Background
Charge limiting is a way used to guard providers from overload. As well as, it may be used to forestall hunger of a multi-tenant useful resource by just a few very giant prospects. At Rockset, we primarily use charge limiting to guard our:
- metadata retailer from overload brought on by too many API requests.
- log retailer from filling up as a consequence of mismatched enter and output charges
- management aircraft from too many state transitions.
We use Redisson RateLimiter which makes use of Redis below the hood to trace charge utilization. At a really fundamental degree, our utilization of the library seems like this (omitting particular enterprise logic for higher readability):
class RedisRateLimiter {
personal ultimate RRateLimiter rateLimitService = ...;
public boolean isNotRateLimited(String key, int requestedTokens) {
return rateLimitService.purchase(key, requestedTokens);
}
}
Let’s not dive into the main points of RRateLimiter, however suffice it to say that this makes a community name to Redis. RedisRateLimiter.purchase will return true if requestedTokens wouldn’t exceed your charge restrict and false in any other case.
Drawback
Lately, we noticed that as a consequence of many requests to Redis, the CPU on our Redis cluster was getting near 100%. The very first thing we tried was vertically scaling up our Redis occasion to purchase us time. Nevertheless, vertical scaling has its personal limits and each few weeks we’d find yourself with one other surge in Redis CPU.
We additionally seen that Redisson makes use of Lua scripting on the server facet and seen that lua compilation was taking over a good chunk of CPU time. One other low hanging fruit we tried was configuring Redisson to cache lua compilation on the server facet, lowering CPU time spent on this process. Since this was a easy config change, it didn’t require a code deploy and was simple to get out.
Other than vertical scaling and bettering configuration, we brainstormed just a few different approaches to the issue:
- We might shard Redis over the speed restrict keys to unfold the load and horizontally scale.
- We might queue charge restrict requests domestically and have a single thread that periodically (i.e. each 50ms) takes n gadgets off the queue and requests a bigger batch of tokens from Redis.
- We might proactively reserve bigger batches of tokens and cache them domestically. When a request for tokens is available in, attempt getting back from the native cache. If that does not exist, go fetch a bigger batch. That is analogous to Malloc not making a sys name each time reminiscence is requested and as a substitute reserving bigger chunks that it manages.
Horizontally scaling Redis by sharding is a superb long-term resolution; it’s most likely one thing we’re going to finish up doing sooner or later.
The issue with the second method is it raises just a few complexities: How incessantly does the thread pull from the queue and ballot? Do you cap the scale of the queue and if that’s the case, what occurs if the queue is full? How do you even set the cap on the queue? What if Redis has 50 tokens and we batch 10 requests every needing 10 tokens (asking Redis for a complete of 100 tokens)? Ideally 5 requests ought to succeed, however in actuality all 10 would fail. These issues are solvable, however would make the implementation fairly complicated. Thus, we ended up implementing the third resolution.
As proven in the direction of the top of the submit, this implementation diminished Redis connections on charge restrict calls by 96%. The remainder of this submit will discover how we carried out the third method. It goes into a few of the pitfalls, complexities, and issues to contemplate when engaged on a batch-oriented resolution resembling this one.
Implementation
Observe that code introduced on this weblog is in Java. Not all error dealing with is proven for simplicity. Additionally, I’ll reference a now() methodology which merely returns the unix timestamp in seconds from epoch.
Let’s begin easy:
class RedisRateLimiter {
personal ultimate RRateLimiter rateLimitService = ...;
personal ultimate lengthy batchSize = ...;
personal ultimate lengthy timeWindowSecs = ...;
personal lengthy reservedTokens = 0;
personal lengthy expirationTs = 0;
public boolean isNotRateLimited(String key, int requestedTokens) {
// On this case, we'd as properly make a direct name to
// simplify issues.
if (requestedTokens > batchSize) {
return rateLimitService.purchase(key, requestedTokens);
}
if (reservedTokens >= requestedTokens && expirationTs <= now()) {
reservedTokens -= requestedTokens;
return true;
}
if (rateLimitService.purchase(key, batchSize)) {
reservedTokens = batchSize - requestedTokens;
expirationTs = now() + timeWindowSecs;
return true;
}
return false;
}
}
This code seems high-quality upon first look, however what occurs if a number of threads have to name isNotRateLimited on the similar time? The above code is definitely not thread secure. I’ll depart as an train to the reader why making reservedTokens into an Atomic variable will not resolve the issue (though do tell us if you happen to give you a intelligent lock-free resolution). If Atomics will not work, we are able to attempt utilizing Locks as a substitute:
class RedisRateLimiter {
personal ultimate RRateLimiter rateLimitService = ...;
personal ultimate lengthy batchSize = ...;
personal ultimate lengthy timeWindowSecs = ...;
personal ultimate Lock lock = new ReentrantLock();
personal lengthy reservedTokens = 0;
personal lengthy expirationTs = 0;
public boolean isNotRateLimited(String key, int requestedTokens) {
// On this case, we'd as properly make a direct name to
// simplify issues.
if (requestedTokens > batchSize) {
return rateLimitService.purchase(key, requestedTokens);
}
lock.lock();
attempt {
if (reservedTokens >= requestedTokens && expirationTs <= now()) {
reservedTokens -= requestedTokens;
return true;
} else if (expirationTs <= now()) {
// Burn up remaining tokens
requestedTokens -= reservedTokens;
reservedTokens = 0;
}
} lastly {
// Straightforward to miss; do not lock throughout the community request.
lock.unlock();
}
if (rateLimitService.purchase(key, batchSize)) {
lock.lock();
reservedTokens = (batchSize - requestedTokens);
expirationTs = now() + timeWindowSecs;
lock.unlock();
return true;
}
return false;
}
}
Whereas at first look this seems appropriate, there may be one refined drawback with it. What occurs if a number of threads see there aren’t sufficient reservedTokens? For example reservedTokens is 0, our batchSize is 100, and 5 threads request 20 tokens every concurrently.
All 5 threads will see that there aren’t sufficient reserved tokens and every will fetch 100 tokens. Now, this machine is left with 450 reservedTokens and 5x too many requests to the exterior retailer. Can we do higher? All we actually want is for one thread to go and fetch a batch after which the opposite 4 threads can simply make the most of that batch. 1 community name, and fewer wasted tokens.
With some booleans and situation variables, we are able to fairly simply obtain this. In case you’re unfamiliar with how situation variables work, take a look at the java docs; most languages may have some type of situation variable implementation as properly. This is the code:
class RedisRateLimiter {
personal ultimate RRateLimiter rateLimitService = ...;
personal ultimate lengthy batchSize = ...;
personal ultimate lengthy timeWindowSecs = ...;
personal ultimate Lock lock = new ReentrantLock();
personal ultimate Situation fetchCondition = lock.newCondition();
personal boolean fetchInProgress = false;
personal lengthy reservedTokens = 0;
personal lengthy expirationTs = 0;
public boolean isNotRateLimited(String key, int requestedTokens) {
// On this case, we'd as properly make a direct name to
// simplify issues.
if (requestedTokens > batchSize) {
return rateLimitService.purchase(key, requestedTokens);
}
boolean doFetch = false;
lock.lock();
attempt {
if (reservedTokens >= requestedTokens && expirationTs <= now()) {
reservedTokens -= requestedTokens;
return true;
} else if (expirationTs <= now()) {
requestedTokens -= reservedTokens;
reservedTokens = 0;
}
if (fetchInProgress) {
// Thread is already fetching; let's watch for it to complete.
fetchCondition.await();
if (reservedTokens >= requestedTokens) {
reservedTokens -= requestedTokens;
return true;
}
return false;
} else {
doFetch = true; // This thread ought to fetch the batch
fetchInProgress = true; // Keep away from different threads from fetching.
}
} lastly {
lock.unlock();
}
if (doFetch) {
boolean acquired = rateLimitService.purchase(key, batchSize);
lock.lock();
if (acquired) {
reservedTokens = (batchSize - requestedTokens);
expirationTs = now() + timeWindowSecs;
}
fetchCondition.signalAll(); // Get up ready threads
lock.unlock();
return acquired;
}
return false;
}
}
Now, we are going to solely ever have one thread at a time fetching a batch. Whereas the code is logically appropriate, we’d find yourself charge limiting a thread too aggressively:
For example our batch dimension is 100 and we’ve 5 threads requesting 25 tokens every concurrently. The primary thread (name it T1) will fetch the batch from the exterior service. The opposite 4 threads will wait on the situation variable. Nevertheless, the fifth thread may have waited for no motive as a result of the primary 4 threads will dissipate all of the tokens within the fetched batch. As an alternative, it might need been higher to both:
- Instantly return false for the fifth thread (it will charge restrict too aggressively)
- Or have the fifth thread make a direct name to the exterior service, not ready on the primary thread.
The second resolution is carried out under:
class RedisRateLimiter {
personal ultimate RRateLimiter rateLimitService = ...;
personal ultimate lengthy batchSize = ...;
personal ultimate lengthy timeWindowSecs = ...;
personal ultimate Lock lock = new ReentrantLock();
personal ultimate Situation fetchCondition = lock.newCondition();
personal boolean fetchInProgress = false;
personal lengthy reservedTokens = 0;
personal lengthy expirationTs = 0;
// Variety of tokens that ready threads will dissipate.
personal lengthy unreservedFetchTokens = 0;
// Utilized by ready threads to find out if the fetch they're
// ready for succeeded or not.
personal boolean didFetchSucceed = false;
public boolean isNotRateLimited(String key, int requestedTokens) {
// On this case, we'd as properly make a direct name to
// simplify issues.
if (requestedTokens > batchSize) {
return rateLimitService.purchase(key, requestedTokens);
}
boolean doFetch = false;
lock.lock();
attempt {
if (reservedTokens >= requestedTokens && expirationTimesatmp <= now()) {
reservedTokens -= requestedTokens;
return true;
} else if (expirationTimestamp <= now()) {
requestedTokens -= reservedTokens;
reservedTokens = 0;
}
if (fetchInProgress) {
if (unreservedFetchTokens >= requestedTokens) {
// Reserve your spot in line
unreservedFetchTokens -= requestedTokens;
fetchCondition.await();
// If we get right here and the fetch succeeded, then we
// are high-quality.
return didFetchSucceed;
}
} else {
doFetch = true;
fetchInProgress = true;
unreservedFetchTokens = batch - requestedTokens;
}
} lastly {
lock.unlock();
}
if (doFetch) {
boolean acquired = rateLimitService.purchase(key, batchSize);
lock.lock();
didFetchSucceed = acquired;
if (acquired) {
reservedTokens = unreservedFetchTokens;
expirationTs = now() + timeWindowSecs;
}
fetchCondition.signalAll(); // Get up ready threads
lock.unlock();
return acquired;
}
// If we get right here, it means there weren't sufficient
// unreservedFetchTokens. Let's simply make our personal
// name somewhat than ready in line.
return rateLimitService.purchase(key, tokensRequested);
}
}
Lastly, we have arrived at an appropriate resolution. In observe, the lock competition ought to be minimal as we’re solely setting just a few primitive values. However, as with something, it is best to benchmark this resolution in your use case and see if it is smart.
Setting the batch dimension
One remaining query is the way to set batchSize. There’s a tradeoff right here: If batchSize is just too low, the variety of requests to Redis will method the variety of requests to isNotRateLimited. If batchSize is just too excessive, hosts will reserve too many tokens, ravenous out different hosts. One factor to contemplate is whether or not these hosts may be auto scaled. If that’s the case, as soon as numHosts * batchSize exceeds the speed restrict, different hosts will begin getting starved out even when the variety of requests is below the speed restrict.
To deal with a few of this, it will be fascinating to discover utilizing a dynamically set batch dimension. If this machine used up the complete final batch, possibly it will possibly request 1.5x the batch subsequent time (with a cap after all). Alternatively, if batches are going to waste, maybe solely ask for half the batch subsequent time.
Outcomes
As an preliminary start line, we set the batchSize to be 1/1000 of the speed restrict for a given useful resource. For our workload, this resulted in ~4% of charge restrict requests going to Redis, a large enchancment. This may be seen within the chart under, the place the x-axis is time and the y-axis is p.c of requests hitting Redis:
Enhancing our charge limiting at Rockset is an ongoing course of and this most likely gained’t be the final enchancment we have to make on this space. Keep tuned for extra. And if you happen to’re enthusiastic about fixing a lot of these issues, we’re hiring!
A fast apart
As an apart, the next code has a really refined concurrency bug. Can you notice it?
class RedisRateLimiter {
personal ultimate RRateLimiter rateLimitService = ...;
personal ultimate lengthy batchSize = ...;
personal ultimate lengthy timeWindowSecs = ...;
personal ultimate Lock lock = new ReentrantLock();
personal ultimate Situation fetchCondition = lock.newCondition();
personal boolean fetchInProgress = false;
personal lengthy reservedTokens = 0;
personal lengthy expirationTs = 0;
// Variety of tokens that ready threads will dissipate.
personal lengthy unreservedFetchTokens = 0;
public boolean isNotRateLimited(String key, int requestedTokens) {
// On this case, we'd as properly make a direct name to
// simplify issues.
if (requestedTokens > batchSize) {
return rateLimitService.purchase(key, requestedTokens);
}
boolean doFetch = false;
lock.lock();
attempt {
if (reservedTokens >= requestedTokens) {
reservedTokens -= requestedTokens;
return true;
} else if (expirationTimestamp <= now()) {
requestedTokens -= reservedTokens;
reservedTokens = 0;
}
if (fetchInProgress) {
if (unreservedFetchTokens >= requestedTokens) {
// Reserve your spot in line
unreservedFetchTokens -= requestedTokens;
fetchCondition.await();
if (reservedTokens >= requestedTokens) {
reservedTokens -= requestedTokens;
return true;
}
return false;
}
} else {
doFetch = true;
fetchInProgress = true;
unreservedFetchTokens = batch - requestedTokens;
}
} lastly {
lock.unlock();
}
if (doFetch) {
boolean acquired = rateLimitService.purchase(key, batchSize);
lock.lock();
if (acquired) {
reservedTokens = (batchSize - requestedTokens);
expirationTs = now() + timeWindowSecs;
}
fetchCondition.signalAll(); // Get up ready threads
lock.unlock();
return acquired;
}
// If we get right here, it means there weren't sufficient
// unreservedFetchTokens. Let's simply make our personal
// name somewhat than ready in line.
return rateLimitService.purchase(key, tokensRequested);
}
}
Trace: Even when rateLimitService.purchase all the time returned true, you’ll be able to find yourself in conditions the place isNotRateLimited returns false.