Azure Functions vs Kubernetes

Overview

Kubernetes

Stacks61.2K

Followers52.8K

Votes685

Azure Functions

Stacks785

Followers705

Votes62

Azure Functions vs Kubernetes: What are the differences?

Azure Functions and Kubernetes are both popular cloud computing platforms used for deploying and managing applications. Here are the key differences between the two.

Container Orchestration: Kubernetes is primarily a container orchestration platform that allows developers to manage and run containerized applications across a cluster of nodes. It provides advanced features like auto-scaling, load balancing, and deployment management. Azure Functions, on the other hand, is a serverless compute service. It allows developers to write and deploy functions without worrying about infrastructure management or scaling.
Deployment Model: In Kubernetes, applications are typically deployed as containerized workloads, where each application component runs within its own container. Azure Functions, on the other hand, deploys functions as individual units of code. Each function is executed in response to an event trigger or an HTTP request.
Scaling: Kubernetes enables horizontal scalability by allowing developers to spin up additional pods or nodes to handle increased workloads. It supports autoscaling based on resource utilization or custom metrics. Azure Functions also support autoscaling, but it scales at a function level. Each function can be independently scaled to handle varying workloads.
Serverless Computing: Azure Functions is designed around the concept of serverless computing. It abstracts away the underlying infrastructure and only charges for the actual resource consumption. Kubernetes, on the other hand, requires developers to provision and manage the infrastructure for their applications.
Development Experience: Kubernetes requires more upfront configuration and setup compared to Azure Functions. Developers have to define deployment manifests, services, and other resources to deploy applications. Azure Functions, being a serverless service, provides a simpler development experience. Developers can focus on writing the code for individual functions without worrying about the underlying infrastructure.
Managed Services: Azure Functions is a fully managed service provided by Microsoft. It handles the management of infrastructure, scaling, and high availability automatically. On the other hand, Kubernetes can be self-hosted or managed by a cloud provider. The level of management and support may vary depending on the chosen deployment model.

In summary, Azure Functions is ideal for serverless computing scenarios, while Kubernetes provides advanced capabilities for container orchestration and management.

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Advice on Kubernetes, Azure Functions

Simon

Senior Fullstack Developer at QUANTUSflow Software GmbH

Apr 27, 2020

Decidedon

GitHub

GitHub Pages

Markdown

Our whole DevOps stack consists of the following tools:

@{GitHub}|tool:27| (incl. @{GitHub Pages}|tool:683|/@{Markdown}|tool:1147| for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
Respectively @{Git}|tool:1046| as revision control system
@{SourceTree}|tool:1599| as @{Git}|tool:1046| GUI
@{Visual Studio Code}|tool:4202| as IDE
@{CircleCI}|tool:190| for continuous integration (automatize development process)
@{Prettier}|tool:7035| / @{TSLint}|tool:5561| / @{ESLint}|tool:3337| as code linter
@{SonarQube}|tool:2638| as quality gate
@{Docker}|tool:586| as container management (incl. @{Docker Compose}|tool:3136| for multi-container application management)
@{VirtualBox}|tool:774| for operating system simulation tests
@{Kubernetes}|tool:1885| as cluster management for docker containers
@{Heroku}|tool:133| for deploying in test environments
@{nginx}|tool:1052| as web server (preferably used as facade server in production environment)
@{SSLMate}|tool:2752| (using @{OpenSSL}|tool:3091|) for certificate management
@{Amazon EC2}|tool:18| (incl. @{Amazon S3}|tool:25|) for deploying in stage (production-like) and production environments
@{PostgreSQL}|tool:1028| as preferred database system
@{Redis}|tool:1031| as preferred in-memory database/store (great for caching)

The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:

Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
Scalability: All-in-one framework for distributed systems.
Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.

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Detailed Comparison

Kubernetes	Azure Functions
Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.	Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.
Lightweight, simple and accessible;Built for a multi-cloud world, public, private or hybrid;Highly modular, designed so that all of its components are easily swappable	Easily schedule event-driven tasks across services;Expose Functions as HTTP API endpoints;Scale Functions based on customer demand;Develop how you want, using a browser-based UI or existing tools;Get continuous deployment, remote debugging, and authentication out of the box
Statistics
Stacks 61.2K	Stacks 785
Followers 52.8K	Followers 705
Votes 685	Votes 62
Pros & Cons
Pros 166 Leading docker container management solution 130 Simple and powerful 108 Open source 76 Backed by google 58 The right abstractions Cons 16 Steep learning curve 15 Poor workflow for development 8 Orchestrates only infrastructure 4 High resource requirements for on-prem clusters 2 Too heavy for simple systems	Pros 14 Pay only when invoked 11 Great developer experience for C# 9 Multiple languages supported 7 Great debugging support 5 Can be used as lightweight https service Cons 1 Poor support for Linux environments 1 Sporadic server & language runtime issues 1 Not suited for long-running applications 1 No persistent (writable) file system available
Integrations
Vagrant Docker Rackspace Cloud Servers Microsoft Azure Google Compute Engine Ansible Google Kubernetes Engine	Azure DevOps Java Bitbucket Node.js Microsoft Azure GitHub Visual Studio Code JavaScript Azure Cosmos DB C#

What are some alternatives to Kubernetes, Azure Functions?

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

Docker Compose

With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.

AWS Lambda

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

Docker Swarm

Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself.

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Codefresh

Automate and parallelize testing. Codefresh allows teams to spin up on-demand compositions to run unit and integration tests as part of the continuous integration process. Jenkins integration allows more complex pipelines.

Serverless

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

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Azure Functions vs Kubernetes: What are the differences?

Azure Functions and Kubernetes are both popular cloud computing platforms used for deploying and managing applications. Here are the key differences between the two.

Container Orchestration: Kubernetes is primarily a container orchestration platform that allows developers to manage and run containerized applications across a cluster of nodes. It provides advanced features like auto-scaling, load balancing, and deployment management. Azure Functions, on the other hand, is a serverless compute service. It allows developers to write and deploy functions without worrying about infrastructure management or scaling.
Deployment Model: In Kubernetes, applications are typically deployed as containerized workloads, where each application component runs within its own container. Azure Functions, on the other hand, deploys functions as individual units of code. Each function is executed in response to an event trigger or an HTTP request.
Scaling: Kubernetes enables horizontal scalability by allowing developers to spin up additional pods or nodes to handle increased workloads. It supports autoscaling based on resource utilization or custom metrics. Azure Functions also support autoscaling, but it scales at a function level. Each function can be independently scaled to handle varying workloads.
Serverless Computing: Azure Functions is designed around the concept of serverless computing. It abstracts away the underlying infrastructure and only charges for the actual resource consumption. Kubernetes, on the other hand, requires developers to provision and manage the infrastructure for their applications.
Development Experience: Kubernetes requires more upfront configuration and setup compared to Azure Functions. Developers have to define deployment manifests, services, and other resources to deploy applications. Azure Functions, being a serverless service, provides a simpler development experience. Developers can focus on writing the code for individual functions without worrying about the underlying infrastructure.
Managed Services: Azure Functions is a fully managed service provided by Microsoft. It handles the management of infrastructure, scaling, and high availability automatically. On the other hand, Kubernetes can be self-hosted or managed by a cloud provider. The level of management and support may vary depending on the chosen deployment model.

In summary, Azure Functions is ideal for serverless computing scenarios, while Kubernetes provides advanced capabilities for container orchestration and management.