Gubernator is a distributed, high performance, cloud native and stateless rate-limiting service.

• Gubernator evenly distributes rate limit requests across the entire cluster, which means you can scale the system by simply adding more nodes.
• Gubernator doesn’t rely on external caches like memcached or redis, as such there is no deployment synchronization with a dependant service. This makes dynamically growing or shrinking the cluster in an orchestration system like kubernetes or nomad trivial.
• Gubernator holds no state on disk, It’s configuration is passed to it by the client on a per-request basis.
• Gubernator provides both GRPC and HTTP access to the API.
• It Can be run as a sidecar to services that need rate limiting or as a separate service.
• It Can be used as a library to implement a domain-specific rate limiting service.
• Supports optional eventually consistent rate limit distribution for extremely high throughput environments. (See GLOBAL behavior architecture.md)
• Gubernator is the english pronunciation of governor in Russian, also it sounds cool.

Gubernator is stateless in that it doesn’t require disk space to operate. No configuration or cache data is ever synced to disk. This is because every request to gubernator includes the config for the rate limit. At first you might think this an unnecessary overhead to each request. However, In reality a rate limit config is made up of only 4, 64bit integers.

# Download the docker-compose file
$ curl -O https://raw.githubusercontent.com/mailgun/gubernator/master/docker-compose.yaml
# Run the docker container
$ docker-compose up -d

Now you can make rate limit requests via CURL

# Hit the HTTP API at localhost:9080 (GRPC is at 9081)
$ curl https://localhost:9080/v1/HealthCheck

# Make a rate limit request
$ curl https://localhost:9080/v1/GetRateLimits \
  --header 'Content-Type: application/json' \
  --data '{
    "requests": [
        {
            "name": "requests_per_sec",
            "uniqueKey": "account:12345",
            "hits": "1",
            "limit": "10",
            "duration": "1000"
        }
    ]
}'

An example rate limit request sent via GRPC might look like the following

rate_limits:
    # Scopes the request to a specific rate limit
  - name: requests_per_sec
    # A unique_key that identifies this instance of a rate limit request
    unique_key: account_id=123|source_ip=172.0.0.1
    # The number of hits we are requesting
    hits: 1
    # The total number of requests allowed for this rate limit
    limit: 100
    # The duration of the rate limit in milliseconds
    duration: 1000
    # The algorithm used to calculate the rate limit  
    # 0 = Token Bucket
    # 1 = Leaky Bucket
    algorithm: 0
    # The behavior of the rate limit in gubernator.
    # 0 = BATCHING (Enables batching of requests to peers)
    # 1 = NO_BATCHING (Disables batching)
    # 2 = GLOBAL (Enable global caching for this rate limit)
    behavior: 0

An example response would be

rate_limits:
    # The status of the rate limit.  OK = 0, OVER_LIMIT = 1
  - status: 0,
    # The current configured limit
    limit: 10,
    # The number of requests remaining
    remaining: 7,
    # A unix timestamp in milliseconds of when the bucket will reset, or if 
    # OVER_LIMIT is set it is the time at which the rate limit will no 
    # longer return OVER_LIMIT.
    reset_time: 1551309219226,
    # Additional metadata about the request the client might find useful
    metadata:
      # This is the name of the coordinator that rate limited this request
      "owner": "api-n03.staging.us-east-1.mailgun.org:9041"

Gubernator currently supports 2 rate limit algorithms.

1. Token Bucket implementation starts with an empty bucket, then each Hit adds a token to the bucket until the bucket is full. Once the bucket is full, requests will return OVER_LIMIT until the reset_time is reached at which point the bucket is emptied and requests will return UNDER_LIMIT. This algorithm is useful for enforcing very bursty limits. (IE: Applications where a single request can add more than 1 hit to the bucket; or non network based queuing systems.) The downside to this implementation is that once you have hit the limit no more requests are allowed until the configured rate limit duration resets the bucket to zero.

2. Leaky Bucket is implemented similarly to Token Bucket where OVER_LIMIT is returned when the bucket is full. However tokens leak from the bucket at a consistent rate which is calculated as duration / limit. This algorithm is useful for metering, as the bucket leaks allowing traffic to continue without the need to wait for the configured rate limit duration to reset the bucket to zero.

In our production environment, for every request to our API we send 2 rate limit requests to gubernator for rate limit evaluation, one to rate the HTTP request and the other is to rate the number of recipients a user can send an email too within the specific duration. Under this setup a single gubernator node fields over 2,000 requests a second with most batched responses returned in under 1 millisecond.

Peer requests forwarded to owning nodes typically respond in under 30 microseconds.

NOTE The above graphs only report the slowest request within the 1 second sample time. So you are seeing the slowest requests that gubernator fields to clients.

Gubernator allows users to choose non-batching behavior which would further reduce latency for client rate limit requests. However because of throughput requirements our production environment uses Behaviour=BATCHING with the default 500 microsecond window. In production we have observed batch sizes of 1,000 during peak API usage. Other users who don’t have the same high traffic demands could disable batching and would see lower latencies but at the cost of throughput.

Users may choose a behavior called DURATION_IS_GREGORIAN which changes the behavior of the Duration field. When Behavior is set to DURATION_IS_GREGORIAN the Duration of the rate limit is reset whenever the end of selected gregorian calendar interval is reached.

This is useful when you want to impose daily or monthly limits on a resource. Using this behavior you know when the end of the day or month is reached the limit on the resource is reset regardless of when the first rate limit request was received by Gubernator.

Given the following Duration values

• 0 = Minutes
• 1 = Hours
• 2 = Days
• 3 = Weeks
• 4 = Months
• 5 = Years

Examples when using Behavior = DURATION_IS_GREGORIAN

• If Duration = 2 (Days) then the rate limit will reset to Current = 0 at the end of the current day the rate limit was created.
• If Duration = 0 (Minutes) then the rate limit will reset to Current = 0 at the end of the minute the rate limit was created.
• If Duration = 4 (Months) then the rate limit will reset to Current = 0 at the end of the month the rate limit was created.

Users may add behavior Behavior_RESET_REMAINING to the rate check request. This will reset the rate limit as if created new on first use.

When using Reset Remaining, the Hits field should be 0.

If you are using golang, you can use Gubernator as a library. This is useful if you wish to implement a rate limit service with your own company specific model on top. We do this internally here at mailgun with a service we creatively called ratelimits which keeps track of the limits imposed on a per account basis. In this way you can utilize the power and speed of Gubernator but still layer business logic and integrate domain specific problems into your rate limiting service.

When you use the library, your service becomes a full member of the cluster participating in the same consistent hashing and caching as a stand alone Gubernator server would. All you need to do is provide the GRPC server instance and tell Gubernator where the peers in your cluster are located. The cmd/gubernator/main.go is a great example of how to use Gubernator as a library.

While the Gubernator server currently doesn't directly support disk persistence, the Gubernator library does provide interfaces through which library users can implement persistence. The Gubernator library has two interfaces available for disk persistence. Depending on the use case an implementor can implement the Loader interface and only support persistence of rate limits at startup and shutdown, or users can implement the Store interface and Gubernator will continuously call OnChange() and Get() to keep the in memory cache and persistent store up to date with the latest rate limit data. Both interfaces can be implemented simultaneously to ensure data is always saved to persistent storage.

For those who choose to implement the Store interface, it is not required to store ALL the rate limits received via OnChange(). For instance; If you wish to support rate limit durations longer than a minute, day or month, calls to OnChange() can check the duration of a rate limit and decide to only persist those rate limits that have durations over a self determined limit.

All methods are accessed via GRPC but are also exposed via HTTP using the GRPC Gateway

Health check returns unhealthy in the event a peer is reported by etcd or kubernetes as up but the server instance is unable to contact that peer via it's advertised address.

rpc HealthCheck (HealthCheckReq) returns (HealthCheckResp)

GET /v1/HealthCheck

Example response:

{
  "status": "healthy",
  "peer_count": 3
}

Rate limits can be applied or retrieved using this interface. If the client makes a request to the server with hits: 0 then current state of the rate limit is retrieved but not incremented.

rpc GetRateLimits (GetRateLimitsReq) returns (GetRateLimitsResp)

POST /v1/GetRateLimits

Example Payload

{
  "requests": [
    {
      "name": "requests_per_sec",
      "uniqueKey": "account:12345",
      "hits": "1",
      "limit": "10",
      "duration": "1000"
    }
  ]
}

Example response:

{
  "responses": [
    {
      "status": "UNDER_LIMIT",
      "limit": "10",
      "remaining": "9",
      "reset_time": "1690855128786",
      "error": "",
      "metadata": {
        "owner": "gubernator:81"
      }
    }
  ]
}

NOTE: Gubernator uses etcd, Kubernetes or round-robin DNS to discover peers and establish a cluster. If you don't have either, the docker-compose method is the simplest way to try gubernator out.

$ docker run -p 8081:81 -p 9080:80 -e GUBER_ETCD_ENDPOINTS=etcd1:2379,etcd2:2379 \
   ghcr.io/mailgun/gubernator:latest

# Hit the HTTP API at localhost:9080
$ curl https://localhost:9080/v1/HealthCheck

# Download the kubernetes deployment spec
$ curl -O https://raw.githubusercontent.com/mailgun/gubernator/master/k8s-deployment.yaml

# Edit the deployment file to change the environment config variables
$ vi k8s-deployment.yaml

# Create the deployment (includes headless service spec)
$ kubectl create -f k8s-deployment.yaml

If your DNS service supports auto-registration, for example AWS Route53 service discovery, you can use same fully-qualified domain name to both let your business logic containers or instances to find gubernator and for gubernator containers/instances to find each other.

Gubernator supports TLS for both HTTP and GRPC connections. You can see an example with self signed certs by running docker-compose-tls.yaml

# Run docker compose
$ docker-compose -f docker-compose-tls.yaml up -d

# Hit the HTTP API at localhost:9080 (GRPC is at 9081)
$ curl --cacert certs/ca.cert --cert certs/gubernator.pem --key certs/gubernator.key  https://localhost:9080/v1/HealthCheck

Gubernator is configured via environment variables with an optional --config flag which takes a file of key/values and places them into the local environment before startup.

See the example.conf for all available config options and their descriptions.

See architecture.md for a full description of the architecture and the inner workings of gubernator.

Gubernator publishes Prometheus metrics for realtime monitoring. See prometheus.md for details.

Gubernator supports OpenTelemetry. See tracing.md for details.