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Kubernetes vs Weave: What are the differences?
Developers describe Kubernetes as "Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops". 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. On the other hand, Weave is detailed as "Weave creates a virtual network that connects Docker containers deployed across multiple hosts". 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.
Kubernetes and Weave can be primarily classified as "Container" tools.
Some of the features offered by Kubernetes are:
- 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
On the other hand, Weave provides the following key features:
- Virtual Ethernet Switch
- Application isolation
- Security
"Leading docker container management solution" is the top reason why over 131 developers like Kubernetes, while over 2 developers mention "Easy setup" as the leading cause for choosing Weave.
Kubernetes and Weave are both open source tools. It seems that Kubernetes with 54.2K GitHub stars and 18.8K forks on GitHub has more adoption than Weave with 5.56K GitHub stars and 512 GitHub forks.
Google, DigitalOcean, and 9GAG are some of the popular companies that use Kubernetes, whereas Weave is used by Excursiopedia, Tutum, and PlanetPass. Kubernetes has a broader approval, being mentioned in 1018 company stacks & 1060 developers stacks; compared to Weave, which is listed in 11 company stacks and 4 developer stacks.
Hello, we have a bunch of local hosts (Linux and Windows) where Docker containers are running with bamboo agents on them. Currently, each container is installed as a system service. Each host is set up manually. I want to improve the system by adding some sort of orchestration software that should install, update and check for consistency in my docker containers. I don't need any clouds, all hosts are local. I'd prefer simple solutions. What orchestration system should I choose?
If you just want the basic orchestration between a set of defined hosts, go with Docker Swarm. If you want more advanced orchestration + flexibility in terms of resource management and load balancing go with Kubernetes. In both cases, you can make it even more complex while making the whole architecture more understandable and replicable by using Terraform.
We develop rapidly with docker-compose orchestrated services, however, for production - we utilise the very best ideas that Kubernetes has to offer: SCALE! We can scale when needed, setting a maximum and minimum level of nodes for each application layer - scaling only when the load balancer needs it. This allowed us to reduce our devops costs by 40% whilst also maintaining an SLA of 99.87%.
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.
Pros of Kubernetes
- Leading docker container management solution166
- Simple and powerful130
- Open source108
- Backed by google76
- The right abstractions58
- Scale services26
- Replication controller20
- Permission managment11
- Supports autoscaling9
- Cheap8
- Simple8
- Self-healing7
- Open, powerful, stable5
- Promotes modern/good infrascture practice5
- Reliable5
- No cloud platform lock-in5
- Scalable4
- Quick cloud setup4
- Cloud Agnostic3
- Custom and extensibility3
- A self healing environment with rich metadata3
- Captain of Container Ship3
- Backed by Red Hat3
- Runs on azure3
- Expandable2
- Sfg2
- Everything of CaaS2
- Gke2
- Golang2
- Easy setup2
Pros of Weave
- Easy setup3
- Seamlessly with mesos/marathon3
- Seamless integration with application layer1
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Cons of Kubernetes
- 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