SpringBoot 集成ES集群CRUD及分页解决方案

冷不防 2023-01-18 04:05 254阅读 0赞

# 1 SpringBoot 集成ES集群 #

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## 1.2 pom ##

<parent>
 <groupId>org.springframework.boot</groupId>
 <artifactId>spring-boot-starter-parent</artifactId>
 <version>2.3.0.RELEASE</version>
 <relativePath/> 
 </parent>
 
 <properties>
 <java.version>1.8</java.version>
 </properties>
 
 
 <dependencies>
 <dependency>
 <groupId>org.springframework.boot</groupId>
 <artifactId>spring-boot-starter-web</artifactId>
 </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-starter-logging</artifactId>
 </dependency>
 
 <dependency>
 <groupId>org.projectlombok</groupId>
 <artifactId>lombok</artifactId>
 </dependency>
 <dependency>
 <groupId>com.alibaba</groupId>
 <artifactId>fastjson</artifactId>
 <version>1.2.71</version>
 </dependency>
 <dependency>
 <groupId>org.apache.commons</groupId>
 <artifactId>commons-collections4</artifactId>
 <version>4.2</version>
 </dependency>
 <dependency>
 <groupId>org.apache.commons</groupId>
 <artifactId>commons-lang3</artifactId>
 </dependency>
 
 
 
 <dependency>
 <groupId>org.elasticsearch.client</groupId>
 <artifactId>elasticsearch-rest-high-level-client</artifactId>
 <version>7.6.2</version>
 </dependency>
 <dependency>
 <groupId>org.elasticsearch.client</groupId>
 <artifactId>elasticsearch-rest-client</artifactId>
 <version>7.6.2</version>
 </dependency>
 </dependencies>
 
 
 <build>
 <plugins>
 <plugin>
 <groupId>org.springframework.boot</groupId>
 <artifactId>spring-boot-maven-plugin</artifactId>
 <version>2.3.0.RELEASE</version>
 </plugin>
 <plugin>
 <groupId>org.apache.maven.plugins</groupId>
 <artifactId>maven-compiler-plugin</artifactId>
 <version>3.8.1</version>
 <configuration>
 <source>1.8</source>
 <target>1.8</target>
 </configuration>
 </plugin>
 </plugins>
 </build>

## 1.3 application.yml ##

server:
      port: 9000
    
    elasticsearch:
      ips: 192.168.38.50:9200,192.168.38.51:9200,192.168.38.52:9200
      scheme: http

## 1.4 ElasticSearchConfig ##

@Configuration
    public class ElasticSearchConfig { 
    
        @Value("${elasticsearch.ips}")
        private String[] ipAddress;
    
    
        @Value("${elasticsearch.scheme}")
        public String scheme;
    
        @Bean
        public RestClientBuilder restClientBuilder() { 
            HttpHost[] hosts = Arrays.stream(ipAddress)
                    .map(this::makeHttpHost)
                    .filter(Objects::nonNull)
                    .toArray(HttpHost[]::new);
    
            return RestClient.builder(hosts).setHttpClientConfigCallback(httpClientBuilder -> httpClientBuilder.setDefaultCredentialsProvider(null));
        }
    
    
        @Bean(name = "highLevelClient")
        public RestHighLevelClient highLevelClient(RestClientBuilder restClientBuilder) { 
            return new RestHighLevelClient(restClientBuilder);
        }
    
    
        private HttpHost makeHttpHost(String s) { 
            String[] address = s.split(":");
            if (address.length == 2) { 
                String ip = address[0];
                int port = Integer.parseInt(address[1]);
                return new HttpHost(ip, port,scheme);
            } else { 
                return null;
            }
        }
    
    }

## 1.5 实体 ##

@AllArgsConstructor
    @NoArgsConstructor
    @Data
    @ToString
    public class JobEntity { 
    
        /** * id */
        private Long id;
    
        /** * 年龄 */
        private Integer age;
    
        /** * 地区 */
        private String area;
    
        /** * 期望 */
        private String expect;
    
        /** * 学历 */
        private String education;
    
        /** * 薪水 */
        private String salary;
    
        /** * 类型 */
        private String type;
    
        /** * 标题 */
        private String title;
    
        /** * 工作描述 */
        private String jobDescription;
    
    
    }

# 2 ES库添加 #

@RunWith(SpringRunner.class)
    @SpringBootTest(classes = ElasticSearchApplication.class)
    @Slf4j
    public class ElasticSearchTest { 
    
    
        // 索引库的名字
        private static final String JOB_IDX = "job_index";
    
        @Qualifier("highLevelClient")
        @Autowired
        private RestHighLevelClient restHighLevelClient;
    
    
        @Test
        public void addJobTest() throws IOException { 
            JobEntity jobEntity = new JobEntity();
            jobEntity.setId(1L);
            jobEntity.setAge(30);
            jobEntity.setArea("北京");
            jobEntity.setExpect("3年工作经验");
            jobEntity.setJobDescription("高级开发工程师，抗压能力强，热爱学习。");
            jobEntity.setSalary("20K/月");
            jobEntity.setEducation("本科及以上");
            jobEntity.setType("全职");
            jobEntity.setTitle("JAVA高级开发工程师");
    
            //1.构建IndexRequest对象，用来描述ES发起请求的数据。
            IndexRequest indexRequest = new IndexRequest(JOB_IDX);
            //2 设置文档ID
            indexRequest.id(String.valueOf(jobEntity.getId()));
            //3 转JSON
            String json = JSON.toJSONString(jobEntity);
            //4 使用IndexRequest.source方法设置文档数据，并设置请求的数据为JSON格式。
            indexRequest.source(json, XContentType.JSON);
            //5 用ES High level client调用index方法发起请求，将一个文档添加到索引中。
            restHighLevelClient.index(indexRequest, RequestOptions.DEFAULT);
            log.info("添加成功");
    
        }
    
    }

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# 3 ES 根据ID查找数据 #

@Test
        public void findJobTest() throws IOException { 
    
            Long id = 1L;
            // 1 构建GetRequest请求。
            GetRequest getRequest = new GetRequest(JOB_IDX, String.valueOf(id));
            // 2 使用RestHighLevelClient.get发送GetRequest请求，并获取到ES服务器的响应。
            GetResponse getResponse = restHighLevelClient.get(getRequest, RequestOptions.DEFAULT);
            // 3 将ES响应的数据转换为JSON字符串
            String json = getResponse.getSourceAsString();
            // 4 并使用FastJSON将JSON字符串转换为JobDetail类对象
            JobEntity jobDetail = JSON.parseObject(json, JobEntity.class);
            // 5 记得：单独设置ID
            jobDetail.setId(id);
            log.info(jobDetail.toString());
        }

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# 4 ES 根据ID更新数据 #

@Test
        public void updateJobTest() throws IOException { 
    
            JobEntity jobEntity = new JobEntity();
            jobEntity.setId(1L);
            jobEntity.setAge(30);
            //地区改成深圳
            jobEntity.setArea("深圳");
            jobEntity.setExpect("3年工作经验");
            jobEntity.setJobDescription("高级开发工程师，抗压能力强，热爱学习。");
            jobEntity.setSalary("20K/月");
            jobEntity.setEducation("本科及以上");
            jobEntity.setType("全职");
            jobEntity.setTitle("JAVA高级开发工程师");
    
            // 1判断对应ID的文档是否存在
            GetRequest getRequest = new GetRequest(JOB_IDX, String.valueOf(jobEntity.getId()));
            boolean exists = restHighLevelClient.exists(getRequest, RequestOptions.DEFAULT);
            //2 如果存在 就更新
            if(exists) { 
                UpdateRequest updateRequest = new UpdateRequest(JOB_IDX, String.valueOf(jobEntity.getId()));
                updateRequest.doc(JSON.toJSONString(jobEntity), XContentType.JSON);
                restHighLevelClient.update(updateRequest, RequestOptions.DEFAULT);
            }
        }

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# 5 根据ID删除 #

@Test
        public void deleteJobTest() throws IOException { 
    
            Long id = 1L;
            // 1. 构建delete请求
            DeleteRequest deleteRequest = new DeleteRequest(JOB_IDX, String.valueOf(id));
    
            // 2. 使用RestHighLevelClient执行delete请求
            restHighLevelClient.delete(deleteRequest, RequestOptions.DEFAULT);
        }

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# 6 根据关键字分页搜索 #

在存在大量数据时，一般我们进行查询都需要进行分页查询。例如：我们指定页码、并指  
定每页显示多少条数据，然后Elasticsearch返回对应页码的数据。

## 6.1 数据准备 ##

https://gitee.com/zhurongsheng/elasticsearch-data/blob/master/es.data

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## 6.2 使用from和size来进行分页 ##

这是ES分页最常用的一种方案，跟mysql类似，from指定查询的起始位置，size表示从起始位置开始的文档数量。看个例子。

GET /bank/_search
    { 
      
      "query": { 
        "match_all": { }
      }, 
      
      "from": 0,
      "size": 10
    }

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ES默认的分页深度是10000，也就是说from+size超过10000就会报错。事实上，ES之所以有这个限制，是因为在分布式环境下深度分页的查询效率会非常低。比如我们现在查询第from=990，size=10这样的条件，这个在业务层就是查询第990页，每页展示10条数据。但是在ES处理的时候，会分别从每个分片上拿到1000条数据，然后在coordinating的节点上根据查询条件聚合出1000条记录，最后返回其中的10条。所以分页越深，ES处理的开销就大，占用内存就越大。

**描述：** JAVA方法。

@RunWith(SpringRunner.class)
    @SpringBootTest(classes = ElasticSearchApplication.class)
    @Slf4j
    public class EsPagingTest { 
    
        // 索引库的名字
        private static final String BANK_INDEX = "bank";
    
        @Qualifier("highLevelClient")
        @Autowired
        private RestHighLevelClient restHighLevelClient;
    
    
        @Test
        public void fromSizeTest() throws IOException { 
    
            //根据Duke关键字搜索第1页数据，每页10条
            JSONObject jsonObject = searchByPage("Duke", 1, 10);
            log.info(jsonObject.toJSONString());
    
        }
    
    
    
        public JSONObject searchByPage(String keywords, int pageNum, int pageSize) throws IOException { 
            // 1.构建SearchRequest检索请求
            SearchRequest searchRequest = new SearchRequest(BANK_INDEX);
            // 2.创建一个SearchSourceBuilder专门用于构建查询条件
            SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
            // 3.使用QueryBuilders.multiMatchQuery构建一个查询条件
            MultiMatchQueryBuilder multiMatchQueryBuilder = QueryBuilders.multiMatchQuery(keywords, "firstname", "lastname", "address");
            // 将查询条件设置到查询请求构建器中
            searchSourceBuilder.query(multiMatchQueryBuilder);
            // 每页显示多少条
            searchSourceBuilder.size(pageSize);
            // 设置从第几条开始查询
            searchSourceBuilder.from((pageNum - 1) * pageSize);
            // 4.调用SearchRequest.source将查询条件设置到检索请求
            searchRequest.source(searchSourceBuilder);
            // 5.执行RestHighLevelClient.search发起请求
            SearchResponse searchResponse = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
            SearchHit[] hitArray = searchResponse.getHits().getHits();
            // 6.遍历结果
            JSONArray jsonArray = new JSONArray();
            for (SearchHit documentFields : hitArray) { 
                // 1)获取命中的结果
                String json = documentFields.getSourceAsString();
    
                // 2)将JSON字符串转换为对象
                JSONObject jsonObject = JSON.parseObject(json);
                // 3)使用SearchHit.getId设置文档ID
                jsonObject.put("id",documentFields.getId());
                jsonArray.add(jsonObject);
            }
    
            // 7 封装返回结果
            long totalNum = searchResponse.getHits().getTotalHits().value;
            JSONObject rs = new JSONObject();
            rs.put("total", totalNum);
            rs.put("content", jsonArray);
            return rs;
        }
    
    
    }

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{ 
    	"total": 2,
    	"content": [
    		{ 
    			"account_number": 1,
    			"firstname": "Amber",
    			"address": "880 Holmes Lane",
    			"balance": 39225,
    			"gender": "M",
    			"city": "Brogan",
    			"employer": "Pyrami",
    			"state": "IL",
    			"id": "1",
    			"age": 32,
    			"email": "amberduke@pyrami.com",
    			"lastname": "Duke"
    		},
    		{ 
    			"account_number": 776,
    			"firstname": "Duke",
    			"address": "520 Doscher Street",
    			"balance": 29177,
    			"gender": "M",
    			"city": "Lafferty",
    			"employer": "Tripsch",
    			"state": "NC",
    			"id": "776",
    			"age": 24,
    			"email": "dukeatkinson@tripsch.com",
    			"lastname": "Atkinson"
    		}
    	]
    }

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## 6.3 使用scroll方式进行分页 ##

在进行大量分页时，每次分页都需要将要查询的数据进行重新排序，这样非常浪费性能。使用scroll是将要用的数据一次性排序好，然后分批取出。性能要比from + size好得多。使用scroll查询后，排序后的数据会保持一定的时间，后续的分页查询都从该快照取数据即可。

GET /bank/_search?scroll=60m
    { 
      
      "query": { 
        "multi_match": { 
          "query": "Street",
          "fields": ["address","firstname","lastname"]
        }
      },
      "from": 0,
      "size": 10
    }

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**描述：** java代

GET _search/scroll
    { 
      "scroll_id": "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAABicWWTROVmNmRHBURDZKS1JiQXhDUDhHUQ==",
      "scroll": "60m"
    }

@Test
 public void testPageByScroll() throws IOException { 
 
 //每页10条数据，根据Street搜索第2页数据
 int pageNum = 2;
 
 JSONObject rs = null;
 
 String scrollId = null;
 
 for (int i = 1; i <= pageNum; i++) { 
 rs = searchByScrollPage("Street", scrollId, 10);
 scrollId = rs.getString("scroll_id");
 }
 
 log.info(rs.toJSONString());
 
 }
 
 
 public JSONObject searchByScrollPage(String keywords, String scrollId, int pageSize) throws IOException { 
 SearchResponse searchResponse = null;
 if (scrollId == null) { 
 // 1.构建SearchRequest检索请求
 SearchRequest searchRequest = new SearchRequest(BANK_INDEX);
 // 2.创建一个SearchSourceBuilder专门用于构建查询条件
 SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
 // 3.使用QueryBuilders.multiMatchQuery构建一个查询条件
 MultiMatchQueryBuilder multiMatchQueryBuilder = QueryBuilders.multiMatchQuery(keywords, "firstname", "lastname", "address");
 // 将查询条件设置到查询请求构建器中
 searchSourceBuilder.query(multiMatchQueryBuilder);
 // 设置高亮
 HighlightBuilder highlightBuilder = new HighlightBuilder();
 highlightBuilder.field("firstname");
 highlightBuilder.field("lastname");
 highlightBuilder.field("address");
 highlightBuilder.preTags("");
 highlightBuilder.postTags("");
 // 给请求设置高亮
 searchSourceBuilder.highlighter(highlightBuilder);
 // 每页显示多少条
 searchSourceBuilder.size(pageSize);
 // 4.调用SearchRequest.source将查询条件设置到检索请求
 searchRequest.source(searchSourceBuilder);
 //--------------------------
 // 设置scroll查询
 //--------------------------
 searchRequest.scroll(TimeValue.timeValueMinutes(5));
 // 5.执行RestHighLevelClient.search发起请求
 searchResponse = restHighLevelClient.search(searchRequest, RequestOptions.DEFAULT);
 
 }
 // 第二次查询的时候，直接通过scroll id查询数据
 else { 
 SearchScrollRequest searchScrollRequest = new SearchScrollRequest(scrollId);
 searchScrollRequest.scroll(TimeValue.timeValueMinutes(5));
 // 使用RestHighLevelClient发送scroll请求
 searchResponse = restHighLevelClient.scroll(searchScrollRequest, RequestOptions.DEFAULT);
 }
 
 
 SearchHit[] hitArray = searchResponse.getHits().getHits();
 // 6.遍历结果
 JSONArray jsonArray = new JSONArray();
 for (SearchHit documentFields : hitArray) { 
 String json = documentFields.getSourceAsString();
 JSONObject jsonObject = JSON.parseObject(json);
 jsonObject.put("id", documentFields.getId());
 jsonArray.add(jsonObject);
 
 // 设置高亮的一些文本到实体类中
 // 封装了高亮
 Map<String, HighlightField> highlightFieldMap = documentFields.getHighlightFields();
 HighlightField fNameHL = highlightFieldMap.get("firstname");
 HighlightField lNameHL = highlightFieldMap.get("lastname");
 HighlightField addressHL = highlightFieldMap.get("address");
 
 if (fNameHL != null) { 
 // 获取指定字段的高亮片段
 Text[] fragments = fNameHL.getFragments();
 // 将这些高亮片段拼接成一个完整的高亮字段
 StringBuilder builder = new StringBuilder();
 for (Text text : fragments) { 
 builder.append(text);
 }
 // 设置到实体类中
 jsonObject.put("firstname", builder.toString());
 }
 
 if (lNameHL != null) { 
 Text[] fragments = lNameHL.getFragments();
 StringBuilder builder = new StringBuilder();
 for (Text text : fragments) { 
 builder.append(text);
 }
 jsonObject.put("lastname", builder.toString());
 }
 
 if (addressHL != null) { 
 Text[] fragments = addressHL.getFragments();
 StringBuilder builder = new StringBuilder();
 for (Text text : fragments) { 
 builder.append(text);
 }
 jsonObject.put("address", builder.toString());
 }
 }
 
 // 8. 将结果封装到Map结构中（带有分页信息）
 long totalNum = searchResponse.getHits().getTotalHits().value;
 
 JSONObject rs = new JSONObject();
 rs.put("scroll_id", searchResponse.getScrollId());
 rs.put("content", jsonArray);
 rs.put("totalNum", totalNum);
 return rs;
 }

**查看结果：**

{ 
 	"totalNum": 385,
 	"scroll_id": "DXF1ZXJ5QW5kRmV0Y2gBAAAAAAAAAM8WZ2M1bHplSFhTWEtGV0p6ZTBfb1lMUQ==",
 	"content": [
 		{ 
 			"account_number": 145,
 			"firstname": "Rowena",
 			"address": "891 Elton Street",
 			"balance": 47406,
 			"gender": "M",
 			"city": "Ripley",
 			"employer": "Asimiline",
 			"state": "NH",
 			"id": "145",
 			"age": 32,
 			"email": "rowenawilkinson@asimiline.com",
 			"lastname": "Wilkinson"
 		},
 		{ 
 			"account_number": 152,
 			"firstname": "Wolfe",
 			"address": "457 Guernsey Street",
 			"balance": 8088,
 			"gender": "M",
 			"city": "Adelino",
 			"employer": "Hivedom",
 			"state": "MS",
 			"id": "152",
 			"age": 21,
 			"email": "wolferocha@hivedom.com",
 			"lastname": "Rocha"
 		},
 		{ 
 			"account_number": 164,
 			"firstname": "Cummings",
 			"address": "308 Schaefer Street",
 			"balance": 9101,
 			"gender": "F",
 			"city": "Chaparrito",
 			"employer": "Comtrak",
 			"state": "WI",
 			"id": "164",
 			"age": 26,
 			"email": "cummingslittle@comtrak.com",
 			"lastname": "Little"
 		},
 		{ 
 			"account_number": 188,
 			"firstname": "Tia",
 			"address": "583 Ainslie Street",
 			"balance": 41504,
 			"gender": "F",
 			"city": "Summerset",
 			"employer": "Jasper",
 			"state": "UT",
 			"id": "188",
 			"age": 24,
 			"email": "tiamiranda@jasper.com",
 			"lastname": "Miranda"
 		},
 		{ 
 			"account_number": 190,
 			"firstname": "Blake",
 			"address": "636 Diamond Street",
 			"balance": 3150,
 			"gender": "F",
 			"city": "Crumpler",
 			"employer": "Quantasis",
 			"state": "KY",
 			"id": "190",
 			"age": 30,
 			"email": "blakedavidson@quantasis.com",
 			"lastname": "Davidson"
 		},
 		{ 
 			"account_number": 195,
 			"firstname": "Kaye",
 			"address": "955 Hopkins Street",
 			"balance": 5025,
 			"gender": "M",
 			"city": "Ola",
 			"employer": "Zork",
 			"state": "WY",
 			"id": "195",
 			"age": 31,
 			"email": "kayegibson@zork.com",
 			"lastname": "Gibson"
 		},
 		{ 
 			"account_number": 203,
 			"firstname": "Eve",
 			"address": "435 Furman Street",
 			"balance": 21890,
 			"gender": "M",
 			"city": "Jamestown",
 			"employer": "Assitia",
 			"state": "MN",
 			"id": "203",
 			"age": 33,
 			"email": "evewyatt@assitia.com",
 			"lastname": "Wyatt"
 		},
 		{ 
 			"account_number": 227,
 			"firstname": "Coleman",
 			"address": "776 Little Street",
 			"balance": 19780,
 			"gender": "M",
 			"city": "Eagleville",
 			"employer": "Exoteric",
 			"state": "WV",
 			"id": "227",
 			"age": 22,
 			"email": "colemanberg@exoteric.com",
 			"lastname": "Berg"
 		},
 		{ 
 			"account_number": 239,
 			"firstname": "Chang",
 			"address": "895 Brigham Street",
 			"balance": 25719,
 			"gender": "M",
 			"city": "Belgreen",
 			"employer": "Qaboos",
 			"state": "NH",
 			"id": "239",
 			"age": 36,
 			"email": "changboyer@qaboos.com",
 			"lastname": "Boyer"
 		},
 		{ 
 			"account_number": 246,
 			"firstname": "Katheryn",
 			"address": "259 Kane Street",
 			"balance": 28405,
 			"gender": "F",
 			"city": "Bath",
 			"employer": "Quantalia",
 			"state": "TX",
 			"id": "246",
 			"age": 21,
 			"email": "katherynfoster@quantalia.com",
 			"lastname": "Foster"
 		}
 	]
 }

**描述：** 结果验证

GET /bank/_search 
    { 
      
      "query": { 
        "multi_match": { 
          "query": "Street",
          "fields": ["address","firstname","lastname"]
        }
      },
      "from": 10,
      "size": 10
    }

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM0MTI1OTk5_size_16_color_FFFFFF_t_70 12]

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