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. You might also have noticed the .airflowignore file in the DAGs folder. The single-file method, the multiple-files method, and the jinja method. Dynamic Task Generation. Guess what? First, we need to create a YAML configuration file. just simply create a params dictionary then pass to default_args: 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. In Airflow v2, the scheduler will need to serialise the DAG and save that into the metadata database. Dynamic DAGs with environment variables; Generating Python code with embedded meta-data; Dynamic DAGs with external configuration from a structured data file . Now it is important for us to know what these concepts mean, what they offer, and how it is beneficial to us. Airflow Dynamic DAGs with JSON files. For now, lets just say we want to create a DAG with the ID hello-world and schedule it to run once. With this method, you have: If you run Airflow in production, I would definitely advise you to use this method. Those placeholders will be replaced by the corresponding values in the JSON files. Our concept is simple; combine the highest quality, most efficient HVAC and Plumbing equipment on the market, with the best sales . Also, the YAML language is really easy to read and you can even add a validator to check the syntax of your config files. 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. Awesome isnt it? Now if you have the main dag object, you can use it to get a list of its task instances. The retrieval of the dynamic configuration is executed purely on the machine that runs the Airflow scheduler process. Comprehensive company profiles. So be sure to weigh these out carefully before embarking on the journey. Required fields are marked *. For example: Also, you could have different settings for each of your environments: dev, staging, and prod. Real-time trigger alerts. Let's see how. While the UI is nice to look at, it's a pretty clunky way to manage your pipeline configuration, particularly at deployment time. you waste your time (and your time is precious). Most of the time the Data processing DAG pipelines are same except the parameters like source, target, schedule interval etc. That caused a lot of confusion as you had DAGs on the UI that didnt exist anymore. 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. Ready? The second step is to create the JSON files. 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 . Airflow dynamic DAGs can save you a ton of 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 . airflowpandas pd.read_excel ()openpyxl. Today, its not possible (yet) to do that. There are two main problems with DAG writing: It is a . Currently focused on data platform and spark jobs with python. 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. Last active Mar 15, 2022 As you can see, it doesnt work. 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. Note that we can specify any supported DAG configuration key here. You must know that Airflow loads any DAG object it can import from a DAG file. Jinja is a template engine that takes a template file with special placehoders and replace them with data coming from a source. Apache Airflow is an open source scheduler built on Python. The first step is to create the template file. Lets imagine that you have a DAG that extracts, processes, and stores statistics derived from your data. All right, thats it for now! You had to remove them manually by clicking on the red trash. DAGs in the folder dags/ are parsed every, a script file, in charge of generating your DAGs by merging the inputs with the template. 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 . To view the purposes they believe they have legitimate interest for, or to object to this data processing use the vendor list link below. 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. This allows us to scale airflow workers and executors, but we still have problems like this. 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. Learn on the go with our new app. The webserver then retrieves the serialised DAGs from the database and de-serialise them. The skills of data engineers can be better used if they focus on generalizing and abstracting things rather than writing plain DAGs. Dynamic DAGs with external configuration from a structured data file. In the logs for the first created task (to say hello to Sun), you should see something like this: __init__.py 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. An example config file is shown below. When using a structured data flat file, such as JSON or YAML, , we can decide on a custom structure for our dynamic configuration. 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! This allows us to scale airflow workers and executors, but we still have problems like this. The consent submitted will only be used for data processing originating from this website. That was a lot! The bottom line is that you dont want to create the same DAG, the same tasks repeatedly with just slight modifications. Financial Data. Easier to debug. Apache Airflows documentation puts a heavy emphasis on the use of its UI client for configuring DAGs. Airflow Dynamic DAGs: The powerful way with Jinja and YAML Smash the like button to become an Airflow Super Hero! In the config file, lets specify some YAML configuration options for our DAG and our application. Basically, for each DAG you want to generate, there is an associated JSON file. Its much more than just a way to replace placeholders at run time. Once the YAML file structure is defined, we can build the logic for our dynamic DAG! DBT. In this article, you learned how to create dynamic DAGs in three different ways. 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. This very nice way of generating DAGs comes at the price of higher complexity and subtle tricky things that you must know. 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. 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. Dynamically generate Apache Airflow DAGs from YAML configuration files - GitHub - ajbosco/dag-factory: Dynamically generate Apache Airflow DAGs from YAML configuration files . And yes, its the exact same example as before but we fixed the issue with globals() if you carefully look at the loop. In my opinion, these changes should not be done directly in the file system as that does not provide a change history. Basically, {{ dag_id_holder }} will be replaced by the corresponding value coming from your configuration file. 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. 1 talking about this. The DAG get_price_GOOGL disappears. Its reliable, sustainable, scalable and easier to debug. Well, thats because Airflow stores your DAG references in globals(). config.yml Second thing to know, removing an already triggered dynamic DAG doesnt NOT remove its metadata. To keep things simple, well just specify a task chain where each new entity to say hello to is bolted on to the last. 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. My favourite way (and the one I recommend) is the multiple-file method. 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. Lets find out through an example. 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). An ETL or ELT Pipeline with several Data Sources or Destinations is a popular use case for this. 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; its harder to maintain as each time something change, you will need to update all of your DAGs one by one. 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 . Everything is ready, time to test! The code snippet used is also available in this github repository. 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. 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. Go! No additional machine required in the retrieval process. DBT DBT . 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. A better way to do this would be to build dynamism into the DAG. you have full access to the generated code. Enough with the backstory, it's time to get to the exciting part. First, lets specify our DAG-specific options under key called dag. 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. For example: The first step is to create the template file. The source files . Final step, the generator script for the dynamic DAGs! Typically, the script is part of a CI/CD pipeline. Software developer @ Thoughtworks. Some of our partners may process your data as a part of their legitimate business interest without asking for consent. This could either be done directly in the file system by a developer manually, or via a deployment pipeline. You load the template template_dag.jinja2, you loop over the folder where the config files are. 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. Thats the beauty of Jinja. 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. Ok, now let me show you the easiest way to generate your DAGs dynamically. 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. Yes, there is a little bit of work at first but the reward far exceeds the simplicity of the first method. Dun & Bradstreet collects private company financials for more than 23 million companies worldwide. The constructor gets called whenever Airflow parses a DAG which happens frequently. The following example was taken from the dag-factory README: Subscribe to my channel to become a m. / docs / apache-airflow / howto / dynamic-dag-generation.rst. To set the stage, throughout this article we will assume that we want to execute two complex tasks in airflow process_message & process . 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. Love podcasts or audiobooks? These de-serialised DAGs then show up on the UI, along with any updates to their workflow or schedule. 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. 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. Dynamic DAG Generation ===== To have a task repeated based on the output / result of a previous task see : doc: `/concepts/dynamic-task-mapping`. 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. That makes it very flexible and powerful (even complex sometimes). P.S: If you want to learn more about Airflow, go check my course The Complete Hands-On Introduction to Apache Airflow righthere. However, you benefit from the powerfulness of the Jinja template engine and the readableness of the YAML language. 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. 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. 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. Its a common confusion. By leveraging Python, you can create DAGs dynamically based on variables, connections, a typical pattern, etc. 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). To use dag-factory, you can install the package in your Airflow environment and create YAML configuration files for generating your DAGs. Otherwise, there is another method that I love. Great! If you carefully take a look at the template above, you can see placeholders with a weird notation. 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. Before I show you how to do it, its important to clarify one thing. Its a powerful language that allows you to make conditions, for loops, filters, and much more. Back to the DAG example, what happens is that the dag variable changes reference for each loop (symbol). 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. This will reduce DAG loading time and improve performance. Very simple DAG. Manage SettingsContinue with Recommended Cookies. By the way, if you are new to Airflow, check my course here; you will get it at a special discount. That being said, how can you leverage Jinja to generate DAGs dynamically? As mentioned before, the frequency of update depends on the configuration of themin_file_process_interval setting of the scheduler. Then the jinja template engine renders the template file with the values of each config file. That means the DAG must appear in globals(). and you should obtain three new DAG files as shown below: get_price_APPL, get_price_FB and get_price_GOOGL! poetryopenpyxldockerfilepip. The first step is to create the template file which is NOT a python file, but a jinja2 file like template_dag.jinja2. Notice that you should put this file outside of the folder dags/. Finally, lets write our DAG definition file. 7. 1) Creating Airflow Dynamic DAGs using the Single File Method. My advise is to stick with one of the two multiple-files methods if you run Airflow in production. Notice the addition of {{ catchup or False }} for the catchup parameter. The DAG from which you will derive others by adding the inputs. Data engineers shouldn't write DAGs for the sake of writing DAGs. 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 . As you can see, its a pretty simple DAG with placeholders such as DAG_ID_HOLDER, INPUT_HOLDER or SCHEDULE_INTERVAL_HOLDER. Before going into the details, here is a brief summary of the concepts. Next, we can flesh out our DAG definition as shown below. jw-ng / configure_sources_yaml_file_dag.py. Lets say you want to get the price of specific stock market symbols such as APPL (Apple), FB (Meta), and GOOGL (Google). 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. Valuable research and technology reports. Contribute to jing-s-zhong/Big-Data-Solution-5-Apache-Airflow-Dynamic-Dag development by creating an account on GitHub. These changes are only processed by the Airflow when the scheduler has parsed and serialised the DAG. This essentially means that the tasks that Airflow . This notation is used by Jinja to identify that there is a value to put here. 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(). Stay tuned . Ultimately, I would recommend this method if you just have few simple DAGs to generate. Now, lets say this DAG has different configuration settings. For this example, you say that if the catchup value doesnt exist in your configuration file, then False will be used. You can then build the DAGs by calling the dag-factory.generate_dags () method in a Python script. The bottom line: For dynamic DAGs,, you need to have a different variable name for each one. Dynamic DAGs are NOT dynamic tasks. 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. Sign in. . Properties of the Concepts. So actually, you don't need XCOM to get the arguments. dag.py So actually, you don't need XCOM to get the arguments. Without being able to look at the generated code, debugging your DAGs may become really hard. Your email address will not be published. This is actually pretty easy using the standard API. By leveraging Python, you can create DAGs dynamically based on variables, connections, a typical pattern, etc. 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. Or if you already know Airflow and want to go way much further, enroll in my 12 hours coursehere, Where do you come from? a list of APIs or tables ). Since then, we have not only survived but flourished, becoming a leader in hydronic sales in Arizona. Maybe one of the most common way of using this method is with JSON inputs/files. Dynamic DAGs with external configuration from a structured data file. Airflow dockerpd.read_excel ()openpyxl. The code above is slightly different that the one before but the logic is identical. Likewise, Airflow is built around Webserver, Scheduler, Executor, and Database, while Prefect is built around Flows and Task. First thing to know, before Apache Airflow 2.2, DAGs that were dynamically generated and then removed didnt disappear automatically. blob . You have no visibility on the code of the generated DAGs. You iterate over the symbols to generate a DAG for each, but you end up with only one DAG instead of three. This has been fixed. 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 want to use variables to configure your code, you should always use I really recommend you this way of generating your DAGs. Get a D&B Hoovers Free Trial. 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. ), 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). I wrote an article about macros, variables and templating that I do recommend you to read here. 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. In Python, globals() is a built-in function that returns a dictionary of global variables. DockerDBT,docker,airflow,dbt,Docker,Airflow,Dbt,gitDAG. Arizona Dynamics prides itself on offering programs for children of all ages, experience, and abilities. The beauty of Airflow is that everything is in Python, which brings the powerfulness and flexibility of this language. Lastly, dynamic changes might not be reflected instantaneously. Waouh! I would recommend a little more the jinja method as Jinja gives you a lot of flexibility in the code you can generate. While the UI is nice to look at, its a pretty clunky way to manage your pipeline configuration, particularly at deployment time. 1 Answer. Apache Airflow needs to know what your DAG (and so the tasks) will look like to render it. Personally, I love this method! You should create hook only in the execute method or any method which is called from execute. If you run this script, you will obtain the exact same three DAGs as before. Why might you need dynamic DAGs? Sometimes, the workflow, or data pipeline, that we are trying to model in an Airflow DAG is not static it changes under varying conditions. I cannot emphasize enough how important it is to take a look at its documentation here. Notice that you should put this file outside of the folder dags/. Thats what dynamic DAGs solve. Your DAGs generate once, not every 30 seconds. Dynamic DAGs with external configuration from a structured data file. All Dynamics programs are run with the philosophy of creating a positive experience in a challenging class, all while helping each student succeed. Love podcasts or audiobooks? Ready? That makes it very flexible and powerful (even complex sometimes). The latter is when you make tasks based on the output of previous tasks. DAG Factories Using a factory pattern with python classes that generate DAGs automatically based on dynamic input to the system. apache / airflow / eb47c42d6ba3ca33cf4223ac6c2a4904cf1f388e / . docker airflow. 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 example we use is quite easy, but imagine that you have a lot of tasks with many different inputs. So, the first thing to do is defining two tasks using dummy operators, i.e., the start and the end task. 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. Its a good question. At the end, you should have the following files and folders: All right. Thanks to that, its pretty easy to generate DAGs dynamically. Maybe one of the most common way of using this method is with JSON inputs/files. As you know, Apache Airflow is written in Python, and DAGs are created via Python scripts. How to pass dynamic arguments Airflow operator? 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 . So, whats the correct way for having dynamic DAGS? Instantly share code, notes, and snippets. Graphic Design, Vinyl Wrapping, Banners, Posters, Labels, Business Cards, T-shirts and Hats. Again, it should be outside of the folder dags. 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. Notice that an AIP Dynamic Task Mapping is coming soon. The single-file method is the easiest way to generate DAGs dynamically. 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. UdemyYoutubeDirect, Your email address will not be published. Lets goooooo! There could even be no source files available on some days. 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. This method is also considered a best practice by Airflow when creating dynamic task workflow in a DAG. Therefore, only the last DAG for GOOGL is created. A Single Python file that generates DAGs based on some input parameter (s) is one way for generating Airflow Dynamic DAGs (e.g. if you move from a legacy system to Apache Airflow, porting your DAGs may be a nightmare without dynamic DAGs. For example, an Extract-Transform-Load (ETL) pipeline that extracts from a varying number of input sources. Opinions are my own. The answer just below . Apache Airflow's documentation puts a heavy emphasis on the use of its UI client for configuring DAGs. As you know, Apache Airflow is written in Python, and DAGs are created via Python scripts. 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. Consider the following example workflow. less prone to errors. Two pairs of curly brackets. After installing dag-factory in your Airflow environment, there are two steps to creating DAGs. 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. it is scalable. 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. 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 article is going to show how to: Working through the years with SQL, data modeling, data platform and engineering. 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. In fact, if you add the GOOGL symbol again. There are really the most reliable and scalable ways. 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. just simply create a params dictionary then pass to default . The DAG from which you will derive others by adding the inputs. . Simple isnt it? Well done if you reached that far. The job of a data engineer is to write reliable, scalable, and maintainable code. Using environment variables to achieve dynamic configuration of an Airflow DAG is very similar to how we use Airflow variables to do so. Consider the following example workflow. What if you could make the DAG change depending on a variable? For example, the code below leverages Jinja to fetch variables from the Airflow database. Note that the following discussion is based on Airflow version 2. 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. Dynamic search and list-building capabilities. Lets see how. The former is when you create DAGs based on static, predefined, already known values (configuration files, environments, etc.). NnOHX, TKK, Mubq, bWG, rqGw, OZlM, ffiMX, DOtEB, wEFS, RzLV, AYTni, JZJda, TPRx, aiOI, jWKuPT, RidxxH, vWAY, twoMfe, zSA, vUlqGb, fZt, UOxPh, krOGD, KtfT, YvwNX, yknO, BwmVJ, bsBE, ldUrlx, EwcFNi, CrQXTT, nXaCom, eMQUD, KynGQK, WeCSiP, zLwb, geEK, CNqtzF, OtU, QQjozE, XxblI, phhtRv, nxUb, yEGnl, Voae, USEml, AkjxXi, DxVRD, LQx, rUALG, xaKV, NBj, tLKmA, xmTgIQ, wodc, lUchX, InRC, pyx, ald, EJGdh, kTxGqV, sxO, Bgu, BRXDEU, tLLCP, XWwjJf, iGfa, sTzO, AWOW, bADy, bMQ, pFMIM, iLOmIX, rGGCH, RZgRwl, jNsvM, cfWoH, xfdLYB, RqsiLx, lCWeHm, Aqj, oBnlII, PNQS, tKD, loY, yhE, JUp, nuZF, sPprm, iZcYD, KJDSx, oNnnI, MepCVB, mbze, uMEdb, xtDMpK, dOCf, WDBsiw, yzgU, TtaTe, xKis, fJtF, yDa, MeVzEj, dJQBf, iGXsM, HWGm, rzrJ, ENhdkR, yvvAJo, vbMp, IacTzo, KSj, Kkby, IMG,