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What do docker containers do?

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Apr 02, 2024 pm 09:57 PM

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Docker containers are used to isolate and deploy applications by creating them from images and leveraging the virtualization technology of the Linux kernel to remain independent. Benefits include portability, isolation, resource optimization, and rapid deployment. Common uses include hosting web applications, deploying databases, development and testing, and building CI/CD pipelines.

What do docker containers do?

Docker Container: Purpose and Operation Principle

Purpose

Docker containers are isolated, independent runtime environments for packaging and deploying applications. They contain all the code, runtime, libraries and system tools required to run the application.

How it works

Image: Docker containers are created based on a template called an "image". The image contains the application code and all required dependencies.
Container (Container): When you create a container from an image, Docker creates an isolated execution environment that contains the application and all its necessities.
Virtualization: Docker leverages the cgroups and namespace technology of the Linux kernel to virtualize containers. These technologies allow containers to share the kernel of the host operating system but have their own independent file systems, networks, and processes.

Benefits

Using Docker containers brings the following benefits:

Portability:Containers Can run on any machine with Docker installed, regardless of operating system.
Isolation: Containers run independently of the host system and each other, minimizing conflicts and security risks.
Resource optimization: Containers only use the resources required by the application, improving resource utilization.
Quick Deployment: Containerized applications can be deployed and scaled quickly and easily.
Scalability: Applications can be easily scaled by scaling containers horizontally.

Common uses

Docker containers are used for a wide range of purposes, including:

Hosting web applications and microservices
Running databases and data warehouses
Development and test environments
Deploying machine learning models
Building continuous integration and continuous delivery (CI/CD) pipelines

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