用Elasticsearch搜索匹配功能实现基于地理位置的查询

1.Redis,MongoDB,Elasticsearch实现地理位置查询比较

1.1 Redis:

优点：Redis提供了地理空间索引功能，可以通过Geo数据类型进行地理位置查询。这使得Redis在处理地理位置查询时非常高效。

缺点：
Redis的地理空间索引功能相对简单，只能支持二维平面坐标系（经纬度）的查询，对于三维坐标系或者不规则地理区域的查询支持不够好。
功能有限：Redis的地理位置查询功能相对简单，仅支持基本的距离计算、范围查询等操作，无法满足复杂的空间查询需求。
存储容量限制：由于Redis数据存储在内存中，其存储容量受限于物理内存大小，对于大规模地理位置数据，可能需要进行分片或其他优化策略。
扩展性受限：Redis对于数据的扩展能力有限，不如Elasticsearch那样容易水平扩展以适应规模的增长。

使用场景：适用于需要快速查询地理位置信息的场景，小型应用，并且对于快速插入和查询地理位置数据有较高的实时性要求，可以考虑使用Redis geo。

1.2 MongoDB ：

优点：
灵活性好：MongoDB支持多种地理位置查询操作，包括点查询、范围查询和多边形查询等。
数据结构简单：MongoDB的文档型结构非常适合存储地理位置数据，容易理解和使用。
高可用性：MongoDB提供了复制集和分片等机制来确保数据的高可用性和扩展性。
然而，MongoDB + 2d索引实现地理位置查询也存在一些缺点：

性能相对较差：相比Elasticsearch，在处理大规模的地理位置查询时，MongoDB的性能可能会受到限制。
功能相对简单：MongoDB的地理位置查询功能较为基础，相比Elasticsearch可能缺乏某些高级查询功能。
不支持部分地理位置操作：例如，MongoDB不支持直接计算两个地理位置之间的距离。

1.3 Elasticsearch geo

使用Elasticsearch geo实现地理位置查询的优点：

高性能：Elasticsearch是一种搜索引擎，使用geo点的经纬度数据可以快速进行空间查询和过滤，具有较高的查询效率。
灵活性：Elasticsearch提供了丰富的地理位置查询功能，例如可以根据距离、范围及其他条件进行查询和排序。
可扩展性：Elasticsearch可以通过分片和副本来实现水平扩展，以应对大规模的地理位置数据查询需求。

使用Elasticsearch geo实现地理位置查询的缺点：
学习成本：学习和配置Elasticsearch需要花费一定的时间和精力。
依赖性：使用Elasticsearch需要安装和维护Elasticsearch服务，这可能增加系统依赖和部署复杂性。
数据存储限制：Elasticsearch适用于小到中等大小的数据集，对于大量地理位置数据，可能需要额外的硬件资源和优化工作。

Elasticsearch为用户提供了基于地理位置的搜索功能。它主要支持两种类型的地理查询：一种是地理点(geo_point),即经纬度查询，另一种是地理形状查询(geo_shape),即支持点、线、圆形和多边形查询等

2.Elasticsearch geo地理位置数据类型

在Elasticsearch中，存在两种地理位置数据类型：geo_point和geo_shape。

geo_point：这是最基本的地理位置类型，通常用于表示一个二维坐标点（经度和纬度）。可以计算落在某个矩形内的点、以某个点为半径(圆)的点、某个多边形内的点等。此外，geo_point还可以用于排序、聚合等操作。
geo_shape：这种数据类型表示一个复杂的图形，使用的是GeoJSON的格式。它可以表达一块地理区域，区域的形状可以是任意多边形，也可以是点、线、面、多点、多线、多面等几何类型。然而，这种数据类型不能进行排序操作。

2.基于geo_point类型实现查询加油站案例

elasticsearch 版本7.12.1

2.1 springboot集成elasticsearch

       <dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-data-elasticsearch</artifactId><version>2.5.11</version></dependency><dependency><groupId>org.locationtech.jts</groupId><artifactId>jts-core</artifactId><version>1.18.1</version></dependency><dependency><groupId>org.locationtech.spatial4j</groupId><artifactId>spatial4j</artifactId><version>0.8</version></dependency>

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
logging.level.org.springframework.data.elasticsearch.core=debug
logging.level.org.springframework.data.elasticsearch.client.WIRE=trace

配置类

@ConfigurationProperties(prefix = "elasticsearch")
@Configuration
@Data
public class ElasticsearchConfig extends AbstractElasticsearchConfiguration {private String host ;private Integer port ;//重写父类方法@Overridepublic RestHighLevelClient elasticsearchClient() {RestClientBuilder builder = RestClient.builder(new HttpHost(host, port));RestHighLevelClient restHighLevelClient = newRestHighLevelClient(builder);return restHighLevelClient;}
}

测试实体类

@ApiModel
@Data
@Builder
@AllArgsConstructor
@NoArgsConstructor
@ToString
@Document(indexName = "stationcc", shards = 3, replicas = 1)
public class ChargingStationDTO {//必须有 id,这里的 id 是全局唯一的标识，等同于 es 中的"_id"@Id@ApiModelProperty(value = "id", example = "111111111111")private Long baseId;/*** 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*/@ApiModelProperty(value = "加油站ID", example = "111111111111")@Field(type = FieldType.Keyword)private String stationId;@ApiModelProperty(value = "运营商ID", example = "395815801")@Field(type = FieldType.Keyword)private String operatorId;@ApiModelProperty(value = "加油站名称", example = "测试加油站")@Field(type = FieldType.Keyword)private String stationName;@ApiModelProperty(value = "运营商名称", example = "测试")@Field(type = FieldType.Keyword)private String operatorName;@GeoPointField@ApiModelProperty(value = "经纬度")private GeoPoint location; @Field(type = FieldType.Keyword)@ApiModelProperty(value = "详细地址", example = "山东路154号")private String address;@ApiModelProperty(value = "距离", example = "1.0")private  double distance;
}

初始化数据

 @Testpublic void saveAll() {//起点 111.000,31.000//终点 121.000,31.000//( 121 , 31 )    -    ( 111 , 31 )    之间的距离为    952.8062737420901 km//96-121,23-40List<ChargingStationDTO> chargingStationDTOList = new ArrayList<>();List<String> stringList = CollUtil.newArrayList("招式", "王五", "基于", "好好", "电动", "反复", "第三十", "十三点", "但是");for (int i = 2000; i < 450000; i++) {ChargingStationDTO chargingStationDTO = new ChargingStationDTO();chargingStationDTO.setBaseId(Long.valueOf(i));chargingStationDTO.setStationId(Long.valueOf(i).toString());chargingStationDTO.setOperatorId(Long.valueOf(i).toString());chargingStationDTO.setStationName(RandomUtil.randomEleList(stringList, 1).get(0));chargingStationDTO.setAddress("地址" + i);//经度范围是0-180°,纬度范围是0-90°//纬度double lat = RandomUtil.randomDouble(23.000, 40.000, 3, RoundingMode.DOWN);//经度double lon = RandomUtil.randomDouble(96.000, 121.000, 3, RoundingMode.DOWN);chargingStationDTO.setLocation(new GeoPoint(lat, lon));chargingStationDTOList.add(chargingStationDTO);if (chargingStationDTOList.size() == 1000) {chargingStationDao.saveAll(chargingStationDTOList);chargingStationDTOList.clear();System.out.println("插入1000,i"+i);}}}

2.2 查询附近加油站（圆形查询）

请求参数

@ApiModel
@Data
@Builder
@AllArgsConstructor
@NoArgsConstructor
public class ChargingStationNearbySearchDTO {@ApiModelProperty(value = "id", example = "1111111111")private Long baseId;@ApiModelProperty(value = "加油站名称", example = "测试加油站")private String stationName;@ApiModelProperty(value = "经度")@NotNull(message = "经度不能为空")private Double lon;@ApiModelProperty(value = "纬度")@NotNull(message = "纬度不能为空")private Double lat;@ApiModelProperty(value = "查找半径")private int radius;@ApiModelProperty(value = "page", example = "1")private Integer page;@ApiModelProperty(value = "pageSize", example = "100")private Integer pageSize;
}

 @PostMapping("/nearby")@ApiOperation(value = "查询附近加油站")public Response<ChargingStationVO> nearbySearch(@RequestBody @Valid @Validated ChargingStationNearbySearchDTO searchDTO) {String fieldName = "location";// NativeSearchQuery实现了SearchQuery接口NativeSearchQueryBuilder nativeSearchQueryBuilder = new NativeSearchQueryBuilder();// 分页PageRequest pageRequest = PageRequest.of(searchDTO.getPage() - 1, searchDTO.getPageSize());nativeSearchQueryBuilder.withPageable(pageRequest);// 定义bool查询BoolQueryBuilder boolQueryBuilder = new BoolQueryBuilder();//https://blog.csdn.net/icanlove/article/details/126425788?spm=1001.2101.3001.6661.1&utm_medium=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-126425788-blog-120678401.235%5Ev38%5Epc_relevant_default_base&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-126425788-blog-120678401.235%5Ev38%5Epc_relevant_default_base&utm_relevant_index=1//使用 minimum_should_match 选项，至少匹配一项should子句。if (StringUtils.isNotBlank(searchDTO.getStationName()) || ObjectUtils.isNotEmpty(searchDTO.getBaseId())) {if (StringUtils.isNotBlank(searchDTO.getStationName())) {// //左右模糊查询,其中fuzziness的参数作用是在查询时，es动态的将查询关键词前后增加或者删除一个词，然后进行匹配QueryBuilder queryBuilder = QueryBuilders.fuzzyQuery("stationName", searchDTO.getStationName()).fuzziness(Fuzziness.ONE);boolQueryBuilder.must(queryBuilder);}if (ObjectUtils.isNotEmpty(searchDTO.getBaseId())) {// //关键字不支持分词QueryBuilder queryBuilder = QueryBuilders.termQuery("baseId", searchDTO.getBaseId());boolQueryBuilder.must(queryBuilder);}}// geo查询，定义中心点，指定查询范围GeoDistanceQueryBuilder geoDistanceQueryBuilder = new GeoDistanceQueryBuilder(fieldName);geoDistanceQueryBuilder.point(searchDTO.getLat(), searchDTO.getLon());geoDistanceQueryBuilder.distance(searchDTO.getRadius(), DistanceUnit.METERS);boolQueryBuilder.must(geoDistanceQueryBuilder);//     外部 bool 过滤器BoolQueryBuilder queryBuilder = new BoolQueryBuilder();queryBuilder.filter(boolQueryBuilder);nativeSearchQueryBuilder.withQuery(queryBuilder);// 按照距离升序GeoDistanceSortBuilder geoDistanceSortBuilder = new GeoDistanceSortBuilder(fieldName, searchDTO.getLat(), searchDTO.getLon());geoDistanceSortBuilder.unit(DistanceUnit.METERS); //距离单位geoDistanceSortBuilder.order(SortOrder.ASC); //升序nativeSearchQueryBuilder.withSort(geoDistanceSortBuilder);NativeSearchQuery nativeSearchQuery = nativeSearchQueryBuilder.build();DslLogUtil.log(elasticsearchOperations, nativeSearchQuery);SearchHits<ChargingStationDTO> searchHits = elasticsearchOperations.search(nativeSearchQuery, ChargingStationDTO.class);log.info("响应数据:{}", LogUtil.getLogJson(searchHits));List<ChargingStationDTO> chargingStationDTOList = null;if (CollectionUtil.isNotEmpty(searchHits.getSearchHits())) {chargingStationDTOList = searchHits.getSearchHits().stream().map(o -> {// 计算两点距离//关于GeoDistance.ARC和GeoDistance.PLANE，前者比后者计算起来要慢，但精确度要比后者高，具体区别可以看。double distance = GeoDistance.ARC.calculate(o.getContent().getLocation().getLat(), o.getContent().getLocation().getLon(), searchDTO.getLat(), searchDTO.getLon(), DistanceUnit.KILOMETERS);ChargingStationDTO chargingStationDTO = o.getContent();chargingStationDTO.setDistance(distance);return chargingStationDTO;}).collect(Collectors.toList());}int count = CollectionUtils.isEmpty(chargingStationDTOList) ? 0 : chargingStationDTOList.size();return Response.success(ChargingStationVO.builder().positions(chargingStationDTOList).count(count).build());}

2.3 查询附近加油站（ geo_bounding_box 矩形查询）

geo_bounding_box语法又称为地理坐标盒模型，在当前语法中，只需选择一个矩阵范围(输入矩阵的左上角的顶点地理坐标和矩阵的右上角的顶点地理坐标，构建成为一个矩阵)，即可计算出当前矩阵中符合条件的元素；
在这里插入图片描述

/*** 给定两个坐标，通过这两个坐标形成对角线，* 平行于地球经纬度从而得到的一个矩阵。* 采用geo_bounding_box语法可以得到坐落于当前矩阵中的元素的信息；** @param searchDTO* @return*/@PostMapping("/box/query")@ApiOperation(value = "矩形查询附近加油站")public Response<ChargingStationVO> boxQuery(@RequestBody @Valid @Validated ChargingStationSearchDTO searchDTO) {// NativeSearchQuery实现了SearchQuery接口NativeSearchQueryBuilder nativeSearchQueryBuilder = new NativeSearchQueryBuilder();// 分页PageRequest pageRequest = PageRequest.of(searchDTO.getPage() - 1, searchDTO.getPageSize());nativeSearchQueryBuilder.withPageable(pageRequest);// 定义bool查询BoolQueryBuilder boolQueryBuilder = new BoolQueryBuilder();//https://blog.csdn.net/icanlove/article/details/126425788?spm=1001.2101.3001.6661.1&utm_medium=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-126425788-blog-120678401.235%5Ev38%5Epc_relevant_default_base&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-126425788-blog-120678401.235%5Ev38%5Epc_relevant_default_base&utm_relevant_index=1//使用 minimum_should_match 选项，至少匹配一项should子句。if (StringUtils.isNotBlank(searchDTO.getStationName()) || ObjectUtils.isNotEmpty(searchDTO.getBaseId())) {if (StringUtils.isNotBlank(searchDTO.getStationName())) {// //左右模糊查询,其中fuzziness的参数作用是在查询时，es动态的将查询关键词前后增加或者删除一个词，然后进行匹配QueryBuilder queryBuilder = QueryBuilders.fuzzyQuery("stationName", searchDTO.getStationName()).fuzziness(Fuzziness.ONE);boolQueryBuilder.must(queryBuilder);}if (ObjectUtils.isNotEmpty(searchDTO.getBaseId())) {// //关键字不支持分词QueryBuilder queryBuilder = QueryBuilders.termQuery("baseId", searchDTO.getBaseId());boolQueryBuilder.must(queryBuilder);}}//给定两个坐标，通过这两个坐标形成对角线，// 平行于地球经纬度从而得到的一个矩阵。// 采用geo_bounding_box语法可以得到坐落于当前矩阵中的元素的信息；// 构造左上点坐标GeoPoint topLeft = new GeoPoint(searchDTO.getPositions().get(0).getLat(), searchDTO.getPositions().get(0).getLon());// 构造右下点坐标GeoPoint bottomRight = new GeoPoint(searchDTO.getPositions().get(1).getLat(), searchDTO.getPositions().get(1).getLon());GeoBoundingBoxQueryBuilder geoBoundingBoxQueryBuilder = new GeoBoundingBoxQueryBuilder("location").setCorners(topLeft, bottomRight);boolQueryBuilder.must(geoBoundingBoxQueryBuilder);//     外部 bool 过滤器BoolQueryBuilder queryBuilder = new BoolQueryBuilder();queryBuilder.filter(boolQueryBuilder);nativeSearchQueryBuilder.withQuery(queryBuilder);NativeSearchQuery nativeSearchQuery = nativeSearchQueryBuilder.build();DslLogUtil.log(elasticsearchOperations, nativeSearchQuery);SearchHits<ChargingStationDTO> searchHits = elasticsearchOperations.search(nativeSearchQuery, ChargingStationDTO.class);log.info("响应数据:{}", LogUtil.getLogJson(searchHits));List<ChargingStationDTO> chargingStationDTOList = null;if (CollectionUtil.isNotEmpty(searchHits.getSearchHits())) {chargingStationDTOList = searchHits.getSearchHits().stream().map(SearchHit::getContent).collect(Collectors.toList());}int count = CollectionUtils.isEmpty(chargingStationDTOList) ? 0 : chargingStationDTOList.size();return Response.success(ChargingStationVO.builder().positions(chargingStationDTOList).count(count).build());}

DSL
请求体：

{"from": 0,"size": 100,"query": {"bool": {"filter": [{"bool": {"must": [{"fuzzy": {"stationName": {"value": "第三十","fuzziness": "1","prefix_length": 0,"max_expansions": 50,"transpositions": true,"boost": 1.0}}}, {"geo_bounding_box": {"location": {"top_left": [120.91224, 30.84623],"bottom_right": [120.93743, 30.8245]},"validation_method": "STRICT","type": "MEMORY","ignore_unmapped": false,"boost": 1.0}}],"adjust_pure_negative": true,"boost": 1.0}}],"adjust_pure_negative": true,"boost": 1.0}},"version": true,"explain": false
}

响应体：

{"code": 200,"message": "成功","data": {"count": 2,"positions": [{"baseId": 431843,"stationId": "431843","operatorId": "431843","stationName": "好好","operatorName": null,"location": {"lat": 30.833,"lon": 120.934,"geohash": "wtmzruvrnry1","fragment": true},"address": "地址431843","distance": 0},{"baseId": 114960,"stationId": "114960","operatorId": "114960","stationName": "第三十","operatorName": null,"location": {"lat": 30.84,"lon": 120.919,"geohash": "wtmzrw680btm","fragment": true},"address": "地址114960","distance": 0}]},"extraData": {}
}

2.4 多边形查询附近加油站（geo-polygon-多边形查询）

ES的geo_polygon语法，可以通过指定多个坐标点，从而构成一个多边形，然后从当前多边形中召回坐落其中的元素进行召回；在当前语法中，最少需要3个坐标，从而构成一个多边形；
在这里插入图片描述

  @PostMapping("/polygon/query")@ApiOperation(value = "多边形查询附近加油站")public Response<ChargingStationVO> polygonQuery(@RequestBody @Valid @Validated ChargingStationSearchDTO searchDTO) throws IOException {// NativeSearchQuery实现了SearchQuery接口NativeSearchQueryBuilder nativeSearchQueryBuilder = new NativeSearchQueryBuilder();// 分页PageRequest pageRequest = PageRequest.of(searchDTO.getPage() - 1, searchDTO.getPageSize());nativeSearchQueryBuilder.withPageable(pageRequest);// 定义bool查询BoolQueryBuilder boolQueryBuilder = new BoolQueryBuilder();//https://blog.csdn.net/icanlove/article/details/126425788?spm=1001.2101.3001.6661.1&utm_medium=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-126425788-blog-120678401.235%5Ev38%5Epc_relevant_default_base&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-126425788-blog-120678401.235%5Ev38%5Epc_relevant_default_base&utm_relevant_index=1//使用 minimum_should_match 选项，至少匹配一项should子句。if (StringUtils.isNotBlank(searchDTO.getStationName()) || ObjectUtils.isNotEmpty(searchDTO.getBaseId())) {if (StringUtils.isNotBlank(searchDTO.getStationName())) {// //左右模糊查询,其中fuzziness的参数作用是在查询时，es动态的将查询关键词前后增加或者删除一个词，然后进行匹配QueryBuilder queryBuilder = QueryBuilders.fuzzyQuery("stationName", searchDTO.getStationName()).fuzziness(Fuzziness.ONE);boolQueryBuilder.must(queryBuilder);}if (ObjectUtils.isNotEmpty(searchDTO.getBaseId())) {// //关键字不支持分词QueryBuilder queryBuilder = QueryBuilders.termQuery("baseId", searchDTO.getBaseId());boolQueryBuilder.must(queryBuilder);}}//可以通过指定多个坐标点，从而构成一个多边形，//然后从当前多边形中召回坐落其中的元素进行召回；//在当前语法中，最少需要3个坐标，从而构成一个多边形；// 创建多边形几何对象CoordinatesBuilder coordinatesBuilder = new CoordinatesBuilder();for (GpsListDTO gpsListDTO : searchDTO.getPositions()) {coordinatesBuilder.coordinate(gpsListDTO.getLon(), gpsListDTO.getLat());}PolygonBuilder pb = new PolygonBuilder(coordinatesBuilder);GeoShapeQueryBuilder geoShapeQueryBuilder = QueryBuilders.geoShapeQuery("location", pb.buildGeometry());// intersects - 查询的形状与索引的形状有重叠（默认）, 即图形有交集则匹配。//disjoint - 查询的形状与索引的形状完全不重叠。//within - 查询的形状包含索引的形状。//CONTAINS将返回其geo_shape字段包含查询中指定的几何形状的所有文档。//within与CONTAINS的区别// 它们是反比关系：A包含B,B在A内.// `A`是查询中的形状，而`B`是文档中的形状。//`WITHIN`表示`A包含B`   A.contains(B) True// `CONTAINS`表示`B包含A`  B.within(A)  TruegeoShapeQueryBuilder.relation(ShapeRelation.INTERSECTS);boolQueryBuilder.must(geoShapeQueryBuilder);//     外部 bool 过滤器BoolQueryBuilder queryBuilder = new BoolQueryBuilder();queryBuilder.filter(boolQueryBuilder);nativeSearchQueryBuilder.withQuery(queryBuilder);NativeSearchQuery nativeSearchQuery = nativeSearchQueryBuilder.build();DslLogUtil.log(elasticsearchOperations, nativeSearchQuery);SearchHits<ChargingStationDTO> searchHits = elasticsearchOperations.search(nativeSearchQuery, ChargingStationDTO.class);log.info("响应数据:{}", LogUtil.getLogJson(searchHits));List<ChargingStationDTO> chargingStationDTOList = null;if (CollectionUtil.isNotEmpty(searchHits.getSearchHits())) {chargingStationDTOList = searchHits.getSearchHits().stream().map(SearchHit::getContent).collect(Collectors.toList());}int count = CollectionUtils.isEmpty(chargingStationDTOList) ? 0 : chargingStationDTOList.size();return Response.success(ChargingStationVO.builder().positions(chargingStationDTOList).count(count).build());}

DSL
请求体：

{"from": 0,"size": 100,"query": {"bool": {"filter": [{"bool": {"must": [{"fuzzy": {"stationName": {"value": "好好","fuzziness": "1","prefix_length": 0,"max_expansions": 50,"transpositions": true,"boost": 1.0}}}, {"geo_shape": {"location": {"shape": {"type": "Polygon","coordinates": [[[120.92696, 30.83932],[120.91964, 30.82868],[120.95907, 30.81838],[120.96842, 30.83525],[120.94369, 30.84345],[120.92696, 30.83932]]]},"relation": "intersects"},"ignore_unmapped": false,"boost": 1.0}}],"adjust_pure_negative": true,"boost": 1.0}}],"adjust_pure_negative": true,"boost": 1.0}},"version": true,"explain": false
}

响应体：

{"empty": false,"maxScore": 0.0,"searchHits": [{"content": {"address": "地址431843","baseId": 431843,"distance": 0.0,"location": {"fragment": true,"geohash": "wtmzruvrnry1","lat": 30.833,"lon": 120.934},"operatorId": "431843","stationId": "431843","stationName": "好好"},"highlightFields": {},"id": "431843","index": "stationcc","innerHits": {},"matchedQueries": [],"score": 0.0,"sortValues": []}],"totalHits": 1,"totalHitsRelation": "EQUAL_TO"
}

2.5 查询沿途加油站（一次查询多个圆点）

   @PostMapping("/route")@ApiOperation(value = "查询沿途加油站")public Response<ChargingStationVO> routeSearch(@RequestBody @Valid @Validated ChargingStationSearchDTO searchDTO) {String fieldName = "location";// NativeSearchQuery实现了SearchQuery接口NativeSearchQueryBuilder nativeSearchQueryBuilder = new NativeSearchQueryBuilder();// 分页PageRequest pageRequest = PageRequest.of(searchDTO.getPage() - 1, searchDTO.getPageSize());nativeSearchQueryBuilder.withPageable(pageRequest);// 定义bool查询BoolQueryBuilder boolQueryBuilder = new BoolQueryBuilder();//https://blog.csdn.net/icanlove/article/details/126425788?spm=1001.2101.3001.6661.1&utm_medium=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-126425788-blog-120678401.235%5Ev38%5Epc_relevant_default_base&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-2%7Edefault%7ECTRLIST%7ERate-1-126425788-blog-120678401.235%5Ev38%5Epc_relevant_default_base&utm_relevant_index=1//使用 minimum_should_match 选项，至少匹配一项should子句。boolQueryBuilder.minimumShouldMatch(1);if (StringUtils.isNotBlank(searchDTO.getStationName()) || ObjectUtils.isNotEmpty(searchDTO.getBaseId())) {if (StringUtils.isNotBlank(searchDTO.getStationName())) {// //左右模糊查询,其中fuzziness的参数作用是在查询时，es动态的将查询关键词前后增加或者删除一个词，然后进行匹配QueryBuilder queryBuilder = QueryBuilders.fuzzyQuery("stationName", searchDTO.getStationName()).fuzziness(Fuzziness.ONE);boolQueryBuilder.must(queryBuilder);}if (ObjectUtils.isNotEmpty(searchDTO.getBaseId())) {// //关键字不支持分词QueryBuilder queryBuilder = QueryBuilders.termQuery("baseId", searchDTO.getBaseId());boolQueryBuilder.must(queryBuilder);}}if (CollectionUtil.isNotEmpty(searchDTO.getPositions())) {boolQueryBuilder.minimumShouldMatch(1);for (GpsListDTO position : searchDTO.getPositions()) {// geo查询，定义中心点，指定查询范围GeoDistanceQueryBuilder geoDistanceQueryBuilder = new GeoDistanceQueryBuilder(fieldName);geoDistanceQueryBuilder.point(position.getLat(), position.getLon());geoDistanceQueryBuilder.distance(searchDTO.getRadius(), DistanceUnit.METERS);boolQueryBuilder.should(geoDistanceQueryBuilder);}}//     外部 bool 过滤器
//        Elasticsearch 查询条件和过滤条件的区别？
//        Elasticsearch中的查询条件和过滤条件都是用于搜索和过滤文档的条件，但它们之间有一些区别。
//        查询条件是用于计算文档相关度得分的条件，它会将所有符合条件的文档按照相关度得分从高到低排序，并返回前N个文档。查询条件可以使用各种类型的查询，如match、term、range、bool等。查询条件会计算每个文档的相关度得分，因此查询条件可以用于搜索和排序。
//        过滤条件是用于过滤文档的条件，它会将所有符合条件的文档返回，但不会计算相关度得分。过滤条件可以使用各种类型的过滤器，如term、range、bool、geo_distance等。过滤条件不会计算相关度得分，因此过滤条件可以用于过滤和聚合。
//        查询条件和过滤条件的区别在于，查询条件会计算每个文档的相关度得分，而过滤条件不会计算得分。因此，如果只需要过滤文档而不需要计算得分，应该使用过滤条件。另外，过滤条件可以缓存结果，提高查询性能，而查询条件不能缓存结果。
//        需要注意的是，查询条件和过滤条件都可以使用bool查询和bool过滤器来组合多个条件。bool查询和bool过滤器都是用于组合多个查询或过滤器的逻辑运算符，可以使用must、should、must_not三个子句来组合多个查询或过滤器。BoolQueryBuilder queryBuilder = new BoolQueryBuilder();queryBuilder.filter(boolQueryBuilder);nativeSearchQueryBuilder.withQuery(queryBuilder);NativeSearchQuery nativeSearchQuery = nativeSearchQueryBuilder.build();DslLogUtil.log(elasticsearchOperations, nativeSearchQuery);SearchHits<ChargingStationDTO> searchHits = elasticsearchOperations.search(nativeSearchQuery, ChargingStationDTO.class);log.info("响应数据:{}", LogUtil.getLogJson(searchHits));List<ChargingStationDTO> chargingStationDTOList = null;if (CollectionUtil.isNotEmpty(searchHits.getSearchHits())) {chargingStationDTOList = searchHits.getSearchHits().stream().map(SearchHit::getContent).collect(Collectors.toList());}int count = CollectionUtils.isEmpty(chargingStationDTOList) ? 0 : chargingStationDTOList.size();return Response.success(ChargingStationVO.builder().positions(chargingStationDTOList).count(count).build());}

请求DSL语句：

{"from": 0,"size": 10000,"query": {"bool": {"filter": [{"bool": {"must": [{"fuzzy": {"stationName": {"value": "王五","fuzziness": "1","prefix_length": 0,"max_expansions": 50,"transpositions": true,"boost": 1.0}}}],"should": [{"geo_distance": {"location": [114.7, 31.0],"distance": 10000.0,"distance_type": "arc","validation_method": "STRICT","ignore_unmapped": false,"boost": 1.0}}, {"geo_distance": {"location": [116.935, 31.0],"distance": 10000.0,"distance_type": "arc","validation_method": "STRICT","ignore_unmapped": false,"boost": 1.0}}, {"geo_distance": {"location": [117.261, 31.0],"distance": 10000.0,"distance_type": "arc","validation_method": "STRICT","ignore_unmapped": false,"boost": 1.0}}, {"geo_distance": {"location": [116.569, 31.0],"distance": 10000.0,"distance_type": "arc","validation_method": "STRICT","ignore_unmapped": false,"boost": 1.0}}, {"geo_distance": {"location": [117.639, 31.0],"distance": 10000.0,"distance_type": "arc","validation_method": "STRICT","ignore_unmapped": false,"boost": 1.0}}, {"geo_distance": {"location": [119.236, 31.0],"distance": 10000.0,"distance_type": "arc","validation_method": "STRICT","ignore_unmapped": false,"boost": 1.0}}],"adjust_pure_negative": true,"minimum_should_match": "1","boost": 1.0}}],"adjust_pure_negative": true,"boost": 1.0}},"version": true,"explain": false
}

响应数据:

{"empty": false,"maxScore": 0.0,"searchHits": [{"content": {"address": "地址4031","baseId": 4031,"distance": 0.0,"location": {"fragment": true,"geohash": "wtkzbygzuwxz","lat": 30.932,"lon": 119.218},"operatorId": "4031","stationId": "4031","stationName": "王五"},"highlightFields": {},"id": "4031","index": "stationcc","innerHits": {},"matchedQueries": [],"score": 0.0,"sortValues": []}, {"content": {"address": "地址26708","baseId": 26708,"distance": 0.0,"location": {"fragment": true,"geohash": "wte2df6z32vx","lat": 31.039,"lon": 117.195},"operatorId": "26708","stationId": "26708","stationName": "王五"},"highlightFields": {},"id": "26708","index": "stationcc","innerHits": {},"matchedQueries": [],"score": 0.0,"sortValues": []}, {"content": {"address": "地址156487","baseId": 156487,"distance": 0.0,"location": {"fragment": true,"geohash": "wt988d3zmbcx","lat": 31.039,"lon": 114.634},"operatorId": "156487","stationId": "156487","stationName": "王五"},"highlightFields": {},"id": "156487","index": "stationcc","innerHits": {},"matchedQueries": [],"score": 0.0,"sortValues": []}, {"content": {"address": "地址131631","baseId": 131631,"distance": 0.0,"location": {"fragment": true,"geohash": "wtdb78u6echc","lat": 30.986,"lon": 116.527},"operatorId": "131631","stationId": "131631","stationName": "王五"},"highlightFields": {},"id": "131631","index": "stationcc","innerHits": {},"matchedQueries": [],"score": 0.0,"sortValues": []}, {"content": {"address": "地址265815","baseId": 265815,"distance": 0.0,"location": {"fragment": true,"geohash": "wte8ks47qs3x","lat": 31.004,"lon": 117.623},"operatorId": "265815","stationId": "265815","stationName": "王五"},"highlightFields": {},"id": "265815","index": "stationcc","innerHits": {},"matchedQueries": [],"score": 0.0,"sortValues": []}],"totalHits": 16,"totalHitsRelation": "EQUAL_TO"
}

打印完整DSL语句工具类

@Slf4j
public class DslLogUtil {public static void log(ElasticsearchOperations elasticsearchOperations, NativeSearchQuery nativeSearchQuery) {if (elasticsearchOperations instanceof ElasticsearchRestTemplate) {try {ElasticsearchRestTemplate elasticsearchRestTemplate = (ElasticsearchRestTemplate) elasticsearchOperations;Method searchRequest = ReflectionUtils.findMethod(Class.forName("org.springframework.data.elasticsearch.core.RequestFactory"), "searchRequest", Query.class, Class.class, IndexCoordinates.class);searchRequest.setAccessible(true);Object o = ReflectionUtils.invokeMethod(searchRequest, elasticsearchRestTemplate.getRequestFactory(), nativeSearchQuery, ChargingStationDTO.class, elasticsearchRestTemplate.getIndexCoordinatesFor(ChargingStationDTO.class));Field source =ReflectionUtils.findField(Class.forName("org.elasticsearch.action.search.SearchRequest"), "source");source.setAccessible(true);Object s = ReflectionUtils.getField(source, o);log.info("请求DSL语句:{}", s);} catch (ClassNotFoundException e) {e.printStackTrace();}}}}