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