我使用 INCR 和 EXPIRE 实现速率限制，例如每分钟 5 次请求：
if EXISTS counter
count = INCR counter
else
EXPIRE counter 60
count = INCR counter
if count > 5
print "Exceeded the limit"
然而，在最后一秒钟可以发送5个请求，在第二分钟的第一秒钟又可以发送5个请求，即在两秒钟内发送10个请求。
如何避免这个问题？
更新：我想出了这个列表实现。这是一个好方法吗？
times = LLEN counter
if times < 5
LPUSH counter now()
else
time = LINDEX counter -1
if now() - time < 60
print "Exceeded the limit"
else
LPUSH counter now()
LTRIM counter 5
counter = current_time # for example 15:03
count = INCR counter
EXPIRE counter 60 # just to make sure redis doesn't store it forever
if count > 5
print "Exceeded the limit"
counter = Time.now.to_i # this is Ruby and it returns the number of milliseconds since 1/1/1970
key = "counter:" + counter
INCR key
EXPIRE key 60
number_of_requests = KEYS "counter"*"
if number_of_requests > 5
print "Exceeded the limit"
KEYS关键字。我们可以使用sets代替：（参考链接）
counter = Time.now.to_i # this is Ruby and it returns the number of milliseconds since 1/1/1970
set = "my_set"
SADD set counter 1
members = SMEMBERS set
# remove all set members which are older than 1 minute
members {|member| SREM member if member[key] < (Time.now.to_i - 60000) }
if (SMEMBERS set).size > 5
print "Exceeded the limit"
有一个实现此算法的Redis模块可在GitHub上找到: https://github.com/brandur/redis-cell
var Ioredis = require('ioredis');
var redis = new Ioredis();
// Rolling window rate limiter
//
// key is a unique identifier for the process or function call being limited
// exp is the expiry in milliseconds
// maxnum is the number of function calls allowed before expiry
var redis_limiter_rolling = function(key, maxnum, exp, next) {
redis.multi([
['incr', 'limiter:num:' + key],
['time']
]).exec(function(err, results) {
if (err) {
next(err);
} else {
// unique incremented list number for this key
var listnum = results[0][1];
// current time
var tcur = (parseInt(results[1][1][0], 10) * 1000) + Math.floor(parseInt(results[1][1][1], 10) / 1000);
// absolute time of expiry
var texpiry = tcur - exp;
// get number of transacation in the last expiry time
var listkey = 'limiter:list:' + key;
redis.multi([
['zadd', listkey, tcur.toString(), listnum],
['zremrangebyscore', listkey, '-inf', texpiry.toString()],
['zcard', listkey]
]).exec(function(err, results) {
if (err) {
next(err);
} else {
// num is the number of calls in the last expiry time window
var num = parseInt(results[2][1], 10);
if (num <= maxnum) {
// does not reach limit
next(null, false, num, exp);
} else {
// limit surpassed
next(null, true, num, exp);
}
}
});
}
});
};
这里是一种锁定式的速率限制器：
// Lockout window rate limiter
//
// key is a unique identifier for the process or function call being limited
// exp is the expiry in milliseconds
// maxnum is the number of function calls allowed within expiry time
var util_limiter_lockout = function(key, maxnum, exp, next) {
// lockout rate limiter
var idkey = 'limiter:lock:' + key;
redis.incr(idkey, function(err, result) {
if (err) {
next(err);
} else {
if (result <= maxnum) {
// still within number of allowable calls
// - reset expiry and allow next function call
redis.expire(idkey, exp, function(err) {
if (err) {
next(err);
} else {
next(null, false, result);
}
});
} else {
// too many calls, user must wait for expiry of idkey
next(null, true, result);
}
}
});
};
这里是函数的要点。如果您发现任何问题，请告诉我。
private final String COUNT = "count";
@Autowired
private StringRedisTemplate stringRedisTemplate;
private HashOperations hashOperations;
@PostConstruct
private void init() {
hashOperations = stringRedisTemplate.opsForHash();
}
@Override
public boolean isRequestAllowed(String key, long limit, long timeout, TimeUnit timeUnit) {
Boolean hasKey = stringRedisTemplate.hasKey(key);
if (hasKey) {
Long value = hashOperations.increment(key, COUNT, -1l);
return value > 0;
} else {
hashOperations.put(key, COUNT, String.valueOf(limit));
stringRedisTemplate.expire(key, timeout, timeUnit);
}
return true;
}
这是我使用 Redis Lists 实现的漏桶算法速率限制代码。
注意：以下代码是php的示例实现，您可以用自己熟悉的编程语言实现它。
$list = $redis->lRange($key, 0, -1); // get whole list
$noOfRequests = count($list);
if ($noOfRequests > 5) {
$expired = 0;
foreach ($list as $timestamp) {
if ((time() - $timestamp) > 60) { // Time difference more than 1 min == expired
$expired++;
}
}
if ($expired > 0) {
$redis->lTrim($key, $expired, -1); // Remove expired requests
if (($noOfRequests - $expired) > 5) { // If still no of requests greater than 5, means fresh limit exceeded.
die("Request limit exceeded");
}
} else { // No expired == all fresh.
die("Request limit exceeded");
}
}
$redis->rPush($key, time()); // Add this request as a genuine one to the list, and proceed.
@Autowired
private StringRedisTemplate stringRedisTemplate;
private HashOperations hashOperations;
@PostConstruct
private void init() {
hashOperations = stringRedisTemplate.opsForHash();
}
@Override
public boolean isRequestAllowed(String key, long limit, long timeout, TimeUnit timeUnit) {
Long value = hashOperations.increment(key, COUNT, 1l);
if (value == 1) {
stringRedisTemplate.expire(key, timeout, timeUnit);
}
return value > limit;
}
你的更新是非常好的算法，尽管我做了一些修改：
times = LLEN counter
if times < 5
LPUSH counter now()
else
time = LINDEX counter -1
if now() - time <= 60
print "Exceeded the limit"
else
LPUSH counter now()
RPOP counter
// Remove older request entries
while (!RequestTimeList.isEmpty() && (now() - RequestTimeList.get(0)) > interval) {
RequestTimeList.remove(0)
}
if (RequestTimeList.length < throughput) {
RequestTimeList.add(now())
} else {
throw err;
}
时间间隔/固定窗口中的请求
我尝试了LIST、EXPIRE和PTTL。
如果每秒钟tps为5，则
吞吐量= 5
rampup = 1000（1000毫秒= 1秒）
间隔= 200毫秒
local tpsKey = KEYS[1]
local throughput = tonumber(ARGV[1])
local rampUp = tonumber(ARGV[2])
-- Minimum interval to accept the next request.
local interval = rampUp / throughput
local currentTime = redis.call('PTTL', tpsKey)
-- -2 if the key does not exist, so set an year expiry
if currentTime == -2 then
currentTime = 31536000000 - interval
redis.call('SET', tpsKey, 31536000000, "PX", currentTime)
end
local previousTime = redis.call('GET', tpsKey)
if (previousTime - currentTime) >= interval then
redis.call('SET', tpsKey, currentTime, "PX", currentTime)
return true
else
redis.call('ECHO',"0. ERR - MAX PERMIT REACHED IN THIS INTERVAL")
return false
end
使用List的另一种方式
local counter = KEYS[1]
local throughput = tonumber(ARGV[1])
local rampUp = tonumber(ARGV[2])
local interval = rampUp / throughput
local times = redis.call('LLEN', counter)
if times == 0 then
redis.call('LPUSH', counter, rampUp)
redis.call('PEXPIRE', counter, rampUp)
return true
elseif times < throughput then
local lastElemTTL = tonumber(redis.call('LINDEX', counter, 0))
local currentTTL = redis.call('PTTL', counter)
if (lastElemTTL-currentTTL) < interval then
return false
else
redis.call('LPUSH', counter, currentTTL)
return true
end
else
return false
end
这里有一种替代方法。如果目标是限制每个用户在Y秒内向X请求的数量，并且计时器从第一个请求接收时开始，则可以为要跟踪的每个用户创建两个键：一个用于记录第一个请求接收的时间，另一个用于记录请求次数。
key = "123"
key_count = "ct:#{key}"
key_timestamp = "ts:#{key}"
if (not redis[key_timestamp].nil?) && (not redis[key_count].nil?) && (redis[key_count].to_i > 3)
puts "limit reached"
else
if redis[key_timestamp].nil?
redis.multi do
redis.set(key_count, 1)
redis.set(key_timestamp, 1)
redis.expire(key_timestamp,30)
end
else
redis.incr(key_count)
end
puts redis[key_count].to_s + " : " + redis[key_timestamp].to_s + " : " + redis.ttl(key_timestamp).to_s
end
这个内容很小，你可能不需要对它进行哈希处理。
local f,k,a,b f=redis.call k=KEYS[1] a=f('incrby',k,ARGV[1]) b=f('pttl',k) if b<0 then f('pexpire',k,ARGV[2]) end return a
参数如下：
KEYS[1] = 键名，例如可以是限制速率的操作
ARGV[1] = 增加的数量，通常为1，但您可以在客户端每10或100毫秒间隔批量处理
ARGV[2] = 以毫秒为单位的时间窗口，用于限制速率
返回值：新增的值，然后可以将其与代码中的值进行比较，以查看是否超过了速率限制。
使用此方法不会将ttl设置回基础值，它将继续滑动，直到键过期，然后在下一次调用时使用ARGV[2] ttl重新开始。
counter是有意义的，特别是如果你有很多counter键。 - keunenow() - time < 60)分钟的时间间隔... - Kanagavelu Sugumar