What is the difference between openshift and docker
Dec 29, 2021 am 09:30 AMDifference: 1. Docker as a project only focuses on runtime containers, while openshift includes both runtime containers and RESTAPI coordination web interface; 2. Docker’s default file system is AUFS and Overlay, while openShift’s The default file system is Etcd.
The operating environment of this tutorial: linux7.3 system, docker-1.13.1 version, Dell G3 computer.
What is the difference between openshift and docker
The main difference is:
Docker as a project only focuses on runtime Containers, and OpenShift (as a system) includes both runtime containers and REST APIs, orchestration and web interfaces to deploy and manage individual containers.
Comparing only runtime containers, both OpenShift and Docker use the kernel isolation feature to separate tenant processes.
For Docker primarily through LXC and OpenShift primarily through SELinux and Multi-Class Security (MCS). Both use cgroups to limit the tenant's CPU, memory and IO.
Upstream OpenShift is looking for LXC to reduce long-term work.
Docker uses AUFS for advanced disk and file-on-write copy-on-write sharing, OpenShift is neither required nor compatible with such a system.
Inside the container, OpenShift models functional units (web servers, databases) through "boxes", which are a set of shell script hooks that are called when the system is called. The API is described here. Cartridges are roughly similar to docker images.
Openshift also describes the API through which an agent (coordinator) communicates with a node (server hosting multiple tenant containers) to call endpoints in that container.
Recommended learning: "docker video tutorial"
The above is the detailed content of What is the difference between openshift and docker. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undress AI Tool
Undress images for free

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

1. The Origin of .NETCore When talking about .NETCore, we must not mention its predecessor .NET. Java was in the limelight at that time, and Microsoft also favored Java. The Java virtual machine on the Windows platform was developed by Microsoft based on JVM standards. It is said to be the best performance Java virtual machine at that time. However, Microsoft has its own little abacus, trying to bundle Java with the Windows platform and add some Windows-specific features. Sun's dissatisfaction with this led to a breakdown of the relationship between the two parties, and Microsoft then launched .NET. .NET has borrowed many features of Java since its inception and gradually surpassed Java in language features and form development. Java in version 1.6

To develop a complete Python Web application, follow these steps: 1. Choose the appropriate framework, such as Django or Flask. 2. Integrate databases and use ORMs such as SQLAlchemy. 3. Design the front-end and use Vue or React. 4. Perform the test, use pytest or unittest. 5. Deploy applications, use Docker and platforms such as Heroku or AWS. Through these steps, powerful and efficient web applications can be built.

Docker and Kubernetes are leaders in containerization and orchestration. Docker focuses on container lifecycle management and is suitable for small projects; Kubernetes is good at container orchestration and is suitable for large-scale production environments. The combination of the two can improve development and deployment efficiency.

There are three ways to view the process information inside the Docker container: 1. Use the dockertop command to list all processes in the container and display PID, user, command and other information; 2. Use dockerexec to enter the container, and then use the ps or top command to view detailed process information; 3. Use the dockerstats command to display the usage of container resources in real time, and combine dockertop to fully understand the performance of the container.

Deploying a PyTorch application on Ubuntu can be done by following the steps: 1. Install Python and pip First, make sure that Python and pip are already installed on your system. You can install them using the following command: sudoaptupdatesudoaptinstallpython3python3-pip2. Create a virtual environment (optional) To isolate your project environment, it is recommended to create a virtual environment: python3-mvenvmyenvsourcemyenv/bin/activatet

Deploying and tuning Jenkins on Debian is a process involving multiple steps, including installation, configuration, plug-in management, and performance optimization. Here is a detailed guide to help you achieve efficient Jenkins deployment. Installing Jenkins First, make sure your system has a Java environment installed. Jenkins requires a Java runtime environment (JRE) to run properly. sudoaptupdatesudoaptininstallopenjdk-11-jdk Verify that Java installation is successful: java-version Next, add J

An efficient way to batch stop a Docker container includes using basic commands and tools. 1. Use the dockerstop$(dockerps-q) command and adjust the timeout time, such as dockerstop-t30$(dockerps-q). 2. Use dockerps filtering options, such as dockerstop$(dockerps-q--filter"label=app=web"). 3. Use the DockerCompose command docker-composedown. 4. Write scripts to stop containers in order, such as stopping db, app and web containers.

There are two ways to compare the differences in different Docker image versions: 1. Use the dockerdiff command to view changes in the container file system; 2. Use the dockerhistory command to view the hierarchy difference in the image building. These methods help to understand and optimize image versioning.
