ElasticSearch2.1 基于空间位置geo_query距离计算

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