The scheduler is at the core of Airflow and manages anything and everything related to DAG runs, tasks, the task runs, parsing, and storing DAGs while also taking care of other aspects like worker pool management, and SLAs, and many more. Managing Roles on the Airflow Web Server UI We can even restrict if someone can trigger/view a DAG. It provides the ability to manage user permissions at a very granular level. One just needs to set `webserver.rbac` it to True in order to enable this interface. User Management: Airflow Web Server also comes with an option to enable Role-Based Access Controls (RBAC).Configuration Management: The Web Server UI also provides options to manage various configs like variables, and connections and view the Airflow default configuration on the UI itself.API Endpoints: Airflow Web Server also provides a set of REST APIs that can be used to perform various tasks like triggering DAGs, tasks, or getting the status of each task instance.One can also view other things like the DAG code, time taken by each task for each run, logs, and more to help debug DAG runs.Īirflow UI showing a graphical representation of a DAG Visualizing DAGs: The UI also has a section for visualizing the DAG flow, a tree view to represent all the recent runs, and the status of each task for these runs.Monitoring DAGs: The Airflow Web Server homepage provides a brief overview of the statuses of the DAGs and their recent runs.Īirflow Web Server HomePage Showing a List of DAGs and statuses of their most recent runs.The most important capabilities of a Web Server are: Apache Airflow Web Server:Īirflow’s Web Server comes with a well-equipped built-in user interface that provides control over each pipeline, including the ability to visualize various aspects of them. Let’s cover each of these components in detail. The Scheduler also updates this information in this metadata database. The Web Server shows the DAGs’ states and their runs from the database. This database stores metadata about DAGs, their runs, and other Airflow configurations like users, roles, and connections. Metadata Database: Airflow supports a variety of databases for its metadata store.We will cover the details later in this blog. People usually select the executor that suits their use case best. There are various types of executors that come with Airflow, such as SequentialExecutor, LocalExecutor, CeleryExecutor, and the KubernetesExecutor. Executor: While the Scheduler orchestrates the tasks, the executors are the components that actually execute tasks.Scheduler: This is the most important part of Airflow, which orchestrates various DAGs and their tasks, taking care of their interdependencies, limiting the number of runs of each DAG so that one DAG doesn’t overwhelm the entire system, and making it easy for users to schedule and run DAGs on Airflow.The Web Server also provides the ability to manage users, roles, and different configurations for the Airflow setup. Web Server: This is the UI of Airflow, that can be used to get an overview of the overall health of different Directed Acyclic Graphs (DAG) and also help in visualizing different components and states of each DAG.Understanding the components and modular architecture of Airflow allows you to understand how its various components interact with each other and seamlessly orchestrate data pipelines. Airflow Architecture diagram for Celery Executor-based Configurationīefore we start using Apache Airflow to build and manage pipelines, it is important to understand how Airflow works.
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