Serverless Kubernetes With Kubeless : Event-Driven Microservices

The concept is the same whether it is referred to as Serverless, Event-driven computing, or Functions As A Service (FaaS) dynamically assigns resources to run distinct functions, or microservices, that are triggered by events. Application developers can concentrate on the application rather than the underlying infrastructure and all of its maintenance aspects thanks to serverless computing platforms.

Although serverless platforms are offered by most cloud providers, you may create your own with just two materials. One is the container orchestration system Kubernetes, which has established itself as a common foundation for developing resilient, componentized systems. The second is any of several systems that Kubernetes uses to create serverless application patterns.

KEDA is a kubernetes-based event driven autoscaler. It helps in scaling the applications based on the events from various sources such as Messaging Queue, Databases etc.. It works through monitoring the event sources and adjust the number of kubernetes pods accordingly. On using the KEDA in kubernetes users can use various event sources such as Azure Queue, RabbitMQ, Prometheus metrics and many more. KEDA integrates seamlessly with kubernetes and can scale any container not just functions only.

Knative is a kubernetes based platform that comes with components such as Knative Server and Knative Eventing to facilitate in deploy, manage and scaling of serverless applications. Knative Serving helps in deploying and running the serverless workloads and Knative Eventing helps in managing the event-driven architecture. It simplifies in building, deploying and managing of serverless applications on kubernetes.

On top of it is an open-source serverless computing technology called Kubeless. Code can be deployed using Kubeless without requiring infrastructure management. Kubeless performs auto-scaling, routing, monitoring, and troubleshooting using Kubernetes resources. It is necessary to develop and implement routines that can be accessed through three distinct trigger methods.

• pub-sub triggered
• HTTP triggered
• schedule triggered

HTTP triggered, exposed with its services and scheduling function, translates to a task; Pubsub triggered is managed using Kafka cluster, an integrated part of the Kubeless installation package. At the moment, Netcore, Ruby, NodeJS, and Python are supported.

For this to be implemented you’ll need the:

• A Kubernetes cluster (kind or minikube will work in a pinch).
• Cluster admin access to your cluster (Kubeless installs CRDs and creates ClusterRoles).
• kubectl installed and configured to communicate with your cluster.

• To get started with Kubeless, you need a running Kubernetes cluster. You can use a local cluster like Minikube or a cloud-based solution such as Google Kubernetes Engine (GKE) or Amazon EKS.

Installing Kubeless

• Kubeless contains two pieces: a controller that’s running on your Kubernetes cluster, and a CLI that runs on your development machine.
• To install Kubeless on your Kubernetes cluster, you can use the following commands:

kubectl create ns kubeless
kubectl create -f https://github.com/kubeless/kubeless/releases/download/v1.0.8/kubeless-v1.0.8.yaml

• The kubeless controller manager should be created in the kubeless namespace once the yaml files are installed. Additionally, CRDs such as functions, HTTP triggers, and cronjob triggers must to be built.
• You can check the status of the deployment by running the command below:

kubectl get pod -n kubeless

The following points guides you in deploying your first kubeless function. Before diving lets understand about kubeless function and Triggers:

Kubeless function

Kubeless’s primary building block is a function. Kubeless allows functions to be created in a variety of languages, including go, python, ruby, and java. A function always receives two arguments when it is called via an HTTP call, cron trigger, etc. Situation and Background. One may think of an event as an input to the standard functions. On the other hand, context is the attribute that holds the metadata.

Triggers

Triggers are the piece of code that will automatically respond ( or invoke a function ) to events like an HTTP call, life-cycle events, or on a schedule. The triggers that are currently available in kubeless are

1. HTTP Trigger
2. CronJob Trigger
3. Kafka Trigger
4. NATS Trigger

• We’re now ready to create a function. We’ll keep things easy by writing a function that says hello and echos back the data it gets.
• Open your favorite IDE, create a file named hello.py and paste the below code:

Regardless of the language or event source, all functions in Kubeless have the same structure. Generally speaking, each function

1. It receives an object event as the initial input. All of the event source’s information is contained in this option. The content of the function request should be included in the key ‘data’ specifically.
2. It obtains a second object context containing general function information.
3. It gives back a string or object that can be utilized to reply to the caller.

Create the function with the kubeless CLI:

• The following function is used for creating a function with the kubeless CLI:

• The below function screenshot specifies regarding the deployment function.

Let’s take a closer look at the command:

1. hello: This is the name of the function we want to deploy.
2. –runtime python3.4: This is the runtime we want to use to run our function. Run kubeless ‘get-server-config’ to see all the available options.
3. –from-file hello.py: This is the file containing the function code. This can be a file or a zip file of up to 1MB of size.
4. –handler function.hello: This specifies the file and the exposed function that will be used when receiving requests.

• Yes, your first function is now deployed. You can check the functions created by using the command

kubeless function ls

• Once the function is ready, you can call it by running:

kubeless function call hello --data 'Hey'

• Now that your function has started, good to go. Next steps, what should I do? Now let’s use the HTTP Trigger to call the function.
• For your function to be accessible to the public, you would require an Ingress controller.
• Any ingress controller will work. For the sake of this essay, we’ll be using the Nginx Ingress controller.
• Now let’s use Helm to install the Ingress controller.

helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
helm repo update
helm install ingress-nginx ingress-nginx/ingress-nginx
kubectl get pods -l app.kubernetes.io/name=ingress-nginx 

• You should now have an Ingress controller running in your Kubernetes cluster.
• Let us now create an HTTP trigger using the kubeless command. If you observe my command precisely, I create an HTTP trigger event with the name hello-http-trigger and at the path env.
• This means that we will be able to invoke the function by sending an HTTP request to the endpoint http://<ingress-ip>/env.

##Create a HTTP trigger
kubeless trigger http create hello-http-trigger --function-name hello --path env 

##Get the IP of the Ingress resource
ip=$(kubectl get ing hello-http-trigger -o jsonpath='{.status.loadBalancer.ingress[0].ip}')

##Get the hostname of the Ingress resource
host=$(kubectl get ing hello-http-trigger -o jsonpath='{.spec.rules[0].host}')

##Invoke the function by triggering a HTTP request
curl --data 'HOSTNAME'   --header "Host: $host"   --header "Content-Type:application/json" $ip/env;echo

Monitoring and Logging

• Utilize Kubernetes tools and additional monitoring solutions to monitor the performance and logs of your serverless functions.

Cleanup

• You can delete the function using the command below:

kubeless function delete hello
kubeless function ls

Redesigning of autoscaling infrastructure for the event driven applications helps in focus on the integrating the event driven mechanisms. This event driven mechanism dynamically responds to the workloads changes. On utilizing the tools like KEDA facilitates with efficient scaling based on the specific event triggers. It helps in ensuring the application with scaling up or down in real time as the event loads are fluctuating. The following are the some of the key points regarding the redesign autoscaling infrastructure for the event driven applications:

• Event Source Integration: It supports in connecting various resources such as Messaging Queues, Databases etc to trigger for scaling.
• Custom Metrics: It comes up with defining custom metrics to accurately measure the workload and trigger autoscaling.
• Monitoring and Logging: It set up the efficient monitoring and logging to track the performances and scaling events.

The integration of KEDA with Knative provides the enhanced scalability for the serverless applications by providing event-driven autoscaling. This integration improves the ability of KEDA to scale the kubernetes deployments based on the external events and Knative’s serverless platform. It provides a seamless solution for efficient management of workloads. The following are the some of the key insights on integration of KEDA with Knative:

• Event-Driven Autoscaling: On using KEDA, we can setup automatic sclaing of knative services based on the events from the sources like Kafka, RabbitMQ and databases.
• Seamless Deployment: It deploys the KEDA as a part of Knative setup enhancing its autoscaling capabilities without interrupting the existing workflows.
• Operational Simplicity: This integration helps in simplifying the operations by combining the strengthsof KEDA’s event-driven model with Knative’s serverless deployment model.

In Kubernetes, custom metrics supports the users in defining and collecting specific performance data tailored to their applications’ needs. Unlike built-in metrics like CPU and memory usage, custom metrics are user-defined and can represent any aspect of application performance, such as request latency, queue length, or database connections. These metrics are typically exposed by applications through APIs or other endpoints and collected by monitoring systems like Prometheus. Kubernetes Horizontal Pod Autoscaler (HPA) can then utilize these custom metrics to dynamically adjust the number of pod replicas based on workload demands, enabling more efficient and fine-grained autoscaling. Custom metrics offer greater flexibility in scaling decisions, enabling Kubernetes to adapt more precisely to diverse application requirements and workload patterns.

The following are the best practices of Kubeless:

1. Assign Each Function a Minimal Role: Consider the idea of least privilege when granting roles and permissions to the serverless functionalities. The smallest set of permissions needed for any function to carry out its specified duties should be granted. This lessens the area that could be attacked and lessens the effect of any security flaws.
2. Keep an eye on the information flow: It is essential to track and observe the information flow within the serverless application in order to spot any unusual activity or possible security breaches. By tracking and analyzing the information flow, logging and monitoring solutions—such as third-party tools or the built-in monitoring features of Kubernetes—can be implemented to enable the proactive discovery and mitigation of security vulnerabilities.
3. Incorporate Tests for Production, CI/CD, and Service Configuration: For production settings, continuous integration and deployment (CI/CD), and service configuration, a strong testing approach is needed. Include automated tests at every development lifecycle stage to verify the security and functionality of your Kubeless functions.
4. Dependencies for Secure Applications: Make sure the dependencies your serverless functions use are current and safe. To find and fix any security vulnerabilities, update the dependencies on a regular basis and run vulnerability checks. To provide an additional degree of protection, think about utilizing technologies for scanning container images.

The following are the differences between kubernetes, keda and HPA:

The following are the differences between Kubernetes and Openshift:

In conclusion, serverless Kubernetes with Kubeless offers a powerful and flexible platform for building event-driven microservices. It simplifies the process of deploying, scaling, and managing serverless functions by leveraging the capabilities of Kubernetes.

This approach enables the creation of scalable, responsive, and efficient microservices that can seamlessly integrate with other Kubernetes services and resources. With the ability to trigger functions based on various events, such as HTTP requests, cron jobs, or custom events, Kubeless empowers developers to build applications that are highly responsive to real-time data streams, webhooks, and IoT device messages.

What is Serverless Architecture in Kubernetes?

Serverless Kubernetes is a deployment framework for container management in the cloud in which you get the benefits of serverless architecture with the fast, reliable performance of Kubernetes.

What is the use of Serverless in Microservices Architecture?

While serverless microservices carry the general advantages of serverless architecture, such as less overhead and improved cost efficiency, their primary benefit is the ease with which you can combine serverless functions and other managed services

What is the maximum memory size for Serverless?

By default, your functions have 128 MB of memory allocated. You can increase that value up to 10 GB.

What is the function of kubeless?

Kubeless allows deploying code without having to worry about infrastructure. Kubeless uses kubernetes resources for auto-scaling, routing, monitoring and troubleshooting.

What is the difference between Kubeless and Knative?

Kubeless builds an image out of code and starts it on Kubernetes. Knative does the same, but uses a more modular approach, enabling different components to plug into and adapt to different deployment scenarios. Knative and Kubeless are both categorized as serverless and task processing tools.