GEO就是Geolocation的简写形式,代表地理坐标。Redis在3.2版本中加入了对GEO的支持,允许存储地理坐标信息,帮助我们根据经纬度来检索数据。
常见的命令有:
GEOADD:添加一个地理空间信息,包含:经度(longitude)、纬度(latitude)、值(member)
GEODIST:计算指定的两个点之间的距离并返回
GEOHASH:将指定member的坐标转为hash字符串形式并返回
GEOPOS:返回指定member的坐标
GEORADIUS:指定圆心、半径,找到该圆内包含的所有member,并按照与圆心之间的距离排序后返回。6.2以后已废弃
GEOSEARCH:在指定范围内搜索member,并按照与指定点之间的距离排序后返回。范围可以是圆形或矩形。6.2.新功能
GEOSEARCHSTORE:与GEOSEARCH功能一致,不过可以把结果存储到一个指定的key。 6.2.新功能
一个点评平台,有多个频道,每个频道都有很多个店铺。
我们按照频道类型做分组,相同频道的店铺作为同一组,以频道id为key存入同一个GEO集合中即可。如下图所示:
其中,Value值存储店铺的id。
在项目中编写一个测试方法将店铺数据导入Redis中:
@Test
void loadShopData() {
//1.查询店铺信息
List<Shop> list = shopService.list();
//2.把店铺分组,按照typeId分组,id一致的放到一个集合
Map<Long, List<Shop>> map = list.stream().collect(Collectors.groupingBy(Shop::getTypeId));
//3.分批完成写入
for (Map.Entry<Long, List<Shop>> entry : map.entrySet()) {
//3.1.获取类型id
Long typeId = entry.getKey();
String key = "shop:geo:" + typeId;
//3.2.获取同类型的店铺的集合
List<Shop> value = entry.getValue();
List<RedisGeoCommands.GeoLocation<String>> locations = new ArrayList<>(value.size());
//3.3.写入reids GEOADD key 经度 维度 member
for (Shop shop : value) {
//stringRedisTemplate.opsForGeo().add(key, new Point(shop.getX(), shop.getY()), shop.getId().toString());
locations.add(new RedisGeoCommands.GeoLocation<>(
shop.getId().toString(),
new Point(shop.getX(), shop.getY())
));
}
stringRedisTemplate.opsForGeo().add(key, locations);
}
}
前端传递如下参数:
后端控制器代码如下:
@GetMapping("/of/type")
public Result queryShopByType(
@RequestParam("typeId") Integer typeId,
@RequestParam(value = "current", defaultValue = "1") Integer current,
@RequestParam(value = "x",required = false) Double x,
@RequestParam(value = "y",required = false) Double y) {
return shopService.queryShopByType(typeId,current,x,y);
}
业务代码如下:
@Override
public Result queryShopByType(Integer typeId, Integer current, Double x, Double y) {
//1.判断是否需要根据坐标查询
if (x == null || y == null) {
//不需要坐标查询,按数据库查询
Page<Shop> page = lambdaQuery()
.eq(Shop::getTypeId, typeId)
.page(new Page<>(current, SystemConstants.DEFAULT_PAGE_SIZE));
//返回数据
return Result.ok(page.getRecords());
}
//2.计算分页参数
int from = (current - 1) * SystemConstants.DEFAULT_PAGE_SIZE;
int end = current * SystemConstants.DEFAULT_PAGE_SIZE;
//3.查询redis 按照距离排序、分页。 结果:shopId distance
String key = SHOP_GEO_KEY + typeId;
GeoResults<RedisGeoCommands.GeoLocation<String>> results = stringRedisTemplate.opsForGeo()
.search(
key,
GeoReference.fromCoordinate(x, y),
new Distance(5, Metrics.KILOMETERS),
RedisGeoCommands.GeoSearchCommandArgs.newGeoSearchArgs().includeDistance().limit(end)
);
//4.解析出id
if (results == null) {
return Result.ok(Collections.emptyList());
}
List<GeoResult<RedisGeoCommands.GeoLocation<String>>> list = results.getContent();
if (list.size() <= from) {
return Result.ok(Collections.emptyList());
}
//4.1.截取 from-end 部分
List<Long> ids = new ArrayList<>(list.size());
HashMap<String, Distance> distanceMap = new HashMap<>(list.size());
list.stream().skip(from).forEach(result -> {
//4.2.获取店铺id
String shopIdStr = result.getContent().getName();
ids.add(Long.valueOf(shopIdStr));
Distance distance = result.getDistance();
distanceMap.put(shopIdStr, distance);
});
//5.根据id查询shop
String idStr = StrUtil.join(",", ids);
List<Shop> shops = lambdaQuery().in(Shop::getId, ids)
.last("ORDER BY FIELD(id," + idStr + ")")
.list().stream().map(shop -> {
shop.setDistance(distanceMap.get(shop.getId().toString()).getValue());
return shop;
}).collect(Collectors.toList());
//6.返回
return Result.ok(shops);
}