ICode9
精准搜索请尝试： 精确搜索
首页
编程语言>
数据库
系统相关
互联网
其他分享
Python
Java
JavaScript
android
PHP
首页 > 编程语言> 文章详细

附近的门店功能JAVA版实现

2021-03-24 18:01:41  阅读：232  来源： 互联网

标签：loc 功能 dist BigDecimal 门店 JAVA null id name


文章目录

前言

最近公司项目需要实现附近的门店功能，通过查询资料发现很多方法都可以实现。
包括Mysql,Redis,Mongodb,PostgreSQL等
其中分别选择了redis和mongodb进行实现。

一、附近门店功能

redis实现

redis4.0.14版本，使用redis自带的geo命令来实现功能。具体的命令详情可参考官方文档 https://redis.io/commands/geoadd
在这里插入图片描述
1、引入要使用的jar包，工程是springboot项目，直接maven引入redis依赖

		<dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-redis</artifactId>
        </dependency>
        <dependency>
            <groupId>redis.clients</groupId>
            <artifactId>jedis</artifactId>
        </dependency>

2、代码实现redis操作
包括了插入、删除数据，以及查询附近的数据

@Data
public class Geo<T> {

    private T object;

    private double distance;
}


@Component
public class RedisGeo<T> {

    @Resource(name = "redisTemplateBusiness")
    private RedisTemplate redisTemplate;

    public void setGeo(String key, double longitude, double latitude, T object) {
        redisTemplate.opsForGeo().add(key, new Point(longitude, latitude), object);
    }

    public void removeGeo(String key, T object) {
        redisTemplate.opsForGeo().remove(key, object);
    }

    public List<Geo<T>> getNearbyByGeo(String key, double longitude, double latitude, int distance, int limit) {
        List<Geo<T>> geos = new ArrayList<>();
        BoundGeoOperations boundGeoOperations = redisTemplate.boundGeoOps(key);
        Point point = new Point(longitude, latitude);
        Circle within = new Circle(point, distance);
        RedisGeoCommands.GeoRadiusCommandArgs geoRadiusArgs = RedisGeoCommands.GeoRadiusCommandArgs.newGeoRadiusArgs();
        geoRadiusArgs = geoRadiusArgs.includeDistance();
        geoRadiusArgs.limit(limit);
        geoRadiusArgs.sortAscending();
        GeoResults<RedisGeoCommands.GeoLocation<Object>> geoResults = boundGeoOperations.radius(within, geoRadiusArgs);
        List<GeoResult<RedisGeoCommands.GeoLocation<Object>>> geoResultList = geoResults.getContent();
        Geo geo;
        for (GeoResult<RedisGeoCommands.GeoLocation<Object>> geoResult : geoResultList) {
            geo = new Geo();
            geo.setObject(geoResult.getContent());
            geo.setDistance(geoResult.getDistance().getValue());
            geos.add(geo);
        }
        return geos;
    }
}

3、初始化数据
选择一个经纬度坐标，插入40万个周边的坐标点数据

@Data
public class StoreBean {

    private int id;

    private String name;

    private double[] loc;

    private double dist;

}

public void insertRedisData(){
        double longitude=114.068245;
        double latitude=22.546195;
        StoreBean storeBean;
       for(int i=1;i<=100000;i++){
           storeBean=new StoreBean();
           storeBean.setId(i);
           storeBean.setName("门店"+i);
           double d=Double.parseDouble(i+"")/10000;
           BigDecimal b1 = new BigDecimal(longitude+d);
           BigDecimal b2 = new BigDecimal(latitude);
           redisGeo.setGeo("store",b1.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue() ,b2.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue(), storeBean);
       }
        for(int i=1;i<=100000;i++){
            storeBean=new StoreBean();
            storeBean.setId(i+100000);
            storeBean.setName("门店"+(i+100000));
            double d=Double.parseDouble(i+"")/10000;
            BigDecimal b1 = new BigDecimal(longitude);
            BigDecimal b2 = new BigDecimal(latitude+d);
            redisGeo.setGeo("store",b1.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue() ,b2.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue(), storeBean);
        }
        for(int i=1;i<=100000;i++){
            storeBean=new StoreBean();
            storeBean.setId(i+200000);
            storeBean.setName("门店"+(i+200000));
            double d=Double.parseDouble(i+"")/10000;
            BigDecimal b1 = new BigDecimal(longitude-d);
            BigDecimal b2 = new BigDecimal(latitude);
            redisGeo.setGeo("store",b1.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue() ,b2.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue(), storeBean);
        }
        for(int i=1;i<=100000;i++){
            storeBean=new StoreBean();
            storeBean.setId(i+300000);
            storeBean.setName("门店"+(i+300000));
            double d=Double.parseDouble(i+"")/10000;
            BigDecimal b1 = new BigDecimal(longitude);
            BigDecimal b2 = new BigDecimal(latitude-d);
            redisGeo.setGeo("store",b1.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue() ,b2.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue(), storeBean);
        }
    }

4、查询数据
查询该坐标附近200米的点，并取前20条

public void testRedis() {
        double longitude=114.068245;
        double latitude=22.546195;
        int distance = 2000;
        int limit=20;
        List<Geo<StoreBean>> list = redisGeo.getNearbyByGeo("store", longitude, latitude, distance,limit);
        if (CollectionUtils.isNotEmpty(list)) {
            for (Geo<StoreBean> geo : list) {
                System.out.println(JsonUtil.toJson(geo.getObject()) + "----------" + geo.getDistance());
            }
        }
    }

查询结果会根据距离远近排好序返回

{"name":{"id":1,"name":"门店1","loc":null,"dist":0.0},"point":null}----------10.1641
{"name":{"id":200001,"name":"门店200001","loc":null,"dist":0.0},"point":null}----------10.2266
{"name":{"id":300001,"name":"门店300001","loc":null,"dist":0.0},"point":null}----------11.1206
{"name":{"id":100001,"name":"门店100001","loc":null,"dist":0.0},"point":null}----------11.157
{"name":{"id":2,"name":"门店2","loc":null,"dist":0.0},"point":null}----------20.6339
{"name":{"id":200002,"name":"门店200002","loc":null,"dist":0.0},"point":null}----------20.6964
{"name":{"id":300002,"name":"门店300002","loc":null,"dist":0.0},"point":null}----------22.1145
{"name":{"id":100002,"name":"门店100002","loc":null,"dist":0.0},"point":null}----------22.1509
{"name":{"id":3,"name":"门店3","loc":null,"dist":0.0},"point":null}----------30.5529
{"name":{"id":200003,"name":"门店200003","loc":null,"dist":0.0},"point":null}----------30.6154
{"name":{"id":300003,"name":"门店300003","loc":null,"dist":0.0},"point":null}----------33.3911
{"name":{"id":100003,"name":"门店100003","loc":null,"dist":0.0},"point":null}----------33.4275
{"name":{"id":4,"name":"门店4","loc":null,"dist":0.0},"point":null}----------41.023
{"name":{"id":200004,"name":"门店200004","loc":null,"dist":0.0},"point":null}----------41.0855
{"name":{"id":300004,"name":"门店300004","loc":null,"dist":0.0},"point":null}----------44.3861
{"name":{"id":100004,"name":"门店100004","loc":null,"dist":0.0},"point":null}----------44.4225
{"name":{"id":5,"name":"门店5","loc":null,"dist":0.0},"point":null}----------51.4932
{"name":{"id":200005,"name":"门店200005","loc":null,"dist":0.0},"point":null}----------51.5557
{"name":{"id":300005,"name":"门店300005","loc":null,"dist":0.0},"point":null}----------55.663
{"name":{"id":100005,"name":"门店100005","loc":null,"dist":0.0},"point":null}----------55.6995

5、性能测试
服务器 CPU 2.00GHz 4核
基础数据40W条，本机16线程分别进行1万次附近200m,2000m,20000m查询结果如下:

 ----200m
mean rate = 1740.76 calls/second
     1-minute rate = 1740.60 calls/second
     5-minute rate = 1740.60 calls/second
    15-minute rate = 1740.60 calls/second
               min = 1.70 milliseconds
               max = 152.20 milliseconds
              mean = 9.11 milliseconds
            stddev = 6.79 milliseconds
            median = 7.61 milliseconds
              75% <= 9.06 milliseconds
              95% <= 17.78 milliseconds
              98% <= 27.31 milliseconds
              99% <= 34.27 milliseconds
            99.9% <= 62.15 milliseconds

---2km
mean rate = 591.55 calls/second
     1-minute rate = 578.13 calls/second
     5-minute rate = 575.03 calls/second
    15-minute rate = 574.48 calls/second
               min = 7.33 milliseconds
               max = 167.37 milliseconds
              mean = 27.84 milliseconds
            stddev = 7.75 milliseconds
            median = 26.16 milliseconds
              75% <= 29.36 milliseconds
              95% <= 42.00 milliseconds
              98% <= 45.53 milliseconds
              99% <= 49.26 milliseconds
            99.9% <= 58.36 milliseconds
			
			
-----20km		
mean rate = 57.33 calls/second
     1-minute rate = 57.47 calls/second
     5-minute rate = 55.08 calls/second
    15-minute rate = 54.06 calls/second
               min = 25.43 milliseconds
               max = 608.66 milliseconds
              mean = 291.61 milliseconds
            stddev = 48.28 milliseconds
            median = 286.57 milliseconds
              75% <= 310.07 milliseconds
              95% <= 370.45 milliseconds
              98% <= 414.06 milliseconds
              99% <= 433.93 milliseconds
            99.9% <= 608.53 milliseconds

从结果可以看出查询出的附近的门店越多，性能越慢

mongodb实现

mongodb版本4.4.4，由于从4.0版本起，已经废弃了geoNear命令，所以我们通过$geoNear aggregation 来实现
1、引入要使用的jar包，工程是springboot项目，直接maven引入redis依赖

		<dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-mongodb</artifactId>
        </dependency>

2、代码实现查询附近的店铺操作
直接使用mongoTemplate操作

@Data
public class StoreBean {

    private int id;

    private String name;

    private double[] loc;

    private double dist;

}

@Service
public class StoreService {

    @Autowired
    private MongoTemplate mongoTemplate;

    public List<StoreBean> findNear(Query query,double lng, double lat, double dist, int page, int pageSize){
        if(query==null){
            query=new Query();
        }
        List<AggregationOperation> aggregationList = new ArrayList<>();
        aggregationList.add(new GeoNearDocument(query, new Point(lng,lat),  dist));
        aggregationList.add(Aggregation.skip((long)(page-1) * pageSize));
        aggregationList.add(Aggregation.limit(pageSize));
        Aggregation agg = Aggregation.newAggregation(
                aggregationList);
        AggregationResults<StoreBean> results = mongoTemplate.aggregate(agg, "store", StoreBean.class);
        return results.getMappedResults();
    }
}

public class GeoNearDocument implements AggregationOperation {

    private Document nearQuery;

    public GeoNearDocument(Query query, Point point, double maxDistance) {
        this.nearQuery = new Document("near", new Document("type", "Point")
                .append("coordinates", new double[]{point.getX(), point.getY()}))
                .append("distanceField", "dist")
                .append("query", query.getQueryObject())
                .append("maxDistance", maxDistance)
                .append("spherical", true);
    }

    @Override
    public Document toDocument(AggregationOperationContext context) {
        Document command = context.getMappedObject(nearQuery);
        return new Document("$geoNear", command);
    }

}

对应的mongo查询语句

db.getCollection('store').aggregate([   
   {
     $geoNear: {
        near: { type: "Point", coordinates: [ 114.06821, 22.546212 ] },
        distanceField: "loc",        
        maxDistance: 200,        
        spherical: true        
     }
   }, 
   {
       $skip:0
    }
   ,  
    {
          $limit:20
    }
])

3、初始化数据
选择一个经纬度坐标，插入40万个周边的坐标点数据,使用了批量插入功能，性能挺不错的

public void insertMongoData(){
         StoreBean storeBean = new StoreBean();
        double longitude=114.068245;
        double latitude=22.546195;
        BulkOperations ops = mongoTemplate.bulkOps(BulkOperations.BulkMode.UNORDERED, "store");
        for (int i = 1; i <= 100000; i++) {
            double d = Double.parseDouble(i + "") / 10000;
            storeBean.setId(i);
            storeBean.setName("门店" + i);
            BigDecimal b1 = new BigDecimal(longitude + d);
            BigDecimal b2 = new BigDecimal(latitude);
            storeBean.setLoc(new double[]{b1.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue(), b2.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue()});
            ops.insert(storeBean);
            if (i % 10000 == 0) {
                ops.execute();
                ops = mongoTemplate.bulkOps(BulkOperations.BulkMode.UNORDERED, "store");
            }
        }
        for(int i=1;i<=100000;i++){
            double d=Double.parseDouble(i+"")/10000;
            storeBean.setId(i+100000);
            storeBean.setName("门店" + (i+100000));
            BigDecimal b1 = new BigDecimal(longitude);
            BigDecimal b2 = new BigDecimal(latitude+d);
            storeBean.setLoc(new double[]{b1.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue(), b2.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue()});
            ops.insert(storeBean);
            if(i%10000==0){
                ops.execute();
                ops = mongoTemplate.bulkOps(BulkOperations.BulkMode.UNORDERED, "store");
            }
        }
        for(int i=1;i<=100000;i++){
            double d=Double.parseDouble(i+"")/10000;
            storeBean.setId(i+200000);
            storeBean.setName("门店" + (i+200000));
            BigDecimal b1 = new BigDecimal(longitude-d);
            BigDecimal b2 = new BigDecimal(latitude);
            storeBean.setLoc(new double[]{b1.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue(), b2.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue()});
            ops.insert(storeBean);
            if(i%10000==0){
                ops.execute();
                ops = mongoTemplate.bulkOps(BulkOperations.BulkMode.UNORDERED, "store");
            }
        }
        for(int i=1;i<=100000;i++){
            double d=Double.parseDouble(i+"")/10000;
            storeBean.setId(i+300000);
            storeBean.setName("门店" + (i+300000));
            BigDecimal b1 = new BigDecimal(longitude);
            BigDecimal b2 = new BigDecimal(latitude-d);
            storeBean.setLoc(new double[]{b1.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue(), b2.setScale(6, BigDecimal.ROUND_HALF_UP).doubleValue()});
            ops.insert(storeBean);
            if(i%10000==0){
                ops.execute();
                ops = mongoTemplate.bulkOps(BulkOperations.BulkMode.UNORDERED, "store");
            }
        }
    }

使用$geoNear需要为loc字段创建2dsphere索引，mongodb控制台创建索引

db.getCollection('store').createIndex({loc:"2dsphere"})

4、查询数据
查询该坐标附近200米的点，并取前20条。
如果有其他的查询条件，可以使用Query来进行数据的过滤

public void testMongodb(){
         double longitude=114.068245;
        double latitude=22.546195;
        double distance = 200;
        int page=1;
        int pageSize=20;
        List<StoreBean> results=storeService.findNear(new Query(),longitude,latitude,distance,page,pageSize);
        for(StoreBean storeBean:results){
            System.out.println(JsonUtil.toJson(storeBean));
        }
    }

查询结果会根据距离远近排好序返回

{"id":1,"name":"门店1","loc":[114.068345,22.546195],"dist":10.281082270657112}
{"id":200001,"name":"门店200001","loc":[114.068145,22.546195],"dist":10.281082272980736}
{"id":100001,"name":"门店100001","loc":[114.068245,22.546295],"dist":11.131884501985395}
{"id":300001,"name":"门店300001","loc":[114.068245,22.546095],"dist":11.131884502057591}
{"id":2,"name":"门店2","loc":[114.068445,22.546195],"dist":20.56216454244631}
{"id":200002,"name":"门店200002","loc":[114.068045,22.546195],"dist":20.562164544376795}
{"id":100002,"name":"门店100002","loc":[114.068245,22.546395],"dist":22.263769004327976}
{"id":300002,"name":"门店300002","loc":[114.068245,22.545995],"dist":22.26376900447237}
{"id":3,"name":"门店3","loc":[114.068545,22.546195],"dist":30.84324681436141}
{"id":200003,"name":"门店200003","loc":[114.067945,22.546195],"dist":30.84324681666289}
{"id":100003,"name":"门店100003","loc":[114.068245,22.546495],"dist":33.395653506415094}
{"id":300003,"name":"门店300003","loc":[114.068245,22.545895],"dist":33.39565350648729}
{"id":4,"name":"门店4","loc":[114.068645,22.546195],"dist":41.12432908631526}
{"id":200004,"name":"门店200004","loc":[114.067845,22.546195],"dist":41.12432908710427}
{"id":100004,"name":"门店100004","loc":[114.068245,22.546595],"dist":44.5275380079574}
{"id":300004,"name":"门店300004","loc":[114.068245,22.545795],"dist":44.52753800907162}
{"id":5,"name":"门店5","loc":[114.068745,22.546195],"dist":51.40541135767077}
{"id":200005,"name":"门店200005","loc":[114.067745,22.546195],"dist":51.40541135876105}
{"id":100005,"name":"门店100005","loc":[114.068245,22.546695],"dist":55.65942251060517}
{"id":300005,"name":"门店300005","loc":[114.068245,22.545695],"dist":55.65942251100064}

5、性能测试
服务器 CPU 2.00GHz 4核
基础数据40W条，本机16线程分别进行1万次附近200m,2000m,20000m查询:。
查询数据条数:
200m:72条
2km:746条
20km:7482条

性能结果如下

-----200m
mean rate = 636.68 calls/second
     1-minute rate = 615.66 calls/second
     5-minute rate = 610.90 calls/second
    15-minute rate = 610.23 calls/second
               min = 5.19 milliseconds
               max = 232.36 milliseconds
              mean = 23.86 milliseconds
            stddev = 13.49 milliseconds
            median = 22.30 milliseconds
              75% <= 28.58 milliseconds
              95% <= 43.58 milliseconds
              98% <= 49.93 milliseconds
              99% <= 56.90 milliseconds
            99.9% <= 231.33 milliseconds
-----2km
      mean rate = 159.02 calls/second
     1-minute rate = 158.04 calls/second
     5-minute rate = 157.00 calls/second
    15-minute rate = 156.74 calls/second
               min = 35.76 milliseconds
               max = 220.84 milliseconds
              mean = 103.05 milliseconds
            stddev = 20.75 milliseconds
            median = 101.69 milliseconds
              75% <= 115.24 milliseconds
              95% <= 138.29 milliseconds
              98% <= 150.54 milliseconds
              99% <= 157.01 milliseconds
            99.9% <= 175.21 milliseconds
-----20km			
 mean rate = 20.14 calls/second
     1-minute rate = 20.38 calls/second
     5-minute rate = 20.32 calls/second
    15-minute rate = 20.53 calls/second
               min = 364.77 milliseconds
               max = 1170.82 milliseconds
              mean = 827.92 milliseconds
            stddev = 122.23 milliseconds
            median = 836.73 milliseconds
              75% <= 901.60 milliseconds
              95% <= 1005.59 milliseconds
              98% <= 1057.28 milliseconds
              99% <= 1078.33 milliseconds
            99.9% <= 1170.82 milliseconds

结果同样是扫描的数据越多性能越低

总结

本文通过java使用redis和mongodb分别实现了附近的门店功能，从查询结果来看redis和mongodb的距离计算结果有细微的差别，不过影响不大。
从性能对比来看，本文中的数据测试结果redis的性能优于mongodb。主要影响性能的是单次查询需要扫描的数据.

标签：loc,功能,dist,BigDecimal,门店,JAVA,null,id,name
来源： https://blog.csdn.net/lyzhhh_1985/article/details/115168295

本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享；
2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关；
3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关；
4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除；
5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。

关于我们 | 联系我们 | 留言反馈

专注分享技术，共同学习，共同进步。侵权联系[81616952@qq.com]

Copyright (C)ICode9.com, All Rights Reserved.

ICode9版权所有