Redis中GEO数据结构实现附近商户搜索

Redis的版本必须是6.2以上

在测试类中将数据导入Redis

@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，typeId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Redis
        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获取同类型的店铺集合
            List<Shop> value = entry.getValue();
            List<RedisGeoCommands.GeoLocation<String>> locations = new ArrayList<>(value.size());
            //3.3 写入redis GEOADD key 经度 纬度 member
            for (Shop shop : value) {
//                stringRedisTemplate.opsForGeo().add(key, new Point(shop.getX(),shop.getY()), shop.getId().toString());
                //通过批量来写
                locations.add(new RedisGeoCommands.GeoLocation<>(
                        shop.getId().toString(),
                        new Point(shop.getX(), shop.getY())
                ));
                stringRedisTemplate.opsForGeo().add(key, locations);
            }
        }
    }

控制层代码

    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);
    }

服务层代码

@Override
    public Result queryShopByType(Integer typeId, Integer current, Double x, Double y) {
        //判断是否需要根据坐标进行查询
        if (x == null || y == null) {
            //不需要坐标查询，按数据库查询
            Page<Shop> page = query()
                    .eq("type_id", typeId)
                    .page(new Page<>(current, SystemConstants.DEFAULT_PAGE_SIZE));
            // 返回数据
            return Result.ok(page.getRecords());
        }
        //计算分页参数
        int from = (current - 1) * SystemConstants.DEFAULT_PAGE_SIZE;
        int end = current * SystemConstants.DEFAULT_PAGE_SIZE;

        //查询Redis,按照距离排序、分页。结果：shopId、distance  ，通过limit获取的是0-end,之后进行截取
        String key = SHOP_GEO_KEY + typeId;
        GeoResults<RedisGeoCommands.GeoLocation<String>> results = stringRedisTemplate.opsForGeo().search(
                key,
                GeoReference.fromCoordinate(x, y),
                new Distance(5000),
                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){
            //没有下一页了，结束
            return Result.ok(Collections.emptyList());
        }
        //4.1截取from-end的部分
        List<Long> ids = new ArrayList<>(list.size());
        Map<String, Distance> distanceMap = new HashMap<>(list.size());
        list.stream().skip(from).forEach(result ->{
            //获取店铺id
            String shopIdStr = result.getContent().getName();
            ids.add(Long.parseLong(shopIdStr));
            //获取距离
            Distance distance = result.getDistance();
            distanceMap.put(shopIdStr, distance);
        });
        //根据id查询shop
        String idStr = StrUtil.join(",", ids);
        List<Shop> shops = query().in("id", ids).last("ORDER BY FIELD(id," + idStr + ")").list();
        for (Shop shop : shops) {
            shop.setDistance(distanceMap.get(shop.getImages().toString()).getValue());
        }
        return Result.ok(shops);
    }

原文地址：https://blog.csdn.net/weixin_53515812/article/details/142712829

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