ElasticSearch2.1 基于空间位置geo_query距离计算

小鱼儿 2024-02-17 17:07 46阅读 0赞

首先声明 我使用的ES版本是2.1，不同版本api可能不尽相同

1、首先创建索引mapping

（1）方法1

// 创建索引方法1
        public static void createIndex2(String indexName, String indexType) throws IOException {
            Settings settings = Settings.settingsBuilder()
                    .put("cluster.name", "cluster_wubing")
                    .build();
            InetAddress inetAddress = InetAddress.getByName("192.168.1.107");;
    
            Client esClient = TransportClient.builder().settings(settings).build()
                    .addTransportAddress(new InetSocketTransportAddress(inetAddress, 9300));
            //创建mapping,我这里使用的分词器analyzer/search_analyzer为ik,注意位置location类型为geo_point
            String mapping = "{\"properties\": {\n" +
                    "      \"id\": {\n" +
                    "        \"type\": \"integer\",\n" +
                    "        \"index\": \"not_analyzed\",\n" +
                    "        \"include_in_all\": false\n" +
                    "      },\n" +
                    "      \"title\": {\n" +
                    "        \"type\": \"string\",\n" +
                    "        \"store\": \"no\",\n" +
                    "        \"term_vector\": \"with_positions_offsets\",\n" +
                    "        \"analyzer\": \"ik_max_word\",\n" +
                    "        \"search_analyzer\": \"ik_max_word\",\n" +
                    "        \"include_in_all\": true\n" +
                    "      },\n" +
                    "      \"city\": {\n" +
                    "        \"type\": \"string\",\n" +
                    "        \"store\": \"no\",\n" +
                    "        \"term_vector\": \"with_positions_offsets\",\n" +
                    "        \"analyzer\": \"ik_max_word\",\n" +
                    "        \"search_analyzer\": \"ik_max_word\",\n" +
                    "        \"include_in_all\": true\n" +
                    "      },\n" +
                    "      \"location\":{\n" +
                    "        \"type\":\"geo_point\",\n" +
                    "        \"index\": \"not_analyzed\",\n" +
                    "        \"include_in_all\": false\n" +
                    "      }\n" +
                    "    }}";
            esClient.admin().indices().prepareCreate(indexName).execute().actionGet();
            PutMappingResponse response  = esClient.admin().indices().preparePutMapping(indexName)
                    .setType(indexType)
                    .setSource(mapping)
                    .get();
    
            if (!response.isAcknowledged()) {
                System.out.println("Could not define mapping for type [" + indexName + "]/[" + indexType + "].");
            } else {
                System.out.println("Mapping definition for [" + indexName + "]/[" + indexType + "] succesfully created.");
            }
        }

2.方法2此方法mapping易读。City类自己创建

// 创建索引
        public static void createIndex(String indexName, String indexType) throws IOException {
            Settings settings = Settings.settingsBuilder()
                    .put("cluster.name", "cluster_wubing")
                    .build();
            InetAddress inetAddress = InetAddress.getByName("192.168.1.107");;
    
            Client esClient = TransportClient.builder().settings(settings).build()
                    .addTransportAddress(new InetSocketTransportAddress(inetAddress, 9300));
            // 创建Mapping
            XContentBuilder mapping = createMapping(indexType);
            System.out.println("mapping:" + mapping.string());
            // 创建一个空索引
            esClient.admin().indices().prepareCreate(indexName).execute().actionGet();
            PutMappingRequest putMapping = Requests.putMappingRequest(indexName).type(indexType).source(mapping);
            PutMappingResponse response = esClient.admin().indices().putMapping(putMapping).actionGet();
            if (!response.isAcknowledged()) {
                System.out.println("Could not define mapping for type [" + indexName + "]/[" + indexType + "].");
            } else {
                System.out.println("Mapping definition for [" + indexName + "]/[" + indexType + "] succesfully created.");
            }
        }
    
        // 创建mapping
        public static XContentBuilder createMapping(String indexType) {
            XContentBuilder mapping = null;
            try {
                mapping = jsonBuilder().startObject()
                        // 索引库名（类似数据库中的表）
                        .startObject(indexType).startObject("properties")
                                // ID
                        .startObject("id").field("type", "long").endObject()
                                // 城市
                        .startObject("city").field("type", "string").endObject()
                                // 位置
                        .startObject("location").field("type", "geo_point").endObject()
                                // 标题
                        .startObject("title").field("type", "string").endObject()
    
                        .endObject().endObject().endObject();
            } catch (IOException e) {
                e.printStackTrace();
            }
            return mapping;
        }

2、向索引里添加数据

// 添加数据
        public static Integer addIndexData(String indexName, String indexType) throws Exception {
            Client client = ElasticSerachUtil.getClient();
            List<String> cityList = new ArrayList<String>();
    
            City city1 = new City(1L, "北京", 116.395645, 39.929986, "中国人民站起来了，北京人民可以天天站在天安门广场吃烤鸭了");
            City city2 = new City(2L, "天津", 117.210813, 39.143931, "中国人民站起来了，天津人民可以天天在迎宾广场吃麻花了");
            City city3 = new City(3L, "青岛", 120.384428, 36.105215, "中国人民站起来了，青岛人民可以天天在！最后一次，不要错过今天");
            City city4 = new City(4L, "哈尔滨", 126.657717, 45.773225, "中国人民站起来了，哈尔滨人民可以天天站在索菲亚广场吃红肠了");
            City city5 = new City(5L, "乌鲁木齐", 87.564988, 43.840381, "中国人民站起来了，乌鲁木齐人民可以天天在人民广场啃羊腿了");
            City city6 = new City(6L, "三亚", 109.522771, 18.257776, "中国人民站起来了，三亚人民可以让青岛政府去丢吧，让他们创城去吧！");
    
            cityList.add(obj2JsonUserData(city1));
            cityList.add(obj2JsonUserData(city2));
            cityList.add(obj2JsonUserData(city3));
            cityList.add(obj2JsonUserData(city4));
            cityList.add(obj2JsonUserData(city5));
            cityList.add(obj2JsonUserData(city6));
    
            // 创建索引库
            List<IndexRequest> requests = new ArrayList<IndexRequest>();
            for (int i = 0; i < cityList.size(); i++) {
                IndexRequest request = client.prepareIndex(indexName, indexType).setSource(cityList.get(i)).request();
                requests.add(request);
            }
    
            // 批量创建索引
            BulkRequestBuilder bulkRequest = client.prepareBulk();
            for (IndexRequest request : requests) {
                bulkRequest.add(request);
            }
    
            BulkResponse bulkResponse = bulkRequest.execute().actionGet();
            if (bulkResponse.hasFailures()) {
                System.out.println("批量创建索引错误！");
            }
            return bulkRequest.numberOfActions();
        }
    
        public static String obj2JsonUserData(City city) {
            String jsonData = null;
            try {
                // 使用XContentBuilder创建json数据
                XContentBuilder jsonBuild = XContentFactory.jsonBuilder();
                jsonBuild.startObject().field("id", city.getId())
                        .field("city", city.getCity())
                        //注意纬度在前，经度在后 location类型
                        .startArray("location").value(city.getLat()).value(city.getLon()).endArray()
                        .field("title", city.getTitle())
                        .endObject();
                jsonData = jsonBuild.string();
                System.out.println(jsonData);
            } catch (IOException e) {
                e.printStackTrace();
            }
            return jsonData;
        }

3、最后创建search搜索和距离计算

// 模糊查询
        public static void query(String query) throws Exception {
            Client client = ElasticSerachUtil.getClient();
            QueryStringQueryBuilder qsqb = new QueryStringQueryBuilder(query);
            // qsqb.analyzer("ik").field("title");
            qsqb.field("title");
            client.admin().indices().prepareRefresh().execute().actionGet();
    
            SearchResponse searchResponse = client.prepareSearch("testes").setTypes("xq").setQuery(qsqb)
                    // .setScroll(new TimeValue(60000))
                    .addFields("id", "title", "updatetime")
                            // .addSort("updatetime", SortOrder.DESC)
                    .addSort("_score", SortOrder.DESC)
                            // .addHighlightedField("title")
                    .setHighlighterEncoder("UTF-8").execute().actionGet();
            // 搜索耗时
            Float usetime = searchResponse.getTookInMillis() / 1000f;
            // 命中记录数
            Long hits = searchResponse.getHits().totalHits();
            System.out.println("查询到记录数=" + hits);
    
            for (SearchHit hit : searchResponse.getHits()) {
                // 打分
                Float score = hit.getScore();
                Integer id = Integer.parseInt(hit.getFields().get("id").value().toString());
                String title = hit.getFields().get("title").value().toString();
                System.out.println(title);
            }
        }
    
        // 获取附近的城市
        public static void testGetNearbyCities(Client client, String index, String type, double lat, double lon) throws ExecutionException, InterruptedException {
            SearchRequestBuilder srb = client.prepareSearch(index).setTypes(type);
           // srb.setPos
            // wx4g0th9p0gk 为北京的geohash 范围为lt(小于) 1500km内的数据
            QueryBuilder builder = geoDistanceRangeQuery("location")
                    .point(lat,lon)//注意纬度在前，经度在后
                    .from("0km")
                    .to("10000km")
                    .includeLower(true)
                    .includeUpper(false)
                    .optimizeBbox("memory")
                    .geoDistance(GeoDistance.ARC);
    
            srb.setQuery(builder);
            // 获取距离多少公里 这个才是获取点与点之间的距离的
            //GeoDistanceSortBuilder sort = SortBuilders.geoDistanceSort("location");
            GeoDistanceSortBuilder sort = new GeoDistanceSortBuilder("location");
            sort.unit(DistanceUnit.KILOMETERS);//距离单位公里
            sort.order(SortOrder.ASC);
            sort.point(lat,lon);//注意纬度在前，经度在后
    
            srb.addSort(sort);
            SearchResponse searchResponse = srb.execute().actionGet();
    
            SearchHits hits = searchResponse.getHits();
            SearchHit[] searchHists = hits.getHits();
            System.out.println("北京附近的城市(" + hits.getTotalHits() + "个)：");
            for (SearchHit hit : searchHists) {
                String city = (String) hit.getSource().get("city");
                String title = (String) hit.getSource().get("title");
                // 获取距离值，并保留两位小数点
                BigDecimal geoDis = new BigDecimal((Double) hit.getSortValues()[0]);
                Map<String, Object> hitMap = hit.getSource();
                // 在创建MAPPING的时候，属性名的不可为geoDistance。
                hitMap.put("geoDistance", geoDis.setScale(2, BigDecimal.ROUND_HALF_DOWN));
                System.out.println(city + "距离北京" + hit.getSource().get("geoDistance") + DistanceUnit.KILOMETERS.toString() + "---" + title);
            }
    
        }

4、写main方法测试

public static void main(String[] args) throws Exception {
            Settings settings = Settings.settingsBuilder()
                    .put("cluster.name", "cluster_wubing")
                    .build();
            InetAddress inetAddress = InetAddress.getByName("192.168.1.107");;
    
            Client client = TransportClient.builder().settings(settings).build()
                    .addTransportAddress(new InetSocketTransportAddress(inetAddress, 9300));
            String index = "testes2";
            String type = "xq2";
            //createIndex2(index, type);
            //addIndexData(index, type);
    
            double lat = 39.929986;
            double lon = 116.395645;
            long start = System.currentTimeMillis();
            testGetNearbyCities(client, index, type, lat, lon);
            query("政府");
            long end = System.currentTimeMillis();
            System.out.println((end - start) + "毫秒");
            client.close();
        }

搜索结果  
![Center][]

[Center]: https://image.dandelioncloud.cn/pgy_files/images/2024/01/30/401489626258451aa3dee697ed12987f.png