![]() Internally, instances of tasks are instances of TaskInstance, identified by the task’s task_id plus the execution_date. Tasks, like DAGs are also identified by a textual id. A DAGRun is identified by the id of the DAG postfixed by the execution_date (not when it’s running, ie. one that is running for 06:00:00 is called a DAGRun. it will start the next hour’s jobs even if the last hour hasn’t completed, as long as dependencies permit and overlap limits permit. Airflow will interleave slow running DAG instances, ie. The DAG contains the first date when these tasks should (have been) run (called start_date), the recurrence interval if any (called schedule_interval), and whether the subsequent runs should depend on each other (called depends_on_past). This is important, because this is used to identify the DAG (and it’s hourly/daily instances) throughout Airflow changing the dag_id will break dependencies in the state! ![]() ![]() DAGs are identified by the textual dag_id given to them in the. py file and looks for instances of class DAG. Although you can tell Airflow to execute just one task, the common thing to do is to load a DAG, or all DAGs in a subdirectory. In Airflow, the unit of execution is a Task. Since everything is stored in the database, the web server component of Airflow is an independent gunicorn process which reads and writes the database. Python binary serialization.Īirflow also has a webserver which shows dashboards and lets users edit metadata like connection strings to data sources. Unlike Luigi, Airflow supports shipping the task’s code around to different nodes using pickle, ie. you can specify that a DAG should run every hour or every day, and the Airflow scheduler process will execute it. Unlike Luigi, Airflow supports the concept of calendar scheduling, ie. ![]() Like in Luigi, tasks depend on each other (and not on datasets). This concept is missing from Airflow, it never checks for the existence of targets to decide whether to run a task. it checks the existence of targets when deciding whether to run a task (if all output targets exists, there’s no need to run the task). Luigi knows that tasks operate on targets (datasets, files) and includes this abstraction eg. Similarly to Luigi, workflows are specified as a DAG of tasks in Python code. This allows for very clean separation of high-level functionality, such as persisting the state itself (done by the database itself), and scheduling, web dashboard, etc. It would be fair to call the core of Airflow “an SQLAlchemy app”. As such much of the logic is implemented as database calls. It uses SQLAlchemy for abstracting away the choice of and querying the database. ArchitectureĪirflow is designed to store and persist its state in a relational database such as Mysql or Postgresql. Check out Building the Fetchr Data Science Infra on AWS with Presto and Airflow. Also, I've been using Airflow in production at Fetchr for a while. Note: Airflow has come a long way since I wrote this. ![]() I wrote this after my Luigi review, so I make comparisons to Luigi throughout the article. It has a nice web dashboard for seeing current and past task state, querying the history and making changes to metadata such as connection strings. It supports calendar scheduling (hourly/daily jobs, also visualized on the web dashboard), so it can be used as a starting point for traditional ETL. It supports defining tasks and dependencies as Python code, executing and scheduling them, and distributing tasks across worker nodes. Airflow is a workflow scheduler written by Airbnb. ![]()
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