The scheduler examines all of the DAGs and stores pertinent information, like schedule intervals, statistics from each run, and task instances. Airflow uses SQLAlchemy and Object Relational Mapping (ORM) to connect to the metadata database. The state of the DAGs and their associated tasks are saved in the database to ensure the schedule remembers metadata information. It shows the status of jobs and allows the user to interact with the databases and read log files from remote file stores, like S3, Google Cloud Storage, Microsoft Azure blobs, etc. The web server is Airflow’s user interface. It periodically checks active tasks to initiate. When dependencies for a task are met, the scheduler will initiate the task. The scheduler monitors all DAGs and their associated tasks. There are four main components that make up this robust and scalable workflow scheduling platform: These can be directly installed in your Airflow environment. Note: Apache Airflow has community-maintained packages that include the core operators and hooks for services such as Google and Amazon. It’s stored within Airflow’s encrypted metadata database. No secure information is contained in hooks. They’re like building blocks for operators. With hooks, you can connect to outside databases and APIs, such as MySQL, Hive, GCS, and more. Hooks allow Airflow to interface with third-party systems. Operators that run until certain conditions are met.Operators that move data from one system to another.Operators that carry out an action or request a different system to carry out an action.These operators are used to specify actions to execute in Python, MySQL, email, or bash. There are operators for many general tasks, such as: An operator is like a template or class for executing a particular task.Īll operators originate from BaseOperator. While DAGs define the workflow, operators define the work. The DAG will show in the UI of the web server as “Example1’’ and will run once. Note: A DAG defines how to execute the tasks, but doesn’t define what particular tasks do.Ī DAG can be specified by instantiating an object of the, as shown in the below example. We could say, “execute Y only after X is executed, but Z can be executed independently at any time.” We can define additional constraints, like the number of retries to execute for a failing task and when to begin a task.
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