Just as you start with a basic recipe and add your personal touch, Docker images begin with a foundation and get customized to perfection. It's like a complete cooking plan, combining files, libraries, and app settings. Imagine a Docker image as a special recipe card □ that lists every ingredient and step. Let's explore Docker's architecture using easy-to-understand analogies that make learning a piece of cake: Docker Hub acts as a centralized registry for sharing and accessing images. These images are layered, enabling faster deployments and reduced storage overhead. Docker Images serve as blueprints for containers created through Dockerfiles. Containers are lightweight, portable units encapsulating applications and their dependencies, isolated from the host system and other containers. At its core lies the Docker Engine, consisting of a Docker Daemon that manages containers and a Docker CLI for user interactions. □□□ □□□□ □□ □□□□□□□□ □□□ □□□□□□ĭockers and containers have become fundamental to any modern software development and deployment strategies.ĭocker's architecture is built around a client-server model that facilitates efficient containerization of applications. A common way of specifying a relation between tasks is using the > operator which works for tasks and collection of tasks (for example list or sets). And respectively task_a is in upstream of both task_b and task_c. In our example task_b and task_c are downstream of task_a. Set of tasks that will be executed after this task. These include:Ī set of tasks that will be executed before this particular task. Each task has a set of dependencies that define its relationships to other tasks. Once we have this baseline, we can start adding tasks to our DAG:įrom _operator import DummyOperatorĮvery task in a Airflow DAG is defined by the operator (we will dive into more details soon) and has its own task_id that has to be unique within a DAG. If the date is in the future you can still trigger the dag manually. It is common to use the days_ago function to specify this value. It can be None and then the DAG will not be scheduled by Airflow but it can still be triggered manually or externally.Ī date (datetime object) from which the DAG will start running. It can be timedelta object for example timedelta(days=2) or a string cron expression * * * * *. Which defines when the DAG should be run. Additionally, to create a DAG we need to specify: A single DAG file may contain multiple DAG definitions, although it is recommended to keep one DAG per file.įirst of all, DAG is identified by unique dag_id which has to be unique in whole Airflow deployment. Workflows are defined in Airflow by DAGs (Directed Acyclic Graphs) and are nothing more than a python file.
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