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Programming language: Go
License: MIT License
Tags: Utilities    
Latest version: v0.0.1

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README

Distributed rate limiters for Golang

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Rate limiters for distributed applications in Golang with configurable back-ends and distributed locks.
Any types of back-ends and locks can be used that implement certain minimalistic interfaces. Most common implementations are already provided.

  • Token bucket

    • in-memory (local)
    • redis
    • etcd

    Allows requests at a certain input rate with possible bursts configured by the capacity parameter.
    The output rate equals to the input rate.
    Precise (no over or under-limiting), but requires a lock (provided).

  • Leaky bucket

    • in-memory (local)
    • redis
    • etcd

    Puts requests in a FIFO queue to be processed at a constant rate.
    There are no restrictions on the input rate except for the capacity of the queue.
    Requires a lock (provided).

  • Fixed window counter

    • in-memory (local)
    • redis

    Simple and resources efficient algorithm that does not need a lock.
    Precision may be adjusted by the size of the window.
    May be lenient when there are many requests around the boundary between 2 adjacent windows.

  • Sliding window counter

    • in-memory (local)
    • redis

    Smoothes out the bursts around the boundary between 2 adjacent windows.
    Needs as twice more memory as the Fixed Window algorithm (2 windows instead of 1 at a time).
    It will disallow all the requests in case when a client is flooding the service with requests. It's the client's responsibility to handle a disallowed request properly: wait before making a new one again.

  • Concurrent buffer

    • in-memory (local)
    • redis

    Allows concurrent requests up to the given capacity.
    Requires a lock (provided).

gRPC example

Global token bucket rate limiter for a gRPC service example:

// examples/example_grpc_simple_limiter_test.go
rate := time.Second * 3
limiter := limiters.NewTokenBucket(
    2,
    rate,
    limiters.NewLockerEtcd(etcdClient, "/ratelimiter_lock/simple/", limiters.NewStdLogger()),
    limiters.NewTokenBucketRedis(
        redisClient,
        "ratelimiter/simple",
        rate, false),
    limiters.NewSystemClock(), limiters.NewStdLogger(),
)

// Add a unary interceptor middleware to rate limit all requests.
s := grpc.NewServer(grpc.UnaryInterceptor(
    func(ctx context.Context, req interface{}, info *grpc.UnaryServerInfo, handler grpc.UnaryHandler) (resp interface{}, err error) {
        w, err := limiter.Limit(ctx)
        if err == limiters.ErrLimitExhausted {
            return nil, status.Errorf(codes.ResourceExhausted, "try again later in %s", w)
        } else if err != nil {
            // The limiter failed. This error should be logged and examined.
            log.Println(err)
            return nil, status.Error(codes.Internal, "internal error")
        }
        return handler(ctx, req)
    }))

For something close to a real world example see the IP address based gRPC global rate limiter in the [examples](examples/example_grpc_ip_limiter_test.go) directory.

Distributed locks

Some algorithms require a distributed lock to guarantee consistency during concurrent requests.
In case there is only 1 running application instance then no distributed lock is needed as all the algorithms are thread-safe (use LockNoop).

Supported backends:

Testing

Run tests locally:

docker-compose up -d  # start etcd and Redis
ETCD_ENDPOINTS="127.0.0.1:2379" REDIS_ADDR="127.0.0.1:6379" ZOOKEEPER_ENDPOINTS="127.0.0.1" CONSUL_ADDR="127.0.0.1:8500" go test -race -v 

Run Drone CI tests locally:

for p in "go1.13" "go1.12" "go1.11" "lint"; do drone exec --pipeline=${p}; done