diff --git a/docs/getting_started/astro-cli-quickstart.rst b/docs/getting_started/astro-cli-quickstart.rst index 824e7e97f0..288231e7ee 100644 --- a/docs/getting_started/astro-cli-quickstart.rst +++ b/docs/getting_started/astro-cli-quickstart.rst @@ -3,7 +3,7 @@ .. title:: Run Cosmos with the Astro CLI Run Cosmos with the Astro CLI ------------------------------ +============================= Work locally with `Apache Airflow® `_, dbt, and Astronomer Cosmos using the `Astro CLI `__. While Cosmos fully works with standard Airflow and independently of the Astro CLI, the Astro CLI can simplify creating and running Airflow projects. If you want to get started with Cosmos using only Airflow, see `Getting Started on Open Source Airflow `_. @@ -16,7 +16,7 @@ By the end of this quickstart you will: - Run a simple Dag that uses dbt to load, run, and test sample e-commerce data Prerequisites -+++++++++++++ +~~~~~~~~~~~~~ - `Python `_ - Install `git `_ @@ -27,7 +27,7 @@ Prerequisites Depending on your operating system, you might also need to install a separate installation manager like `Homebrew `_ or `WinGet `_. Clone the demo repo -+++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~ 1. Open a terminal in the directory where you want to clone your sample repo. 2. Clone the ``cosmos-demo`` repo. @@ -37,7 +37,7 @@ Clone the demo repo git clone https://github.com/astronomer/cosmos-demo.git Start Apache Airflow® locally -++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. Open a terminal at the root of the ``cosmos-demo`` repo. 2. Run ``astro dev start --verbosity debug`` to start your Aiflow instance. Including the ``--verbosity debug`` flag allows the command to display the steps the Astro CLI takes to set up your Airflow project. @@ -47,7 +47,7 @@ Start Apache Airflow® locally The `Airflow UI `_ enables you to start, stop, troubleshoot, or manage your Dags. Run a simple Cosmos Dag -+++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~ 1. From the Airflow Dashboard, click **Dags**. This opens a view where you can see all available Dags. Or, you can see if there were problems loading Dags to your Airflow project. 2. Select **Simple Dag** from the list to access the `Dag view `_ in the Airflow UI. Click **Code** to see the Dag code. @@ -81,7 +81,7 @@ For example in the ``stg_customers``, task group, in the ``run`` task, the logs This log indicates that the Dag triggers Cosmos to initiate the ``dbt run`` command following the sql actions defined in the ``stg_customers.sql``. View results with a database viewer -+++++++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To view the transformations completed by the Dag, you must use a database viewer to access the views and materializations completed by your dbt project. @@ -116,7 +116,7 @@ You have several options to locate this kind of information in dbt projects. For :alt: dBeaver user interface displaying the Customers table view produced by the dbt code. This table includes data that has been joined together from three separate raw database sources. Key Concepts -++++++++++++ +~~~~~~~~~~~~ Congratulations! You ran a dbt project successfully on Airflow! This quickstart includes the minimal steps required to get started working with Cosmos. Specifically it includes: @@ -133,7 +133,7 @@ This demo repo also includes a dbt project with configurations that allow you to Cosmos does not require you to use the specific project architecture shown in the ``cosmos-demo`` to run successfully. However, it can serve as a template or example for you to adapt your dbt or Airflow projects to work cohesively. Next steps -++++++++++ +~~~~~~~~~~ - Follow one of the Getting Started Guides where you can bring your own dbt projects and/or Dag code: - `Getting Started on Open-Source `__ diff --git a/docs/getting_started/astro.rst b/docs/getting_started/astro.rst index 33e436b140..03e5afd895 100644 --- a/docs/getting_started/astro.rst +++ b/docs/getting_started/astro.rst @@ -1,7 +1,7 @@ .. _astro: Getting Started with Cosmos on Astro ------------------------------------- +==================================== While it is possible to use Cosmos on Astro with all :ref:`Execution Modes `, we recommend using the ``local`` execution mode. It's the simplest to set up and use. @@ -10,7 +10,7 @@ If you'd like to see a fully functional project to run in Astro (CLI or Cloud), Below you can find a step-by-step guide to run your own dbt project within Astro. Pre-requisites -++++++++++++++ +~~~~~~~~~~~~~~ To get started, you should have: @@ -19,7 +19,7 @@ To get started, you should have: - A dbt project. The `jaffle shop example `_ is a good example. Create a virtual environment -++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Create a virtual environment in your ``Dockerfile`` using the sample below. Be sure to replace ```` with the actual adapter you need (i.e. ``dbt-redshift``, ``dbt-snowflake``). It's recommended to use a virtual environment because dbt and `Apache Airflow® `_ can have conflicting dependencies. @@ -34,7 +34,7 @@ Create a virtual environment in your ``Dockerfile`` using the sample below. Be s An example of dbt adapter is ``dbt-postgres``. Install Cosmos -++++++++++++++ +~~~~~~~~~~~~~~ Add Cosmos to your project's ``requirements.txt``. @@ -44,7 +44,7 @@ Add Cosmos to your project's ``requirements.txt``. Move your dbt project into the DAGs directory -+++++++++++++++++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Make a new folder, ``dbt``, inside your local project's ``dags`` folder. Then, copy/paste your dbt project into the directory and create a file called ``my_cosmos_dag.py`` in the root of your DAGs directory. Your project structure should look like this: @@ -84,7 +84,7 @@ For example, if you wanted to put your dbt project in a directory relative to yo ) Create a dagfile -++++++++++++++++ +~~~~~~~~~~~~~~~~ In your ``my_cosmos_dag.py`` file, import the ``DbtDag`` class from Cosmos and create a new DAG instance. Make sure to use the ``dbt_executable_path`` argument to point to the virtual environment you created in step 1. @@ -128,7 +128,7 @@ In your ``my_cosmos_dag.py`` file, import the ``DbtDag`` class from Cosmos and c This error can be resolved by increasing the value of the Airflow configuration `core.dagbag_import_timeout `_. Start your project -++++++++++++++++++ +~~~~~~~~~~~~~~~~~~ Start your project with ``astro dev start``. You should see your Airflow DAG in the Airflow UI (``localhost:8080`` by default), where you can trigger it. diff --git a/docs/getting_started/bring-your-own.rst b/docs/getting_started/bring-your-own.rst index a3178d1048..97b005938c 100644 --- a/docs/getting_started/bring-your-own.rst +++ b/docs/getting_started/bring-your-own.rst @@ -1,7 +1,7 @@ .. _bring-your-own: Get started with your dbt project ---------------------------------- +================================= If you have a sample project that you want to use to test out Cosmos functionality, or, you want to see how to set up a more custom project, check out the Bring your Own project guides. These guides provide the general structure for how to set up a dbt project in Cosmos. @@ -14,7 +14,7 @@ These guides provide the general structure for how to set up a dbt project in Co When you're ready to set up and customize your Cosmos project, see the :ref:`guides`. Example Demo: Jaffle Shop Project -+++++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you don't have your own project that you want to set up as a demo, you can explore a practical example in the **Bring your own project** guides to see how Cosmos can convert the dbt workflow into an `Apache Airflow® `_ Dag. diff --git a/docs/getting_started/core-concepts.rst b/docs/getting_started/core-concepts.rst index 33e83ea7f0..aba3a620af 100644 --- a/docs/getting_started/core-concepts.rst +++ b/docs/getting_started/core-concepts.rst @@ -1,18 +1,18 @@ .. _core-concepts: Cosmos core concepts --------------------- +==================== **Cosmos** is an open-source library that helps you to run dbt code in Apache Airflow. Because it operates at the interface between Airflow and dbt, becoming familiar with the commonly used terminology and foundational concepts can help you get started. Similar dbt and Apache Airflow® concepts -+++++++++++++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ dbt and Airflow each solve different kinds of data engineering problems. However, they do share some useful concepts. See :ref:`dbt-airflow-concepts`. How Cosmos works -++++++++++++++++ +~~~~~~~~~~~~~~~~ Cosmos provides an exceptional amount of control and ability to customize how Cosmos runs your dbt project in Airflow. When starting with Cosmos, you can think of it as performing two core functions: @@ -22,7 +22,7 @@ Cosmos provides an exceptional amount of control and ability to customize how Co DbtDag and DbtTaskGroup -+++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~ During the dbt code parsing phase, you can choose how you want your dbt workflow to translate into an Airflow Dag: @@ -32,17 +32,17 @@ During the dbt code parsing phase, you can choose how you want your dbt workflow You can see example Dags for both a simple `DbtDag `_ and `DbtTaskGroup `_ in the Cosmos `dev/dags directory `_. ProjectConfig -+++++++++++++ +~~~~~~~~~~~~~ The ``ProjectConfig`` contains information about which dbt project a Cosmos Dag or task group executes, as well as configurations that apply to both, rendering and execution. See :ref:`project-config`. ProfileConfig -+++++++++++++ +~~~~~~~~~~~~~ The ``ProfileConfig`` class determines which data warehouse Cosmos connects to when executing the dbt SQL. Connecting Cosmos to your data warehouse -'''''''''''''''''''''''''''''''''''''''' +++++++++++++++++++++++++++++++++++++++++ There are two ways to connect Cosmos projects to the data warehouse used by your dbt project: @@ -52,7 +52,7 @@ There are two ways to connect Cosmos projects to the data warehouse used by your See :ref:`profile-config` for more information about the ``ProfileConfig``, ``profiles.yml``, and ``profile_mapping``. Execution Modes -+++++++++++++++ +~~~~~~~~~~~~~~~ You define the execution mode for your project by using the ``ExecutionConfig`` class, which determines where and how dbt commands are run within Cosmos. @@ -70,28 +70,28 @@ There are two main categories of execution modes: See :ref:`execution-modes` and :ref:`execution-config`. Parsing -+++++++ +~~~~~~~ Parsing generally refers to the processes and configurations that Cosmos uses to parse your dbt project. See :ref:`parsing-methods`. Invocation Mode -+++++++++++++++ +~~~~~~~~~~~~~~~ The method that Cosmos uses to parse the dbt object, whether its by using the Python ``subprocess`` module or with a ``dbt_runner``. See `Invocation modes `_. Rendering -+++++++++ +~~~~~~~~~ After parsing your dbt project, Cosmos *renders* the project as an Airflow Dag or Task Group. Depending on the specifics of your dbt project, you can choose customizations that speed up overall performance. You can find more information about rendering options in the `RenderConfig <../guides/translate_dbt_to_airflow/render-config.html>`_. Testing Strategy -'''''''''''''''' +++++++++++++++++ By default, Cosmos adds a dbt test after it completes a model. However, you can change this behavior by configuring the test behavior in the ``RenderConfig``. Learn more in :ref:`testing-behavior`. Select and exclude -'''''''''''''''''' +++++++++++++++++++ You can filter your dbt project to only parse a subset of your dbt project by using the ``RenderConfig``, and define the ``select`` and ``exclude`` parameters. See :ref:`selecting-excluding`. diff --git a/docs/getting_started/dbt-airflow-concepts.rst b/docs/getting_started/dbt-airflow-concepts.rst index 8030f55aab..0d6dc78f06 100644 --- a/docs/getting_started/dbt-airflow-concepts.rst +++ b/docs/getting_started/dbt-airflow-concepts.rst @@ -1,7 +1,7 @@ .. _dbt-airflow-concepts: Similar dbt and Apache Airflow® concepts ------------------------------------------ +========================================= While dbt is an open source tool for data transformations and analysis, using SQL, `Apache Airflow® `_ focuses on being a platform for the development, scheduling and monitoring of batch-oriented workflows, using Python. Although both tools have many diff --git a/docs/getting_started/gcc.rst b/docs/getting_started/gcc.rst index 3ff341193d..96d505c3b9 100644 --- a/docs/getting_started/gcc.rst +++ b/docs/getting_started/gcc.rst @@ -1,12 +1,12 @@ .. _gcc: Getting Started on Google Cloud Composer (GCC) ----------------------------------------------- +============================================== Because there's no straightforward way of creating a Python virtual environment in Google Cloud Composer (GCC) , we recommend using Cosmos' built-in virtual environment functionality to run dbt. Install Cosmos -++++++++++++++ +~~~~~~~~~~~~~~ Add the following to your base project ``requirements.txt``: @@ -16,7 +16,7 @@ Add the following to your base project ``requirements.txt``: Move your dbt project into the DAGs directory -+++++++++++++++++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Make a new folder, ``dbt``, inside your local ``dags`` folder. Then, copy/paste your dbt project into the directory and create a file called ``my_cosmos_dag.py`` in the root of your DAGs directory. @@ -43,7 +43,7 @@ For example, if you wanted to put your dbt project in the ``/usr/local/airflow/d You can also exclude the ``manifest_path=...`` from the ``ProjectConfig``. Excluding a ``manifest_path`` file will by default use Cosmos's ``custom`` parsing method, which may be less accurate at parsing a dbt project compared to providing a ``manifest.json``. Create your DAG -+++++++++++++++ +~~~~~~~~~~~~~~~ In your ``my_cosmos_dag.py`` file, import the ``DbtDag`` class from Cosmos and create a new DAG instance. You need to supply additional arguments in the ``operator_args`` dictionary to tell Cosmos which packages are required. diff --git a/docs/getting_started/how-cosmos-works.rst b/docs/getting_started/how-cosmos-works.rst index af86dc9863..47c7e2a907 100644 --- a/docs/getting_started/how-cosmos-works.rst +++ b/docs/getting_started/how-cosmos-works.rst @@ -1,7 +1,7 @@ .. _how-cosmos-works: How Cosmos works ----------------- +================ Cosmos is an open-source Python package developed by Astronomer under the Apache License 2.0. The package includes classes that automatically turn dbt Core and dbt Fusion projects into `Apache Airflow® `_ @@ -12,7 +12,7 @@ advanced orchestration capabilities to integrate your dbt projects into your end run automatically. Overview -++++++++ +~~~~~~~~ Cosmos creates an interface between a dbt project and Airflow, allowing you to translate your dbt project into a Dag. Then, your Cosmos configuration provides the necessary configurations to Airflow so that it can schedule and initiate running your dbt code when you want. @@ -26,12 +26,12 @@ You have a number of configuration options, but fundamentally, Cosmos provides t :alt: Diagram showing that Cosmos parses dbt projects, with options defined by the ProfileConfig and RenderConfig. Then, Cosmos executes the resulting Dag, with options defined by ExecutionConfig and ProjectConfig. Quickstart -++++++++++ +~~~~~~~~~~ Even though Cosmos is highly extensible, and you have many advanced customization options, you can run a demo with the `Astro CLI `_ in just a few minutes. This demo introduces you to the key elements required for Cosmos to parse dbt projects and run Dags. Bring your own project -++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~ If you have existing resources for Airflow or dbt, and want to start exploring how to get started with a project more similar to your use case, check out the **Get started with Cosmos** guides. These resources provide more general recommendations for how to create a new project. diff --git a/docs/getting_started/index.rst b/docs/getting_started/index.rst index ef58a29a99..62bf6a4841 100644 --- a/docs/getting_started/index.rst +++ b/docs/getting_started/index.rst @@ -1,7 +1,7 @@ .. _getting_started: Getting started with Cosmos ---------------------------- +=========================== .. toctree:: :maxdepth: 0 @@ -10,7 +10,7 @@ Getting started with Cosmos self Cosmos fundamentals -+++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~ Learn the important concepts and key ideas for how Cosmos works with Airflow and dbt. @@ -23,7 +23,7 @@ Learn the important concepts and key ideas for how Cosmos works with Airflow and Cosmos core concepts Quickstart -++++++++++ +~~~~~~~~~~ Run Cosmos in just a few minutes using a pre-configured demo, such as the `cosmos-demo `_. These guides are an ideal place to start if you don't have a particular project that you want to try out, and just want walk through the process of setting up Cosmos, Airflow, and a dbt project to work together. @@ -36,7 +36,7 @@ These guides are an ideal place to start if you don't have a particular project Astro CLI quickstart Bring your own project -++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~ An example workflow for setting up Cosmos, depending on how you run Airflow. For guides that describe specific scenarios or platforms that you want to use, see the following: diff --git a/docs/getting_started/mwaa.rst b/docs/getting_started/mwaa.rst index 6f6f363b6e..5e28ac2b94 100644 --- a/docs/getting_started/mwaa.rst +++ b/docs/getting_started/mwaa.rst @@ -1,7 +1,7 @@ .. _mwaa: Getting Started with Cosmos on Amazon Managed Workflows -------------------------------------------------------- +======================================================= Users can face Python dependency issues when trying to use the Cosmos `Local Execution Mode `_ in Amazon Managed Workflows for `Apache Airflow® `_ (MWAA). @@ -10,7 +10,7 @@ This step-by-step illustrates how to use the Local Execution Mode, together with the ``dbt_executable_path`` argument. Create a Startup Script -+++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~ MWAA allows users to run a startup script before the scheduler and webserver are started. This is a great place to install dbt into a virtual environment. @@ -32,7 +32,7 @@ To do so: Install Cosmos -++++++++++++++ +~~~~~~~~~~~~~~ Add the following to your base project ``requirements.txt``: @@ -42,7 +42,7 @@ Add the following to your base project ``requirements.txt``: Move your dbt project into the DAGs directory -+++++++++++++++++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Make a new folder, ``dbt``, inside your local ``dags`` folder. Then, copy/paste your dbt project into the directory and create a file called ``my_cosmos_dag.py`` in the root of your DAGs directory. Your folder structure should look like this: @@ -78,7 +78,7 @@ For example, if you wanted to put your dbt project in the ``/usr/local/airflow/d Create your DAG -+++++++++++++++ +~~~~~~~~~~~~~~~ In your ``my_cosmos_dag.py`` file, import the ``DbtDag`` class from Cosmos and create a new DAG instance. Make sure to use the ``dbt_executable_path`` argument to point to the virtual environment you created in step 1. diff --git a/docs/getting_started/open-source.rst b/docs/getting_started/open-source.rst index d7265cabff..9fef7d373f 100644 --- a/docs/getting_started/open-source.rst +++ b/docs/getting_started/open-source.rst @@ -1,12 +1,12 @@ .. _open-source: Getting Started with Cosmos on Open-source Apache Airflow® ------------------------------------------------------------ +=========================================================== When running open-source `Apache Airflow® `_, your setup may vary. This guide assumes you have access to edit the underlying image. Create a virtual environment -++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Create a virtual environment in your ``Dockerfile`` using the sample below. Be sure to replace ```` with the actual adapter you need (i.e. ``dbt-redshift``, ``dbt-snowflake``). It's recommended to use a virtual environment because dbt and Airflow can have conflicting dependencies. @@ -20,13 +20,13 @@ Create a virtual environment in your ``Dockerfile`` using the sample below. Be s Install Cosmos -++++++++++++++ +~~~~~~~~~~~~~~ Install ``astronomer-cosmos`` however you install Python packages in your environment. Move your dbt project into the DAGs directory -+++++++++++++++++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Make a new folder, ``dbt``, inside your local project's ``dags`` folder. Then, copy/paste your dbt project into the directory and create a file called ``my_cosmos_dag.py`` in the root of your DAGs directory. diff --git a/docs/getting_started/oss-quickstart.rst b/docs/getting_started/oss-quickstart.rst index 09269a25bc..65328e6af6 100644 --- a/docs/getting_started/oss-quickstart.rst +++ b/docs/getting_started/oss-quickstart.rst @@ -1,7 +1,7 @@ .. _oss-quickstart: Run Cosmos in open-source Apache Airflow® ------------------------------------------- +========================================== Quickly get started working locally with `Apache Airflow® `_, dbt, and Astronomer Cosmos using the `Open-source Airflow `_. @@ -15,13 +15,13 @@ By the end of this quickstart, you will: - (Optional) View your Dag run output Prerequisites -+++++++++++++ +~~~~~~~~~~~~~ - A Python version supported by `Airflow `_. - (Optional) Install a database viewer. This guide uses `dBeaver `_. 1. Set up your project environment -++++++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. Create your demo project directory. @@ -55,7 +55,7 @@ The Cosmos project includes Airflow as a dependency, so when you install Cosmos 2. Create your Cosmos project structure -+++++++++++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. Set up separate subdirectories in your demo directory for Dags and dbt project: @@ -72,7 +72,7 @@ Your project structure should look like this: :: └── dbt_project/micro_project/ 3. Create a minimal dbt project -+++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For the demo dbt project, you need to make some essential components for your project. The dbt commands for this demo take two SQL files, a ``base_model`` that creates a table with greetings in your database, and an ``enriched_model`` table that has transformed values for greetings in the base model. @@ -148,7 +148,7 @@ Add the following content to your new project definition. 4. Create an Airflow Dag -++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~ 1. Now, in your ``dags`` directory, create an Airflow Dag with the following commands: @@ -187,7 +187,7 @@ This Dag tells Airflow and Cosmos where to find the dbt project and profile conf 5. Set environment variables -++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To successfully launch, Airflow needs you to define the some environment variables. These identify the project home directory, ``AIRFLOW_HOME``, and disable additional Airflow and Cosmos features that are not required for local execution. @@ -199,7 +199,7 @@ To successfully launch, Airflow needs you to define the some environment variabl 6. Run Airflow -++++++++++++++ +~~~~~~~~~~~~~~ At this point, you have completed the following project setup steps: @@ -219,7 +219,7 @@ At this point, you have completed the following project setup steps: 2. Airflow autogenerates credentials when it launches that you must use to access the local Airflow UI. Open the ``simple_auth_manager_passwords.json.generated`` file in your ``oss-quickstart`` directory. This file contains the ``{"username": "password"}`` key-value pair for you to use to login to ``localhost:8080``. Use this username and password to log in. 7. Trigger your Dag -+++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~ 1. Open the **Dags** view in Airflow. @@ -228,7 +228,7 @@ At this point, you have completed the following project setup steps: 3. After the Dag successfully finishes, you can explore the `Airflow UI Dag views `_ to view the logs or check the Dag code. 8. (Optional) View results with a database viewer -+++++++++++++++++++++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To view the transformations completed by the Dag, you must use a database viewer to access the views and materializations completed by your dbt project. @@ -262,10 +262,10 @@ To view the transformations completed by the Dag, you must use a database viewer .. _troubleshoot-quickstart: Troubleshooting the quickstart -++++++++++++++++++++++++++++++ +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Can't log in to the Airflow UI -'''''''''''''''''''''''''''''' +++++++++++++++++++++++++++++++ 1. Confirm that you are using a version of Python that is supported by Airflow. These versions are usually included in the `Airflow installation guide `_. @@ -285,7 +285,7 @@ To resolve, open your ``profiles.yml`` file and confirm that you correctly inclu .. _no-quickstart-dags: Dags do not load -'''''''''''''''' +++++++++++++++++ If you encounter issues, like error messages that say **Cosmos Dag not loading** or the **Dags** page on your Airflow UI does not show your Dags, try resetting the Airflow database and reserializing with the following commands. @@ -299,7 +299,7 @@ Then, relaunch Airflow with ``airflow standalone``. .. _quickstart-dags-stall: Example Dags stall -'''''''''''''''''' +++++++++++++++++++ If your ``base_model_run`` Dag gets stuck in **Running** mode, even though the **Logs** show that it completed successfully, and the next Dag does not automatically start.