使用Lucene-Spatial实现集成地理位置的全文检索

Lucene通过Spatial包提供了对基于地理位置的全文检索的支持，最典型的应用场景就是：“搜索中关村附近1公里内的火锅店，并按远近排序”。使用Lucene-Spatial添加对地理位置的支持，和之前普通文本搜索主要有两点区别：

1. 将坐标信息转化为笛卡尔层，建立索引

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private void indexLocation(Document document, JSONObject jo)

throws Exception {



double longitude = jo.getDouble("longitude");

double latitude = jo.getDouble("latitude");



document.add(new Field("lat", NumericUtils

.doubleToPrefixCoded(latitude), Field.Store.YES,

Field.Index.NOT_ANALYZED));

document.add(new Field("lng", NumericUtils

.doubleToPrefixCoded(longitude), Field.Store.YES,

Field.Index.NOT_ANALYZED));



for (int tier = startTier; tier <= endTier; tier++) {

ctp = new CartesianTierPlotter(tier, projector,

CartesianTierPlotter.DEFALT_FIELD_PREFIX);

final double boxId = ctp.getTierBoxId(latitude, longitude);

document.add(new Field(ctp.getTierFieldName(), NumericUtils

.doubleToPrefixCoded(boxId), Field.Store.YES,

Field.Index.NOT_ANALYZED_NO_NORMS));

}

}

2. 搜索时，指定使用DistanceQueryFilter

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DistanceQueryBuilder dq = new DistanceQueryBuilder(latitude,

longitude, miles, "lat", "lng",

CartesianTierPlotter.DEFALT_FIELD_PREFIX, true, startTier,

endTier);

DistanceFieldComparatorSource dsort = new DistanceFieldComparatorSource(

dq.getDistanceFilter());

Sort sort = new Sort(new SortField("geo_distance", dsort));

下面是基于Lucene3.2.0和JUnit4.8.2的完整代码。

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<dependencies>

<dependency>

<groupId>junit</groupId>

<artifactId>junit</artifactId>

<version>4.8.2</version>

<type>jar</type>

<scope>test</scope>

</dependency>

<dependency>

<groupId>org.apache.lucene</groupId>

<artifactId>lucene-core</artifactId>

<version>3.2.0</version>

<type>jar</type>

<scope>compile</scope>

</dependency>

<dependency>

<groupId>org.apache.lucene</groupId>

<artifactId>lucene-spatial</artifactId>

<version>3.2.0</version>

<type>jar</type>

<scope>compile</scope>

</dependency>

<dependency>

<groupId>org.json</groupId>

<artifactId>json</artifactId>

<version>20100903</version>

<type>jar</type>

<scope>compile</scope>

</dependency>

</dependencies>

首先准备测试用的数据：

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{"id":12,"title":"时尚码头美容美发热烫特价","longitude":116.3838183,"latitude":39.9629015}

{"id":17,"title":"审美个人美容美发套餐","longitude":116.386564,"latitude":39.966102}

{"id":23,"title":"海底捞吃300送300","longitude":116.38629,"latitude":39.9629573}

{"id":26,"title":"仅98元！享原价335元李老爹","longitude":116.3846175,"latitude":39.9629125}

{"id":29,"title":"都美造型烫染美发护理套餐","longitude":116.38629,"latitude":39.9629573}

{"id":30,"title":"仅售55元！原价80元的老舍茶馆相声下午场","longitude":116.0799914,"latitude":39.9655391}

{"id":33,"title":"仅售55元！原价80元的新笑声客栈早场","longitude":116.0799914,"latitude":39.9655391}

{"id":34,"title":"仅售39元（红色礼盒）！原价80元的平谷桃","longitude":116.0799914,"latitude":39.9655391}

{"id":46,"title":"仅售38元！原价180元地质礼堂白雪公主","longitude":116.0799914,"latitude":39.9655391}

{"id":49,"title":"仅99元！享原价342.7元自助餐","longitude":116.0799914,"latitude":39.9655391}

{"id":58,"title":"桑海教育暑期学生报名培训九折优惠券","longitude":116.0799914,"latitude":39.9655391}

{"id":59,"title":"全国发货：仅29元！贝玲妃超模粉红高光光","longitude":116.0799914,"latitude":39.9655391}

{"id":65,"title":"海之屿生态水族用品店抵用券","longitude":116.0799914,"latitude":39.9655391}

{"id":67,"title":"小区东门时尚烫染个人护理美发套餐","longitude":116.3799914,"latitude":39.9655391}

{"id":74,"title":"《郭德纲相声专辑》CD套装","longitude":116.0799914,"latitude":39.9655391}

根据上面的测试数据，编写测试用例，分别搜索坐标（116.3838183, 39.9629015）3千米以内的“美发”和全部内容，分别得到的结果应该是4条和6条。

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import static org.junit.Assert.assertEquals;

import static org.junit.Assert.fail;



import java.util.List;



import org.junit.Test;



public class LuceneSpatialTest {



private static LuceneSpatial spatialSearcher = new LuceneSpatial();



@Test

public void testSearch() {

try {

long start = System.currentTimeMillis();

List<String> results = spatialSearcher.search("美发", 116.3838183, 39.9629015, 3.0);

System.out.println(results.size()

+ "个匹配结果，共耗时 "

+ (System.currentTimeMillis() - start) + "毫秒。\n");

assertEquals(4, results.size());

} catch (Exception e) {

fail("Exception occurs...");

e.printStackTrace();

}

}



@Test

public void testSearchWithoutKeyword() {

try {

long start = System.currentTimeMillis();

List<String> results = spatialSearcher.search(null, 116.3838183, 39.9629015, 3.0);

System.out.println( results.size()

+ "个匹配结果，共耗时 "

+ (System.currentTimeMillis() - start) + "毫秒.\n");

assertEquals(6, results.size());

} catch (Exception e) {

fail("Exception occurs...");

e.printStackTrace();

}

}

}

下面是LuceneSpatial类，在构造函数中初始化变量和创建索引：

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public class LuceneSpatial {



private Analyzer analyzer;

private IndexWriter writer;

private FSDirectory indexDirectory;

private IndexSearcher indexSearcher;

private IndexReader indexReader;

private String indexPath = "c:/lucene-spatial";



// Spatial

private IProjector projector;

private CartesianTierPlotter ctp;

public static final double RATE_MILE_TO_KM = 1.609344; //英里和公里的比率

public static final String LAT_FIELD = "lat";

public static final String LON_FIELD = "lng";

private static final double MAX_RANGE = 15.0; // 索引支持的最大范围，单位是千米

private static final double MIN_RANGE = 3.0; // 索引支持的最小范围，单位是千米

private int startTier;

private int endTier;



public LuceneSpatial() {

try {

init();

} catch (Exception e) {

e.printStackTrace();

}

}



private void init() throws Exception {

initializeSpatialOptions();



analyzer = new StandardAnalyzer(Version.LUCENE_32);



File path = new File(indexPath);



boolean isNeedCreateIndex = false;



if (path.exists() && !path.isDirectory())

throw new Exception("Specified path is not a directory");



if (!path.exists()) {

path.mkdirs();

isNeedCreateIndex = true;

}



indexDirectory = FSDirectory.open(new File(indexPath));



//建立索引

if (isNeedCreateIndex) {

IndexWriterConfig indexWriterConfig = new IndexWriterConfig(

Version.LUCENE_32, analyzer);

indexWriterConfig.setOpenMode(OpenMode.CREATE_OR_APPEND);

writer = new IndexWriter(indexDirectory, indexWriterConfig);

buildIndex();

}



indexReader = IndexReader.open(indexDirectory, true);

indexSearcher = new IndexSearcher(indexReader);



}



@SuppressWarnings("deprecation")

private void initializeSpatialOptions() {

projector = new SinusoidalProjector();

ctp = new CartesianTierPlotter(0, projector,

CartesianTierPlotter.DEFALT_FIELD_PREFIX);

startTier = ctp.bestFit(MAX_RANGE / RATE_MILE_TO_KM);

endTier = ctp.bestFit(MIN_RANGE / RATE_MILE_TO_KM);

}







private int mile2Meter(double miles) {

double dMeter = miles * RATE_MILE_TO_KM * 1000;



return (int) dMeter;

}



private double km2Mile(double km) {

return km / RATE_MILE_TO_KM;

}

创建索引的具体实现：

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private void buildIndex() {

BufferedReader br = null;

try {

//逐行添加测试数据到索引中，测试数据文件和源文件在同一个目录下

br = new BufferedReader(new InputStreamReader(

LuceneSpatial.class.getResourceAsStream("data")));

String line = null;

while ((line = br.readLine()) != null) {

index(new JSONObject(line));

}



writer.commit();

} catch (Exception e) {

e.printStackTrace();

} finally {

if (br != null) {

try {

br.close();

} catch (IOException e) {

e.printStackTrace();

}

}

}

}



private void index(JSONObject jo) throws Exception {

Document doc = new Document();



doc.add(new Field("id", jo.getString("id"), Field.Store.YES,

Field.Index.ANALYZED));



doc.add(new Field("title", jo.getString("title"), Field.Store.YES,

Field.Index.ANALYZED));



//将位置信息添加到索引中

indexLocation(doc, jo);



writer.addDocument(doc);

}



private void indexLocation(Document document, JSONObject jo)

throws Exception {



double longitude = jo.getDouble("longitude");

double latitude = jo.getDouble("latitude");



document.add(new Field("lat", NumericUtils

.doubleToPrefixCoded(latitude), Field.Store.YES,

Field.Index.NOT_ANALYZED));

document.add(new Field("lng", NumericUtils

.doubleToPrefixCoded(longitude), Field.Store.YES,

Field.Index.NOT_ANALYZED));



for (int tier = startTier; tier <= endTier; tier++) {

ctp = new CartesianTierPlotter(tier, projector,

CartesianTierPlotter.DEFALT_FIELD_PREFIX);

final double boxId = ctp.getTierBoxId(latitude, longitude);

document.add(new Field(ctp.getTierFieldName(), NumericUtils

.doubleToPrefixCoded(boxId), Field.Store.YES,

Field.Index.NOT_ANALYZED_NO_NORMS));

}

}

搜索的具体实现：

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public List<String> search(String keyword, double longitude,

double latitude, double range) throws Exception {

List<String> result = new ArrayList<String>();



double miles = km2Mile(range);



DistanceQueryBuilder dq = new DistanceQueryBuilder(latitude,

longitude, miles, "lat", "lng",

CartesianTierPlotter.DEFALT_FIELD_PREFIX, true, startTier,

endTier);



//按照距离排序

DistanceFieldComparatorSource dsort = new DistanceFieldComparatorSource(

dq.getDistanceFilter());

Sort sort = new Sort(new SortField("geo_distance", dsort));



Query query = buildQuery(keyword);



//搜索结果

TopDocs hits = indexSearcher.search(query, dq.getFilter(),

Integer.MAX_VALUE, sort);

//获得各条结果相对应的距离

Map<Integer, Double> distances = dq.getDistanceFilter()

.getDistances();



for (int i = 0; i < hits.totalHits; i++) {

final int docID = hits.scoreDocs[i].doc;



final Document doc = indexSearcher.doc(docID);



final StringBuilder builder = new StringBuilder();

builder.append("找到了: ")

.append(doc.get("title"))

.append("， 距离: ")

.append(mile2Meter(distances.get(docID)))

.append("米。");

System.out.println(builder.toString());



result.add(builder.toString());

}



return result;

}



private Query buildQuery(String keyword) throws Exception {

//如果没有指定关键字，则返回范围内的所有结果

if (keyword == null || keyword.isEmpty()) {

return new MatchAllDocsQuery();

}

QueryParser parser = new QueryParser(Version.LUCENE_32, "title",

analyzer);



parser.setDefaultOperator(Operator.AND);



return parser.parse(keyword.toString());

}



执行测试用例，可以得到下面的结果：

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找到了: 时尚码头美容美发热烫特价， 距离: 0米。

找到了: 都美造型烫染美发护理套餐， 距离: 210米。

找到了: 审美个人美容美发套餐， 距离: 426米。

找到了: 小区东门时尚烫染个人护理美发套餐， 距离: 439米。

4个匹配结果，共耗时 119毫秒。



找到了: 时尚码头美容美发热烫特价， 距离: 0米。

找到了: 仅98元！享原价335元李老爹， 距离: 68米。

找到了: 海底捞吃300送300， 距离: 210米。

找到了: 都美造型烫染美发护理套餐， 距离: 210米。

找到了: 审美个人美容美发套餐， 距离: 426米。

找到了: 小区东门时尚烫染个人护理美发套餐， 距离: 439米。

6个匹配结果，共耗时 3毫秒.

参考文献：

Lucene-Spatial的原理介绍：http://www.nsshutdown.com/projects/lucene/whitepaper/locallucene.htm

GeoHash：http://en.wikipedia.org/wiki/Geohash

两篇示例（其中大部分代码就来自于这里）：

Spatial
search with Lucene

Lucene Spatial Example

使用 Apache Lucene 和 Solr 进行位置感知搜索