From Newsgroup: rec.sport.rowing
<div>I know this is due to the auth_backends not being set up properly. My question is what should I add to the Helm chart's value.yaml file in order for it to initialize the auth_backends in the config? The Airflow docs seem outdated on this point.</div><div></div><div></div><div>I am trying to deploy Airflow using Helm charts, for a personal POC, but I have been facing some issues with the deployment and could not find clear instructions to solve my issue - which is why I am seeking help here.</div><div></div><div></div><div></div><div>airflow helm chart download</div><div></div><div>Download File ✫✫✫
https://t.co/hLFhEz1VCP</div><div></div><div></div><div></div><div></div><div></div><div></div><div>A bit of background of the POC first - I want to deploy a K8S cluster that hosts airflow, connect it to a git repo that hosts the dags and has the metastore and cache hosted externally from K8S Airflow.</div><div></div><div></div><div>I have successfully deployed Airflow to a local Kubernetes cluster using kind and Airflow's default helm chart. On the helm chart I have specified that the executor mode to be used must be KubernetesExecutor.</div><div></div><div></div><div>I am having issues with connecting Airflow with the external services - I have created an Azure PostgreSQL server, created a airflow database, created a admin user on the psql as follows:</div><div></div><div></div><div>Perhaps remove config for externalDatabase and resultBackendConnection. Why? - because resultBackendConnection will use metadataConnection if it is not configured. I haven't seen the externalDatabase key in my current config file v2.2.4. Are you using -f values.yaml to override helm install with the correct values.yaml?</div><div></div><div></div><div>While there are some successes with using other tools like poetry or pip-tools, they do not share the same workflow as pip - especially when it comes to constraint vs. requirements management. Installing via Poetry or pip-tools is not currently supported. If you wish to install airflow using those tools you should use the constraint files and convert them to appropriate format and workflow that your tool requires.</div><div></div><div></div><div>As for your question on Forum Post v GitHub Issue, I think this is the right way to do it mgoeke! We monitor our forum frequently and help provide guidance as much as we can. Questions here are more easily searchable in Google and thus are better positioned to help other users running into the same problem. If we identify and validate a bug that needs to be addressed in airflow-chart, ap-airflow etc, our team can take care of creating a GH issue in the right place.</div><div></div><div></div><div>Connections in Airflow are a way to store credentials for databases, APIs, cloud services, and other systems that require authentication. You can add connections to your Airflow deployment by defining them in the values.yaml file under the airflow.connections section.</div><div></div><div></div><div></div><div></div><div></div><div></div><div>Variables in Airflow are a way to store and retrieve arbitrary content or settings as a simple key-value store within Airflow. You can add variables to your Airflow deployment by defining them in the values.yaml file under the airflow.variables section.</div><div></div><div></div><div>Environment variables can be used to set values that are needed by your DAGs. You can add environment variables to your Airflow deployment by defining them in the values.yaml file under the airflow.extraEnv section.</div><div></div><div></div><div>After you've defined your connections, variables, and environment variables in the values.yaml file, you can deploy your Airflow instance with these settings by running the helm install or helm upgrade command.</div><div></div><div></div><div>The values.yaml file is a crucial component in Helm charts, which are packages for Kubernetes applications. This file allows you to customize your Helm chart with key-value pairs that can be used throughout the chart.</div><div></div><div></div><div>The extraSecrets section allows you to define additional secrets that will be managed by the Helm chart. This is useful when you need to add more secrets than the ones defined in the secret section or when you want to override the existing secrets.</div><div></div><div></div><div>In this example, the extraSecrets section is defining a secret named .Release.Name -airflow-connections of type Opaque. The secret contains two pieces of data (AIRFLOW_CONN_GCP and AIRFLOW_CONN_AWS) and one string data (AIRFLOW_CONN_OTHER).</div><div></div><div></div><div>The values.yaml file is a default configuration file used in Helm charts. It contains key-value pairs that define the default configuration values for a Helm chart. However, you might want to override these default values with your own custom values. This can be done using an override.yaml file.</div><div></div><div></div><div>You can inject environment variables into Airflow using the Helm chart by modifying the values.yaml file. The env section of the values.yaml file allows you to specify environment variables that will be applied to all Airflow containers.</div><div></div><div></div><div>The Helm chart also provides built-in secrets that are set via environment variables by default. You can enable or disable these built-in secrets using the enableBuiltInSecretEnvVars section of the values.yaml file. Here is an example:</div><div></div><div></div><div>The Airflow Helm chart also provides built-in secrets that are set via environment variables by default. If you want to provide some of these variables with _CMD or _SECRET variable, you should disable setting of those variables by setting the relevant configuration to false under enableBuiltInSecretEnvVars. Here is an example:</div><div></div><div></div><div>In this example, two secrets are created. The first secret, .Release.Name -airflow-connections, contains connection strings for GCP and AWS, which are base64 encoded. It also contains another connection string AIRFLOW_CONN_OTHER that is not base64 encoded. The second secret, .Release.Name -other-secret-name-suffix, can contain any other data you want to include.</div><div></div><div></div><div>I hardly try to install an official airflow chart on my Kubernetes cluster - and it seems pretty easy based on -chart/1.4.0/. But in real life I meet issues, such that pods cannot find persistent volumes in my system for logs, Postgres, and Redis</div><div></div><div></div><div>I checked a lot of different tutorials about how to set up correctly airflow 2 on Kubernetes - but in all of them, I didn't mention this issue. I know how to create volumes by myself for the cluster - but looks like I do something wrong because everyone else setup without it</div><div></div><div></div><div>Apache Airflow is an open source workflow management tool used to author, schedule, and monitor ETL pipelines and machine learning workflows among other uses. To make easy to deploy a scalable Apache Arflow in production environments, Bitnami provides an Apache Airflow Helm chart comprised, by default, of three synchronized nodes: web server, scheduler, and workers. You can add more nodes at deployment time or scale the solution once deployed.</div><div></div><div></div><div>Users of Airflow create Directed Acyclic Graph (DAG) files to define the processes and tasks that must be executed, in what order, and their relationships and dependencies. DAG files can be loaded into the Airflow chart. This tutorial shows how to deploy the Bitnami Helm chart for Apache Airflow loading DAG files from a Git repository at deployment time. In addition, you will learn how to add new DAG files to your repository and upgrade the deployment to update your DAGs dashboard. This process is the same that you should follow in case you want to introduce any change in your DAG files.</div><div></div><div></div><div>To successfully load your custom DAGs into the chart from a GitHub repository, it is necessary to only store DAG files in the repository you will synchronize with your deployment. If you use the example repository provided in this guide, clone or fork the whole repository and then, make sure that you move the airflow-dag-examples folder content to a standalone repository.</div><div></div><div></div><div>The Apache Airflow chart requires to provide an external URL at installation time. This can be done once the chart is deployed. In that case, you'll need to configure Airflow with a resolvable host and later, upgrade the chart. To avoid the possibility of overwriting the initial parameters you added to load your DAG files, the command above includes the required parameter to configure the URL of your service.</div><div></div><div></div><div>Go to your DevOps Project, click Build Pipelines, select your build pipeline for airflow-helm, then click Parameters tab and add the following parameters:</div><div></div><div></div><div>Create a new stage to create ingress for airflow in OKE, select Apply Manifest to your Kubernetes cluster. Since we set up an ingress controller to access our microservices from outside OKE, we also need to set up for the airflow.</div><div></div><div></div><div>Update your build pipeline for airflow and add a trigger to deployment. In order to start the deployment automatically after the build pipeline is completed, we need to add a trigger on the airflow-helm build pipeline.</div><div></div><div></div><div>Note: The go-microservices is a piece of code that will interact with the OCI resources. Since we just created the build pipeline for airflow, we need to make go-microservices aware of the build pipeline it needs to call. For that, we will do some changes on the configmap values that will be injected inside the container in OKE.</div><div></div><div></div><div>The initial way of setting up an airflow environment is usually Standalone. In this initial form, use is made of a SequentialExecutor, which uses a SQLite as a backend, which entails a loss of parallelism. This mode runs a single process on the same machine as the Scheduler and allows the execution of a single task simultaneously, as the name suggests.</div><div></div><div></div><div>One important concept used in Airflow is DAGs (Directed Acyclical Graphs).</div><div></div><div>A DAG is a graph without any cycles. In other words, a node in your graph may never point back to a node higher up in your workflow.</div><div></div><div>DAGs are used to model your workflows/pipelines, which essentially means that you are building and executing graphs when working with Airflow.</div><div></div><div>You can read more about DAGs here: -airflow/stable/core-concepts/dags.html</div><div></div><div></div><div>Note: The example configuration references the pre-built elyra/airflow container image, which has the airflow-notebook package pre-installed. This package contains the operator that supports running of Jupyter notebooks or Python scripts as tasks.</div><div></div><div> df19127ead</div>
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