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Copy file name to clipboardExpand all lines: docs/modules/airflow/pages/getting_started/first_steps.adoc
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== Airflow
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An Airflow cluster is made of up three components:
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An Airflow cluster is made of up five components, two of which are optional:
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* `webserver`: this provides the main UI for user-interaction
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* `executors`: the CeleryExecutor or KubernetesExecutor nodes over which the job workload is distributed by the scheduler
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* `scheduler`: responsible for triggering jobs and persisting their metadata to the backend database
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* `dagProcessors`: (Optional) responsible for monitoring, parsing and preparing DAGs for processing.
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If this role is not specified then the process will be started as a scheduler subprocess (Airflow 2.x), or as a standalone process in the same container as the scheduler (Airflow 3.x+)
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* `triggerers`: (Optional) DAGs making use of deferrable operators can be used together with one or more triggerer processes to free up worker slots.
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This deferral process is also useful for providing a measure of high availability
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Create a file named `airflow.yaml` with the following contents:
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airflow-redis-replicas 1/1 16m
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airflow-scheduler-default 1/1 11m
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airflow-webserver-default 1/1 11m
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airflow-celery-executor-default 2/2 11m
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airflow-celery-executor-default 1/1 11m
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airflow-dagprocessor-default 1/1 11m
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airflow-triggerer-default 1/1 11m
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----
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When the Airflow cluster has been created and the database is initialized, Airflow can be opened in the
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=== Custom resources
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The AirflowCluster is the resource for the configuration of the Airflow instance.
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The resource defines three xref:concepts:roles-and-role-groups.adoc[roles]: `webserver`, `worker` and `scheduler` (the `worker` role is embedded within `spec.celeryExecutors`: this is described in the next section).
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The resource defines five xref:concepts:roles-and-role-groups.adoc[roles]: `webserver`, `worker`, `scheduler`, `dagProcessor` and `triggerer` (the `worker` role is embedded within `spec.celeryExecutors`: this is described in the next section).
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The `dagProcessor` and `triggerer` roles are optional.
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The various configuration options are explained in the xref:usage-guide/index.adoc[].
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It helps you tune your cluster to your needs by configuring xref:usage-guide/storage-resources.adoc[resource usage], xref:usage-guide/security.adoc[security], xref:usage-guide/logging.adoc[logging] and more.
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...
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=== DAG-Processors
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In Airflow 2.x, a DAG-Processor can be started either as a standalone process or a subprocess within the scheduler component.
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For Airflow 3.x+ it _must_ be started as a standalone process, either in a separate container or in the scheduler container.
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In each case the default will be applied (subprocess or combined in the scheduler container) if the role is not specified.
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=== Triggerers
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DAGs using deferrable operators can be combined with the triggerer component to free up worker slots and/or provide high availability.
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For more information, please refer to the {deferrable-operators}[documentation {external-link-icon}^].
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=== Kubernetes resources
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Based on the custom resources you define, the operator creates ConfigMaps, StatefulSets and Services.
Copy file name to clipboardExpand all lines: docs/modules/airflow/pages/usage-guide/storage-resources.adoc
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= Resource Requests
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:description: Find out about minimal HA Airflow requirements for CPU and memory, with defaults for schedulers, Celery executors, webservers using Kubernetes resource limits.
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:description: Find out about minimal HA Airflow requirements for CPU and memory, with defaults for schedulers, Celery executors, webservers, dagProcessors and triggerers using Kubernetes resource limits.
A minimal HA setup consisting of 2 schedulers, 2 workers and 2 webservers has the following https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/[resource requirements]:
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A minimal HA setup consisting of 2 schedulers, 2 workers, 2 webservers, 2 dag-processors and 1 triggerer has the following https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/[resource requirements]:
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* `8700m` CPU request
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* `17400m` CPU limit
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* `15872Mi` memory request and limit
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* `11600` CPU request
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* `23200` CPU limit
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* `18432Mi` memory request and limit
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Corresponding to the values above, the operator uses the following resource defaults:
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