Spring boot基于redis实现附近的人

package com.karle.redis.biz; import java.util.ArrayList; import java.util.Date; import java.util.List; import java.util.Map; import java.util.Set; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor; import org.springframework.stereotype.Service; import com.alibaba.fastjson.JSONObject; import com.karle.redis.dao.RedisDao; import com.karle.redis.vo.NearbyBO; import com.karle.redis.vo.NearbyPO; import com.karle.redis.vo.Result; /** * 博客：http://csdn.karle.vip/ * * @author Karle * */ @Service public class NearbyBiz { /** 2017-09-01 毫秒值/1000 (秒) **/ private static final int BASE_SORT_NUM = 1504195200; /** 最大距离 **/ private static final int MAX_DISTANCE = 3000; /** 8小时（秒） **/ private static final int EIGHT_HOUR_SECOND = 60 * 60 * 8; /** 附近的人缓存key值，p1-城市编号，p2-地区编号 **/ private static final String NEARBY_CACHE_KEY = "nearby_%s_%s"; /** 附近的人用户缓存key值，p1-城市编号，p2-地区编号,p3-用户id **/ private static final String NEARBY_USER_CACHE_KEY = "nearby_user_%s_%s_%s"; @Autowired private RedisDao redisDao; // 线程池 @Autowired private ThreadPoolTaskExecutor threadPoolTaskExecutor; // 附近的人最新版 public Result<List<NearbyBO>> nearbyNew(NearbyPO paramObj) { // 缓存key值 String cacheKey = String.format(NEARBY_CACHE_KEY, paramObj.getCityCode(), paramObj.getAdCode()); long currentTimeMillis = System.currentTimeMillis(); // 设置当前用户缓存时间 paramObj.setSaveTime(currentTimeMillis); // 使用hash更加快速的定位到用户缓存信息，便于删除更新 redisDao.hset(cacheKey, paramObj.getId() + "", JSONObject.toJSONString(paramObj)); // 从当前地区缓存中获取全部用户（包括用户自己） Map<String, String> cacheAll = redisDao.hGetAll(cacheKey); if (cacheAll.isEmpty() || cacheAll.size() == 1) { return new Result<List<NearbyBO>>(true, new ArrayList<>()); } // 结果集，-1是要排除用户自己 List<NearbyBO> result = new ArrayList<NearbyBO>(cacheAll.size() - 1); for (String item : cacheAll.keySet()) { NearbyPO cacheData = JSONObject.parseObject(cacheAll.get(item), NearbyPO.class); // 计算缓存时长 long twoDayMinute = (cacheData.getSaveTime() - currentTimeMillis) / 60000; // 八小时有效 if (twoDayMinute > 480) { // 被动触发删除过期缓存 redisDao.hdel(cacheKey, paramObj.getId() + ""); continue; } // 排除用户自己 if (paramObj.getId().equals(cacheData.getId())) { continue; } double distance = countDistance(paramObj.getLongitude(), paramObj.getLatitude(), cacheData.getLongitude(), cacheData.getLatitude()); // 10KM之内有效 if (distance > 10000) { continue; } result.add(new NearbyBO(cacheData.getId(), cacheData.getName(), distance)); } return new Result<List<NearbyBO>>(true, result); } // 附近的人 @Deprecated public Result<List<NearbyBO>> nearby(NearbyPO paramObj) { int nowSortNum = (int) (new Date().getTime() / 1000); // 此处仅为了减低排序的序号（ 获取缓存集合最大排序下标） int endIndex = nowSortNum - BASE_SORT_NUM; // 缓存key值 String cacheKey = String.format(NEARBY_CACHE_KEY, paramObj.getCityCode(), paramObj.getAdCode()); // 取同一城市地区&&八小时区间范围数据(八小时之前缓存数据会删除) Set<String> redisNearby = redisDao.getSetByKeyAndScore(cacheKey, endIndex - EIGHT_HOUR_SECOND, endIndex); // 开启新线程写入数据(让主线程“专心”处理主业务) threadPoolTaskExecutor.execute(new InsertCache(paramObj, cacheKey, endIndex)); if (redisNearby.size() == 0) return new Result<List<NearbyBO>>(false, "附近查无用户", null); List<NearbyBO> result = new ArrayList<NearbyBO>(redisNearby.size()); boolean oneself = true; for (String item : redisNearby) { NearbyPO cacheNearby = JSONObject.parseObject(item, NearbyPO.class); // 缓存里可能有用户自己 if (cacheNearby.getId().intValue() == paramObj.getId()) continue; double distance = countDistance(paramObj.getLongitude(), paramObj.getLatitude(), cacheNearby.getLongitude(), cacheNearby.getLatitude()); // 大于限定距离 if (distance > MAX_DISTANCE) continue; result.add(new NearbyBO(cacheNearby.getId(), cacheNearby.getName(), distance)); oneself = false; } if (oneself) return new Result<List<NearbyBO>>(false, "附近查无用户", null); return new Result<List<NearbyBO>>(true, "获取成功", result); } // 把用户定位信息写入缓存 private class InsertCache implements Runnable { // 用户提交的最新坐标信息 private NearbyPO paramObj; // “附近的人”缓存集合key private String cacheKey; // 获取缓存集合最大排序下标 private Integer endIndex; public InsertCache(NearbyPO paramObj, String cacheKey, Integer endIndex) { this.paramObj = paramObj; this.cacheKey = cacheKey; this.endIndex = endIndex; } @Override public void run() { String userCacheKey = String.format(NEARBY_USER_CACHE_KEY, paramObj.getCityCode(), paramObj.getAdCode(), paramObj.getId()); String cacheNewData = JSONObject.toJSONString(paramObj); String cacheUserPosition = redisDao.getOneStringByKey(userCacheKey); // 确保用户坐标信息缓存清除慢于“附近的人”坐标信息 redisDao.setOneStringByKey(userCacheKey, cacheNewData, EIGHT_HOUR_SECOND + 60); // 保存用户坐标信息至“附近的人”缓存集合 redisDao.addOneStringToZSet(cacheKey, cacheNewData, cacheUserPosition, endIndex); } } /** * 计算两经纬度点之间的距离（单位：米） * * @param longitude1 * 坐标1经度 * @param latitude1 * 坐标1纬度 * @param longitude2 * 坐标2经度 * @param latitude2 * 坐标1纬度 * @return */ private static double countDistance(double longitude1, double latitude1, double longitude2, double latitude2) { double radLat1 = Math.toRadians(latitude1); double radLat2 = Math.toRadians(latitude2); double a = radLat1 - radLat2; double b = Math.toRadians(longitude1) - Math.toRadians(longitude2); double s = 2 * Math.asin(Math.sqrt( Math.pow(Math.sin(a / 2), 2) + Math.cos(radLat1) * Math.cos(radLat2) * Math.pow(Math.sin(b / 2), 2))); s = s * 6378137.0; s = Math.round(s * 10000) / 10000; return s; } }