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

java 复制代码
       <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

java 复制代码
# 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

java 复制代码
@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;
    }
}

测试实体类

java 复制代码
@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

java 复制代码
 @Test
    public 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

        //96-121,23-40
        List<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

java 复制代码
@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;
}
java 复制代码
 @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;

java 复制代码
/**
     * 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:

java 复制代码
{
	"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:

java 复制代码
{
  "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;

java 复制代码
  @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)  True
        geoShapeQueryBuilder.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:

java 复制代码
{
	"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:

java 复制代码
{
	"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)

java 复制代码
   @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:

java 复制代码
{
	"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:

java 复制代码
{
	"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

java 复制代码
@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();
            }
        }

    }


}

参考:
https://www.kancloud.cn/yiyanan/elasticsearch_7_6/1670492

https://www.kancloud.cn/apachecn/elasticsearch-doc-zh/1945207

https://learnku.com/docs/elasticsearch73/7.3/5210-geo-distance-aggregation/8043

相关推荐
Acrelhuang29 分钟前
安科瑞5G基站直流叠光监控系统-安科瑞黄安南
大数据·数据库·数据仓库·物联网
皓74137 分钟前
服饰电商行业知识管理的创新实践与知识中台的重要性
大数据·人工智能·科技·数据分析·零售
Mephisto.java40 分钟前
【大数据学习 | kafka高级部分】kafka的kraft集群
大数据·sql·oracle·kafka·json·hbase
Mephisto.java42 分钟前
【大数据学习 | kafka高级部分】kafka的文件存储原理
大数据·sql·oracle·kafka·json
筱源源1 小时前
Elasticsearch-linux环境部署
linux·elasticsearch
ycsdn101 小时前
Caused by: org.apache.flink.api.common.io.ParseException: Row too short:
大数据·flink
DolphinScheduler社区3 小时前
Apache DolphinScheduler + OceanBase,搭建分布式大数据调度平台的实践
大数据
时差9534 小时前
MapReduce 的 Shuffle 过程
大数据·mapreduce
kakwooi5 小时前
Hadoop---MapReduce(3)
大数据·hadoop·mapreduce
数新网络5 小时前
《深入浅出Apache Spark》系列②:Spark SQL原理精髓全解析
大数据·sql·spark