What is K9s and what are its top alternatives?
K9s is a powerful terminal-based UI to interact with Kubernetes clusters. It provides a comprehensive view of resources, logs, and other cluster information in real-time. Key features include cluster resource management, pod navigation, log streaming, and resource editing capabilities. However, some limitations include a steep learning curve for beginners and potential performance issues with large cluster sizes.
- Octant: Octant is a web-based user interface for Kubernetes that simplifies cluster monitoring and troubleshooting. It offers a visual representation of resources and allows for easy navigation and management. Pros include an intuitive interface and support for plugins, but it may lack some advanced features compared to K9s.
- Lens: Lens is a desktop application for Kubernetes management that provides a rich set of features for cluster monitoring and resource management. It offers a multi-cluster view, real-time logs, and resource utilization metrics. Pros include a user-friendly interface and support for multiple clusters, but it may consume more system resources than K9s.
- Konstellate: Konstellate is a visual tool for creating and editing Kubernetes YAML manifests. It allows users to design their cluster architecture graphically and export the configuration as YAML files. Pros include a visual representation of resources and easy editing capabilities, but it may not offer real-time cluster monitoring like K9s.
- Portainer: Portainer is a container management platform that supports Kubernetes alongside other container runtimes. It offers a web-based interface for managing clusters, deploying applications, and monitoring resources. Pros include an easy setup process and support for multiple Kubernetes clusters, but it may lack some advanced features compared to K9s.
- KubeSphere: KubeSphere is a Kubernetes platform for managing containerized applications across hybrid cloud environments. It provides a unified interface for cluster management, application deployment, and CI/CD pipelines. Pros include a comprehensive feature set and native support for Kubernetes, but it may be more complex to set up and configure than K9s.
- Rancher: Rancher is a Kubernetes management platform that simplifies cluster deployment, monitoring, and scaling operations. It offers a centralized dashboard for managing multiple clusters and applications. Pros include a user-friendly interface and support for a wide range of Kubernetes distributions, but it may require additional resources compared to K9s.
- Supertubes: Supertubes is a Kubernetes dashboard for monitoring and managing clusters in real-time. It provides insights into resource usage, pod statuses, and alerting capabilities. Pros include a customizable dashboard and support for cluster automation, but it may lack certain advanced features found in K9s.
- Kuboard: Kuboard is a concise Kubernetes dashboard for visualizing cluster resources and managing workloads. It offers a streamlined interface for resource monitoring, pod management, and cluster health checks. Pros include a lightweight design and easy installation process, but it may not have the same level of functionality as K9s.
- Kiali: Kiali is a service mesh observability platform for monitoring the behavior of microservices in a Kubernetes environment. It provides detailed insights into service dependencies, traffic patterns, and performance metrics. Pros include comprehensive service mesh monitoring capabilities, but it may not focus on overall cluster management like K9s.
- Cockpit: Cockpit is a web-based interface for managing Linux servers, including containerized workloads running on Kubernetes. It offers system monitoring, resource utilization data, and container management features. Pros include a simple and intuitive interface, but it may require additional setup and configuration compared to K9s.
Top Alternatives to K9s
OctantA tool for developers to understand how applications run on a Kubernetes cluster. It aims to be part of the developer's toolkit for gaining insight and approaching complexity found in Kubernetes. ...
KubernetesKubernetes 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. ...
Docker ComposeWith 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. ...
RancherRancher 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 SwarmSwarm 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. ...
ArgoArgo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition). ...
PortainerIt 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. ...
Docker MachineMachine lets you create Docker hosts on your computer, on cloud providers, and inside your own data center. It creates servers, installs Docker on them, then configures the Docker client to talk to them. ...
K9s alternatives & related posts
Octant
- Web-based and on compatible with common OS1
- Open Source1
related Octant posts
Kubernetes
- Leading docker container management solution164
- Simple and powerful128
- Open source106
- Backed by google76
- The right abstractions58
- Scale services25
- Replication controller20
- Permission managment11
- Supports autoscaling9
- Cheap8
- Simple8
- Self-healing6
- No cloud platform lock-in5
- Promotes modern/good infrascture practice5
- Open, powerful, stable5
- Reliable5
- Scalable4
- Quick cloud setup4
- Cloud Agnostic3
- Captain of Container Ship3
- A self healing environment with rich metadata3
- Runs on azure3
- Backed by Red Hat3
- Custom and extensibility3
- Sfg2
- Gke2
- Everything of CaaS2
- Golang2
- Easy setup2
- Expandable2
- Steep learning curve16
- Poor workflow for development15
- Orchestrates only infrastructure8
- High resource requirements for on-prem clusters4
- Too heavy for simple systems2
- Additional vendor lock-in (Docker)1
- More moving parts to secure1
- Additional Technology Overhead1
related Kubernetes posts
How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:
Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.
Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:
https://eng.uber.com/distributed-tracing/
(GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)
Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark
Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.
Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.
After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...
- Multi-container descriptor123
- Fast development environment setup110
- Easy linking of containers79
- Simple yaml configuration68
- Easy setup60
- Yml or yaml format16
- Use Standard Docker API12
- Open source8
- Go from template to application in minutes5
- Can choose Discovery Backend5
- Scalable4
- Easy configuration4
- Kubernetes integration4
- Quick and easy3
- Tied to single machine9
- Still very volatile, changing syntax often5
related Docker Compose posts
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis 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.
Recently I have been working on an open source stack to help people consolidate their personal health data in a single database so that AI and analytics apps can be run against it to find personalized treatments. We chose to go with a #containerized approach leveraging Docker #containers with a local development environment setup with Docker Compose and nginx for container routing. For the production environment we chose to pull code from GitHub and build/push images using Jenkins and using Kubernetes to deploy to Amazon EC2.
We also implemented a dashboard app to handle user authentication/authorization, as well as a custom SSO server that runs on Heroku which allows experts to easily visit more than one instance without having to login repeatedly. The #Backend was implemented using my favorite #Stack which consists of FeathersJS on top of Node.js and ExpressJS with PostgreSQL as the main database. The #Frontend was implemented using React, Redux.js, Semantic UI React and the FeathersJS client. Though testing was light on this project, we chose to use AVA as well as ESLint to keep the codebase clean and consistent.
- Easy to use103
- Open source and totally free79
- Multi-host docker-compose support63
- Load balancing and health check included58
- Simple58
- Rolling upgrades, green/blue upgrades feature44
- Dns and service discovery out-of-the-box42
- Only requires docker37
- Multitenant and permission management34
- Easy to use and feature rich29
- Cross cloud compatible11
- Does everything needed for a docker infrastructure11
- Simple and powerful8
- Next-gen platform8
- Very Docker-friendly7
- Support Kubernetes and Swarm6
- Application catalogs with stack templates (wizards)6
- Supports Apache Mesos, Docker Swarm, and Kubernetes6
- Rolling and blue/green upgrades deployments6
- High Availability service: keeps your app up 24/76
- Easy to use service catalog5
- Very intuitive UI4
- IaaS-vendor independent, supports hybrid/multi-cloud4
- Awesome support4
- Scalable3
- Requires less infrastructure requirements2
- Hosting Rancher can be complicated10
related Rancher posts
Docker Swarm
- Docker friendly55
- Easy to setup46
- Standard Docker API40
- Easy to use38
- Native23
- Free22
- Clustering made easy13
- Simple usage12
- Integral part of docker11
- Cross Platform6
- Labels and annotations5
- Performance5
- Easy Networking3
- Shallow learning curve3
- Low adoption9
related Docker Swarm posts
Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.
Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.
After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis 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.
- Open Source3
- Autosinchronize the changes to deploy2
- Online service, no need to install anything1
related Argo posts
- Simple35
- Great UI26
- Friendly19
- Easy to setup, gives a practical interface for Docker12
- Because it just works, super simple yet powerful11
- Fully featured11
- A must for Docker DevOps9
- Free and opensource7
- API5
- It's simple, fast and the support is great5
- Template Support4
related Portainer posts
Docker Docker Compose Portainer ELK Elasticsearch Kibana Logstash nginx
I've found Portainer to be a like the 8 tooled jacknife I need for Docker and am loving it. Wasn't hard to get up and going and is well rounded enough to do everything I need. Win win.
- Easy docker hosts management12