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  5. K9s vs kubectl flame

K9s vs kubectl flame

OverviewComparisonAlternatives

Overview

K9s
K9s
Stacks75
Followers103
Votes2
GitHub Stars31.7K
Forks2.0K
kubectl flame
kubectl flame
Stacks0
Followers7
Votes0

K9s vs kubectl flame: What are the differences?

Introduction

In this article, we will discuss the key differences between K9s and kubectl flame, which are tools used for managing and monitoring Kubernetes clusters.

  1. User Interface: K9s provides a user-friendly and intuitive terminal-based user interface (TUI) that allows users to navigate and interact with their Kubernetes clusters. On the other hand, kubectl flame is a command-line tool that visualizes CPU and memory usage within a cluster in a flame graph format.

  2. Functionality: K9s offers a wide range of features for managing Kubernetes resources, such as pods, deployments, services, and more. It allows users to view resources, edit configurations, delete resources, and even deploy new resources. In contrast, kubectl flame focuses solely on visualizing resource usage and does not provide management capabilities.

  3. Compatibility: K9s is compatible with any Kubernetes cluster as long as users have the necessary credentials and permissions. It can be used with any cloud provider or self-hosted cluster. On the other hand, kubectl flame relies on the kubectl command-line tool and is compatible with any cluster where kubectl is installed.

  4. Monitoring Capabilities: K9s allows users to monitor various aspects of their cluster, such as resource usage, logs, events, and even perform real-time monitoring. It provides a comprehensive view of the cluster's health and performance. In contrast, kubectl flame is primarily focused on monitoring CPU and memory usage, providing visual insights into resource consumption.

  5. Customization: K9s provides a high level of customization options, allowing users to configure various settings and customize the layout of the TUI according to their preferences. Users can also define custom views, filters, and shortcuts. On the other hand, kubectl flame does not offer as much customization and mainly relies on predefined visualizations.

  6. Ease of Use: K9s provides a more user-friendly and interactive experience compared to kubectl flame. The TUI interface of K9s makes it easier to navigate and manage resources, especially for users who are not familiar with command-line interfaces. On the other hand, kubectl flame requires users to have a good understanding of Kubernetes concepts and commands.

Summary

In summary, K9s is a versatile tool with a user-friendly interface that allows users to manage and monitor Kubernetes clusters, while kubectl flame is focused on visualizing CPU and memory usage in a flame graph format. K9s offers more extensive functionality and customization options compared to kubectl flame.

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Detailed Comparison

K9s
K9s
kubectl flame
kubectl flame

K9s provides a curses based terminal UI to interact with your Kubernetes clusters. The aim of this project is to make it easier to navigate, observe and manage your applications in the wild. K9s continually watches Kubernetes for changes and offers subsequent commands to interact with observed resources.

Kubectl plugin for effortless profiling on kubernetes. It allows you to profile production applications with low-overhead by generating FlameGraphs. Running it does not require any modification to existing pods.

-
Profiling Kubernetes Pod; Profiling Alpine based container; Profiling sidecar container
Statistics
GitHub Stars
31.7K
GitHub Stars
-
GitHub Forks
2.0K
GitHub Forks
-
Stacks
75
Stacks
0
Followers
103
Followers
7
Votes
2
Votes
0
Pros & Cons
Pros
  • 2
    Nice UI and fast way to manage my kubernetes clusters
No community feedback yet
Integrations
Kubernetes
Kubernetes
No integrations available

What are some alternatives to K9s, kubectl flame?

Kubernetes

Kubernetes

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.

Rancher

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

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.

Docker Swarm

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

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

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.

Codefresh

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.

CAST.AI

CAST.AI

It is an AI-driven cloud optimization platform for Kubernetes. Instantly cut your cloud bill, prevent downtime, and 10X the power of DevOps.

k3s

k3s

Certified Kubernetes distribution designed for production workloads in unattended, resource-constrained, remote locations or inside IoT appliances. Supports something as small as a Raspberry Pi or as large as an AWS a1.4xlarge 32GiB server.

Flocker

Flocker

Flocker is a data volume manager and multi-host Docker cluster management tool. With it you can control your data using the same tools you use for your stateless applications. This means that you can run your databases, queues and key-value stores in Docker and move them around as easily as the rest of your app.

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