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Fatih BALTACI
Django Rest Framework Throttling with API Key

Django Rest Framework Throttling with API Key

Introduction: 

How to use Django Rest Framework with API Key Throttling to write an Endpoint

In this article, we will write an endpoint using the Django REST framework with API Key throttling (rate limiting). We will first use Django default cache configuration Local Memory Caching for throttling and we will see that this method does not work for multiple workers on gunicorn or multiple replicas on Kubernetes. We will change the Django cache configuration to Redis to solve this problem. We will test throttling with the open-source load testing tool Anteon.

From Django Docs: Each process will have its own private cache instance, which means no cross-process caching is possible. This also means the local memory cache isn’t particularly memory-efficient, so it’s probably not a good choice for production environments. It’s nice for development.

Prerequisites

Create a Project

In this section, we will create a Django application with rate limiting based on API key header.

Create Django Environment

Let’s create an python environment env and activate it.

python3 -m venv env
source env/bin/activate

Create requirements.txt

Django==4.0.7
djangorestframework==3.13.1
gunicorn==20.1.0
redis==4.3.4

Install Django Dependencies

pip3 install -r requirements.txt

Create Django Project and Application

Let’s create an Django project throttling and application core.

django-admin startproject throttling
cd throttling
python3 manage.py startapp core

Update Django Settings

  • Add rest_framework into INSTALLED_APPS list.
  • Add REST_FRAMEWORK dictionary configuration.
INSTALLED_APPS = [
    'django.contrib.admin',
    'django.contrib.auth',
    'django.contrib.contenttypes',
    'django.contrib.sessions',
    'django.contrib.messages',
    'django.contrib.staticfiles',

    'rest_framework',
]

REST_FRAMEWORK = {
    'DEFAULT_RENDERER_CLASSES': (
        'rest_framework.renderers.JSONRenderer',
    ),
}

Create a custom throttling class

ConcurrencyThrottleApiKey class uses SimpleRateThrottle from Django REST framework. Last request information for throttling is stored in the default Django cache. For more: Django Docs.

  • Allow one request per second (1/s).
  • Use api_key from request url query parameters for the cache key. So requests with the same api_key query parameters will be rate limited.
from rest_framework.throttling import SimpleRateThrottle

class ConcurrencyThrottleApiKey(SimpleRateThrottle):
    rate = "1/s"

    def get_cache_key(self, request, view):
        return request.query_params['api_key']

You will change this cache implementation later in this article.

Create a basic endpoint

We will create a basic GenericAPIView DjangoThrottlingAPIView endpoint with GET method. This endpoint uses throttling.ConcurrencyThrottleApiKey throttling class for rate limiting.

  • If the request is valid and not throttled, the response will be HTTP 200 OK
  • If the request is valid and throttled, the response will be HTTP 429 Too Many Requests
from rest_framework import status
from rest_framework.generics import GenericAPIView
from rest_framework.response import Response

from core import throttling


class DjangoThrottlingAPIView(GenericAPIView):
    throttle_classes = [throttling.ConcurrencyThrottleApiKey]

    def get(self, request):
        return Response("ok", status=status.HTTP_200_OK)

Update Endpoint URLs

Update Django application URLs.

from django.urls import path
import core.views as core_views

urlpatterns = [
    path('', core_views.DjangoThrottlingAPIView.as_view(), name="throtling"),
]

Update Django root URLs.

from django.urls import path

urlpatterns = [
    path('', include('core.urls')),
]

Run gunicorn HTTP Server with 1 worker

gunicorn --workers 1 --bind 0.0.0.0:9018 throttling.wsgi

Test Endpoint with cURL

Let’s test the endpoint http://localhost:9018/?api_key=1234 with cURL.


We can use random api_key query parameter for the URL. We used 1234 as the api_key in this example. Throttling is determined by the api_key query parameter.
 

curl http://localhost:9018/?api_key=1234

We should see ok in the response of the request.

Load Test Endpoint with Anteon

Make a load test with Anteon by sending 100 requests for 10 seconds (10 requests per second).

anteon -t http://localhost:9018/?api_key=1234 -n 100 -d 10

 

Anteon output shows that 90 requests are throttled, and 10 requests are not throttled, which we expect.

⚙️  Initializing...
🔥 Engine fired.

...

Status Code (Message) :Count
  200 (OK)                   :10
  429 (Too Many Requests)    :90

Run gunicorn HTTP Server with 6 workers

Let’s increase the gunicorn worker size from 1 to 6 to be able to serve more users.

gunicorn --workers 6 --bind 0.0.0.0:9018 throttling.wsgi

Load Test Endpoint with Anteon

Let’s test it again with Anteon by sending 100 requests for 10 seconds (10 requests per second).

anteon -t http://localhost:9018/?api_key=1234 -n 100 -d 10

Anteon output shows that 65 requests are throttled, and 35 requests are not throttled, which we do not expect. So why is that? Maybe we can solve this problem by deploying to Kubernetes with 6 replicas and run gunicorn with 1 worker. Let’s try.

⚙️  Initializing...
🔥 Engine fired.

...

Status Code (Message) :Count
  200 (OK)                   :35
  429 (Too Many Requests)    :65

Kubernetes Django Deployment

In order to deploy the Django application to Kubernetes, we need to dockerize the application. Create a Dockerfile and entrypoint.sh on the root of the project.

Create Docker Image

#!/bin/bash
cd throttling && gunicorn --workers 1 --bind 0.0.0.0:9018 throttling.wsgi


Make sure to give executable permission to `entrypoint.sh``

chmod +x entrypoint.sh

 

Create Dockerfile with python:3.9.10-slim base.

FROM python:3.9.10-slim

COPY ./requirements.txt .
RUN pip --no-cache-dir install -r requirements.txt

WORKDIR /workspace
COPY . /workspace

CMD ["/workspace/entrypoint.sh"]

Build the Dockerfile.

docker build -t django_throttling .

Run the docker image django_throttling.

docker run -it --rm -p 9018:9018 django_throttling

Test the application with cURL.

curl http://localhost:9018/?api_key=1234

We should see ok in the response of the request. The docker application is ready. Next step is the deploy the image to Kubernetes.

Create Kubernetes Cluster


If you have already installed Kubernetes, you can skip this step.
 

We will use Docker Desktop to enable single-node Kubernetes cluster.

Docker Desktop Kubernetes

Test Kubernetes with kubectl:

kubectl get nodes

We should see the docker-desktop node with Ready status.

NAME             STATUS   ROLES           AGE   VERSION
docker-desktop   Ready    control-plane   99m   v1.24.2

Create Deployment and Service on Kubernetes

We will deploy django application into Kubernetes with 6 replicas.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: throttling-deployment
  labels:
    app: throttling
spec:
  # highlight-start
  replicas: 6
  # highlight-end
  selector:
    matchLabels:
      app: throttling
  template:
    metadata:
      labels:
        app: throttling
        appType: anteon
    spec:
      containers:
        - name: throttling
          # highlight-start
          image: django_throttling:latest
          # highlight-end
          imagePullPolicy: Never
          ports:
            - containerPort: 9018
              name: throttling-port
---
apiVersion: v1
kind: Service
metadata:
  name: throttling-service
spec:
  type: NodePort
  selector:
    app: throttling
  ports:
    - protocol: TCP
      port: 9018
      targetPort: 9018
      # highlight-start
      nodePort: 31534
      # highlight-end

Apply the yaml file.

kubectl apply -f k8s_django.yaml

Show the pods.

kubectl get pods

We should see the pods with Running status. Since we set the replicas to 6 in the yaml file, there should be 6 pods.

NAME                                     READY   STATUS    RESTARTS   AGE
throttling-deployment-6d545fdd88-2c5s2   1/1     Running   0          107s
throttling-deployment-6d545fdd88-5jb9d   1/1     Running   0          107s
throttling-deployment-6d545fdd88-8l48d   1/1     Running   0          107s
throttling-deployment-6d545fdd88-fmpst   1/1     Running   0          107s
throttling-deployment-6d545fdd88-g2d6b   1/1     Running   0          107s
throttling-deployment-6d545fdd88-pskjc   1/1     Running   0          107s

Show the services.

kubectl get svc

We should see the service throttling-service with NodePort type.

NAME                 TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
kubernetes           ClusterIP   10.96.0.1       <none>        443/TCP          115m
throttling-service   NodePort    10.109.83.155   <none>        9018:31534/TCP   89m

The service is accesible from http://localhost:31534/?api_key=1234.

Let’s test the endpoint with cURL.

curl http://localhost:31534/?api_key=1234

We should see ok in the response of the request.

Load Test Endpoint with Anteon

Let’s test the endpoint with Anteon by sending 100 requests for 10 seconds (10 requests per second).

anteon -t http://localhost:31534/?api_key=1234 -n 100 -d 10
⚙️  Initializing...
🔥 Engine fired.

...

Status Code (Message) :Count
  200 (OK)                   :35
  429 (Too Many Requests)    :65

Anteon output shows that 61 requests are throttled, and 39 requests are not throttled, which we do not expect. Since all the pods run a separate Django application, the default Django caches are not global. You need a global cache for throttling.

Change the Throttling Cache

We need a global cache for consistent throttling. Deploy a redis to Kubernetes with 1 replica.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis-deployment
  labels:
    app: redis
spec:
  replicas: 1
  selector:
    matchLabels:
      app: redis
  template:
    metadata:
      labels:
        app: redis
        appType: anteon
    spec:
      containers:
        - name: redis
          image: redis:6.2.7
          imagePullPolicy: Always
          ports:
            - containerPort: 6379
              name: redis-port
---
apiVersion: v1
kind: Service
metadata:
  name: redis-service
spec:
  type: NodePort
  selector:
    app: redis
  ports:
    - protocol: TCP
      port: 6379
      targetPort: 6379
      nodePort: 31514

Apply the yaml file.

kubectl apply -f k8s_redis.yaml

Update Django Settings

CACHES = {
    'alternate': {
        'BACKEND': 'django.core.cache.backends.redis.RedisCache',
        'LOCATION': f'redis://redis-service:6379',
    },
}

Update the custom throttling class

Change the default Django cache to Redis cache.

from rest_framework.throttling import SimpleRateThrottle
from django.core.cache import caches

class ConcurrencyThrottleApiKey(SimpleRateThrottle):
    cache = caches['alternate']
    rate = "1/s"

    def get_cache_key(self, request, view):
        return request.query_params['api_key']

Let’s deploy to Kubernetes with 6 gunicorn workers. Update workers to 6 in entrypoint.sh.

#!/bin/bash
cd throttling && gunicorn --workers 6 --bind 0.0.0.0:9018 throttling.wsgi

Build the Dockerfile.

docker build -t django_throttling .

Delete and apply the yaml file.

kubectl delete -f k8s_django.yaml && kubectl apply -f k8s_django.yaml

Load Test Endpoint with Anteon

Let’s test the endpoint with Anteon by sending 100 requests for 10 seconds (10 requests per second).

anteon -t http://localhost:31534/?api_key=1234 -n 100 -d 10
⚙️  Initializing...
🔥 Engine fired.

...

Status Code (Message) :Count
  200 (OK)                   :10
  429 (Too Many Requests)    :90

Anteon output shows that 90 requests are throttled, and 10 requests are not throttled, which we expect. The results show that the global Redis cache solved the problem.

The Final Word:

How to use Django Rest Framework with API Key Throttling to write an Endpoint

In this article, we built a simple Django application with throttling using API key. First, the default Django cache is used for throttling. If we use more than 1 worker on gunicorn or more than 1 replica on Kubernetes deployment, the throttling results were unexpected. Then a global cache with Redis is used and tested again with multiple workers on gunicorn and multiple replicas on Kubernetes deployment. The global Redis cache solved the problem. Throughout this process, we utilized Anteon to load test our endpoints and verify the effectiveness of our throttling strategy. 

Tags:
DjangodrfThrottlingRate LimitingLoad TestingAnteon

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