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  1. Stackups
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  4. Container Tools
  5. Weave vs minikube

Weave vs minikube

OverviewComparisonAlternatives

Overview

Weave
Weave
Stacks50
Followers72
Votes7
minikube
minikube
Stacks110
Followers262
Votes3
GitHub Stars31.1K
Forks5.1K

Weave vs minikube: What are the differences?

Introduction: In the world of container orchestration, both Weave and Minikube serve specific purposes in managing and deploying containerized applications. Understanding the key differences between them can help users make informed decisions based on their specific requirements.

  1. Deployment Environment: Weave primarily focuses on simplifying networking and communication between containers within a cluster, whereas Minikube is designed to set up a single-node Kubernetes cluster locally for development and testing purposes.

  2. Scalability: Weave is more suitable for medium to large-scale deployments as it provides advanced networking features and management capabilities for complex clusters. On the other hand, Minikube is ideal for small-scale developments and learning Kubernetes basics on a single machine.

  3. Resource Consumption: Weave tends to require more resources and operational overhead compared to Minikube, which is lightweight and quick to set up for local development environments. Developers looking for a minimalistic tool may prefer Minikube over the more robust Weave.

  4. Networking Capabilities: Weave offers advanced networking functionalities such as service discovery, load balancing, and network segmentation, making it a preferred choice for production environments with complex networking requirements. Minikube, while functional, may lack some of the advanced networking features provided by Weave.

  5. Community Support: Minikube, being a part of the Kubernetes ecosystem, benefits from a large and active community contributing to its development, documentation, and support resources. Weave also has a supportive community but may not be as extensive or widely recognized as Minikube's community.

  6. Use Case Focus: Weave is tailored more towards DevOps teams and enterprises managing larger Kubernetes deployments, offering advanced networking solutions and operational insights. Meanwhile, Minikube caters to individual developers and small organizations looking to learn and experiment with Kubernetes on a local machine before scaling up to production environments.

In Summary, understanding the key differences between Weave and Minikube helps users choose the right tool based on their deployment environment, scalability needs, resource consumption preferences, networking capabilities, community support, and primary use cases.

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Detailed Comparison

Weave
Weave
minikube
minikube

Weave can traverse firewalls and operate in partially connected networks. Traffic can be encrypted, allowing hosts to be connected across an untrusted network. With weave you can easily construct applications consisting of multiple containers, running anywhere.

It implements a local Kubernetes cluster on macOS, Linux, and Windows. Its goal is to be the tool for local Kubernetes application development and to support all Kubernetes features that fit.

Virtual Ethernet Switch;Application isolation;Security;Host network integration;Service export;Service import;Multi-cloud networking;Multi-hop routing;Dynamic topologies;Container mobility;Fault tolerance
Local Kubernetes; LoadBalancer; Multi-cluster
Statistics
GitHub Stars
-
GitHub Stars
31.1K
GitHub Forks
-
GitHub Forks
5.1K
Stacks
50
Stacks
110
Followers
72
Followers
262
Votes
7
Votes
3
Pros & Cons
Pros
  • 3
    Easy setup
  • 3
    Seamlessly with mesos/marathon
  • 1
    Seamless integration with application layer
Pros
  • 1
    Let's me test k8s config locally
  • 1
    Easy setup
  • 1
    Can use same yaml config I'll use for prod deployment
Integrations
Docker
Docker
boot2docker
boot2docker
Windows
Windows
Linux
Linux
macOS
macOS

What are some alternatives to Weave, minikube?

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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