Airflow django11/19/2023 ![]() airflow.cfg is the Airflow configuration file which is accessed by the Web Server, Scheduler, and Workers.The Worker(s) are separate processes which also interact with the other components of the Airflow architecture and the metadata repository.The Executor is shown separately above, since it is commonly discussed within Airflow and in the documentation, but in reality it is NOT a separate process, but run within the Scheduler.Web Server and Scheduler: The Airflow web server and Scheduler are separate processes run (in this case) on the local machine and interact with the database mentioned above.Alternate databases supported with Airflow include MySQL. In the diagram above, this is represented as Postgres which is extremely popular with Airflow. Metadata Database: Airflow uses a SQL database to store metadata about the data pipelines being run.You're the data engineer and you got to work with a database to do that, say Apache Spark for example. Note that Airflow on its own does not move data. Lets now look at the core concepts of Airflow, but the architecture first which sort of simplifies it all. Enable the example_bash_operator dag in the home pageĪirflow tasks run example_bash_operator runme_0 Īirflow dags backfill example_bash_operator \ # visit localhost:8080 in the browser and use the admin account you just# created to login. # open a new terminal or else run webserver with ``-D`` option to run it as a daemon email start the web server, default port is 8080 # but you can lay foundation somewhere else if you prefer The installation is quick and straightforward, however do the following first if you are on a Linux debian distribution sudo apt-get updateĪnd then go on to run this, (update the username and pwd as per what you like to choose) # airflow needs a home, ~/airflow is the default, ![]() ![]() Scalable: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers.Parameterizing your scripts is built into the core of Airflow using the powerful Jinja templating engine (Its exploited better than it was in Django and thats pure magnificence). Elegant: Airflow pipelines are lean and explicit.Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.This allows for writing code that instantiates pipelines dynamically. Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation.Airflow is not in the Spark Streaming or Storm space, it is more comparable to Oozie or Azkaban.Īs per Airflow documentation Airflow is built on the following Principles: Tasks do not move data from one to the other (though tasks can exchange metadata!). Airflow is not a data streaming solution.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |