You are browsing documentation for an outdated version. See the latest documentation here.

Using Redis for rate-limiting

Kong can rate-limit your traffic without any external dependency. In such a case, Kong stores the request counters in-memory and each Kong node applies the rate-limiting policy independently. There is no synchronization of information being done in this case. But if Redis is available in your cluster, Kong can take advantage of it and synchronize the rate-limit information across multiple Kong nodes and enforce a slightly different rate-limiting policy.

This guide walks through the steps of using Redis for rate-limiting in a multi-node Kong deployment.

Installation

Please follow the deployment documentation to install the Kubernetes Ingress Controller on your Kubernetes cluster.

Testing Connectivity to Kong

This guide assumes that the PROXY_IP environment variable is set to contain the IP address or URL pointing to Kong. Please follow one of the deployment guides to configure this environment variable.

If everything is setup correctly, making a request to Kong should return HTTP 404 Not Found.

$ curl -i $PROXY_IP
HTTP/1.1 404 Not Found
Date: Fri, 21 Jun 2019 17:01:07 GMT
Content-Type: application/json; charset=utf-8
Connection: keep-alive
Content-Length: 48
Server: kong/1.2.1

{"message":"no Route matched with those values"}

This is expected as Kong does not yet know how to proxy the request.

Setup a Sample Service

For the purpose of this guide, we will setup an httpbin service in the cluster and proxy it.

kubectl apply -f https://bit.ly/k8s-httpbin
service/httpbin created
deployment.apps/httpbin created

Create an Ingress rule to proxy the httpbin service we just created:

$ echo '
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: demo
  annotations:
    konghq.com/strip-path: "true"
spec:
  ingressClassName: kong
  rules:
  - http:
      paths:
      - path: /foo
        pathType: ImplementationSpecific
        backend:
          service:
            name: httpbin
            port:
              number: 80
' | kubectl apply -f -
ingress.extensions/demo created

Test the Ingress rule:

$ curl -i $PROXY_IP/foo/status/200
HTTP/1.1 200 OK
Content-Type: text/html; charset=utf-8
Content-Length: 0
Connection: keep-alive
Server: gunicorn/19.9.0
Date: Wed, 17 Jul 2019 19:25:32 GMT
Access-Control-Allow-Origin: *
Access-Control-Allow-Credentials: true
X-Kong-Upstream-Latency: 2
X-Kong-Proxy-Latency: 1
Via: kong/3.1.1

Set up rate-limiting

We will start by creating a global rate-limiting policy:

$ echo "
apiVersion: configuration.konghq.com/v1
kind: KongClusterPlugin
metadata:
  name: global-rate-limit
  annotations:
    kubernetes.io/ingress.class: kong
  labels:
    global: \"true\"
config:
  minute: 5
  policy: local
plugin: rate-limiting
" | kubectl apply -f -
kongclusterplugin.configuration.konghq.com/global-rate-limit created

Here we are configuring the Kubernetes Ingress Controller to rate-limit traffic from any client to 5 requests per minute, and we are applying this policy in a global sense, meaning the rate-limit will apply across all services.

You can set this up for a specific Ingress or a specific service as well, please follow using KongPlugin resource guide on steps for doing that.

Next, test the rate-limiting policy by executing the following command multiple times and observe the rate-limit headers in the response:

$ curl -I $PROXY_IP/foo/headers

As there is a single Kong instance running, Kong correctly imposes the rate-limit and you can make only 5 requests in a minute.

Scale the controller to multiple pods

Now, let’s scale up the Kubernetes Ingress Controller deployment to 3 pods, for scalability and redundancy:

$ kubectl scale --replicas 3 -n kong deployment ingress-kong
deployment.extensions/ingress-kong scaled

It will take a couple minutes for the new pods to start up. Once the new pods are up and running, test the rate-limiting policy by executing the following command and observing the rate-limit headers:

$ curl -I $PROXY_IP/foo/headers

You will observe that the rate-limit is not consistent anymore and you can make more than 5 requests in a minute.

To understand this behavior, we need to understand how we have configured Kong. In the current policy, each Kong node is tracking a rate-limit in-memory and it will allow 5 requests to go through for a client. There is no synchronization of the rate-limit information across Kong nodes. In use-cases where rate-limiting is used as a protection mechanism and to avoid over-loading your services, each Kong node tracking its own counter for requests is good enough as a malicious user will hit rate-limits on all nodes eventually. Or if the load-balancer in-front of Kong is performing some sort of deterministic hashing of requests such that the same Kong node always receives the requests from a client, then we won’t have this problem at all.

In some cases, a synchronization of information that each Kong node maintains in-memory is needed. For that purpose, Redis can be used. Let’s go ahead and set this up next.

Deploy Redis to your Kubernetes cluster

First, we will deploy redis in our Kubernetes cluster:

$ kubectl apply -n kong -f https://bit.ly/k8s-redis
deployment.apps/redis created
service/redis created

Once this is deployed, let’s update our KongClusterPlugin configuration to use Redis as a data store rather than each Kong node storing the counter information in-memory:

$ echo "
apiVersion: configuration.konghq.com/v1
kind: KongClusterPlugin
metadata:
  name: global-rate-limit
  annotations:
    kubernetes.io/ingress.class: kong
  labels:
    global: \"true\"
config:
  minute: 5
  policy: redis
  redis_host: redis
plugin: rate-limiting
" | kubectl apply -f -
kongclusterplugin.configuration.konghq.com/global-rate-limit configured

Notice, how the policy is now set to redis and we have configured Kong to talk to the redis server available at redis DNS name, which is the Redis node we deployed earlier.

Test it

Now, if you go ahead and execute the following commands, you should be able to make only 5 requests in a minute:

$ curl -I $PROXY_IP/foo/headers

This guide shows how to use Redis as a data-store for rate-limiting plugin, but this can be used for other plugins which support Redis as a data-store like proxy-cache.