Integration guide for AirFlow class libraries and docker containers

Integration guide for AirFlow class libraries and docker containers Foreword: Airflow is an open source data pipeline tool, which is widely used in scheduling and monitoring data processing tasks.It provides a simple, flexible and scalable platform for workflows for defining, scheduling and monitoring tasks.Docker is a popular containerized tool that provides a lightweight, transplant and scalable operating environment.This article will introduce how to integrate AirFlow and Docker containers in order to better manage and run data processing tasks. Step 1: Installation and configuration docker First, Docker needs to be installed on the local machine.You can choose the appropriate installation method according to your own operating system.After the installation is completed, the docker needs to be basic configuration, including setting mirror accelerators, configuration resource restrictions, etc. Step 2: Create a docker mirror image Next, we need to create a Docker mirror containing AirFlow.You can obtain the image of AirFlow in the Docker's official warehouse, or you can customize it based on your own needs.When customized mirrors, you need to create a dockerfile and define the installation and configuration steps of AirFlow.For example, you can use the following dockerfile to create AirFlow mirror: dockerfile FROM apache/airflow:2.0.1 RUN pip install --no-cache-dir some_custom_package COPY airflow.cfg /opt/airflow/airflow.cfg In the above DockerFile, we use the official AirFlow image as the basic image and install a custom Python package.At the same time, we also copied a pre -configured `Airflow.cfg` file to the specified position of the container to cover the default configuration. Step 3: Create a docker container When the mirror is created, we can create a Docker container based on the image to run AirFlow.When creating a container, you need to specify the name of the container, the port of the mapping, the data volume mounted, etc.For example, you can use the following commands to create a Docker container called `Airflow_Container`: shell docker run -d -p 8080:8080 -v /path/to/dags:/opt/airflow/dags -v /path/to/logs:/opt/airflow/logs --name airflow_container airflow_image In the above commands, we map the local `/path/to/dags` directory to the `/opt/AirFlow/DAGS` directory of the container, and map the local`/path/to/logs `directory to/OPT/AirFlow/LOGS` directory.In this way, we can edit and manage DAG files locally and save log files to the local. Step 4: Visit the AirFlow Web interface Once the container starts, we can access the Web interface of AirFlow through the browser.By default, the Web interface of AirFlow runs on the 8080 port of the container.We only need to enter the `http: // localhost: 8080` in the browser to open the Web interface of Airflow. Step 5: Configuration and management task In the Web interface of Airflow, we can configure and manage the workflow of tasks.We can create and edit Dag files, schedule tasks, view task status and logs in the interface. Appendix: complete docker compose configuration If you use Docker Compose to manage the Docker container, you can use the following configuration files to define the AirFlow container service: yaml version: '3' services: airflow: image: airflow_image ports: - 8080:8080 volumes: - /path/to/dags:/opt/airflow/dags - /path/to/logs:/opt/airflow/logs In the above configuration file, we define a service called `Airflow`, using the AirFlow image created earlier, and the configuration of port mapping and data rolls. in conclusion: This article introduces how to integrate AirFlow with Docker container to achieve better task management and operation.By deploying AirFlow in the Docker container, we can easily build a scheduling platform for data processing tasks, and at the same time, we can also enjoy the advantages of docker lightweight and portability.I hope this article can help readers better use Airflow and Docker to manage and run data processing tasks.