Elasticsearch - Spring Data 框架集成；Spark Streaming 框架集成；Flink 框架集成（九）

柔光的暖阳◎ 2023-09-27 09:07 116阅读 0赞

#### 阅读本文前可先参考 ####

[Elasticsearch - Elasticsearch详解；安装部署（一）\_MinggeQingchun的博客-CSDN博客][Elasticsearch - Elasticsearch_MinggeQingchun_-CSDN]

[https://blog.csdn.net/MinggeQingchun/article/details/126762570][https_blog.csdn.net_MinggeQingchun_article_details_126762570]

[https://blog.csdn.net/MinggeQingchun/article/details/126717979][https_blog.csdn.net_MinggeQingchun_article_details_126717979]

## 一、Spring Data 框架集成 ##

### （一）Spring Data简介 ###

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

#### 1、Spring Data官网地址： ####

[Spring Data][]

#### 2、Spring Data常用功能模块   ![b90d86c64d21477d9ad3f3f1ae81f3d5.png][] ####

#### 3、Spring Data Elasticsearch ####

[Spring Data Elasticsearch][]

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

#### 4、Spring Data Elasticsearch 和 SpringBoot、Spring Data版本对比 ####

官网地址：

[Spring Data Elasticsearch - Reference Documentation][]

![decb85f1989546e1a09e7450a10586d6.png][]

### （二）Spring Data 框架集成 ###

1、创建Springboot项目elasticsearch-7\_15\_0-springdata

2、添加依赖

<parent>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-parent</artifactId>
            <version>2.5.14</version>
            <relativePath/> 
        </parent>
    
        <groupId>com.company</groupId>
        <artifactId>elasticsearch-7_15_0-springdata</artifactId>
        <version>1.0.0</version>
    
        <name>elasticsearch-7_15_0-springdata</name>
        <description>Demo project for Spring Boot</description>
    
        <properties>
            <java.version>1.8</java.version>
            <maven.compiler.source>8</maven.compiler.source>
            <maven.compiler.target>8</maven.compiler.target>
        </properties>
    
        <dependencies>
            <dependency>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-starter</artifactId>
            </dependency>
    
            <dependency>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-starter-test</artifactId>
                <scope>test</scope>
            </dependency>
    
            <dependency>
                <groupId>org.projectlombok</groupId>
                <artifactId>lombok</artifactId>
            </dependency>
    
            <dependency>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-starter-data-elasticsearch</artifactId>
            </dependency>
    
        </dependencies>
    
        <build>
            <plugins>
                <plugin>
                    <groupId>org.springframework.boot</groupId>
                    <artifactId>spring-boot-maven-plugin</artifactId>
                </plugin>
            </plugins>
        </build>

3、修改application.properties配置文件

# 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.company.es=debug

4、数据实体类Product

@Data
    @NoArgsConstructor
    @AllArgsConstructor
    @ToString
    @Document(indexName = "product", shards = 3, replicas = 1)
    public class Product {
        @Id
        private Long id;//商品唯一标识
    
        @Field(type = FieldType.Text)
        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
    }

5、 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（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

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;
        }
    }

6、DAO对象访问

@Repository
    public interface ProductDao extends ElasticsearchRepository<Product,Long> {
        Iterable<Product> search(TermQueryBuilder termQueryBuilder, PageRequest pageRequest);
    
        Iterable<Product> search(TermQueryBuilder termQueryBuilder);
    }

7、 实体类Product映射

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 = "product", 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、索引操作 ####

import com.company.es.domain.Product;
    import org.junit.jupiter.api.Test;
    import org.springframework.beans.factory.annotation.Autowired;
    import org.springframework.boot.test.context.SpringBootTest;
    import org.springframework.data.elasticsearch.core.ElasticsearchRestTemplate;
    
    @SpringBootTest
    class SpringDataESIndexTest {
        @Autowired
        private ElasticsearchRestTemplate elasticsearchRestTemplate;
    
        //创建索引并增加映射配置
        @Test
        public void createIndex(){
            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
            String flg = elasticsearchRestTemplate.delete(Product.class);
            System.out.println("删除索引 = " + flg);
        }
    }

#### 2、文档操作 ####

import com.company.es.dao.ProductDao;
    import com.company.es.domain.Product;
    import org.junit.jupiter.api.Test;
    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 java.util.ArrayList;
    import java.util.List;
    
    @SpringBootTest
    class SpringDataESDocTest {
    
        @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.xx/hw.jpg");
            productDao.save(product);
        }
        //修改
        @Test
        public void update(){
            Product product = new Product();
            product.setId(1L);
            product.setTitle("小米 2 手机");
            product.setCategory("手机");
            product.setPrice(9999.0);
            product.setImages("http://www.xx/xm.jpg");
            productDao.save(product);
        }
        //根据 id 查询
        @Test
        public void findById(){
            Product product = productDao.findById(1L).get();
            System.out.println(product);
        }
        
        //查询所有
        @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(1L);
            productDao.delete(product);
        }
        //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.xx/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);
            }
        }
    }

#### 3、文档搜索 ####

@SpringBootTest
    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);
            }
        }
    }

## 二、Spark Streaming 框架集成 ##

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，可以将Spark 的机器学习和图形处理算法应用于数据流

![d7fde08e1e28487c905bddd231a4a7fc.png][]

1、创建maven模块 elasticsearch-7\_15\_0-sparkstreaming

2、添加依赖

<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>7.15.0</version>
            </dependency>
    
            <dependency>
                <groupId>org.elasticsearch.client</groupId>
                <artifactId>elasticsearch-rest-high-level-client</artifactId>
                <version>7.15.0</version>
            </dependency>
    
            
            <dependency>
                <groupId>org.apache.logging.log4j</groupId>
                <artifactId>log4j-api</artifactId>
                <version>2.11.1</version>
            </dependency>
            <dependency>
                <groupId>org.apache.logging.log4j</groupId>
                <artifactId>log4j-core</artifactId>
                <version>2.11.1</version>
            </dependency>
    
            <dependency>
                <groupId>com.fasterxml.jackson.core</groupId>
                <artifactId>jackson-databind</artifactId>
                <version>2.10.4</version>
            </dependency>
    
            
            <dependency>
                <groupId>junit</groupId>
                <artifactId>junit</artifactId>
                <version>4.12</version>
            </dependency>
        </dependencies>

3、在IDEA中安装Scala插件

![9ad3fe32e72b49ab9a3bd2045a5a5869.png][]

4、maven模块项目添加 Framework Support

![53cf2686fd214708890a9075c2b2e1dc.png][]

![3a55f28918ab43d78d4feb60cd24e65e.png][]

下载相应版本即可

![43903755141c43dc9f141770d862593c.png][]

5、添加Scala文件测试

![052014e2bafa46a0a1cc74232425fa45.png][]

6、编写代码

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, IndexResponse}
    import org.elasticsearch.client.{RequestOptions, RestClient, RestHighLevelClient}
    import org.elasticsearch.common.xcontent.XContentType
    
    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 => {
            rdd.foreach(
              data => {
                val client = new RestHighLevelClient(
                  RestClient.builder(new HttpHost("localhost",9200, "http"))
                )
    
                val ss = data.split(" ")
    
                val request = new IndexRequest()
                request.index("product").id(ss(0))
                val json =
                  s"""
                     | {  "data" : "${ss(1)}" }
                     |""".stripMargin
                request.source(json, XContentType.JSON)
    
                val response: IndexResponse = client.index(request, RequestOptions.DEFAULT)
                println(response.getResult)
                client.close()
              }
            )
          }
        )
    
        ssc.start()
        ssc.awaitTermination()
      }
    }

## 三、Flink 框架集成 ##

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，赢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：

（1）数据精准一次性处理（Exactly-Once）

（2）乱序数据，迟到数据

（3）低延迟，高吞吐，准确性

（4）容错性

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。

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、创建maven项目elasticsearch-7\_15\_0-flink

2、添加依赖

<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>
    
            
            <dependency>
                <groupId>com.fasterxml.jackson.core</groupId>
                <artifactId>jackson-core</artifactId>
                <version>2.11.1</version>
            </dependency>
        </dependencies>

3、功能测试

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.flink.table.descriptors.Elasticsearch;
    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 {
    
            // 构建Flink环境对象
            StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
    
            // Source : 数据的输入
            DataStreamSource<String> source = env.socketTextStream("localhost", 9999);
    
            // 使用ESBuilder构建输出
            List<HttpHost> hosts = new ArrayList<>();
            hosts.add(new HttpHost("127.0.0.1", 9200, "http"));
            ElasticsearchSink.Builder<String> esBuilder = new ElasticsearchSink.Builder<>(hosts,
                     new ElasticsearchSinkFunction<String>() {
    
                         @Override
                         public void process(String s, RuntimeContext runtimeContext, RequestIndexer requestIndexer) {
                             Map<String, String> jsonMap = new HashMap<>();
                             jsonMap.put("data", s);
    
                             IndexRequest indexRequest = Requests.indexRequest();
                             indexRequest.index("flink-index");
                             indexRequest.id("9001");
                             indexRequest.source(jsonMap);
    
                             requestIndexer.add(indexRequest);
                         }
                     });
    
            // Sink : 数据的输出
            esBuilder.setBulkFlushMaxActions(1);
            source.addSink(esBuilder.build());
    
            // 执行操作
            env.execute("flink-es");
    
        }
    }

[Elasticsearch - Elasticsearch_MinggeQingchun_-CSDN]: https://blog.csdn.net/MinggeQingchun/article/details/126618387
[https_blog.csdn.net_MinggeQingchun_article_details_126762570]: https://blog.csdn.net/MinggeQingchun/article/details/126762570
[https_blog.csdn.net_MinggeQingchun_article_details_126717979]: https://blog.csdn.net/MinggeQingchun/article/details/126717979
[Spring Data]: https://spring.io/projects/spring-data
[b90d86c64d21477d9ad3f3f1ae81f3d5.png]: https://img-blog.csdnimg.cn/b90d86c64d21477d9ad3f3f1ae81f3d5.png
[Spring Data Elasticsearch]: https://spring.io/projects/spring-data-elasticsearch
[Spring Data Elasticsearch - Reference Documentation]: https://docs.spring.io/spring-data/elasticsearch/docs/current/reference/html/#preface.versions
[decb85f1989546e1a09e7450a10586d6.png]: https://img-blog.csdnimg.cn/decb85f1989546e1a09e7450a10586d6.png
[d7fde08e1e28487c905bddd231a4a7fc.png]: https://img-blog.csdnimg.cn/d7fde08e1e28487c905bddd231a4a7fc.png
[9ad3fe32e72b49ab9a3bd2045a5a5869.png]: https://img-blog.csdnimg.cn/9ad3fe32e72b49ab9a3bd2045a5a5869.png
[53cf2686fd214708890a9075c2b2e1dc.png]: https://img-blog.csdnimg.cn/53cf2686fd214708890a9075c2b2e1dc.png
[3a55f28918ab43d78d4feb60cd24e65e.png]: https://img-blog.csdnimg.cn/3a55f28918ab43d78d4feb60cd24e65e.png
[43903755141c43dc9f141770d862593c.png]: https://img-blog.csdnimg.cn/43903755141c43dc9f141770d862593c.png
[052014e2bafa46a0a1cc74232425fa45.png]: https://img-blog.csdnimg.cn/052014e2bafa46a0a1cc74232425fa45.png