使用elasticsearch1.5.2查询指定距离范围内的城市（类似微信附近的人）

获取附近的人
mongodb实现方式：http://www.infoq.com/cn/articles/depth-study-of-Symfony2
mysql实现功能：http://www.wubiao.info/470
在此使用elasticsearch,简称es实现：
pom.xml:

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion>

<groupId>com.heli</groupId>
<artifactId>ElasticSearch</artifactId>
<version>0.0.1-SNAPSHOT</version>
<packaging>jar</packaging>

<name>ElasticSearch</name>
<url>http://maven.apache.org</url>

<properties>
<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
<es.version>1.5.2</es.version>
<lucene.maven.version>4.10.4</lucene.maven.version>
</properties>

<dependencies>
<dependency>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch</artifactId>
<version>${es.version}</version>
</dependency>

<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.8.2</version>
<scope>test</scope>
</dependency>
</dependencies>
</project>

实体City：

package com.heli.es;

public class City {

private long id;// id
private String city;// 城市名
private double lat;// 纬度
private double lon;// 经度
private double[] location;// 经纬度数组，第一个元素纬度，第二个元素经度
private String title;// 标题

public City(long id, String city, double lon, double lat, String title) {
super();
this.id = id;
this.city = city;
this.lat = lat;
this.lon = lon;
this.title = title;
}

public long getId() {
return id;
}

public void setId(long id) {
this.id = id;
}

public double getLat() {
return lat;
}

public void setLat(double lat) {
this.lat = lat;
}

public double getLon() {
return lon;
}

public void setLon(double lon) {
this.lon = lon;
}

public String getCity() {
return city;
}

public void setCity(String city) {
this.city = city;
}

public double[] getLocation() {
return location;
}

public void setLocation(double[] location) {
this.location = location;
}

public String getTitle() {
return title;
}

public void setTitle(String title) {
this.title = title;
}

}

测试类：

package com.heli.es;

import static org.elasticsearch.common.xcontent.XContentFactory.jsonBuilder;
import static org.elasticsearch.index.query.FilterBuilders.geoDistanceRangeFilter;

import java.io.IOException;
import java.math.BigDecimal;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;

import org.elasticsearch.action.admin.indices.mapping.put.PutMappingRequest;
import org.elasticsearch.action.admin.indices.mapping.put.PutMappingResponse;
import org.elasticsearch.action.bulk.BulkRequestBuilder;
import org.elasticsearch.action.bulk.BulkResponse;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.action.search.SearchRequestBuilder;
import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.client.Client;
import org.elasticsearch.client.Requests;
import org.elasticsearch.client.transport.TransportClient;
import org.elasticsearch.common.geo.GeoDistance;
import org.elasticsearch.common.transport.InetSocketTransportAddress;
import org.elasticsearch.common.unit.DistanceUnit;
import org.elasticsearch.common.xcontent.XContentBuilder;
import org.elasticsearch.common.xcontent.XContentFactory;
import org.elasticsearch.index.query.FilterBuilder;
import org.elasticsearch.index.query.QueryStringQueryBuilder;
import org.elasticsearch.node.Node;
import org.elasticsearch.search.SearchHit;
import org.elasticsearch.search.SearchHits;
import org.elasticsearch.search.sort.GeoDistanceSortBuilder;
import org.elasticsearch.search.sort.SortBuilders;
import org.elasticsearch.search.sort.SortOrder;
import static org.elasticsearch.node.NodeBuilder.*;

public class ES {

// 创建索引
public static void createIndex(String indexName, String indexType) throws IOException {
Client esClient = new TransportClient().addTransportAddress(new InetSocketTransportAddress("127.0.0.1", 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;
}

// 添加数据
public static Integer addIndexData(String indexName, String indexType) {
Client client = new TransportClient().addTransportAddress(new InetSocketTransportAddress("127.0.0.1", 9300));
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五四广场吃海鲜了，虾TM就是贵点儿，38元一只，38元最后一次！！！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，三亚人民可以天天在明珠广场吃鲍鱼了，三亚人民这次没丢脸，脸让青岛政府去丢吧，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()).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;
}

// 模糊查询
public static void query(String query) {
Client client = new TransportClient().addTransportAddress(new InetSocketTransportAddress("127.0.0.1", 9300));
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) {
SearchRequestBuilder srb = client.prepareSearch(index).setTypes(type);
// wx4g0th9p0gk 为北京的geohash 范围为lt(小于) 1500km内的数据
FilterBuilder builder = geoDistanceRangeFilter("location").point(lon, lat).from("1km").to("1000km").optimizeBbox("memory").geoDistance(GeoDistance.PLANE);
srb.setPostFilter(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");
sort.unit(DistanceUnit.KILOMETERS);
sort.order(SortOrder.ASC);
sort.point(lon, lat);
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);
}

}

public static void main(String[] args) throws IOException {
Client client = new TransportClient().addTransportAddress(new InetSocketTransportAddress("127.0.0.1", 9300));
String index = "testes";
String type = "xq";
// createIndex("testes", "xq");
// addIndexData("testes", "xq");
//
double lat = 39.929986;
double lon = 116.395645;
long start = System.currentTimeMillis();
testGetNearbyCities(client, index, type, lat, lon);
// query("*海鲜*");
long end = System.currentTimeMillis();
System.out.println((end - start) + "毫秒");
client.close();
}
}

输出结果：

北京附近的城市(2个)：
天津距离北京98.69km---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
青岛距离北京486.53km---中国人民站起来了，青岛人民可以天天在五四广场吃海鲜了，虾TM就是贵点儿，38元一只，38元最后一次！！！最后一次，不要错过今天
1192毫秒

注：server 和client版本使用的是1.5.2，如果server版本用elasticsearch-rtf-master，sort的时候总是报：

Exception in thread "main" org.elasticsearch.action.search.SearchPhaseExecutionException: Failed to execute phase [query], all shards failed; shardFailures {[alee59cPQNuzRP4go6-5vw][testes][4]: SearchParseException[[testes][4]: from[-1],size[-1]: Parse Failure [Failed to parse source [{"post_filter":{"geo_distance_range":{"location":"wx4g0th9p0gk","from":"1km","to":"2000km","include_lower":true,"include_upper":true,"distance_type":"arc","optimize_bbox":"memory"}},"sort":[{"_geo_distance":{"location":[{"lat":39.929986,"lon":116.395645}],"unit":"km","distance_type":"arc"}}]}]]]; nested: ElasticsearchParseException[Numeric value expected]; }{[alee59cPQNuzRP4go6-5vw][testes][0]: SearchParseException[[testes][0]: from[-1],size[-1]: Parse Failure [Failed to parse source [{"post_filter":{"geo_distance_range":{"location":"wx4g0th9p0gk","from":"1km","to":"2000km","include_lower":true,"include_upper":true,"distance_type":"arc","optimize_bbox":"memory"}},"sort":[{"_geo_distance":{"location":[{"lat":39.929986,"lon":116.395645}],"unit":"km","distance_type":"arc"}}]}]]]; nested: ElasticsearchParseException[Numeric value expected]; }{[alee59cPQNuzRP4go6-5vw][testes][1]: SearchParseException[[testes][1]: from[-1],size[-1]: Parse Failure [Failed to parse source [{"post_filter":{"geo_distance_range":{"location":"wx4g0th9p0gk","from":"1km","to":"2000km","include_lower":true,"include_upper":true,"distance_type":"arc","optimize_bbox":"memory"}},"sort":[{"_geo_distance":{"location":[{"lat":39.929986,"lon":116.395645}],"unit":"km","distance_type":"arc"}}]}]]]; nested: ElasticsearchParseException[Numeric value expected]; }{[alee59cPQNuzRP4go6-5vw][testes][2]: SearchParseException[[testes][2]: from[-1],size[-1]: Parse Failure [Failed to parse source [{"post_filter":{"geo_distance_range":{"location":"wx4g0th9p0gk","from":"1km","to":"2000km","include_lower":true,"include_upper":true,"distance_type":"arc","optimize_bbox":"memory"}},"sort":[{"_geo_distance":{"location":[{"lat":39.929986,"lon":116.395645}],"unit":"km","distance_type":"arc"}}]}]]]; nested: ElasticsearchParseException[Numeric value expected]; }{[alee59cPQNuzRP4go6-5vw][testes][3]: SearchParseException[[testes][3]: from[-1],size[-1]: Parse Failure [Failed to parse source [{"post_filter":{"geo_distance_range":{"location":"wx4g0th9p0gk","from":"1km","to":"2000km","include_lower":true,"include_upper":true,"distance_type":"arc","optimize_bbox":"memory"}},"sort":[{"_geo_distance":{"location":[{"lat":39.929986,"lon":116.395645}],"unit":"km","distance_type":"arc"}}]}]]]; nested: ElasticsearchParseException[Numeric value expected]; }

换成1.5.2结果就好了，还有

.point(lon, lat)

必须经度在前，纬度在后，不然查询为空，跟一朋友聊说这个可能是个bug

另外查询速度太慢，应该哪个地方配置的问题，回头下周研究研究，周末愉快。

经过试验，原来创建client消耗了1秒左右，查询80毫秒，非常快

借鉴http://blog.csdn.net/loveisnull/article/details/45914115