Airflow 2.0 plugins8/11/2023 ![]() ![]() Many tools like this have a rather obsolete appearance, which makes their use less comfortable. Airflow UI - what has changed?Īfter installing Airflow 2.0, you will quickly realize Airflow’s new UI looks different from the previous one - it looks better in fact. You will find that Airflow 2.0 is more user-friendly and intuitive.ĭo you want to know if changes in Airflow UI are significant? We’ve analysed Airflow 2.0’s new features, and we will be happy to tell you more about the new user interface design. A new user interface design - improving the UI and UX design of the software is a crucial task when upgrading a digital product.DAG versioning - DAG versioning will solve the problem of “no-status” tasks in the history overview.DAG serialization - DAG serialization reduces the load on the web servers and thanks to this, Airflow 2.0’s performance is better compared to the old Airflow version.Smart sensors - the smart sensors introduced in Airflow 2.0 have significantly improved the overall efficiency of the software.REST API - after years of using Airflow’s “Experimental API”, your data engineers will surely be relieved that Airflow 2.0 provides them with a full REST API.The new scheduler works faster and is highly scalable. An efficient scheduler - the scheduler is one of the most important components of Airflow, and improving its performance was crucial for those who filled out the Community Survey.You will be able to find the information you need much faster than before.Ĭheck out the most important improvements: What we can tell you straight away is that you will not be disappointed in the new Airflow 2.0. Some people would rather stick with the old version of the system if the changes to efficiency are not substantial. It is not surprising that before moving to the new version of some software, a business user like you does some research to gather information about significant changes. Find out more about Data Pipelines A new version of Airflow is available - what’s new? In this article, we’ll focus on how the user interface has been modified and how it is different from the previous Airflow UI. We have already created a list of the most important new features. Its users are certainly aware that relatively small modifications can disturb DAG processing. Read our article to see what the Airflow 2.0 UI improvements are.Īpache Airflow is the kind of software whose efficiency can be affected even by even minor changes. Moving to Apache Airflow version 2.0 is certainly worth considering. Fortunately, the changes in Airflow’s new UI are only for the better. After all, end users get used to what they see on the screen and can feel slightly (or even very) confused. I wonder if I can omit the _init_.py file inside plugins, but since everything is working I didn't try doing that.Releasing a new version of some software with a modified user interface can be a gamble. This also facilitates testing since the imports within the custom classes are relative to the sub modules within the folder plugins. It is necessary to restart the webserver and scheduler. Then in my DAG files, I can do # sample_dag.pyįrom _name import MyOperator In my custom operators which use my custom hooks, I import them like # my_operator.py In plugin_name.py, I extend the AirflowPlugin class # plugin_name.pyįrom ugins_manager import AirflowPlugin ![]() This is the final structure of my AIRFLOW_HOME folder airflow I figured this out by doing some trial and error. Do I have to modify my Airflow configurations to point to my repo every time I run unit tests on my hooks/ops? What are the best practices for testing custom plugins? I would also prefer a drag and drop option.įinally, it clearly doesn't make sense for my code repo to be the plugins folder itself, but if I separate them testing becomes inconvenient. The official documentation also shows this way of extending the AirflowPlugin class, but I'm not sure where this "interface" should reside. I've looked at solutions proposed in similar questions and they don't work either. I restart my workers, scheduler and webserver every time but that doesn't seem to be the issue. I get another import error though, this time saying that my_hook cannot be found. ![]() So I changed all the imports to read directly from ugin_type instead. I read up a little about it and apparently Airflow loads the plugins into its core module so they can be imported like # my_operator.py However, when I tried moving my entire repository into the plugins folder, I get an import error after running airflow list_dags saying that plugins cannot be found. My custom operators use my custom hooks, and all the imports are relative to the top level folder plugins. I've been following astronomer.io's examples here. My AIRFLOW_HOME is structured like airflow This is actually two questions combined into one. ![]()
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