Enhancing Concurrency in Redis Fee Limiting System

Background

Fee limiting is a method 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 clients. At Rockset, we primarily use fee limiting to guard our:

1. metadata retailer from overload attributable to too many API requests.
2. log retailer from filling up resulting from mismatched enter and output charges
3. management airplane from too many state transitions.

We use Redisson RateLimiter which makes use of Redis below the hood to trace fee utilization. At a really fundamental degree, our utilization of the library appears to be like like this (omitting particular enterprise logic for higher readability):

class RedisRateLimiter {
  non-public 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 fee restrict and false in any other case.

Downside

Not too long ago, we noticed that resulting from 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 might find yourself with one other surge in Redis CPU.

We additionally observed that Redisson makes use of Lua scripting on the server facet and observed 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 straightforward to get out.

Aside from vertical scaling and bettering configuration, we brainstormed just a few different approaches to the issue:

1. We may shard Redis over the speed restrict keys to unfold the load and horizontally scale.
2. We may queue fee 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.
3. We may proactively reserve bigger batches of tokens and cache them domestically. When a request for tokens is available in, strive coming 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 an alternative 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 in some unspecified time in the future.

The issue with the second method is it raises just a few complexities: How often does the thread pull from the queue and ballot? Do you cap the dimensions 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 advanced. Thus, we ended up implementing the third resolution.

As proven in direction of the top of the publish, this implementation decreased Redis connections on fee restrict calls by 96%. The remainder of this publish will discover how we applied the third method. It goes into a number of the pitfalls, complexities, and issues to think about when engaged on a batch-oriented resolution akin to this one.

Implementation

Be aware that code offered 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 {
  non-public ultimate RRateLimiter rateLimitService = ...;
  non-public ultimate lengthy batchSize = ...;
  non-public ultimate lengthy timeWindowSecs = ...;
  non-public lengthy reservedTokens = 0;
  non-public lengthy expirationTs = 0;

  public boolean isNotRateLimited(String key, int requestedTokens) {
    // On this case, we'd as effectively 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 appears to be like positive upon first look, however what occurs if a number of threads have to name isNotRateLimited on the identical time? The above code is actually not thread secure. I’ll go away as an train to the reader why making reservedTokens into an Atomic variable will not remedy the issue (though do tell us in case you give you a intelligent lock-free resolution). If Atomics will not work, we will strive utilizing Locks as an alternative:

class RedisRateLimiter {
  non-public ultimate RRateLimiter rateLimitService = ...;
  non-public ultimate lengthy batchSize = ...;
  non-public ultimate lengthy timeWindowSecs = ...;
  non-public ultimate Lock lock = new ReentrantLock();
  non-public lengthy reservedTokens = 0;
  non-public lengthy expirationTs = 0;

  public boolean isNotRateLimited(String key, int requestedTokens) {
    // On this case, we'd as effectively make a direct name to
    // simplify issues.
    if (requestedTokens > batchSize) {
      return rateLimitService.purchase(key, requestedTokens);
    }

    lock.lock();
    strive {
      if (reservedTokens >= requestedTokens && expirationTs <= now()) {
        reservedTokens -= requestedTokens;
        return true;
      } else if (expirationTs <= now()) {
        // Deplete remaining tokens
        requestedTokens -= reservedTokens;
        reservedTokens = 0;
      }
    } lastly {
      // Simple 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 appears to be like appropriate, there’s one delicate downside with it. What occurs if a number of threads see there aren’t sufficient reservedTokens? For instance 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 will fairly simply obtain this. Should you’re unfamiliar with how situation variables work, try the java docs; most languages can have some type of situation variable implementation as effectively. This is the code:

class RedisRateLimiter {
  non-public ultimate RRateLimiter rateLimitService = ...;
  non-public ultimate lengthy batchSize = ...;
  non-public ultimate lengthy timeWindowSecs = ...;
  non-public ultimate Lock lock = new ReentrantLock();
  non-public ultimate Situation fetchCondition = lock.newCondition();
  non-public boolean fetchInProgress = false;
  non-public lengthy reservedTokens = 0;
  non-public lengthy expirationTs = 0;

  public boolean isNotRateLimited(String key, int requestedTokens) {
    // On this case, we'd as effectively make a direct name to
    // simplify issues.
    if (requestedTokens > batchSize) {
      return rateLimitService.purchase(key, requestedTokens);
    }

    boolean doFetch = false;
    lock.lock();
    strive {
      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 fee limiting a thread too aggressively:

For instance our batch measurement is 100 and we have now 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 can have waited for no purpose as a result of the primary 4 threads will deplete all of the tokens within the fetched batch. As an alternative, it may need been higher to both:

1. Instantly return false for the fifth thread (this may fee restrict too aggressively)
2. Or have the fifth thread make a direct name to the exterior service, not ready on the primary thread.

The second resolution is applied under:

class RedisRateLimiter {
  non-public ultimate RRateLimiter rateLimitService = ...;
  non-public ultimate lengthy batchSize = ...;
  non-public ultimate lengthy timeWindowSecs = ...;
  non-public ultimate Lock lock = new ReentrantLock();
  non-public ultimate Situation fetchCondition = lock.newCondition();
  non-public boolean fetchInProgress = false;
  non-public lengthy reservedTokens = 0;
  non-public lengthy expirationTs = 0;
  // Variety of tokens that ready threads will deplete.
  non-public lengthy unreservedFetchTokens = 0;
  // Utilized by ready threads to find out if the fetch they're
  // ready for succeeded or not.
  non-public boolean didFetchSucceed = false;

  public boolean isNotRateLimited(String key, int requestedTokens) {
    // On this case, we'd as effectively make a direct name to
    // simplify issues.
    if (requestedTokens > batchSize) {
      return rateLimitService.purchase(key, requestedTokens);
    }

    boolean doFetch = false;
    lock.lock();
    strive {
      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 positive.
          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 fairly than ready in line.
    return rateLimitService.purchase(key, tokensRequested);
  }
}

Lastly, we have arrived at an appropriate resolution. In follow, the lock competition must be minimal as we’re solely setting just a few primitive values. However, as with something, you must benchmark this resolution in your use case and see if it is smart.

Setting the batch measurement

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 think about is whether or not these hosts might be auto scaled. In that 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 could be fascinating to discover utilizing a dynamically set batch measurement. If this machine used up your entire final batch, possibly it could 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 fee 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 fee limiting at Rockset is an ongoing course of and this most likely received’t be the final enchancment we have to make on this space. Keep tuned for extra. And in case you’re fascinated with fixing all these issues, we’re hiring!

A fast apart

As an apart, the next code has a really delicate concurrency bug. Can you see it?

class RedisRateLimiter {
  non-public ultimate RRateLimiter rateLimitService = ...;
  non-public ultimate lengthy batchSize = ...;
  non-public ultimate lengthy timeWindowSecs = ...;
  non-public ultimate Lock lock = new ReentrantLock();
  non-public ultimate Situation fetchCondition = lock.newCondition();
  non-public boolean fetchInProgress = false;
  non-public lengthy reservedTokens = 0;
  non-public lengthy expirationTs = 0;
  // Variety of tokens that ready threads will deplete.
  non-public lengthy unreservedFetchTokens = 0;

  public boolean isNotRateLimited(String key, int requestedTokens) {
    // On this case, we'd as effectively make a direct name to
    // simplify issues.
    if (requestedTokens > batchSize) {
      return rateLimitService.purchase(key, requestedTokens);
    }

    boolean doFetch = false;
    lock.lock();
    strive {
      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 fairly than ready in line.
    return rateLimitService.purchase(key, tokensRequested);
  }
}

Trace: Even when rateLimitService.purchase at all times returned true, you may find yourself in conditions the place isNotRateLimited returns false.