Bettering Concurrency in Redis Fee Limiting System


Background

Fee 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 massive prospects. At Rockset, we primarily use price limiting to guard our:

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

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

class RedisRateLimiter {
  personal remaining 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 price restrict and false in any other case.

Drawback

Lately, we noticed that because 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 observed that Redisson makes use of Lua scripting on the server facet and observed that lua compilation was taking on 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 job. Since this was a easy config change, it didn’t require a code deploy and was simple to get out.

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

  1. We might shard Redis over the speed restrict keys to unfold the load and horizontally scale.
  2. We might queue price 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 might 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 answer; it’s in all probability one thing we’re going to finish up doing in some unspecified time in the future.

The issue with the second strategy is it raises just a few complexities: How steadily does the thread pull from the queue and ballot? Do you cap the scale of the queue and in that 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 answer.

As proven in the direction of the tip of the submit, this implementation decreased Redis connections on price restrict calls by 96%. The remainder of this submit will discover how we carried out the third strategy. It goes into a few of the pitfalls, complexities, and issues to contemplate when engaged on a batch-oriented answer similar to this one.

Implementation

Notice that code introduced on this weblog is in Java. Not all error dealing with is proven for simplicity. Additionally, I’ll reference a now() technique which merely returns the unix timestamp in seconds from epoch.

Let’s begin easy:

class RedisRateLimiter {
  personal remaining RRateLimiter rateLimitService = ...;
  personal remaining lengthy batchSize = ...;
  personal remaining 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 to be high quality upon first look, however what occurs if a number of threads must name isNotRateLimited on the identical time? The above code is actually not thread secure. I’ll depart as an train to the reader why making reservedTokens into an Atomic variable will not clear up the issue (though do tell us in the event you provide you with a intelligent lock-free answer). If Atomics will not work, we will strive utilizing Locks as an alternative:

class RedisRateLimiter {
  personal remaining RRateLimiter rateLimitService = ...;
  personal remaining lengthy batchSize = ...;
  personal remaining lengthy timeWindowSecs = ...;
  personal remaining 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();
    strive {
      if (reservedTokens >= requestedTokens && expirationTs <= now()) {
        reservedTokens -= requestedTokens;
        return true;
      } else if (expirationTs <= now()) {
        // Deplete 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 to be right, there may be one delicate downside with it. What occurs if a number of threads see there aren’t sufficient reservedTokens? To illustrate 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. For those who’re unfamiliar with how situation variables work, try the java docs; most languages could have some form of situation variable implementation as properly. Here is the code:

class RedisRateLimiter {
  personal remaining RRateLimiter rateLimitService = ...;
  personal remaining lengthy batchSize = ...;
  personal remaining lengthy timeWindowSecs = ...;
  personal remaining Lock lock = new ReentrantLock();
  personal remaining 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();
    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 anticipate 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’ll solely ever have one thread at a time fetching a batch. Whereas the code is logically right, we’d find yourself price limiting a thread too aggressively:

To illustrate our batch measurement is 100 and we’ve got 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 could 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 may need been higher to both:

  1. Instantly return false for the fifth thread (this may price 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 answer is carried out under:

class RedisRateLimiter {
  personal remaining RRateLimiter rateLimitService = ...;
  personal remaining lengthy batchSize = ...;
  personal remaining lengthy timeWindowSecs = ...;
  personal remaining Lock lock = new ReentrantLock();
  personal remaining 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();
    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 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 slightly than ready in line.
    return rateLimitService.purchase(key, tokensRequested);
  }
}

Lastly, we have arrived at a suitable answer. In follow, the lock competition ought to be minimal as we’re solely setting just a few primitive values. However, as with something, it’s best to benchmark this answer to your use case and see if it is sensible.

Setting the batch measurement

One remaining query is learn how to set batchSize. There’s a tradeoff right here: If batchSize is just too low, the variety of requests to Redis will strategy 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 will 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 beneath the speed restrict.

To deal with a few of this, it might be fascinating to discover utilizing a dynamically set batch measurement. If this machine used up the whole final batch, possibly it will probably 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 price restrict requests going to Redis, an enormous 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:

how-we-improved-the-concurrency-and-scalability-of-our-redis-rate-limiting - figure1

Bettering our price limiting at Rockset is an ongoing course of and this in all probability received’t be the final enchancment we have to make on this space. Keep tuned for extra. And in the event you’re excited by fixing a lot of these issues, we’re hiring!

A fast apart

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

class RedisRateLimiter {
  personal remaining RRateLimiter rateLimitService = ...;
  personal remaining lengthy batchSize = ...;
  personal remaining lengthy timeWindowSecs = ...;
  personal remaining Lock lock = new ReentrantLock();
  personal remaining 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();
    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 slightly than ready in line.
    return rateLimitService.purchase(key, tokensRequested);
  }
}

Trace: Even when rateLimitService.purchase all the time returned true, you possibly can find yourself in conditions the place isNotRateLimited returns false.



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