Overview
What is Kubernetes?
Kubernetes is an open-source container cluster manager.
MatbaRofex Argentina exchange and K8S
Kubernetes is the answer but, what was the question?
Kubernetes - Open-Source container management
Kubernetes Review
"Best orchestration tool for your applications container"
Kubernetes makes managing umpty number of applications/databases Easy
Kubernetes, a perfect home-grown container infrastructure management solution
Great way to deploy/manage your servers.
Kubernetes, a good cluster management system to place bets on
Kubernetes, managing the future
Kubernetes - the App Deployment Platform of the Future Today!
Kubernetes is "the" tool for Docker orchestration
Worth the Learning Curve
Poor man's review
- Whole organization.
- Used as a PaaS.
- Used to deploy mostly stateless and cloud-ready apps.
- Solves the problem of immutable infrastructure. …
Easiest containerized deploy, bar none!
Reviewer Pros & Cons
Pricing
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
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Alternatives Pricing
What is Vultr?
Vultr is an independent cloud computing platform on a mission to provide businesses and developers around the world with unrivaled ease of use, price-to-performance, and global reach.
Product Demos
Kubernetes Beginner Tutorial 8 | Step by Step Play with Kubernetes (K8s) Demo
Demo: Intro to Rancher container management
[ Kube 68 ] Kubernetes RBAC Demo | Creating Users and Roles
Kubernetes for the Absolute Beginners - Setup Kubernetes - kubeadm
Kubernetes Deployment Tutorial - yaml explained + Demo
Product Details
- About
- Tech Details
What is Kubernetes?
Kubernetes Technical Details
Operating Systems | Unspecified |
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Mobile Application | No |
Comparisons
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Reviews and Ratings
(166)Community Insights
- Business Problems Solved
- Pros
- Cons
- Recommendations
Telcos have found Kubernetes to be a valuable tool for deploying and managing their legacy telco applications. By converting these applications into Kubernetes objects, telcos have been able to improve uptime and scalability. The simplicity and speed of Kubernetes make it ideal for managing microservices, enabling easy deployment, service discovery, configuration management, autoscaling, and fault tolerance. This has been particularly useful for organizations like LinkedIn, which has used Kubernetes as an experimental product for building and managing Machine Learning pipelines and accessing GPU clusters. Additionally, Kubernetes is widely adopted as a PaaS solution throughout organizations, solving the problem of immutable infrastructure and providing a low learning curve for users. It offers scalability and reliability, making it suitable for managing developer and customer environments at both departmental and organizational levels. Moreover, Kubernetes excels in orchestration across diverse hardware infrastructures, including data centers and multiple cloud providers. It effectively manages containerization applications consisting of hundreds of containers deployed on physical machines, virtual machines, or cloud machines. This addresses resource allocation and scheduling challenges by creating and tearing down containers based on resource demand. Furthermore, Kubernetes serves as a powerful tool for containerizing on-premises servers for seamless deployment to the cloud. Its versatility and standard deployment through Helm have made it the preferred microservice container orchestration platform for deploying web-based applications. Overall, Kubernetes offers a wide range of use cases that enhance the deployment, management, and scalability of various applications in different environments.
Flexibility in Customization: Many reviewers have praised Kubernetes for its flexibility in choosing networking, storage, monitoring, and other solutions, allowing them to customize their workload according to their needs. This feature has been appreciated by a significant number of users.
Seamless Upgrades: Users have mentioned that Kubernetes provides the ability to upgrade applications to a new version without any downtime, making it seamless and efficient. Several reviewers have highlighted this as a valuable feature of the platform.
High Portability: The high level of portability offered by Kubernetes has been positively acknowledged by many users. They appreciate being able to move their applications to different environments easily.
Complex Application Design: Several users have found designing applications on Kubernetes to be complex and time-consuming, especially when manually writing YAML manifests and validating them for errors.
Steep Learning Curve: Many reviewers have mentioned that the learning curve for Kubernetes is slow due to a large number of objects and new concepts. They suggest adding GUI-based operations to help with tasks like finding latency points or identifying resource-consuming pods.
Difficulty in Troubleshooting and Documentation: Users have encountered challenges in understanding and troubleshooting Kubernetes, particularly for beginners. Some users have also found it difficult to find relevant information as the documentation is scattered. They suggest better documentation and versioning for easier access to relevant information.
Based on user reviews, users commonly recommend the following for Kubernetes:
Consider using Kubernetes for companies with a large microservice environment. Users believe that Kubernetes is helpful for managing complex applications and recommend it specifically for organizations with a significant number of microservices.
Acquire a basic understanding and knowledge of Kubernetes before using it. Users suggest that having some familiarity with Kubernetes before implementation is beneficial in order to fully utilize its features and capabilities.
Utilize specialized support and platforms like Rancher when deploying Kubernetes. Users recommend seeking assistance from specialized companies that provide support for Kubernetes, as well as using platforms like Rancher in conjunction with Kubernetes.
Overall, users emphasize the importance of evaluating specific requirements and capabilities before choosing Kubernetes as the container management solution, acquiring knowledge beforehand, and leveraging external support to enhance the deployment experience.
Reviews
(1-7 of 7)Kubernetes is the answer but, what was the question?
- Makes sure that the workload remains UP & running by maintaining the desired state.
- Gives a lot of flexibility in choosing the networking, storage, monitoring, etc solutions of your choice.
- The biggest advantage is to upgrade the application with a new version without any downtime.
- Portability of the code is possible up to a great extent.
- Flexibility gives birth to complexity & therefore designing an application on K8s is also complex.
- Writing Yaml manifests manually & then validating them for errors is a pain that should be worked upon with a solution that can write YAMLs & Helm charts in the background with the user designing the application on a GUI-based sketch. Just like they do in OpenStack.
- The overall approach of operations should be shifted from CLI to GUI-based for ease of use.
- Due to a lot of objects & new concepts, the learning curve is really flat i.e. slow.
- Adding GUI-based operations like finding the exact point causing latency OR showing the POD consuming the highest CPU/RAM would be of great help.
Kubernetes is a good choice - When the application needs quick scaling, is already in microservice-based architecture, has no fixed traffic pattern, most of the employees already have desired skills.
- Resource allocation and scheduling.
- Managing container instances and run-files.
- Allowing for infrastructure as code.
- Usability and user friendliness.
- There is no front end and anything attempting to provide a self-service model must be created currently.
- It uses pretty new technologies so there is a relatively steep learning curve.
Kubernetes, a good cluster management system to place bets on
- Complex cluster management can be done with simple commands with strong authentication and authorization schemes
- Exhaustive documentation and open community smoothens the learning process
- As a user a few concepts like pod, deployment and service are sufficient to go a long way
- We had several problems with its NFS, which is responsible for syncing the code across the cluster
- On several instances the pods go into UNKNOWN state in which case restarting the entire node is the only solution
- As a user of the existing setup given to me, I wasn't able to allocate only some CPU cores on a single host. It was either all or zero making cluster utilization sub-optimal
- Kubernetes is very easy to get started and to set up
- It has various deployment options, file systems and service types making it suitable for several use cases besides Machine Learning
- Extends the functionality of Docker's rich functionality making it a deadly combination
- The rough edges in file system, utilization and resource management should be fixed to be adopted as a standard in a company
- Its extremely vast Python library makes it easy to build services on top of kubernetes. However the API is quite complex and documentation is quite poor
Kubernetes is "the" tool for Docker orchestration
- Kubernetes can run anywhere, i.e in in-house datacenters as well as in Public cloud
- Very efficient management of containers and self healing.
- Out of the box Automated deployment and rollbacks. And support for many deployment strategies like blue-green, rolling update and recreate.
- Efficient secret and configuration management
- Understanding Kubernetes is little hard and has a steep learning curve.
- Kubernetes is complex, it has its own concepts called pods, services and deployments.
- Debugging and troubleshooting in Kubernetes is quite hard and requires experience.
Worth the Learning Curve
- Fault tolerance - the things it does under the hood to handle failure is near magical.
- Configuration management - the ease of managing configs and secrets in kubernetes makes it a snap for integrating services.
- Service discovery - getting services to talk to each other with automated internal DNS and service-discovery makes shipping service dependencies easy.
- Speed of error detection - many times, in attempting to fix a problem, I found that kubernetes just had a delay in handling an automated fix. By changing the system, I was playing a cat and mouse game with kubernetes' attempts to auto-fix the error.
- Sensible logging - many of the logs are difficult to decipher and too verbose to be useful.
- The learning curve is high - it took many months of working with Google, in which both I and Google Support Engineers learned a lot about how Kubernetes works. The learning curve is not for people looking for quick and easy out of the box.
Poor man's review
- Whole organization.
- Used as a PaaS.
- Used to deploy mostly stateless and cloud-ready apps.
- Solves the problem of immutable infrastructure. No need for Chef, Puppet or Ansible.
- Low learning curve for users.
- Apps start on failure, can auto scale; burst into cloud;
- Infra is cloud agnostic; works in an in-house datacenter too. Gives leverage to mangement for negotiations with cloud providers
- Container orchestration
- Application scale up and down
- Good PaaS with fluentd, service discovery, secrets etc.
- Huge community support
- Free kubeconfig video, which is awesome
- Quick releases (every quarter)
- Extensive documentation. Design discussion and decisions are all documented.
- Huge ecosystem and a lot of tools built around it. A lot of companies are behind it (Google, Microsoft, Coreos etc.). This project is not going anywhere.
- Better documentation; no document versioning. Stuff is all over. It's difficult to find the right stuff sometimes.
- Easy installation. kubeadm is partly there but not fully HA; minikube is awesome but does not work for multi-node installation; other installation such as kops, kargo are Anisible based, not fully immutable.
Easiest containerized deploy, bar none!
- Single process microservice containerization that can be scaled up and down at a moments notice.
- Rolling deployments with zero downtime.
- Artifactory/DockerHub integration for deploying from an artifact repo.
- Spring Boot integration with configmaps & secret managment.
- Ingress is HTTP only, so something that is TCP only must be in the cluster.
- Multi-process containers don't behave well.
- Sizing constraints cause slow startups for Spring Boot apps.
- Ingress' are slow to start up.