What is Docker? And Why is it so popular?

If you’ve been anywhere near the IT industry over the last five years, you’ve very likely heard of the container platform Docker. Docker and containers are a new way of running software that is revolutionizing software development and delivery.

What is Docker?

Docker is a new technology that allows development teams to build, manage, and secure apps anywhere.

It’s not possible to explain what Docker is without explaining what containers are, so let’s look at a quick explanation of containers and how they work.

A container is a special type of process that is isolated from other processes. Containers are assigned resources that no other process can access, and they cannot access any resources not explicitly assigned to them.

So what’s the big deal?

Processes that are not “containerized” can ask the operating system for access to any file on disk or any network socket.

Until containers became widely available, there was no reliable, guaranteed way to isolate a process to its own set of resources. A properly functioning container has absolutely no way to reach outside its resource “sandbox” to touch resources that were not explicitly assigned to it.

For example, two containers running on the same computer might as well be on two completely different computers, miles away from each other. They are entirely and effectively isolated from each other.

This isolation has several advantages:

• Two containerized processes can run side-by-side on the same computer, but they can’t interfere with each other.
• They can’t access each other’s data unless explicitly configured to do so.
• Two different applications can run containers on the same hardware with confidence that their processes and data are secure.
• Shared hardware means less hardware. Gone are the days when a company needs thousands of servers to run applications. That hardware can be shared between different business units or entirely different enterprise clients. The result is massive new economies of scale for private and public centers alike.

#docker #devops

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Docker Explained: Docker Architecture | Docker Registries

Following the second video about Docker basics, in this video, I explain Docker architecture and explain the different building blocks of the docker engine; docker client, API, Docker Daemon. I also explain what a docker registry is and I finish the video with a demo explaining and illustrating how to use Docker hub

In this video lesson you will learn:

• What is Docker Host
• What is Docker Engine
• Learn about Docker Architecture
• Learn about Docker client and Docker Daemon
• Docker Hub and Registries
• Simple demo to understand using images from registries

#docker #docker hub #docker host #docker engine #docker architecture #api

Docker Architecture Overview & Docker Components [For Beginners]

If you have recently come across the world of containers, it’s probably not a bad idea to understand the underlying elements that work together to offer containerisation benefits. But before that, there’s a question that you may ask. What problem do containers solve?

After building an application in a typical development lifecycle, the developer sends it to the tester for testing purposes. However, since the development and testing environments are different, the code fails to work.

Now, predominantly, there are two solutions to this – either you use a Virtual Machine or a containerised environment such as Docker. In the good old times, organisations used to deploy VMs for running multiple applications.

So, why did they started adopting containerisation over VMs? In this article, we will provide detailed explanations of all such questions.

#docker containers #docker engine #docker #docker architecture

Apache/Airflow and PostgreSQL with Docker and Docker Compose

Hello, in this post I will show you how to set up official Apache/Airflow with PostgreSQL and LocalExecutor using docker and docker-compose. In this post, I won’t be going through Airflow, what it is, and how it is used. Please checktheofficial documentation for more information about that.

Before setting up and running Apache Airflow, please install Docker and Docker Compose.

For those in hurry…

In this chapter, I will show you files and directories which are needed to run airflow and in the next chapter, I will go file by file, line by line explaining what is going on.

Firstly, in the root directory create three more directories: dags, logs, and scripts. Further, create following files: **.env, docker-compose.yml, entrypoint.sh **and **dummy_dag.py. **Please make sure those files and directories follow the structure below.

#project structure

root/
├── dags/
│   └── dummy_dag.py
├── scripts/
│   └── entrypoint.sh
├── logs/
├── .env
└── docker-compose.yml

Created files should contain the following:

#docker-compose.yml

version: '3.8'
services:
    postgres:
        image: postgres
        environment:
            - POSTGRES_USER=airflow
            - POSTGRES_PASSWORD=airflow
            - POSTGRES_DB=airflow
    scheduler:
        image: apache/airflow
        command: scheduler
        restart_policy:
            condition: on-failure
        depends_on:
            - postgres
        env_file:
            - .env
        volumes:
            - ./dags:/opt/airflow/dags
            - ./logs:/opt/airflow/logs
    webserver:
        image: apache/airflow
        entrypoint: ./scripts/entrypoint.sh
        restart_policy:
            condition: on-failure
        depends_on:
            - postgres
            - scheduler
        env_file:
            - .env
        volumes:
            - ./dags:/opt/airflow/dags
            - ./logs:/opt/airflow/logs
            - ./scripts:/opt/airflow/scripts
        ports:
            - "8080:8080"

#entrypoint.sh
#!/usr/bin/env bash
airflow initdb
airflow webserver

#.env
AIRFLOW__CORE__SQL_ALCHEMY_CONN=postgresql+psycopg2://airflow:airflow@postgres/airflow
AIRFLOW__CORE__EXECUTOR=LocalExecutor

#dummy_dag.py
from airflow import DAG
from airflow.operators.dummy_operator import DummyOperator
from datetime import datetime
with DAG('example_dag', start_date=datetime(2016, 1, 1)) as dag:
    op = DummyOperator(task_id='op')

Positioning in the root directory and executing “docker-compose up” in the terminal should make airflow accessible on localhost:8080. Image bellow shows the final result.

If you encounter permission errors, please run “chmod -R 777” on all subdirectories, e.g. “chmod -R 777 logs/”

For the curious ones...

In Leyman’s terms, docker is used when managing individual containers and docker-compose can be used to manage multi-container applications. It also moves many of the options you would enter on the docker run into the docker-compose.yml file for easier reuse. It works as a front end "script" on top of the same docker API used by docker. You can do everything docker-compose does with docker commands and a lot of shell scripting.

Before running our multi-container docker applications, docker-compose.yml must be configured. With that file, we define services that will be run on docker-compose up.

The first attribute of docker-compose.yml is version, which is the compose file format version. For the most recent version of file format and all configuration options click here.

Second attribute is services and all attributes one level bellow services denote containers used in our multi-container application. These are postgres, scheduler and webserver. Each container has image attribute which points to base image used for that service.

For each service, we define environment variables used inside service containers. For postgres it is defined by environment attribute, but for scheduler and webserver it is defined by .env file. Because .env is an external file we must point to it with env_file attribute.

By opening .env file we can see two variables defined. One defines executor which will be used and the other denotes connection string. Each connection string must be defined in the following manner:

dialect+driver://username:password@host:port/database

Dialect names include the identifying name of the SQLAlchemy dialect, a name such as sqlite, mysql, postgresql, oracle, or mssql. Driver is the name of the DBAPI to be used to connect to the database using all lowercase letters. In our case, connection string is defined by:

postgresql+psycopg2://airflow:airflow@postgres/airflow

Omitting port after host part denotes that we will be using default postgres port defined in its own Dockerfile.

Every service can define command which will be run inside Docker container. If one service needs to execute multiple commands it can be done by defining an optional .sh file and pointing to it with entrypoint attribute. In our case we have entrypoint.sh inside the scripts folder which once executed, runs airflow initdb and airflow webserver. Both are mandatory for airflow to run properly.

Defining depends_on attribute, we can express dependency between services. In our example, webserver starts only if both scheduler and postgres have started, also the scheduler only starts after postgres have started.

In case our container crashes, we can restart it by restart_policy. The restart_policy configures if and how to restart containers when they exit. Additional options are condition, delay, max_attempts, and window.

Once service is running, it is being served on containers defined port. To access that service we need to expose the containers port to the host's port. That is being done by ports attribute. In our case, we are exposing port 8080 of the container to TCP port 8080 on 127.0.0.1 (localhost) of the host machine. Left side of : defines host machines port and the right-hand side defines containers port.

Lastly, the volumes attribute defines shared volumes (directories) between host file system and docker container. Because airflows default working directory is /opt/airflow/ we need to point our designated volumes from the root folder to the airflow containers working directory. Such is done by the following command:

#general case for airflow
- ./<our-root-subdir>:/opt/airflow/<our-root-subdir>
#our case
- ./dags:/opt/airflow/dags
- ./logs:/opt/airflow/logs
- ./scripts:/opt/airflow/scripts
           ...

This way, when the scheduler or webserver writes logs to its logs directory we can access it from our file system within the logs directory. When we add a new dag to the dags folder it will automatically be added in the containers dag bag and so on.

Originally published by Ivan Rezic at Towardsdatascience

#docker #how-to #apache-airflow #docker-compose #postgresql

Docker manifest - A peek into image's manifest.json files

docker manifest – An experimental feature !

The image manifest provides a configuration and a set of layers for a container image.

This is an experimental feature. To enable this feature in the Docker CLI, one can edit the config.json file found in ~/.docker/config.json like :

{
        "auths": {
                "https://index.docker.io/v1/": {
                        "auth": "XXXXXXX"
                }
        },
        "HttpHeaders": {
                "User-Agent": "Docker-Client/19.03.8 (linux)"
        },
        "experimental": "enabled",
        "debug": true
}

What is ‘docker manifest’ ?

The docker manifest command does not work independently to perform any action. In order to work with the docker manifest or manifest list, we use sub-commands along with it. This manifest sub-command can enable us to interact with the image manifests. Furthermore, it also gives information about the OS and the architecture, that a particular image was built for.

A single manifest comprises of information about an image, it’s size, the layers and digest.

A manifest list is a list of image layers (manifests) that are, created by specifying one or more image names. It can then be used in the same way as an image name in docker pull and docker run commands.

Commands to get started with :

After enabling this feature, one would be able to access the following command :

These commands are easy to use. It basically avoids the need for pulling and running and then testing the images locally, from a docker registry.

Next, to inspect an image manifest, follow this syntax,

 docker manifest inspect image-name

.

#devops #docker #devops #docker #docker learning #docker-image

Docker Tutorial for Beginners 8 - Build and Run C++ Applications in a Docker Container

Docker is an open platform that allows use package, develop, run, and ship software applications in different environments using containers.
In this course We will learn How to Write Dockerfiles, Working with the Docker Toolbox, How to Work with the Docker Machine, How to Use Docker Compose to fire up multiple containers, How to Work with Docker Kinematic, Push images to Docker Hub, Pull images from a Docker Registery, Push stacks of servers to Docker Hub.
How to install Docker on Mac.

#docker tutorial #c++ #docker container #docker #docker hub #devopstools