PS:在网上看到的案例进行学习

1、分析秒杀业务流程

image.png

2、利用 Redis 缓存 INCR拦截流量

首先通过数据控制模块，提前将秒杀商品缓存到读写分离 Redis，并设置秒杀开始标记如下：

skuId_start: 0 　　开始标记，0表示秒杀还没开始
skuId_count: 10000 　　表示总数
skuId_access: 12000　　表示接受抢购数
秒杀开始前，服务集群读取 skuId_start 为 0，直接返回未开始。之所以设置这个值而不是根据时间判断是否开始，是因为服务时间可能不一致（相差几百毫秒）这样可能导致流量倾斜（其他服务没开始，会将大量的流量堆积到开始的服务上）

数据控制模块将 skuId_start 改为1，标志秒杀开始。
当接受下单数达到 skuId_count*1.2 后，继续拦截所有请求。

3、利用 Redis 缓存加速库存扣量

skuId_booked: 0 表示没有抢购

4、将用户订单数据写入mq

5、监听mq入库

6、代码实现

SeckillService.java

@Service
public class SeckillService {

    private static final String secStartPrefix = "skuId_start_";   //一开始要在redis初始化0_1554045087    
    private static final String secAccess = "skuId_access_";
    private static final String secCount = "skuId_count_"; //一开始要在redis初始化数量“0”
    private static final String filterName = "skuId_bloomfilter_";
    private static final String bookedName = "skuId_booked_"; //一开始要在redis初始化数量“0”


    @Resource
    private RedisService redisService;

    public String seckill(int uid, int skuId) {
        //流量拦截层
        //1、判断秒杀是否开始   0_1554045087    开始标识_开始时间
        String isStart = (String) redisService.get(secStartPrefix + skuId);
        if (StringUtils.isBlank(isStart)) {
            return "还未开始";
        }
        if (isStart.contains("_")) {
            Integer isStartInt = Integer.parseInt(isStart.split("_")[0]);
            Integer startTime = Integer.parseInt(isStart.split("_")[1]);
            if (isStartInt == 0) {
                if (startTime > getNow()) {
                    return "还未开始";
                } else {
                    //代表秒杀已经开始
                    redisService.set(secStartPrefix + skuId, 1 + "");
                }
            } else {
                return "系统异常";
            }
        } else {
            if (Integer.parseInt(isStart) != 1) {
                return "系统异常";
            }
        }
        //2、流量拦截
        String skuIdAccessName = secAccess + skuId;
        Integer accessNumInt = 0;
        String accessNum = (String) redisService.get(skuIdAccessName);
        if (StringUtils.isNotBlank(accessNum)) {
            accessNumInt = Integer.parseInt(accessNum);
        }
        String skuIdCountName = secCount + skuId;
        Integer countNumInt = Integer.parseInt((String) redisService.get(skuIdCountName));
        if (countNumInt * 1.2 < accessNumInt) {
            return "抢购已经完成,欢迎下次参与";
        } else {
            redisService.incr(skuIdAccessName);
        }
        //信息校验层
        if (redisService.bloomFilterExists(filterName, uid)) {
            return "您已经抢购过该商品，请勿重复下发!";
        } else {
            redisService.bloomFilterAdd(filterName, uid);
        }
        Boolean isSuccess = redisService.getAndIncrLua(bookedName + skuId);
        if (isSuccess) {
            return "恭喜您抢购成功！！！";
        } else {
            return "抢购结束,欢迎下次参与";
        }
    }

    private long getNow() {
        return System.currentTimeMillis() / 1000;
    }
}

工具类 RedisService.java

@Service
public class RedisService {

    @Autowired
    private RedisTemplate redisTemplate;

    private static double size = Math.pow(2, 32);


    /**
     * 写入缓存
     *
     * @param key
     * @param offset 位 8Bit=1Byte
     * @return
     */
    public boolean setBit(String key, long offset, boolean isShow) {
        boolean result = false;
        try {
            ValueOperations<Serializable, Object> operations = redisTemplate.opsForValue();
            operations.setBit(key, offset, isShow);
            result = true;
        } catch (Exception e) {
            e.printStackTrace();
        }
        return result;
    }

    /**
     * 写入缓存
     *
     * @param key
     * @param offset
     * @return
     */
    public boolean getBit(String key, long offset) {
        boolean result = false;
        try {
            ValueOperations<Serializable, Object> operations = redisTemplate.opsForValue();
            result = operations.getBit(key, offset);
        } catch (Exception e) {
            e.printStackTrace();
        }
        return result;
    }


    /**
     * 写入缓存
     *
     * @param key
     * @param value
     * @return
     */
    public boolean set(final String key, Object value) {
        boolean result = false;
        try {
            ValueOperations<Serializable, Object> operations = redisTemplate.opsForValue();
            redisTemplate.opsForList();
            operations.set(key, value);
            result = true;
        } catch (Exception e) {
            e.printStackTrace();
        }
        return result;
    }


    /**
     * 写入缓存
     *
     * @param key
     * @return
     */
    public Object get(final String key) {
        boolean result = false;
        try {
            ValueOperations<Serializable, Object> operations = redisTemplate.opsForValue();
            return operations.get(key);
        } catch (Exception e) {
            e.printStackTrace();
            return null;
        }
    }


    /**
     * 写入缓存
     *
     * @param key
     * @param value
     * @return
     */
    public boolean decr(final String key, int value) {
        boolean result = false;
        try {
            ValueOperations<Serializable, Object> operations = redisTemplate.opsForValue();
            operations.increment(key, -value);
            result = true;
        } catch (Exception e) {
            e.printStackTrace();
        }
        return result;
    }


    /**
     * 写入缓存
     *
     * @param key
     * @return
     */
    public boolean incr(final String key) {
        boolean result = false;
        try {
            ValueOperations<Serializable, Object> operations = redisTemplate.opsForValue();
            operations.increment(key, 1);
            result = true;
        } catch (Exception e) {
            e.printStackTrace();
        }
        return result;
    }

    /**
     * 写入缓存设置时效时间
     *
     * @param key
     * @param value
     * @return
     */
    public boolean set(final String key, Object value, Long expireTime) {
        boolean result = false;
        try {
            ValueOperations<Serializable, Object> operations = redisTemplate.opsForValue();
            operations.set(key, value);
            redisTemplate.expire(key, expireTime, TimeUnit.SECONDS);
            result = true;
        } catch (Exception e) {
            e.printStackTrace();
        }
        return result;
    }

    /**
     * 批量删除对应的value
     *
     * @param keys
     */
    public void remove(final String... keys) {
        for (String key : keys) {
            remove(key);
        }
    }


    /**
     * 删除对应的value
     *
     * @param key
     */
    public void remove(final String key) {
        if (exists(key)) {
            redisTemplate.delete(key);
        }
    }

    /**
     * 判断缓存中是否有对应的value
     *
     * @param key
     * @return
     */
    public boolean exists(final String key) {
        return redisTemplate.hasKey(key);
    }

    /**
     * 读取缓存
     *
     * @param key
     * @return
     */
    public Object genValue(final String key) {
        Object result = null;
        ValueOperations<String, String> operations = redisTemplate.opsForValue();
        result = operations.get(key);
        return result;
    }

    /**
     * 哈希 添加
     *
     * @param key
     * @param hashKey
     * @param value
     */
    public void hmSet(String key, Object hashKey, Object value) {
        HashOperations<String, Object, Object> hash = redisTemplate.opsForHash();
        hash.put(key, hashKey, value);
    }

    /**
     * 哈希获取数据
     *
     * @param key
     * @param hashKey
     * @return
     */
    public Object hmGet(String key, Object hashKey) {
        HashOperations<String, Object, Object> hash = redisTemplate.opsForHash();
        return hash.get(key, hashKey);
    }

    /**
     * 列表添加
     *
     * @param k
     * @param v
     */
    public void lPush(String k, Object v) {
        ListOperations<String, Object> list = redisTemplate.opsForList();
        list.rightPush(k, v);
    }

    /**
     * 列表获取
     *
     * @param k
     * @param l
     * @param l1
     * @return
     */
    public List<Object> lRange(String k, long l, long l1) {
        ListOperations<String, Object> list = redisTemplate.opsForList();
        return list.range(k, l, l1);
    }

    /**
     * 集合添加
     *
     * @param key
     * @param value
     */
    public void add(String key, Object value) {
        SetOperations<String, Object> set = redisTemplate.opsForSet();
        set.add(key, value);
    }

    /**
     * 集合获取
     *
     * @param key
     * @return
     */
    public Set<Object> setMembers(String key) {
        SetOperations<String, Object> set = redisTemplate.opsForSet();
        return set.members(key);
    }

    /**
     * 有序集合添加
     *
     * @param key
     * @param value
     * @param scoure
     */
    public void zAdd(String key, Object value, double scoure) {
        ZSetOperations<String, Object> zset = redisTemplate.opsForZSet();
        zset.add(key, value, scoure);
    }

    /**
     * 有序集合获取
     *
     * @param key
     * @param scoure
     * @param scoure1
     * @return
     */
    public Set<Object> rangeByScore(String key, double scoure, double scoure1) {
        ZSetOperations<String, Object> zset = redisTemplate.opsForZSet();
        redisTemplate.opsForValue();
        return zset.rangeByScore(key, scoure, scoure1);
    }


    //第一次加载的时候将数据加载到redis中
    public void saveDataToRedis(String name) {
        double index = Math.abs(name.hashCode() % size);
        long indexLong = new Double(index).longValue();
        boolean availableUsers = setBit("availableUsers", indexLong, true);
    }

    //第一次加载的时候将数据加载到redis中
    public boolean getDataToRedis(String name) {

        double index = Math.abs(name.hashCode() % size);
        long indexLong = new Double(index).longValue();
        return getBit("availableUsers", indexLong);
    }

    /**
     * 有序集合获取排名
     *
     * @param key   集合名称
     * @param value 值
     */
    public Long zRank(String key, Object value) {
        ZSetOperations<String, Object> zset = redisTemplate.opsForZSet();
        return zset.rank(key, value);
    }


    /**
     * 有序集合获取排名
     *
     * @param key
     */
    public Set<ZSetOperations.TypedTuple<Object>> zRankWithScore(String key, long start, long end) {
        ZSetOperations<String, Object> zset = redisTemplate.opsForZSet();
        Set<ZSetOperations.TypedTuple<Object>> ret = zset.rangeWithScores(key, start, end);
        return ret;
    }

    /**
     * 有序集合添加
     *
     * @param key
     * @param value
     */
    public Double zSetScore(String key, Object value) {
        ZSetOperations<String, Object> zset = redisTemplate.opsForZSet();
        return zset.score(key, value);
    }


    /**
     * 有序集合添加分数
     *
     * @param key
     * @param value
     * @param scoure
     */
    public void incrementScore(String key, Object value, double scoure) {
        ZSetOperations<String, Object> zset = redisTemplate.opsForZSet();
        zset.incrementScore(key, value, scoure);
    }


    /**
     * 有序集合获取排名
     *
     * @param key
     */
    public Set<ZSetOperations.TypedTuple<Object>> reverseZRankWithScore(String key, long start, long end) {
        ZSetOperations<String, Object> zset = redisTemplate.opsForZSet();
        Set<ZSetOperations.TypedTuple<Object>> ret = zset.reverseRangeByScoreWithScores(key, start, end);
        return ret;
    }

    /**
     * 有序集合获取排名
     *
     * @param key
     */
    public Set<ZSetOperations.TypedTuple<Object>> reverseZRankWithRank(String key, long start, long end) {
        ZSetOperations<String, Object> zset = redisTemplate.opsForZSet();
        Set<ZSetOperations.TypedTuple<Object>> ret = zset.reverseRangeWithScores(key, start, end);
        return ret;
    }


    public Boolean bloomFilterAdd(String filterName, int value) {
        DefaultRedisScript<Boolean> bloomAdd = new DefaultRedisScript<>();
        bloomAdd.setScriptSource(new ResourceScriptSource(new ClassPathResource("bloomFilterAdd.lua")));
        bloomAdd.setResultType(Boolean.class);
        List<Object> keyList = new ArrayList<>();
        keyList.add(filterName);
        keyList.add(value + "");
        Boolean result = (Boolean) redisTemplate.execute(bloomAdd, keyList);
        return result;
    }


    public Boolean bloomFilterExists(String filterName, int value) {
        DefaultRedisScript<Boolean> bloomExists = new DefaultRedisScript<>();
        bloomExists.setScriptSource(new ResourceScriptSource(new ClassPathResource("bloomFilterExist.lua")));
        bloomExists.setResultType(Boolean.class);
        List<Object> keyList = new ArrayList<>();
        keyList.add(filterName);
        keyList.add(value + "");
        Boolean result = (Boolean) redisTemplate.execute(bloomExists, keyList);
        return result;
    }

    public Boolean getAndIncrLua(String key) {
        DefaultRedisScript<Boolean> bloomExists = new DefaultRedisScript<>();
        bloomExists.setScriptSource(new ResourceScriptSource(new ClassPathResource("secKillIncr.lua")));
        bloomExists.setResultType(Boolean.class);
        List<Object> keyList = new ArrayList<>();
        keyList.add(key);
        Boolean result = (Boolean) redisTemplate.execute(bloomExists, keyList);
        return result;
    }
}

基于 Lua 脚本实现 Spring Boot 和布隆过滤器的整合
（1）bloomFilterAdd.lua

local bloomName = KEYS[1]
local value = KEYS[2]

-- bloomFilter
local result_1 = redis.call('BF.ADD', bloomName, value)
return result_1

（2）bloomFilterExist.lua

local bloomName = KEYS[1]
local value = KEYS[2]

-- bloomFilter
local result_1 = redis.call('BF.EXISTS', bloomName, value)
return result_1

（3）secKillIncr.lua

local lockKey = KEYS[1]

-- get info
local result_1 = redis.call('GET', lockKey)
if tonumber(result_1) <10000
then
local result_2= redis.call('INCR', lockKey)
return result_1
else
return result_1
end

测试Controller SeckillController.java

@RestController
public class SeckillController {

    @Resource
    private SeckillService seckillService;

    @RequestMapping("/redis/seckill")
    public String secKill(int uid,int skuId){
         return seckillService.seckill(uid,skuId);
    }
}

学习redis布隆过滤器实现秒杀案例

学习redis布隆过滤器实现秒杀案例

PS:在网上看到的案例进行学习

1、分析秒杀业务流程

2、利用 Redis 缓存 INCR拦截流量

3、利用 Redis 缓存加速库存扣量

4、将用户订单数据写入mq

5、监听mq入库

6、代码实现

学习redis布隆过滤器实现秒杀案例

PS:在网上看到的案例进行学习

1、分析秒杀业务流程

2、利用 Redis 缓存 INCR拦截流量

3、利用 Redis 缓存加速库存扣量

4、 将用户订单数据写入mq

5、监听mq入库

6、代码实现

4、将用户订单数据写入mq