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@ -1,7 +1,19 @@ |
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# Bare Metal VM Containers |
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# Bare Metal vs VM vs Containers |
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|
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In this section, we will discuss **bare metal VM containers**, which are virtual machines running directly on the hardware without a hypervisor. This type of container provides better performance compared to traditional virtualization methods, as it eliminates the overhead typically associated with hypervisors. |
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Here is a quick overview of the differences between bare metal, virtual machines, and containers. |
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|
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## How bare metal VM containers work |
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## Bare Metal |
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|
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Bare metal VM containers, also known as container runtimes, are designed to run multiple isolated operating system instances directly on the host's hardware, without the need for a |
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Bare metal is a term used to describe a computer that is running directly on the hardware without any virtualization. This is the most performant way to run an application, but it is also the least flexible. You can only run one application per server, and you cannot easily move the application to another server. |
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|
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## Virtual Machines |
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|
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Virtual machines (VMs) are a way to run multiple applications on a single server. Each VM runs on top of a hypervisor, which is a piece of software that emulates the hardware of a computer. The hypervisor allows you to run multiple operating systems on a single server, and it also provides isolation between applications running on different VMs. |
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|
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## Containers |
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|
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Containers are a way to run multiple applications on a single server without the overhead of a hypervisor. Each container runs on top of a container engine, which is a piece of software that emulates the operating system of a computer. The container engine allows you to run multiple applications on a single server, and it also provides isolation between applications running on different containers. |
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|
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You can learn more from the following resources: |
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|
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- [History of Virtualization](https://courses.devopsdirective.com/docker-beginner-to-pro/lessons/01-history-and-motivation/03-history-of-virtualization) |
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@ -1,26 +1,3 @@ |
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# Introduction |
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|
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In this introductory section, we will discuss the basics of Docker, a powerful platform used by developers and system administrators to simplify the deployment and management of applications within containers. This guide aims to provide a clear understanding of Docker's key concepts, its benefits, and how it can improve your application development and deployment process. |
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|
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## What is Docker? |
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|
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Docker is an open-source platform that automates the deployment, scaling, and management of applications by isolating them into lightweight, portable containers. Containers are standalone executable units that encapsulate all necessary dependencies, libraries, and configuration files required for an application to run consistently across various environments. |
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|
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## Why Use Docker? |
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|
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- **Consistent environments:** Docker containers ensure a consistent environment for both development and production, eliminating the "works on my machine" problem. |
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- **Isolation and security:** Containers isolate applications from each other, reducing security risks and simplifying dependency management. |
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- **Portability:** Containerized applications can be effortlessly moved across environments and platforms. |
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- **Scalability:** Docker makes it easy to create and manage multiple instances of an application, simplifying scaling and load balancing. |
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- **Resource-efficient:** Containers share the host operating system's resources, making them more efficient than traditional virtual machines. |
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|
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## Key Components |
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|
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- **Docker Engine:** The core component responsible for building and running containers. |
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- **Docker Images:** Immutable snapshots of the container's file system, serving as a blueprint for creating a container. |
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- **Docker Containers:** Running instances of Docker images, which can be started, stopped, and restarted. |
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- **Dockerfile:** A text file containing instructions to build a Docker image from scratch or modify an existing one. |
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- **Docker Volumes:** A way to persist data across container restarts and share data between containers. |
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- **Docker Compose:** A tool for defining and running multi-container Docker applications using a YAML configuration file. |
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|
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Throughout this guide, we will dive deeper into these concepts and explore various use-cases of Docker, helping you become proficient in containerization and application deployment. So, let's get started! |
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@ -1,27 +1,14 @@ |
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# Docker Engine |
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|
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Docker Engine is the core component of the Docker platform which allows you to build, ship, and run applications and services in containers. It is a lightweight runtime and a powerful building tool that is designed to simplify the task of application development and deployment. |
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There is often confusion between "Docker Desktop" and "Docker Engine". Docker Engine refers specifically to a subset of the Docker Desktop components which are free and open source and can be installed only on Linux. |
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|
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### Docker Engine Components |
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Docker Engine includes: |
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|
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The Docker Engine consists of three main components: |
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- Docker Command Line Interface (CLI) |
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- Docker daemon (dockerd), exposing the Docker Application Programming Interface (API) |
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|
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- **Docker Daemon (dockerd)**: This is the main part of the Docker Engine that is responsible for running and managing containers on your host server. It listens for Docker API requests and creates, starts, stops, or removes containers. |
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Docker Engine can build container images, run containers from them, and generally do most things that Docker Desktop but is Linux only and doesn't provide all of the developer experience polish that Docker Desktop provides. |
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|
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- **Docker REST API**: Docker Engine exposes an API that allows you to interact with the Docker Daemon. This powerful interface allows you to interact with the Docker system, control container behavior, and access various Docker features using any programming language or application capable of sending HTTP requests. |
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For more information about docker engine see: |
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|
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- **Docker CLI (Command Line Interface)**: The Docker CLI is a command-line tool that provides a convenient and user-friendly way to interact with the Docker REST API. Using the Docker CLI, you can issue commands to build, start, stop, and manage containers, networks, and volumes, among other things. |
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|
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### Docker Engine Editions |
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|
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Docker Engine comes in two main editions, each catering to the specific needs of different developers and organizations: |
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|
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- **Community Edition (CE)**: This edition is designed for individual developers and small teams who want to get started with Docker in their environment. It offers the essential features for building and running containers and is available for free use. |
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|
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- **Enterprise Edition (EE)**: This edition is designed for large organizations and offers more features, including advanced security, enhanced management, and support. It is available as a subscription-based service, with various support tiers to meet the needs of different organization sizes and requirements. |
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|
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### Platforms and Architectures |
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|
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Docker Engine is available for various platforms and architectures, making it an excellent choice for cross-platform development and deployment. The most common platforms supported by Docker Engine include Linux distributions (such as Ubuntu, CentOS, and Red Hat Enterprise Linux), Windows Server, and macOS. |
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|
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By leveraging the Docker Engine, you can ensure a consistent development environment and a predictable deployment experience, regardless of the underlying infrastructure. This flexibility and portability are among the main reasons why Docker has become such a popular choice among developers and IT professionals. |
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- [Docker Engine - Docker Documentation](https://docs.docker.com/engine/) |
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@ -1,61 +1,6 @@ |
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# Installation Setup |
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|
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In this section, we'll discuss the necessary steps to setup Docker on your machine. We'll cover the installation process for various platforms including Windows, macOS, and Linux. |
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Docker provides a desktop application called **Docker Desktop** that simplifies the installation and setup process. There is also another option to install using the **Docker Engine**. |
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|
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### Windows |
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|
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If you are using Windows, Docker provides a desktop application called **Docker Desktop** that simplifies the installation and setup process. Here are the steps to install Docker Desktop on Windows: |
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|
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- Download the installer from the official [Docker Desktop website](https://www.docker.com/products/docker-desktop). |
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- Run the installer and follow the on-screen instructions. |
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- Restart your computer after the installation is complete. |
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- Launch Docker Desktop from the Start menu. |
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|
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_NOTE: Docker Desktop requires Windows 10 Pro, Enterprise or Education edition._ |
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|
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### macOS |
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|
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For macOS users, Docker also provides a desktop application called **Docker Desktop** which makes the installation and setup process hassle-free. Follow these steps to install Docker Desktop on macOS: |
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|
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- Download the installer from the official [Docker Desktop website](https://www.docker.com/products/docker-desktop). |
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- Open the downloaded `.dmg` file and follow the on-screen instructions. |
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- After successfully installing the application, launch "Docker Desktop" from the Applications folder. |
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|
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### Linux |
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|
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Linux users can install Docker using their respective package manager. Below, we'll provide installation instructions for some popular distributions. For other distributions, refer to the [official Docker documentation](https://docs.docker.com/engine/install/). |
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|
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#### Ubuntu |
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|
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Run the following commands in the terminal to install Docker on Ubuntu: |
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|
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```bash |
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sudo apt-get update |
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sudo apt-get install docker.io |
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``` |
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|
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#### Fedora |
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|
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Install Docker on Fedora using the `dnf` command: |
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|
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```bash |
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sudo dnf update |
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sudo dnf install docker |
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``` |
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|
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#### CentOS |
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|
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To install Docker on CentOS, run the following commands: |
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|
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```bash |
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sudo yum update |
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sudo yum install docker |
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``` |
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|
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### Post-Installation Steps |
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|
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After successfully installing Docker, it's essential to perform some post-installation steps to manage Docker as a non-root user and ensure that it starts on system boot. |
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|
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For Linux users, follow the [post-installation steps](https://docs.docker.com/engine/install/linux-postinstall/) provided in the official Docker documentation. |
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|
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Windows and macOS users can configure Docker Desktop settings, such as memory and CPU allocation, by right-clicking the Docker icon in the system tray and selecting "Preferences" or "Settings". |
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- [Docker Desktop website](https://www.docker.com/products/docker-desktop). |
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- [Docker Engine](https://docs.docker.com/engine/install/). |
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@ -1,62 +1,58 @@ |
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# Volume Mounts |
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|
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Volume mounts are a key feature in Docker that helps in managing and persisting data generated by and used by containers. In this section, we will discuss the concept of volume mounts and how to use them with Docker containers. |
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|
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### What are Volume Mounts |
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|
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Volume mounts are a way to map a folder or file on the host system to a folder or file inside a container. This allows the data to persist outside the container even when the container is removed. Additionally, multiple containers can share the same volume, making data sharing between containers easy. |
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|
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### Creating a Volume |
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## Creating a Volume |
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|
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To create a volume in Docker, you need to run the following command: |
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|
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``` |
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```bash |
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docker volume create my-volume |
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``` |
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|
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This command will create a volume named `my-volume`. You can inspect the details of the created volume using the command: |
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|
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``` |
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```bash |
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docker volume inspect my-volume |
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``` |
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|
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### Mounting a Volume in a Container |
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## Mounting a Volume in a Container |
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|
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To mount a volume to a container, you need to use the `-v` or `--mount` flag while running the container. Here's an example: |
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|
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Using `-v` flag: |
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|
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``` |
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```bash |
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docker run -d -v my-volume:/data your-image |
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``` |
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|
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Using `--mount` flag: |
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|
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``` |
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```bash |
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docker run -d --mount source=my-volume,destination=/data your-image |
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``` |
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|
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In both examples above, `my-volume` is the name of the volume we created earlier, and `/data` is the path inside the container where the volume will be mounted. |
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|
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### Sharing Volumes Between Containers |
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## Sharing Volumes Between Containers |
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|
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To share a volume between multiple containers, simply mount the same volume on multiple containers. Here's how to share `my-volume` between two containers running different images: |
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|
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``` |
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```bash |
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docker run -d -v my-volume:/data1 image1 |
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docker run -d -v my-volume:/data2 image2 |
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``` |
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|
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In this example, `image1` and `image2` would have access to the same data stored in `my-volume`. |
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|
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### Removing a Volume |
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## Removing a Volume |
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|
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To remove a volume, you can use the `docker volume rm` command followed by the volume name: |
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|
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``` |
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```bash |
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docker volume rm my-volume |
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``` |
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|
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**Note**: Removing a volume will delete all the data stored inside the volume. Make sure to backup the data beforehand. |
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|
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That's it! Now you have a basic understanding of volume mounts in Docker. You can use them to persist and share data between your containers efficiently and securely. |
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|
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- [Docker Volumes](https://docs.docker.com/storage/volumes/). |
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@ -1,24 +1,9 @@ |
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# Bind Mounts |
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|
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**Bind mounts** are a powerful and flexible mechanism for data persistence in Docker containers. This type of mount effectively maps a specific directory or file from the host system to a specified location within the container. By doing so, the container can read and write data on the host file system, making it possible to preserve state and transfer data between containers or even different hosts. |
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Bind mounts have limited functionality compared to volumes. When you use a bind mount, a file or directory on the host machine is mounted into a container. The file or directory is referenced by its absolute path on the host machine. By contrast, when you use a volume, a new directory is created within Docker’s storage directory on the host machine, and Docker manages that directory’s contents. |
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|
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### How to Use Bind Mounts |
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The file or directory does not need to exist on the Docker host already. It is created on demand if it does not yet exist. Bind mounts are very performant, but they rely on the host machine’s filesystem having a specific directory structure available. |
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|
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When creating a new container, bind mounts can be specified using the `-v` or `--volume` option followed by a colon-separated pair of paths. The first path is the source directory or file on the host system, and the second path is the target location within the container. For example: |
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Learn more about bind mounts here: |
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|
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``` |
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docker run -d -v /path/on/host:/path/in/container my-image |
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``` |
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|
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### Advantages of Bind Mounts |
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|
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- **Flexibility**: Bind mounts can be used to share entire directories, single files, or specific file system subtrees between the host and the container. |
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- **Performance**: Since bind mounts don't rely on network file systems, they generally provide better performance, especially for operations that involve heavy file I/O or random access. |
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- **Ease of use**: By directly mapping host paths into the container, bind mounts offer a simple and familiar way to manage data persistence and sharing between containers. |
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|
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### Disadvantages of Bind Mounts |
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|
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- **Host file system dependency**: As bind mounts directly rely on the host file system, they introduce tight coupling between the container and the host. This can create issues when attempting to move containers to different hosts or platforms, especially if files and directories have specific ownership or permissions requirements. |
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- **Security**: By exposing parts of the host file system to the container, bind mounts can introduce potential security risks. It's important to consider the permissions and visibility of the host data you expose to containers. |
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|
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While bind mounts are a useful tool for managing container data, they are not the only option available. In some cases, using Docker volumes or other tools and strategies may be a better choice for data persistence. Remember to consider the specific requirements and constraints of your application when deciding on a persistence mechanism. |
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- [Docker Bind Mounts](https://docs.docker.com/storage/bind-mounts/). |
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@ -1,91 +1,65 @@ |
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# Using Third Party Images: Databases |
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|
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Databases are an essential component of many applications and services. In this section, we'll discuss how to use third party images for databases within your Docker projects. |
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|
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### Overview |
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|
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Running your database in a Docker container can help streamline your development process and ease deployment. Docker Hub provides numerous pre-made images for popular databases such as MySQL, PostgreSQL, and MongoDB. |
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|
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### Example: Using MySQL Image |
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|
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To use a MySQL database, search for the official image on Docker Hub: |
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|
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``` |
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```bash |
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docker search mysql |
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``` |
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|
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Find the official image, and pull it: |
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|
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``` |
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```bash |
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docker pull mysql |
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``` |
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|
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Now, you can run a MySQL container. Specify the required environment variables, such as `MYSQL_ROOT_PASSWORD`, and optionally map the container's port to your host machine: |
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|
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``` |
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```bash |
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docker run --name some-mysql -e MYSQL_ROOT_PASSWORD=my-secret-pw -p 3306:3306 -d mysql |
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``` |
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|
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This command creates a new container named `some-mysql`, sets the root password to `my-secret-pw`, and maps port 3306 on the host to port 3306 on the container. |
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|
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To connect to the database from another container, use the `--link` flag: |
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|
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``` |
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docker run --name some-app --link some-mysql:mysql -d my-app |
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``` |
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|
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### Example: Using PostgreSQL Image |
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## Example: Using PostgreSQL Image |
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|
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For PostgreSQL, follow similar steps to those outlined above. First, search for the official image: |
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|
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``` |
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```bash |
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docker search postgres |
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``` |
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|
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Pull the image: |
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|
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``` |
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```bash |
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docker pull postgres |
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``` |
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|
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Run a PostgreSQL container, specifying environment variables such as `POSTGRES_PASSWORD`: |
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|
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``` |
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```bash |
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docker run --name some-postgres -e POSTGRES_PASSWORD=my-secret-pw -p 5432:5432 -d postgres |
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``` |
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|
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Link the container to another container to allow communication: |
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|
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``` |
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docker run --name some-app --link some-postgres:postgres -d my-app |
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``` |
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|
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### Example: Using MongoDB Image |
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|
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Running a MongoDB container with Docker follows a similar pattern as previous examples. Search for the official image: |
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|
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``` |
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```bash |
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docker search mongo |
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``` |
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|
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Pull the image: |
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|
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``` |
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```bash |
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docker pull mongo |
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``` |
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|
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Run a MongoDB container: |
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|
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``` |
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```bash |
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docker run --name some-mongo -p 27017:27017 -d mongo |
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``` |
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|
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Link the container to another container: |
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|
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``` |
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docker run --name some-app --link some-mongo:mongo -d my-app |
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``` |
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|
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### Conclusion |
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|
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Docker makes it easy to use third-party images for databases, streamlining your development process and ensuring a consistent environment for your applications. This guide demonstrated examples of using MySQL, PostgreSQL, and MongoDB, but many other database images are available on Docker Hub. |
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@ -1,56 +1,46 @@ |
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# Docker Images |
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|
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In this section, we'll explore the concept of Docker images and how they are useful in the Docker ecosystem. |
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|
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### What are Docker Images? |
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|
||||
Docker images are lightweight, standalone, and executable packages that include everything needed to run an application. These images contain all necessary dependencies, libraries, runtime, system tools, and code to enable the application to run consistently across different environments. |
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|
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Docker images are built and managed using Dockerfiles. A Dockerfile is a script that consists of instructions to create a Docker image, providing a step-by-step guide for setting up the application environment. |
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|
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### Key Benefits of Docker Images |
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- **Consistent**: Docker images enable applications to run with the same behavior across various platforms and environments, reducing the impact of the "it works on my machine" issue. |
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- **Version control**: You can version your Docker images, making it easier to rollback and track changes. |
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- **Reusability**: Docker images can be shared and reused for creating new containers, enhancing productivity and collaboration. |
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- **Isolation**: Each Docker image is isolated from the host system and other containers, eliminating conflicts and improving security. |
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|
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### Working with Docker Images |
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|
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Docker CLI provides several commands to manage and work with Docker images. Some essential commands include: |
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|
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- `docker images`: List all available images on your local system. |
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- `docker image ls`: List all available images on your local system. |
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- `docker build`: Build an image from a Dockerfile. |
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- `docker rmi`: Remove one or more images. |
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- `docker image rm`: Remove one or more images. |
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- `docker pull`: Pull an image from a registry (e.g., Docker Hub) to your local system. |
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- `docker push`: Push an image to a repository. |
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|
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For example, to pull the official Ubuntu image from Docker Hub, you can run the following command: |
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|
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``` |
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```bash |
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docker pull ubuntu:latest |
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``` |
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|
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After pulling the image, you can create and run a container using that image with the `docker run` command: |
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|
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``` |
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```bash |
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docker run -it ubuntu:latest /bin/bash |
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``` |
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|
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This command creates a new container and starts an interactive session inside the container using the `/bin/bash` shell. |
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|
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### Sharing Images |
||||
## Sharing Images |
||||
|
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Docker images can be shared and distributed using container registries, such as Docker Hub, Google Container Registry, or Amazon Elastic Container Registry (ECR). Once your images are pushed to a registry, others can easily access and utilize them. |
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|
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To share your image, you first need to tag it with a proper naming format: |
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|
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``` |
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```bash |
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docker tag <image-id> <username>/<repository>:<tag> |
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``` |
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|
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Then, you can push the tagged image to a registry using: |
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|
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``` |
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```bash |
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docker push <username>/<repository>:<tag> |
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``` |
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|
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|
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@ -1,37 +1,7 @@ |
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# Hot Reloading in Docker |
||||
|
||||
Hot reloading is a powerful feature that can significantly improve the developer experience. It allows the application to automatically reload or refresh upon changes in its source code without the developer having to manually restart the application or the development server. This not only streamlines the development process but also saves time and increases productivity. |
||||
Even though we can speed up the image building with layer caching enable, we don't want to have to rebuild our container image with every code change. Instead, we want the state of our application in the container to reflect changes immediately. We can achieve this through a combination of bind mounts and hot reloading utilities! |
||||
|
||||
In the context of Docker, hot reloading can be achieved using volumes, which are a way to store data and share it between containers, or between a container and the host machine. By mounting your application's source code as a volume, you can ensure that any changes made to the source code are detected by the running container and the application inside it is refreshed accordingly. |
||||
Have a look at the following resources for sample implementations: |
||||
|
||||
Here's how you can enable hot reloading in your Docker-based development environment: |
||||
|
||||
### 1. Configuring the Application |
||||
|
||||
First, make sure that your application supports live reloading. This can usually be done using built-in features or libraries, depending on the programming language and framework you are using. For example, in a React application, you can use the `react-scripts` package to enable hot reloading. Similarly, in a Node.js application, tools like `nodemon` can be used for the same purpose. |
||||
|
||||
### 2. Updating the Docker Compose File |
||||
|
||||
Next, you need to set up a Docker Compose file that defines your services and their configurations. Within this file, add the necessary settings to enable volumes for your application, so that they are shared between your host machine and the running container. Here's an example of how it could look like: |
||||
|
||||
```yaml |
||||
version: '3' |
||||
services: |
||||
app: |
||||
build: . |
||||
image: myapp |
||||
volumes: |
||||
- .:/usr/src/app |
||||
ports: |
||||
- 3000:3000 |
||||
environment: |
||||
- NODE_ENV=development |
||||
``` |
||||
|
||||
In this example, the current directory (`.`, where the source code is located) is being mapped to the `/usr/src/app` directory inside the Docker container. This ensures that any changes made to the source code on the host machine will be detected by the container and trigger a reload or refresh of the application. |
||||
|
||||
### 3. Running the Application with Hot Reloading |
||||
|
||||
With the application and Docker Compose file configured, you can now start your services with `docker-compose up`. This will launch the containers and automatically enable hot reloading. Now, whenever you make changes to your application's source code, the running container will detect those changes and refresh the app as necessary. |
||||
|
||||
By leveraging hot reloading in your Docker-based development environment, you can create a seamless and efficient workflow, allowing you to focus on writing code and delivering results faster. |
||||
- [Hot Reloading - Docker](https://courses.devopsdirective.com/docker-beginner-to-pro/lessons/11-development-workflow/01-hot-reloading) |
||||
@ -1,68 +1,5 @@ |
||||
# Debuggers in Docker |
||||
|
||||
Debuggers are essential tools that allow developers to track down issues and identify the root cause of problems within their applications. In the context of Docker, using debuggers can be a bit more challenging due to the isolated container environment. However, with proper configuration and setup, debuggers can be used effectively and efficiently in Docker projects. |
||||
In order to make developing with containers competitive with developing locally, we need the ability to run and attach to debuggers inside the container. |
||||
|
||||
This guide will cover the essentials of using debuggers for Docker-based applications, explaining how you can configure and utilize them for an improved developer experience. |
||||
|
||||
## Prerequisites |
||||
|
||||
Before diving into debuggers, make sure you're familiar with the following: |
||||
|
||||
- Basic Docker concepts and how to write Dockerfiles |
||||
- Creating and managing containers |
||||
- Docker Compose (optional, but helpful for multi-container setup) |
||||
|
||||
## Configuring your Debugger |
||||
|
||||
In order to use a debugger with your Docker-based application, you'll need to do some initial setup. Here are some high-level steps for setting up debugging in your Docker projects: |
||||
|
||||
- **Choose a Debugger**: First, select a debugger appropriate for your application's programming language (e.g. gdb for C/C++, pdb for Python, or Visual Studio Debugger for .NET applications). |
||||
|
||||
- **Modify your Dockerfile**: Your Dockerfile should be updated to include the necessary debugger tools or packages. Additionally, you may need to adjust your application's build configuration to include debug symbols which will be helpful when examining your code at runtime. |
||||
|
||||
Example: |
||||
```Dockerfile |
||||
FROM python:3.8 AS debug |
||||
|
||||
RUN apt-get update && apt-get install -y gdb |
||||
|
||||
COPY requirements.txt ./ |
||||
|
||||
RUN pip install --no-cache-dir -r requirements.txt |
||||
|
||||
COPY . . |
||||
|
||||
CMD ["gdb", "-ex", "run", "-ex", "bt", "--args", "python", "app.py"] |
||||
``` |
||||
|
||||
- **Expose a Debugging Port**: Most debuggers require a dedicated port for remote connections. Update your Dockerfile and `docker-compose.yml` (if applicable) to expose this port, and forward it to your host when running your container. |
||||
|
||||
Example: |
||||
```yml |
||||
services: |
||||
your_service: |
||||
build: . |
||||
volumes: |
||||
- .:/app |
||||
ports: |
||||
- "8080:8080" |
||||
- "DebuggingPort:DebuggingPort" |
||||
environment: |
||||
# Configure Env Variables |
||||
``` |
||||
|
||||
- **Configure the Debugger**: Depending on the debugger you're using, you may need to configure it within your application's code, with a configuration file, or by setting environment variables. |
||||
|
||||
## Debugging Workflow |
||||
|
||||
Once your debugger is configured, your debugging workflow will involve the following steps: |
||||
|
||||
- Set breakpoints within your application's code, to specify the locations where you want the debugger to pause. |
||||
- Start your Docker container, ensuring that it's running in debug mode and that the debugging port is properly exposed. |
||||
- Attach the debugger to the running container using the exposed debugging port. |
||||
- Interact with your application and use your debugger to step through your code, examine variables, and debug any issues that arise. |
||||
- Once the issue is resolved, update your code accordingly and restart your Docker container to test your changes. |
||||
|
||||
## Wrap Up |
||||
|
||||
Using debuggers with Docker-based applications can greatly improve the developer experience by providing better insights into application behavior and potential issues. By configuring your debugger correctly and following the steps outlined above, you can harness the power of debugging within your Docker projects to ensure a more stable and robust application. |
||||
- [Debuggers in Docker](https://courses.devopsdirective.com/docker-beginner-to-pro/lessons/11-development-workflow/02-debug-and-test) |
||||
@ -1,52 +1,5 @@ |
||||
# Tests |
||||
|
||||
### Benefits of Running Tests in Docker |
||||
We want to run tests in an environment as similar as possible to production, so it only makes sense to do so inside of our containers! |
||||
|
||||
There are several benefits of running tests in Docker: |
||||
|
||||
- **Isolation:** Test environments can be isolated from one another, preventing conflicts or inconsistencies between test runs. |
||||
- **Consistency:** Docker containers ensure that tests are run under the same conditions every time, reducing variability in test results. |
||||
- **Reproducibility:** Tests are quickly and easily reproducible, allowing you to share test environments and results with colleagues. |
||||
- **Ease of Use:** Docker makes it easy to set up and tear down test environments, resulting in a quicker development cycle. |
||||
|
||||
### Writing Tests |
||||
|
||||
When it comes to writing tests, you typically want to use a testing framework or library that is suited for the programming language and framework of your application. Examples include Jest for JavaScript, pytest for Python, or JUnit for Java. Follow best practices for your application's language and framework when writing tests. |
||||
|
||||
### Running Tests with Docker |
||||
|
||||
To run tests within a Docker container, there are a few steps you need to follow: |
||||
|
||||
- **Create a Test Dockerfile:** Create a separate Dockerfile for running tests. This file should be based on the same image as your application's Dockerfile, and may include additional dependencies or libraries needed for testing. |
||||
|
||||
``` |
||||
# Test Dockerfile |
||||
FROM node:12 |
||||
|
||||
# Set the working directory |
||||
WORKDIR /app |
||||
|
||||
# Copy your package.json and install dependencies |
||||
COPY package.json ./ |
||||
RUN npm install |
||||
|
||||
# Copy your source code |
||||
COPY . . |
||||
|
||||
# Run tests |
||||
CMD ["npm", "test"] |
||||
``` |
||||
|
||||
- **Build the Test Image:** Build the Docker image for your tests using the test Dockerfile. |
||||
|
||||
``` |
||||
docker build -t myapp-test -f Test.Dockerfile . |
||||
``` |
||||
|
||||
- **Run the Test Container:** Run a Docker container using the test image, which will execute your tests. |
||||
|
||||
``` |
||||
docker run --name myapp-test-container myapp-test |
||||
``` |
||||
|
||||
Running tests in Docker can help you create a more consistent and reliable testing process, which ultimately leads to a smoother development experience and more stable applications. |
||||
- [Running Tests - Docker](https://courses.devopsdirective.com/docker-beginner-to-pro/lessons/11-development-workflow/03-tests) |
||||
@ -1,35 +1,15 @@ |
||||
# Continuous Integration (CI) |
||||
|
||||
Continuous Integration (CI) is an essential practice in modern software development. CI automates the process of building, testing, and integrating code changes from multiple contributors. By employing CI, you can catch and fix potential issues early in the development lifecycle, improve code quality, and shorten the time it takes to deliver the final product. |
||||
Continuous integration is the idea of executing some actions (for example build, test, etc...) automatically as you push code to your version control system. |
||||
|
||||
### CI and Docker |
||||
For containers, there are a number of things we may want to do: |
||||
|
||||
Docker can significantly enhance the CI process by allowing developers to create lightweight and portable containers that can run applications and their dependencies. These containers can be easily shared, tested, and deployed without worrying about environment inconsistencies or conflicts. |
||||
- Build the container images |
||||
- Eecute tests |
||||
- Scan container images for vulnerabilities |
||||
- Tag images with useful metadata |
||||
- Push to a container registry |
||||
|
||||
### Key Benefits of CI with Docker |
||||
Learn more from the following: |
||||
|
||||
- **Consistency:** Docker helps maintain consistency across development, testing, and production environments. Docker containers can be versioned and shared among team members, reducing the risk of discrepancies or out-of-date dependencies. |
||||
|
||||
- **Isolation:** Docker containers can run multiple services or applications in isolation. This allows for better separation of concerns and the ability to test individual components without affecting the entire application stack. |
||||
|
||||
- **Reproducibility:** Creating a Docker container for your application ensures that it can be reliably reproduced and tested across different environments or platforms. |
||||
|
||||
- **Scalability:** Docker enables you to run multiple instances of your application or its components on a single host or cluster. This makes it easy to scale your CI environment to handle more complex builds or tests. |
||||
|
||||
- **Speed:** By leveraging the Docker cache, builds and tests can be run much faster as Docker reuses existing layers that haven't changed since the last build. |
||||
|
||||
### Implementing CI with Docker |
||||
|
||||
To implement continuous integration with Docker, you need to follow these basic steps: |
||||
|
||||
- **Create a Dockerfile**: Write a Dockerfile for your application, including all dependencies and configurations required to build and run the application. |
||||
|
||||
- **Build Docker Images**: Use Docker to build an image of your application from the Dockerfile. |
||||
|
||||
- **Run Tests**: Execute tests in a Docker container using the built image. This ensures that the testing environment is consistent with the production environment. |
||||
|
||||
- **Push Images**: If tests pass, push the Docker image to a container registry/repository such as Docker Hub or a private registry. |
||||
|
||||
- **Deploy**: Deploy your application to a production environment using the Docker image from the container registry. |
||||
|
||||
By incorporating Docker into your CI pipeline, you can streamline the process of building, testing, and deploying software while reducing inconsistencies and improving overall code quality. |
||||
- [Continuous Integration - Docker](https://courses.devopsdirective.com/docker-beginner-to-pro/lessons/11-development-workflow/04-continuous-integration-github-actions) |
||||
@ -1,31 +1,15 @@ |
||||
# Developer Experience |
||||
|
||||
In the context of Docker, DX revolves around simplifying the process of creating, deploying, and running applications using containers. This can be achieved by leveraging features and tools provided by Docker. |
||||
So far we have only discussed using docker for deploying applications. However, docker is also a great tool for developing applications. There are a few different recommendations that you can adopt to improve your development experience. |
||||
|
||||
This guide covers the following aspects of the Docker Developer Experience: |
||||
- Use `docker-compose` in your application for ease of development. |
||||
- Use bind mounts to mount the code from your local into the container filesystem to avoid having to rebuild the container image with every single change. |
||||
- For auto-reloading, you can use tools like [vite](https://vitejs.dev/) for client side, [nodemon](https://nodemon.io/) for nodejs or [air](https://github.com/cosmtrek/air) for golang. |
||||
- You should also provide a way to debug your applications. For example, look into [delve](https://github.com/go-delve/delve) for Go, enable debugging in node.js using --inspect flag etc. It doesn't matter what you use, but the point is that you should have a way to debug your application running inside the container. |
||||
- You should have a way to run tests inside the container. For example, you could have a separate docker-compose file for running tests. |
||||
- You should have a CI pipeline for production images. |
||||
- Ephemeral environment for each pull request |
||||
|
||||
### 1. Writing Dockerfiles |
||||
For more details and practical examples: |
||||
|
||||
A fundamental aspect of the DX in Docker is writing effective Dockerfiles. Learn best practices for creating minimal, efficient, and maintainable Dockerfiles – the foundation of your containerized environments. |
||||
|
||||
### 2. Multi-stage builds |
||||
|
||||
Optimize your build process through multi-stage builds, which help you create lean and clean images. This improves the development speed and sharing of images. |
||||
|
||||
### 3. Local development |
||||
|
||||
Explore how to efficiently set up your local development environment using Docker Compose, which allows you to define and run multi-container applications. This section covers best practices, tips, and common pitfalls to avoid. |
||||
|
||||
### 4. Debugging |
||||
|
||||
Get practical advice on how to debug issues in your Docker containers, both during development and after deployment. This includes docker-specific debugging strategies, as well as integrating with other debugging tools. |
||||
|
||||
### 5. Continuous Integration and Deployment |
||||
|
||||
Learn how to incorporate Docker into your CI/CD pipelines to automate building, testing, and deploying your applications. Discover how to use the Docker registry to store images, set up automated build triggers, and integrate with popular CI/CD tools. |
||||
|
||||
### 6. Sharing your work |
||||
|
||||
Dive into the world of Docker Hub and other container registries. Learn the advantages of sharing your images with others, both in terms of collaborating on your own projects and contributing to the broader Docker community. |
||||
|
||||
By following this guide, you'll gain a deep understanding of the Docker Developer Experience, and learn how to make the most of its features and best practices to enhance your software development process. |
||||
- [Developer Experience Wishlist - Docker](https://courses.devopsdirective.com/docker-beginner-to-pro/lessons/11-development-workflow/00-devx-wishlist#key-devx-features) |
||||
@ -1,89 +1,5 @@ |
||||
# Nomad: Deploying Containers |
||||
|
||||
[Nomad](https://www.nomadproject.io/) is a powerful and flexible tool for deploying containers. It is designed by HashiCorp, the creators of other popular DevOps tools such as Terraform and Vault. In this section, we'll cover the basics of Nomad and explore how you can use it to easily deploy and manage your containerized applications. |
||||
|
||||
### What is Nomad? |
||||
|
||||
Nomad is a cluster manager and scheduler that enables you to deploy, manage and scale your containerized applications. It automatically handles node failures, resource allocation, and container orchestration. Nomad supports running Docker containers as well as other container runtimes and non-containerized applications. |
||||
|
||||
### Key Features |
||||
|
||||
- **Flexible Deployment**: Nomad supports multiple container runtimes, including Docker, as well as non-containerized applications. |
||||
- **Highly Scalable**: Nomad is designed to scale from a single machine to thousands of nodes, promoting efficient resource utilization. |
||||
- **Resilient**: Nomad automatically handles node failures, maintaining the desired application state and count. |
||||
- **Simple to Use**: Nomad features a single binary and a single configuration file, making it easy to get started. |
||||
- **HashiCorp Ecosystem Integration**: Nomad works seamlessly with other HashiCorp tools such as Consul for service discovery and Vault for secrets management. |
||||
|
||||
### Getting Started with Nomad |
||||
|
||||
To start using Nomad, you'll need to install the Nomad binary on your system. You can download it from the [official website](https://www.nomadproject.io/downloads). Once installed, you can start using Nomad to deploy and manage your containers. |
||||
|
||||
#### Step 1: Set up a Nomad cluster |
||||
|
||||
A Nomad cluster consists of one or more client nodes and one or more server nodes. You'll need to configure and start the server(s) and client(s), specifying their roles and communication settings. |
||||
|
||||
Server configuration example: |
||||
|
||||
```hcl |
||||
data_dir = "/path/to/data-dir" |
||||
|
||||
server { |
||||
enabled = true |
||||
bootstrap_expect = 3 |
||||
} |
||||
``` |
||||
|
||||
Client configuration example: |
||||
|
||||
```hcl |
||||
data_dir = "/path/to/data-dir" |
||||
|
||||
client { |
||||
enabled = true |
||||
servers = ["server1:4647", "server2:4647", "server3:4647"] |
||||
} |
||||
``` |
||||
|
||||
#### Step 2: Define your job specification |
||||
|
||||
Jobs are the unit of work in Nomad, and they are defined using HashiCorp Configuration Language (HCL). You'll create a job specification file for your container deployment. |
||||
|
||||
Example job specification for a Docker container: |
||||
|
||||
```hcl |
||||
job "example" { |
||||
datacenters = ["dc1"] |
||||
|
||||
group "web" { |
||||
task "app" { |
||||
driver = "docker" |
||||
|
||||
config { |
||||
image = "your-docker-image" |
||||
ports = ["http"] |
||||
} |
||||
|
||||
resources { |
||||
cpu = 500 |
||||
memory = 256 |
||||
network { |
||||
mbits = 10 |
||||
port "http" {} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
``` |
||||
|
||||
#### Step 3: Deploy your job |
||||
|
||||
To deploy your job, you'll submit the job specification to Nomad using the `nomad run` command. Nomad will schedule and deploy the containers on the available nodes, handling failures and scaling as needed. |
||||
|
||||
```shell |
||||
$ nomad run example-job.hcl |
||||
``` |
||||
|
||||
### Next Steps |
||||
|
||||
We've covered the basics of Nomad and deploying containers with it. You can now experiment with more advanced features like integrating with Consul and Vault, or explore different deployment strategies like Canary or Blue/Green. To dive deeper into Nomad, check out the [official documentation](https://www.nomadproject.io/docs). |
||||
To dive deeper into Nomad, check out the [official documentation](https://www.nomadproject.io/docs). |
||||
Loading…
Reference in new issue