpHost;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.client.Requests;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class FlinkElasticsearchSinkTest {

	public static void main(String[] args) throws Exception {

		StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
		DataStreamSource<String> source = env.socketTextStream("localhost", 9999);
		List<HttpHost> httpHosts = new ArrayList<>();
		httpHosts.add(new HttpHost("127.0.0.1", 9200, "http"));
		//httpHosts.add(new HttpHost("10.2.3.1", 9200, "http"));

		// use a ElasticsearchSink.Builder to create an ElasticsearchSink
		ElasticsearchSink.Builder<String> esSinkBuilder = new ElasticsearchSink.Builder<>(httpHosts, 
			new ElasticsearchSinkFunction<String>() {
				public IndexRequest createIndexRequest(String element) {
					Map<String, String> json = new HashMap<>();
					json.put("data", element);
					return Requests.indexRequest()
						.index("my-index")
						//.type("my-type")
						.source(json);
				}

				@Override
				public void process(String element, RuntimeContext ctx, RequestIndexer indexer) {
					indexer.add(createIndexRequest(element));
				}
			}
		);
		
		// configuration for the bulk requests; this instructs the sink to emit after every element, otherwise they would be buffered
		esSinkBuilder.setBulkFlushMaxActions(1);

		// provide a RestClientFactory for custom configuration on the internally createdREST client
		// esSinkBuilder.setRestClientFactory(
		// restClientBuilder -> {
			// restClientBuilder.setDefaultHeaders(...)
			// restClientBuilder.setMaxRetryTimeoutMillis(...)
			// restClientBuilder.setPathPrefix(...)
			// restClientBuilder.setHttpClientConfigCallback(...)
		// }
		// );
		source.addSink(esSinkBuilder.build());
		env.execute("flink-es");
	}
}