airflow dynamic dag yaml

Apache Airflow needs to know what your DAG (and so the tasks) will look like to render it. You have your template, the second step is to create the configuration files: This time the config files are in YAML and not in JSON. Easier to debug. Thats what dynamic DAGs solve. The constructor gets called whenever Airflow parses a DAG which happens frequently. As mentioned before, the frequency of update depends on the configuration of themin_file_process_interval setting of the scheduler. Not sure what you mean for 'dynamic', but when yaml file updated, if the reading file process is in dag file body, the dag will be refreshed to apply for the new args from yaml file. Last active Mar 15, 2022 UdemyYoutubeDirect, Your email address will not be published. My advise is to stick with one of the two multiple-files methods if you run Airflow in production. Learn on the go with our new app. Greenfield Dynamics was founded in 2007, just before one the worst economic times in our country's history. My favourite way (and the one I recommend) is the multiple-file method. With our example sources.yaml file, we have the following DAG: As the dynamic configuration now lives in a file that is stored on the same machine as the DAG files, we will need an external process if we want to make changes to the dynamic configuration. Typically, the script is part of a CI/CD pipeline. Such tasks are the ones in which we are going to build upon our DAG by dynamically creating tasks between them at this point this may be a little confusing, but once you see the . 7. Its a common confusion. Lets imagine that you have a DAG that extracts, processes, and stores statistics derived from your data. As the sources are only determined at runtime, the DAG will need to dynamically create the ETL task groups for each source present during runtime. DockerDBT,docker,airflow,dbt,Docker,Airflow,Dbt,gitDAG. Airflow Dynamic DAGs: The powerful way with Jinja and YAML Smash the like button to become an Airflow Super Hero! Thats the beauty of Jinja. There are two main problems with DAG writing: It is a . You can then build the DAGs by calling the dag-factory.generate_dags () method in a Python script. Dynamic DAGs with external configuration from a structured data file. This article is going to show how to: Working through the years with SQL, data modeling, data platform and engineering. You must know that Airflow loads any DAG object it can import from a DAG file. Running Airflow behind a reverse proxy; Running Airflow with systemd; Using the Test Mode Configuration; Define an operator extra link; Email Configuration; Dynamic DAG Generation. just simply create a params dictionary then pass to default . Maybe one of the most common way of using this method is with JSON inputs/files. Note that we can specify any supported DAG configuration key here. Here are other methods, each with their own sets of pros and cons, that you can consider in place of using an external database: Heres an article summarising the comparison of this method against the above 5. Consider the following example workflow. The first step is to create the template file. In the logs for the first created task (to say hello to Sun), you should see something like this: __init__.py I really recommend you this way of generating your DAGs. / docs / apache-airflow / howto / dynamic-dag-generation.rst. apache / airflow / eb47c42d6ba3ca33cf4223ac6c2a4904cf1f388e / . Lets find out through an example. For example: Also, you could have different settings for each of your environments: dev, staging, and prod. So actually, you don't need XCOM to get the arguments. Its a powerful language that allows you to make conditions, for loops, filters, and much more. The bottom line is that you dont want to create the same DAG, the same tasks repeatedly with just slight modifications. That being said, how can you leverage Jinja to generate DAGs dynamically? You had to remove them manually by clicking on the red trash. you waste your time (and your time is precious). With this method, you have: If you run Airflow in production, I would definitely advise you to use this method. a list of APIs or tables ). Two pairs of curly brackets. Airflow dynamic DAGs can save you a ton of time. For example, the code below leverages Jinja to fetch variables from the Airflow database. These changes are only processed by the Airflow when the scheduler has parsed and serialised the DAG. Awesome isnt it? docker airflow. So actually, you don't need XCOM to get the arguments. Before I show you how to do it, its important to clarify one thing. The former is when you create DAGs based on static, predefined, already known values (configuration files, environments, etc.). As you can see, it doesnt work. Lets say you want to get the price of specific stock market symbols such as APPL (Apple), FB (Meta), and GOOGL (Google). If you need to use a more complex meta-data to prepare your DAG structure and you would prefer to keep the data in a structured non-python format, you should export the data to the DAG folder in a file and push it to the DAG folder, rather than try to pull the data by the DAG's top-level code - for the reasons explained . You should create hook only in the execute method or any method which is called from execute. Dynamic DAGs with environment variables; Generating Python code with embedded meta-data; Dynamic DAGs with external configuration from a structured data file . Apache Airflow's documentation puts a heavy emphasis on the use of its UI client for configuring DAGs. Comprehensive company profiles. Basically, {{ dag_id_holder }} will be replaced by the corresponding value coming from your configuration file. The first step is to create the template file. However, you benefit from the powerfulness of the Jinja template engine and the readableness of the YAML language. Dynamic DAGs with external configuration from a structured data file. 1 Answer. . You iterate over the symbols to generate a DAG for each, but you end up with only one DAG instead of three. Let's see how. The DAG get_price_GOOGL disappears. By leveraging Python, you can create DAGs dynamically based on variables, connections, a typical pattern, etc. After installing dag-factory in your Airflow environment, there are two steps to creating DAGs. Some of our partners may process your data as a part of their legitimate business interest without asking for consent. In this article, you learned how to create dynamic DAGs in three different ways. Lets see how. Thanks to that, its pretty easy to generate DAGs dynamically. This allows us to scale airflow workers and executors, but we still have problems like this. Otherwise, there is another method that I love. The DAG from which you will derive others by adding the inputs. That means the DAG must appear in globals(). Next, we can flesh out our DAG definition as shown below. Finally, copy the folder containing the DAG to your Airflow dags directory (usually this is $AIRFLOW_HOME/dags), load the UI client and enable the DAG. Subscribe to my channel to become a m. Apache Airflows documentation puts a heavy emphasis on the use of its UI client for configuring DAGs. And yes, its the exact same example as before but we fixed the issue with globals() if you carefully look at the loop. Very simple DAG. The first step is to create the template file which is NOT a python file, but a jinja2 file like template_dag.jinja2. Our concept is simple; combine the highest quality, most efficient HVAC and Plumbing equipment on the market, with the best sales . This could either be done directly in the file system by a developer manually, or via a deployment pipeline. If you need to use a more complex meta-data to prepare your DAG structure and you would prefer to keep the data in a structured non-python format, you should export the data to the DAG folder in a file and push it to the DAG folder, rather than try to pull the data by the DAG's top-level code - for the reasons explained . If you carefully take a look at the template above, you can see placeholders with a weird notation. Well done if you reached that far. The Possibilities with Multilingual Dashboards in Tableau CRM, Prototyping an NFS connection to LDAP using SSSD, How to Add Your Virtual Environment to the Jupyter Kernel in Windows, Using an external database, such as MongoDB, Using a generated Python code with embedded dynamic configuration. In this article, we will explore using a structured data flat file to store the dynamic configuration as a variable to implement a dynamic workflow. The consent submitted will only be used for data processing originating from this website. Lets also specify some default arguments to pass to operators attached to the DAG and, separately, a list of entities to say hello to under the top level key say_hello. Get a D&B Hoovers Free Trial. You have no visibility on the code of the generated DAGs. Here is an example on how we can do the dynamic configuration changes using another Airflow DAG: One good thing about using another DAG is that we kind of have a change history of the dynamic configuration. In the first story about an airflow architecture (https://medium.com/@nbrgil/scalable-airflow-with-kubernetes-git-sync-63c34d0edfc3), I explained how to use airflow with Kubernetes Executor. config.yml 0 directories, 3 files, # Load the DAG configuration, setting a default if none is present, # Extend the graph with a task for each new name. if you move from a legacy system to Apache Airflow, porting your DAGs may be a nightmare without dynamic DAGs. Love podcasts or audiobooks? At the end, you should have the following files and folders: All right. You could perfectly stick with JSON but I would like to show how to do it with YAML as I feel its an easier to read language. The retrieval of the dynamic configuration is executed purely on the machine that runs the Airflow scheduler process. blob . Without being able to look at the generated code, debugging your DAGs may become really hard. Notice that you should put this file outside of the folder dags/. Also, the YAML language is really easy to read and you can even add a validator to check the syntax of your config files. The source files might all be dropped in a central location, and the DAG is responsible for re-locating them before perform the Extract-Transform-Load (ETL) pipeline for each source. First, lets specify our DAG-specific options under key called dag. The source files . This has been fixed. These de-serialised DAGs then show up on the UI, along with any updates to their workflow or schedule. and you should obtain three new DAG files as shown below: get_price_APPL, get_price_FB and get_price_GOOGL! Financial Data. Required fields are marked *. All Dynamics programs are run with the philosophy of creating a positive experience in a challenging class, all while helping each student succeed. To keep things simple, well just specify a task chain where each new entity to say hello to is bolted on to the last. I had to do something similar in the past, I wrote a DAG which read from a YAML file which defined what tasks to create. You can set or get variables as shown below (here, the variable my_dag): Python stores a variable in globals() when you create it outside of a function, in the global scope. Manage SettingsContinue with Recommended Cookies. The code above is slightly different that the one before but the logic is identical. This method is also considered a best practice by Airflow when creating dynamic task workflow in a DAG. The bottom line: For dynamic DAGs,, you need to have a different variable name for each one. Once the YAML file structure is defined, we can build the logic for our dynamic DAG! Ready? Why might you need dynamic DAGs? The job of a data engineer is to write reliable, scalable, and maintainable code. Notice that you should put this file outside of the folder dags/. Therefore, only the last DAG for GOOGL is created. With the above project structure, we can retrieve our dynamic configuration from a YAML file in our DAG as such: In line 35 to 38, we parse the contents of the YAML file to get the list of sources. A better way to do this would be to build dynamism into the DAG. Go! Dynamically generate Apache Airflow DAGs from YAML configuration files - GitHub - ajbosco/dag-factory: Dynamically generate Apache Airflow DAGs from YAML configuration files . One alternative is to store your DAG configuration in YAML and use it to set the default configuration in the Airflow database when the DAG is first run. Everything is ready, time to test! First, we need to create a YAML configuration file. Apache Airflow is an open source scheduler built on Python. If you would like to change your settings or withdraw consent at any time, the link to do so is in our privacy policy accessible from our home page. The single-file method is the easiest way to generate DAGs dynamically. Contribute to jing-s-zhong/Big-Data-Solution-5-Apache-Airflow-Dynamic-Dag development by creating an account on GitHub. Back to the DAG example, what happens is that the dag variable changes reference for each loop (symbol). Instantly share code, notes, and snippets. jw-ng / configure_sources_yaml_file_dag.py. Notice the addition of {{ catchup or False }} for the catchup parameter. Yes, there is a little bit of work at first but the reward far exceeds the simplicity of the first method. In Airflow v2, the scheduler will need to serialise the DAG and save that into the metadata database. Valuable research and technology reports. Its a good question. You might also have noticed the .airflowignore file in the DAGs folder. The DAG from which you will derive others by adding the inputs. Not sure what you mean for 'dynamic', but when yaml file updated, if the reading file process is in dag file body, the dag will be refreshed to apply for the new args from yaml file. This very nice way of generating DAGs comes at the price of higher complexity and subtle tricky things that you must know. For example: Then the jinja template engine renders the template file with the values of each config file. Sign in. If you need to use a more complex meta-data to prepare your DAG structure and you would prefer to keep the data in a structured non-python format, you should export the data to the DAG folder in a file and push it to the DAG folder, rather than try to pull the data by the DAG's top-level code - for the reasons explained in . Now it is important for us to know what these concepts mean, what they offer, and how it is beneficial to us. dag.py DAG Factories Using a factory pattern with python classes that generate DAGs automatically based on dynamic input to the system. Now if you have the main dag object, you can use it to get a list of its task instances. Before we begin, using a structured data flat file file is not the only way to achieve a dynamic workflow, and it comes with its own set of pros and cons, which we shall dive deeper as we go along. So be sure to weigh these out carefully before embarking on the journey. Its much more than just a way to replace placeholders at run time. First thing to know, before Apache Airflow 2.2, DAGs that were dynamically generated and then removed didnt disappear automatically. This file is necessary to let the Airflow scheduler know which files or folders to ignore when looking for Python files to parse for DAG updates. For example, an Extract-Transform-Load (ETL) pipeline that extracts from a varying number of input sources. To use dag-factory, you can install the package in your Airflow environment and create YAML configuration files for generating your DAGs. You get back the get_price_GOOGL DAG with the already triggered DAG Run as shown below: In addition to those details, there are two major drawbacks with this method: It worth to mention that you should never generate your DAGs based on inputs that come from DB or API requests. One alternative is to store your DAG configuration in YAML and use it to set the default configuration in the Airflow database when the DAG is first run. All right, thats it for now! That makes it very flexible and powerful (even complex sometimes). DBT. Those placeholders will be replaced by the corresponding values in the JSON files. Simple isnt it? Ok, now let me show you the easiest way to generate your DAGs dynamically. poetryopenpyxldockerfilepip. As you can see, its a pretty simple DAG with placeholders such as DAG_ID_HOLDER, INPUT_HOLDER or SCHEDULE_INTERVAL_HOLDER. To view the purposes they believe they have legitimate interest for, or to object to this data processing use the vendor list link below. While the UI is nice to look at, its a pretty clunky way to manage your pipeline configuration, particularly at deployment time. My situation was that the number of tables that I was extracting data from could change every week, instead of re-deploying the DAG to production every time I needed to add a new table I pointed the DAG to a YAML . By the way, if you are new to Airflow, check my course here; you will get it at a special discount. There are really the most reliable and scalable ways. In these situations, it would be implausible to recreate the DAG each time the condition changes that would be highly manual and taxing for the team maintaining the Airflow DAGs. Data engineers shouldn't write DAGs for the sake of writing DAGs. With the above project structure, we can retrieve our dynamic configuration from a YAML file in our DAG as such: In line 35 to 38, we parse the contents of the YAML file to get the list of sources. Learn on the go with our new app. To demonstrate, lets create a simple hello world DAG with an init file (__init__.py), a DAG definition file (dag.py) and a YAML configuration file (config.yml) specifying the default configuration options to use (note: the complete set of files can be found on my GitHub account here). How to pass dynamic arguments Airflow operator? In my opinion, these changes should not be done directly in the file system as that does not provide a change history. Properties of the Concepts. A Single Python file that generates DAGs based on some input parameter (s) is one way for generating Airflow Dynamic DAGs (e.g. Airflow Dynamic DAGs: The powerful way with Jinja and YAML Smash the like button to become an Airflow Super Hero! Subscribe to my channel to become a master of Airflow BECOME A PRO: https://www.udemy.com/course/the-complete-hands-on-course-to-master-apache-airflow/?couponCode=WEBSITE-15 My Patreon: https://www.patreon.com/marclambertiAirflow dynamic DAGs can save you a ton of time. Well, thats because Airflow stores your DAG references in globals(). Jinja is a template engine that takes a template file with special placehoders and replace them with data coming from a source. There could even be no source files available on some days. Using environment variables to achieve dynamic configuration of an Airflow DAG is very similar to how we use Airflow variables to do so. The code snippet used is also available in this github repository. Note that, as PyYAML will deserialize datetimes to Python datetime.datetime instances automatically, we must specify default=str when dumping to JSON to avoid serialization errors as the json module does not support the same automatic serialization/deserialization out of the box. Dynamic DAGs with external configuration from a structured data file. Arizona Dynamics prides itself on offering programs for children of all ages, experience, and abilities. This allows us to scale airflow workers and executors, but we still have problems like this. That caused a lot of confusion as you had DAGs on the UI that didnt exist anymore. Since then, we have not only survived but flourished, becoming a leader in hydronic sales in Arizona. Today, its not possible (yet) to do that. I wrote an article about macros, variables and templating that I do recommend you to read here. just simply create a params dictionary then pass to default_args: The webserver then retrieves the serialised DAGs from the database and de-serialise them. You load the template template_dag.jinja2, you loop over the folder where the config files are. To do this, we need to load the YAML file (using PyYAML), convert its contents to JSON, and use the setdefault method of Airflows Variable class to persist it to the database if no matching key is found, as shown below. This notation is used by Jinja to identify that there is a value to put here. Ready? So, the first thing to do is defining two tasks using dummy operators, i.e., the start and the end task. For this example, you say that if the catchup value doesnt exist in your configuration file, then False will be used. Dynamic DAGs are NOT dynamic tasks. It uses a topological sorting mechanism, called a DAG ( Directed Acyclic Graph) to generate dynamic tasks for execution according to dependency, schedule, dependency task completion, data partition and/or many other possible criteria. This very nice way of generating DAGs comes at the price of higher complexity and subtle tricky things that you must know.Ready?Lets go! For now, lets just say we want to create a DAG with the ID hello-world and schedule it to run once. DAGs in the folder dags/ are parsed every, a script file, in charge of generating your DAGs by merging the inputs with the template. Again, it should be outside of the folder dags. Currently focused on data platform and spark jobs with python. Now, lets say this DAG has different configuration settings. With this method, you have: Without further waiting, here is an example: As you can see, you get the three DAGs get_price_APPL, get_price_FB, get_price_GOOGL. Love podcasts or audiobooks? An ETL or ELT Pipeline with several Data Sources or Destinations is a popular use case for this. This is actually pretty easy using the standard API. The answer just below . We and our partners use cookies to Store and/or access information on a device.We and our partners use data for Personalised ads and content, ad and content measurement, audience insights and product development.An example of data being processed may be a unique identifier stored in a cookie. The example we use is quite easy, but imagine that you have a lot of tasks with many different inputs. Finally, lets write our DAG definition file. The single-file method, the multiple-files method, and the jinja method. DBT DBT . So, whats the correct way for having dynamic DAGS? When using a structured data flat file, such as JSON or YAML, , we can decide on a custom structure for our dynamic configuration. The third and last step is to create the script that will replace the placholders in the template by the values in the config files and generate the DAGs. Waouh! Now, run the DAG get_price_GOOGL one time and once it is completed, remove the GOOGL symbol from the loop and refresh the page again. Notice that an AIP Dynamic Task Mapping is coming soon. Most of the time the Data processing DAG pipelines are same except the parameters like source, target, schedule interval etc. The structure of the project should look like this: For this example, we can leave the init file empty - its just a placeholder file to instruct Airflow to check for a DAG in the folder. An example config file is shown below. Stay tuned . ), staticstics (could be mean, median, standard deviation, all of them or only one of those), destination table (could be a different table for each API route, folder etc). By leveraging Python, you can create DAGs dynamically based on variables, connections, a typical pattern, etc. Create a Python file in your folder dags/ and paste the code below: If you take a look at the Airflow UI, you obtain this. As you know, Apache Airflow is written in Python, and DAGs are created via Python scripts. Your email address will not be published. Maybe one of the most common way of using this method is with JSON inputs/files. Basically, for each DAG you want to generate, there is an associated JSON file. Maybe you dont know it but Apache Airflow uses Jinja to build its webpages as well as to render values in DAG files at run time. This article is going to show how to: Use airflow kubernetes operator to isolate all business rules from airflow pipelines; Create a YAML DAG using schema validations to simplify the usage of airflow for some users; Define a pipeline pattern; Personally, I love this method! This will reduce DAG loading time and improve performance. Opinions are my own. No additional machine required in the retrieval process. This makes it a little more troublesome when it comes to debugging the dynamic behaviour of the DAG based on changes done to the flat file. Real-time trigger alerts. Software developer @ Thoughtworks. The beauty of Airflow is that everything is in Python, which brings the powerfulness and flexibility of this language. Note that the following discussion is based on Airflow version 2. Before setting up the DAG itself, we should first load the YAML config and persist it to the Airflow configuration database if configuration has not yet been defined for our application. The latter is when you make tasks based on the output of previous tasks. less prone to errors. ge dishwasher 5h code Using dynamic SQL, you could write a procedure or function that was called like this: select_by_pos ('hr.employees', 1, 2, 5) The procedure could query all_tab_columns to find, in the given table, what the given columns were, and then produce a query such as SELECT employee_id , first_name , phone_number FROM hr.employees . Xcom push a list (or what ever you need to create the dynamic workflow later) in the subdag that gets executed first (see test1.py def return_list ()) Pass the main dag object as a parameter to your second subdag. Lastly, dynamic changes might not be reflected instantaneously. GATEWAY PROTOCOL(GWP) to Be Listed on Azbit, How to pick a new programming language for a startup of 510 engineers, Make Money With PythonThe Sports Arbitrage Project, Discover Tiime Engine, Ternoas Community Rewards Platform, How Halloween Can Improve Your Technical Writing, Getting students talking with data structure metaphors, (https://medium.com/@nbrgil/scalable-airflow-with-kubernetes-git-sync-63c34d0edfc3. The second step is to create the JSON files. Dynamic DAG Generation ===== To have a task repeated based on the output / result of a previous task see : doc: `/concepts/dynamic-task-mapping`. Using a structured data flat file to store the dynamic configuration might seem like an easy implementation for a dynamic workflow, but it does comes with its own drawbacks. Second thing to know, removing an already triggered dynamic DAG doesnt NOT remove its metadata. In the config file, lets specify some YAML configuration options for our DAG and our application. Dynamic search and list-building capabilities. Or if you already know Airflow and want to go way much further, enroll in my 12 hours coursehere, Where do you come from? Consider the following example workflow. Enough with the backstory, it's time to get to the exciting part. So having a dynamic DAG generator using a templating language can greatly benefit when you have to manage a large number of pipelines at enterprise level. Dynamic Task Generation. you have full access to the generated code. Ultimately, I would recommend this method if you just have few simple DAGs to generate. Your DAGs generate once, not every 30 seconds. it is scalable. I would recommend a little more the jinja method as Jinja gives you a lot of flexibility in the code you can generate. . What to know about the single-file method, ShortCircuitOperator in Apache Airflow: The guide, DAG Dependencies in Apache Airflow: The Ultimate Guide, source (could be a different FTP server, API route etc. Sometimes, the workflow, or data pipeline, that we are trying to model in an Airflow DAG is not static it changes under varying conditions. The biggest drawback from this method is that the flat file containing the dynamic configuration can only be viewed via a separate platform, such as the file system. Its reliable, sustainable, scalable and easier to debug. Final step, the generator script for the dynamic DAGs! Airflow Dynamic DAGs with JSON files. The skills of data engineers can be better used if they focus on generalizing and abstracting things rather than writing plain DAGs. 1) Creating Airflow Dynamic DAGs using the Single File Method. That was a lot! What if you could make the DAG change depending on a variable? Each time the Airflow scheduler parses the DAG file for updates, the create_dag function is called, which in turn executes the Variable.get function to determine the dynamic workflow. In Python, globals() is a built-in function that returns a dictionary of global variables. If you want to use variables to configure your code, you should always use While the UI is nice to look at, it's a pretty clunky way to manage your pipeline configuration, particularly at deployment time. That makes it very flexible and powerful (even complex sometimes). its harder to maintain as each time something change, you will need to update all of your DAGs one by one. Graphic Design, Vinyl Wrapping, Banners, Posters, Labels, Business Cards, T-shirts and Hats. Before going into the details, here is a brief summary of the concepts. Likewise, Airflow is built around Webserver, Scheduler, Executor, and Database, while Prefect is built around Flows and Task. If you run this script, you will obtain the exact same three DAGs as before. Airflow dockerpd.read_excel ()openpyxl. As you know, Apache Airflow is written in Python, and DAGs are created via Python scripts. To set the stage, throughout this article we will assume that we want to execute two complex tasks in airflow process_message & process . Great! 1 talking about this. airflowpandas pd.read_excel ()openpyxl. In our example, our .airflowignore file will have the following content: The biggest benefit is that there is no additional load on any operational database. Dun & Bradstreet collects private company financials for more than 23 million companies worldwide. Guess what? This essentially means that the tasks that Airflow . In fact, if you add the GOOGL symbol again. P.S: If you want to learn more about Airflow, go check my course The Complete Hands-On Introduction to Apache Airflow righthere. The following example was taken from the dag-factory README: Of course, one could always make the manual change even with this DAG around, but that would be a violation of the process flow (user issue). The hook retrieves the auth parameters such as username and password from Airflow backend and passes the params to the airflow.hooks.base.BaseHook.get_connection(). I cannot emphasize enough how important it is to take a look at its documentation here. Lets goooooo! 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