Um den Erfolg und die wachsende Präferenz der Kunden für OSS-Lösungen noch stärker zu berücksichtigen, wird Cloud Composer zu Managed Service for Apache Airflow weiterentwickelt. Durch diese Namensänderung wird das Verständnis der Kunden für unser Portfolio verbessert und gleichzeitig unser Engagement für das offenste Cloud-Ökosystem unterstrichen.

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Google Kubernetes Engine-Operatoren verwenden

Managed Airflow (Gen 3) | Managed Airflow (Gen 2) | Managed Airflow (Legacy Gen 1)

Auf dieser Seite wird beschrieben, wie Sie mit den Google Kubernetes Engine-Operatoren Cluster in Google Kubernetes Engine erstellen und Kubernetes-Pods in diesen Clustern starten.

Mit Google Kubernetes Engine-Operatoren werden Kubernetes-Pods in einem bestimmten Cluster ausgeführt. Dabei kann es sich um einen separaten Cluster handeln, der nicht mit Ihrer Umgebung in Verbindung steht. Im Vergleich dazu führt KubernetesPodOperator Kubernetes-Pods im Cluster Ihrer Umgebung aus.

Auf dieser Seite werden Sie Schritt für Schritt durch einen Beispiel-DAG geführt, mit dem ein Google Kubernetes Engine-Cluster mit dem GKECreateClusterOperator erstellt wird, der verwendet den GKEStartPodOperator mit den folgenden Konfigurationen und löscht den Cluster dann mit GKEDeleteClusterOperator danach:

Minimalkonfiguration: Legt nur die erforderlichen Parameter fest.
Vorlagenkonfiguration: Verwendet Parameter, die Sie für Vorlagen mit Jinja verwenden können.
Pod-Affinitätskonfiguration: Begrenzt die verfügbaren Knoten, für die Pods geplant werden können.
Vollständige Konfiguration: Umfasst alle Konfigurationen.

Hinweis

Wir empfehlen die Verwendung der neuesten Version von Managed Airflow. Diese Version muss mindestens im Rahmen der Einstellungs- und Supportrichtlinie unterstützt werden.

GKE-Operatorkonfiguration

Zum Ausführen dieses Beispiels legen Sie die gesamte gke_operator.py-Datei in den Ordner dags/ Ihrer Umgebung. Alternativ können Sie den entsprechenden Code einem DAG hinzufügen.

Cluster erstellen

Der hier gezeigte Code erstellt einen Google Kubernetes Engine-Cluster mit zwei Knotenpools, pool-0 und pool-1, die jeweils einen Knoten haben. Bei Bedarf können Sie weitere Parameter aus der Google Kubernetes Engine API als Teil von body festlegen.

Wir empfehlen die Verwendung regionaler Cluster. Zonale Cluster sind anfälliger für Zonenausfälle. Sie können beispielsweise die Region us-central1 für Ihren Cluster anstelle der Zone us-central1-a verwenden. Weitere Informationen zu regionsspezifischen Aspekten finden Sie unter Geografie und Regionen.

Vor dem Release von apache-airflow-providers-google Version 5.1.0 war es nicht möglich, das node_pools-Objekt im GKECreateClusterOperator zu übergeben. Wenn Sie Airflow 2 verwenden, achten Sie darauf, dass Ihre Umgebung apache-airflow-providers-google Version 5.1.0 oder höher verwendet. Sie können eine neuere Version dieses PyPI-Pakets installieren, indem Sie apache-airflow-providers-google und >=5.1.0 als erforderliche Version angeben. Als Behelfslösung für Airflow 1-Nutzer verwenden wir die BashOperator und gcloud, um diese Knotenpools zu erstellen.

Airflow 2

# TODO(developer): update with your values
PROJECT_ID = "my-project-id"
# It is recommended to use regional clusters for increased reliability
# though passing a zone in the location parameter is also valid
CLUSTER_REGION = "us-west1"
CLUSTER_NAME = "example-cluster"
CLUSTER = {
    "name": CLUSTER_NAME,
    "node_pools": [
        {"name": "pool-0", "initial_node_count": 1},
        {"name": "pool-1", "initial_node_count": 1},
    ],
}
create_cluster = GKECreateClusterOperator(
    task_id="create_cluster",
    project_id=PROJECT_ID,
    location=CLUSTER_REGION,
    body=CLUSTER,
)

Airflow 1

# TODO(developer): update with your values
PROJECT_ID = "my-project-id"
CLUSTER_ZONE = "us-west1-a"
CLUSTER_NAME = "example-cluster"
CLUSTER = {"name": CLUSTER_NAME, "initial_node_count": 1}
create_cluster = GKECreateClusterOperator(
    task_id="create_cluster",
    project_id=PROJECT_ID,
    location=CLUSTER_ZONE,
    body=CLUSTER,
)
# Using the BashOperator to create node pools is a workaround
# In Airflow 2, because of https://github.com/apache/airflow/pull/17820
# Node pool creation can be done using the GKECreateClusterOperator

create_node_pools = BashOperator(
    task_id="create_node_pools",
    bash_command=f"gcloud container node-pools create pool-0 \
                    --cluster {CLUSTER_NAME} \
                    --num-nodes 1 \
                    --zone {CLUSTER_ZONE} \
                    && gcloud container node-pools create pool-1 \
                    --cluster {CLUSTER_NAME} \
                    --num-nodes 1 \
                    --zone {CLUSTER_ZONE}",
)

Arbeitslasten im Cluster starten

In den folgenden Abschnitten wird jede GKEStartPodOperator-Konfiguration im Beispiel erläutert. Informationen zu den einzelnen Konfigurationsvariablen finden Sie in der Airflow-Referenz für GKE-Operatoren.

Airflow 2



from airflow import models
from airflow.providers.google.cloud.operators.kubernetes_engine import (
    GKECreateClusterOperator,
    GKEDeleteClusterOperator,
    GKEStartPodOperator,
)
from airflow.utils.dates import days_ago

from kubernetes.client import models as k8s_models


with models.DAG(
    "example_gcp_gke",
    schedule=None,  # Override to match your needs
    start_date=days_ago(1),
    tags=["example"],
) as dag:
    # TODO(developer): update with your values
    PROJECT_ID = "my-project-id"
    # It is recommended to use regional clusters for increased reliability
    # though passing a zone in the location parameter is also valid
    CLUSTER_REGION = "us-west1"
    CLUSTER_NAME = "example-cluster"
    CLUSTER = {
        "name": CLUSTER_NAME,
        "node_pools": [
            {"name": "pool-0", "initial_node_count": 1},
            {"name": "pool-1", "initial_node_count": 1},
        ],
    }
    create_cluster = GKECreateClusterOperator(
        task_id="create_cluster",
        project_id=PROJECT_ID,
        location=CLUSTER_REGION,
        body=CLUSTER,
    )

    kubernetes_min_pod = GKEStartPodOperator(
        # The ID specified for the task.
        task_id="pod-ex-minimum",
        # Name of task you want to run, used to generate Pod ID.
        name="pod-ex-minimum",
        project_id=PROJECT_ID,
        location=CLUSTER_REGION,
        cluster_name=CLUSTER_NAME,
        # Entrypoint of the container, if not specified the Docker container's
        # entrypoint is used. The cmds parameter is templated.
        cmds=["echo"],
        # The namespace to run within Kubernetes, default namespace is
        # `default`.
        namespace="default",
        # Docker image specified. Defaults to hub.docker.com, but any fully
        # qualified URLs will point to a custom repository. Supports private
        # gcr.io images if the Composer Environment is under the same
        # project-id as the gcr.io images and the service account that Composer
        # uses has permission to access the Google Container Registry
        # (the default service account has permission)
        image="marketplace.gcr.io/google/ubuntu2204",
    )

    kubernetes_template_ex = GKEStartPodOperator(
        task_id="ex-kube-templates",
        name="ex-kube-templates",
        project_id=PROJECT_ID,
        location=CLUSTER_REGION,
        cluster_name=CLUSTER_NAME,
        namespace="default",
        image="bash",
        # All parameters below are able to be templated with jinja -- cmds,
        # arguments, env_vars, and config_file. For more information visit:
        # https://airflow.apache.org/docs/apache-airflow/stable/macros-ref.html
        # Entrypoint of the container, if not specified the Docker container's
        # entrypoint is used. The cmds parameter is templated.
        cmds=["echo"],
        # DS in jinja is the execution date as YYYY-MM-DD, this docker image
        # will echo the execution date. Arguments to the entrypoint. The docker
        # image's CMD is used if this is not provided. The arguments parameter
        # is templated.
        arguments=["{{ ds }}"],
        # The var template variable allows you to access variables defined in
        # Airflow UI. In this case we are getting the value of my_value and
        # setting the environment variable `MY_VALUE`. The pod will fail if
        # `my_value` is not set in the Airflow UI.
        env_vars={"MY_VALUE": "{{ var.value.my_value }}"},
    )

    kubernetes_affinity_ex = GKEStartPodOperator(
        task_id="ex-pod-affinity",
        project_id=PROJECT_ID,
        location=CLUSTER_REGION,
        cluster_name=CLUSTER_NAME,
        name="ex-pod-affinity",
        namespace="default",
        image="perl",
        cmds=["perl"],
        arguments=["-Mbignum=bpi", "-wle", "print bpi(2000)"],
        # affinity allows you to constrain which nodes your pod is eligible to
        # be scheduled on, based on labels on the node. In this case, if the
        # label 'cloud.google.com/gke-nodepool' with value
        # 'nodepool-label-value' or 'nodepool-label-value2' is not found on any
        # nodes, it will fail to schedule.
        affinity={
            "nodeAffinity": {
                # requiredDuringSchedulingIgnoredDuringExecution means in order
                # for a pod to be scheduled on a node, the node must have the
                # specified labels. However, if labels on a node change at
                # runtime such that the affinity rules on a pod are no longer
                # met, the pod will still continue to run on the node.
                "requiredDuringSchedulingIgnoredDuringExecution": {
                    "nodeSelectorTerms": [
                        {
                            "matchExpressions": [
                                {
                                    # When nodepools are created in Google Kubernetes
                                    # Engine, the nodes inside of that nodepool are
                                    # automatically assigned the label
                                    # 'cloud.google.com/gke-nodepool' with the value of
                                    # the nodepool's name.
                                    "key": "cloud.google.com/gke-nodepool",
                                    "operator": "In",
                                    # The label key's value that pods can be scheduled
                                    # on.
                                    "values": [
                                        "pool-1",
                                    ],
                                }
                            ]
                        }
                    ]
                }
            }
        },
    )
    kubernetes_full_pod = GKEStartPodOperator(
        task_id="ex-all-configs",
        name="full",
        project_id=PROJECT_ID,
        location=CLUSTER_REGION,
        cluster_name=CLUSTER_NAME,
        namespace="default",
        image="perl:5.34.0",
        # Entrypoint of the container, if not specified the Docker container's
        # entrypoint is used. The cmds parameter is templated.
        cmds=["perl"],
        # Arguments to the entrypoint. The docker image's CMD is used if this
        # is not provided. The arguments parameter is templated.
        arguments=["-Mbignum=bpi", "-wle", "print bpi(2000)"],
        # The secrets to pass to Pod, the Pod will fail to create if the
        # secrets you specify in a Secret object do not exist in Kubernetes.
        secrets=[],
        # Labels to apply to the Pod.
        labels={"pod-label": "label-name"},
        # Timeout to start up the Pod, default is 120.
        startup_timeout_seconds=120,
        # The environment variables to be initialized in the container
        # env_vars are templated.
        env_vars={"EXAMPLE_VAR": "/example/value"},
        # If true, logs stdout output of container. Defaults to True.
        get_logs=True,
        # Determines when to pull a fresh image, if 'IfNotPresent' will cause
        # the Kubelet to skip pulling an image if it already exists. If you
        # want to always pull a new image, set it to 'Always'.
        image_pull_policy="Always",
        # Annotations are non-identifying metadata you can attach to the Pod.
        # Can be a large range of data, and can include characters that are not
        # permitted by labels.
        annotations={"key1": "value1"},
        # Optional resource specifications for Pod, this will allow you to
        # set both cpu and memory limits and requirements.
        # Prior to Airflow 2.3 and the cncf providers package 5.0.0
        # resources were passed as a dictionary. This change was made in
        # https://github.com/apache/airflow/pull/27197
        # Additionally, "memory" and "cpu" were previously named
        # "limit_memory" and "limit_cpu"
        # resources={'limit_memory': "250M", 'limit_cpu': "100m"},
        container_resources=k8s_models.V1ResourceRequirements(
            limits={"memory": "250M", "cpu": "100m"},
        ),
        # If true, the content of /airflow/xcom/return.json from container will
        # also be pushed to an XCom when the container ends.
        do_xcom_push=False,
        # List of Volume objects to pass to the Pod.
        volumes=[],
        # List of VolumeMount objects to pass to the Pod.
        volume_mounts=[],
        # Affinity determines which nodes the Pod can run on based on the
        # config. For more information see:
        # https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
        affinity={},
    )
    delete_cluster = GKEDeleteClusterOperator(
        task_id="delete_cluster",
        name=CLUSTER_NAME,
        project_id=PROJECT_ID,
        location=CLUSTER_REGION,
    )

    create_cluster >> kubernetes_min_pod >> delete_cluster
    create_cluster >> kubernetes_full_pod >> delete_cluster
    create_cluster >> kubernetes_affinity_ex >> delete_cluster
    create_cluster >> kubernetes_template_ex >> delete_cluster

Airflow 1



from airflow import models
from airflow.operators.bash_operator import BashOperator
from airflow.providers.google.cloud.operators.kubernetes_engine import (
    GKECreateClusterOperator,
    GKEDeleteClusterOperator,
    GKEStartPodOperator,
)
from airflow.utils.dates import days_ago


with models.DAG(
    "example_gcp_gke",
    schedule_interval=None,  # Override to match your needs
    start_date=days_ago(1),
    tags=["example"],
) as dag:
    # TODO(developer): update with your values
    PROJECT_ID = "my-project-id"
    CLUSTER_ZONE = "us-west1-a"
    CLUSTER_NAME = "example-cluster"
    CLUSTER = {"name": CLUSTER_NAME, "initial_node_count": 1}
    create_cluster = GKECreateClusterOperator(
        task_id="create_cluster",
        project_id=PROJECT_ID,
        location=CLUSTER_ZONE,
        body=CLUSTER,
    )
    # Using the BashOperator to create node pools is a workaround
    # In Airflow 2, because of https://github.com/apache/airflow/pull/17820
    # Node pool creation can be done using the GKECreateClusterOperator

    create_node_pools = BashOperator(
        task_id="create_node_pools",
        bash_command=f"gcloud container node-pools create pool-0 \
                        --cluster {CLUSTER_NAME} \
                        --num-nodes 1 \
                        --zone {CLUSTER_ZONE} \
                        && gcloud container node-pools create pool-1 \
                        --cluster {CLUSTER_NAME} \
                        --num-nodes 1 \
                        --zone {CLUSTER_ZONE}",
    )

    kubernetes_min_pod = GKEStartPodOperator(
        # The ID specified for the task.
        task_id="pod-ex-minimum",
        # Name of task you want to run, used to generate Pod ID.
        name="pod-ex-minimum",
        project_id=PROJECT_ID,
        location=CLUSTER_ZONE,
        cluster_name=CLUSTER_NAME,
        # Entrypoint of the container, if not specified the Docker container's
        # entrypoint is used. The cmds parameter is templated.
        cmds=["echo"],
        # The namespace to run within Kubernetes, default namespace is
        # `default`.
        namespace="default",
        # Docker image specified. Defaults to hub.docker.com, but any fully
        # qualified URLs will point to a custom repository. Supports private
        # gcr.io images if the Composer Environment is under the same
        # project-id as the gcr.io images and the service account that Composer
        # uses has permission to access the Google Container Registry
        # (the default service account has permission)
        image="marketplace.gcr.io/google/ubuntu2204",
    )

    kubernetes_template_ex = GKEStartPodOperator(
        task_id="ex-kube-templates",
        name="ex-kube-templates",
        project_id=PROJECT_ID,
        location=CLUSTER_ZONE,
        cluster_name=CLUSTER_NAME,
        namespace="default",
        image="bash",
        # All parameters below are able to be templated with jinja -- cmds,
        # arguments, env_vars, and config_file. For more information visit:
        # https://airflow.apache.org/docs/apache-airflow/stable/macros-ref.html
        # Entrypoint of the container, if not specified the Docker container's
        # entrypoint is used. The cmds parameter is templated.
        cmds=["echo"],
        # DS in jinja is the execution date as YYYY-MM-DD, this docker image
        # will echo the execution date. Arguments to the entrypoint. The docker
        # image's CMD is used if this is not provided. The arguments parameter
        # is templated.
        arguments=["{{ ds }}"],
        # The var template variable allows you to access variables defined in
        # Airflow UI. In this case we are getting the value of my_value and
        # setting the environment variable `MY_VALUE`. The pod will fail if
        # `my_value` is not set in the Airflow UI.
        env_vars={"MY_VALUE": "{{ var.value.my_value }}"},
    )

    kubernetes_affinity_ex = GKEStartPodOperator(
        task_id="ex-pod-affinity",
        project_id=PROJECT_ID,
        location=CLUSTER_ZONE,
        cluster_name=CLUSTER_NAME,
        name="ex-pod-affinity",
        namespace="default",
        image="perl",
        cmds=["perl"],
        arguments=["-Mbignum=bpi", "-wle", "print bpi(2000)"],
        # affinity allows you to constrain which nodes your pod is eligible to
        # be scheduled on, based on labels on the node. In this case, if the
        # label 'cloud.google.com/gke-nodepool' with value
        # 'nodepool-label-value' or 'nodepool-label-value2' is not found on any
        # nodes, it will fail to schedule.
        affinity={
            "nodeAffinity": {
                # requiredDuringSchedulingIgnoredDuringExecution means in order
                # for a pod to be scheduled on a node, the node must have the
                # specified labels. However, if labels on a node change at
                # runtime such that the affinity rules on a pod are no longer
                # met, the pod will still continue to run on the node.
                "requiredDuringSchedulingIgnoredDuringExecution": {
                    "nodeSelectorTerms": [
                        {
                            "matchExpressions": [
                                {
                                    # When nodepools are created in Google Kubernetes
                                    # Engine, the nodes inside of that nodepool are
                                    # automatically assigned the label
                                    # 'cloud.google.com/gke-nodepool' with the value of
                                    # the nodepool's name.
                                    "key": "cloud.google.com/gke-nodepool",
                                    "operator": "In",
                                    # The label key's value that pods can be scheduled
                                    # on.
                                    "values": [
                                        "pool-1",
                                    ],
                                }
                            ]
                        }
                    ]
                }
            }
        },
    )
    kubernetes_full_pod = GKEStartPodOperator(
        task_id="ex-all-configs",
        name="full",
        project_id=PROJECT_ID,
        location=CLUSTER_ZONE,
        cluster_name=CLUSTER_NAME,
        namespace="default",
        image="perl",
        # Entrypoint of the container, if not specified the Docker container's
        # entrypoint is used. The cmds parameter is templated.
        cmds=["perl"],
        # Arguments to the entrypoint. The docker image's CMD is used if this
        # is not provided. The arguments parameter is templated.
        arguments=["-Mbignum=bpi", "-wle", "print bpi(2000)"],
        # The secrets to pass to Pod, the Pod will fail to create if the
        # secrets you specify in a Secret object do not exist in Kubernetes.
        secrets=[],
        # Labels to apply to the Pod.
        labels={"pod-label": "label-name"},
        # Timeout to start up the Pod, default is 120.
        startup_timeout_seconds=120,
        # The environment variables to be initialized in the container
        # env_vars are templated.
        env_vars={"EXAMPLE_VAR": "/example/value"},
        # If true, logs stdout output of container. Defaults to True.
        get_logs=True,
        # Determines when to pull a fresh image, if 'IfNotPresent' will cause
        # the Kubelet to skip pulling an image if it already exists. If you
        # want to always pull a new image, set it to 'Always'.
        image_pull_policy="Always",
        # Annotations are non-identifying metadata you can attach to the Pod.
        # Can be a large range of data, and can include characters that are not
        # permitted by labels.
        annotations={"key1": "value1"},
        # Resource specifications for Pod, this will allow you to set both cpu
        # and memory limits and requirements.
        # Prior to Airflow 1.10.4, resource specifications were
        # passed as a Pod Resources Class object,
        # If using this example on a version of Airflow prior to 1.10.4,
        # import the "pod" package from airflow.contrib.kubernetes and use
        # resources = pod.Resources() instead passing a dict
        # For more info see:
        # https://github.com/apache/airflow/pull/4551
        resources={"limit_memory": "250M", "limit_cpu": "100m"},
        # If true, the content of /airflow/xcom/return.json from container will
        # also be pushed to an XCom when the container ends.
        do_xcom_push=False,
        # List of Volume objects to pass to the Pod.
        volumes=[],
        # List of VolumeMount objects to pass to the Pod.
        volume_mounts=[],
        # Affinity determines which nodes the Pod can run on based on the
        # config. For more information see:
        # https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
        affinity={},
    )
    delete_cluster = GKEDeleteClusterOperator(
        task_id="delete_cluster",
        name=CLUSTER_NAME,
        project_id=PROJECT_ID,
        location=CLUSTER_ZONE,
    )

    create_cluster >> create_node_pools >> kubernetes_min_pod >> delete_cluster
    create_cluster >> create_node_pools >> kubernetes_full_pod >> delete_cluster
    create_cluster >> create_node_pools >> kubernetes_affinity_ex >> delete_cluster
    create_cluster >> create_node_pools >> kubernetes_template_ex >> delete_cluster

Minimalkonfiguration

Zum Starten eines Pods in Ihrem GKE-Cluster mit GKEStartPodOperator sind nur project_id, location, cluster_name, name, namespace, image und task_id erforderlich.

Wenn Sie das folgende Code-Snippet in einen DAG einfügen, ist die Aufgabe pod-ex-minimum erfolgreich, solange die zuvor aufgeführten Parameter definiert und gültig sind.

Airflow 2

# TODO(developer): update with your values
PROJECT_ID = "my-project-id"
# It is recommended to use regional clusters for increased reliability
# though passing a zone in the location parameter is also valid
CLUSTER_REGION = "us-west1"
CLUSTER_NAME = "example-cluster"
kubernetes_min_pod = GKEStartPodOperator(
    # The ID specified for the task.
    task_id="pod-ex-minimum",
    # Name of task you want to run, used to generate Pod ID.
    name="pod-ex-minimum",
    project_id=PROJECT_ID,
    location=CLUSTER_REGION,
    cluster_name=CLUSTER_NAME,
    # Entrypoint of the container, if not specified the Docker container's
    # entrypoint is used. The cmds parameter is templated.
    cmds=["echo"],
    # The namespace to run within Kubernetes, default namespace is
    # `default`.
    namespace="default",
    # Docker image specified. Defaults to hub.docker.com, but any fully
    # qualified URLs will point to a custom repository. Supports private
    # gcr.io images if the Composer Environment is under the same
    # project-id as the gcr.io images and the service account that Composer
    # uses has permission to access the Google Container Registry
    # (the default service account has permission)
    image="marketplace.gcr.io/google/ubuntu2204",
)

Airflow 1

# TODO(developer): update with your values
PROJECT_ID = "my-project-id"
CLUSTER_ZONE = "us-west1-a"
CLUSTER_NAME = "example-cluster"
kubernetes_min_pod = GKEStartPodOperator(
    # The ID specified for the task.
    task_id="pod-ex-minimum",
    # Name of task you want to run, used to generate Pod ID.
    name="pod-ex-minimum",
    project_id=PROJECT_ID,
    location=CLUSTER_ZONE,
    cluster_name=CLUSTER_NAME,
    # Entrypoint of the container, if not specified the Docker container's
    # entrypoint is used. The cmds parameter is templated.
    cmds=["echo"],
    # The namespace to run within Kubernetes, default namespace is
    # `default`.
    namespace="default",
    # Docker image specified. Defaults to hub.docker.com, but any fully
    # qualified URLs will point to a custom repository. Supports private
    # gcr.io images if the Composer Environment is under the same
    # project-id as the gcr.io images and the service account that Composer
    # uses has permission to access the Google Container Registry
    # (the default service account has permission)
    image="marketplace.gcr.io/google/ubuntu2204",
)

Vorlagenkonfiguration

Airflow unterstützt die Verwendung von Jinja-Vorlagen. Sie müssen die erforderlichen Variablen (task_id, name, namespace, image) mit dem Operator deklarieren. Wie im folgenden Beispiel gezeigt, können Sie alle anderen Parameter mit Jinja als Vorlage verwenden, einschließlich cmds, arguments, env_vars.

Wenn der DAG oder die Umgebung nicht geändert wird, schlägt die Aufgabe ex-kube-templates fehl. Legen Sie eine Airflow-Variable mit dem Namen my_value fest, damit dieser DAG erfolgreich ist.

So legen Sie my_value mit gcloud oder der Airflow-UI fest:

gcloud

Geben Sie für Airflow 2 den folgenden Befehl ein:

gcloud composer environments run ENVIRONMENT \
    --location LOCATION \
    variables set -- \
    my_value example_value

Geben Sie für Airflow 1 den folgenden Befehl ein:

gcloud composer environments run ENVIRONMENT \
    --location LOCATION \
    variables -- \
    --set my_value example_value

Ersetzen Sie:

ENVIRONMENT durch den Namen der Umgebung.
LOCATION durch die Region, in der sich die Umgebung befindet.

Airflow-UI

In der Airflow 2-UI:

Wählen Sie in der Symbolleiste Admin > Variablen aus.
Klicken Sie auf der Seite Listenvariable auf Neuen Eintrag hinzufügen.
Geben Sie auf der Seite Add Variable (Variable hinzufügen) die folgenden Informationen ein:
- Key: my_value
- Val: example_value
Klicken Sie auf Speichern.

In der Airflow 1-UI:

Klicken Sie in der Symbolleiste auf Admin > Variables.
Klicken Sie auf der Seite Variablen auf den Tab Erstellen.
Geben Sie auf der Seite Variable die folgenden Informationen ein:
- Key: my_value
- Val: example_value
Klicken Sie auf Speichern.

Vorlagenkonfiguration

Airflow 2

# TODO(developer): update with your values
PROJECT_ID = "my-project-id"
# It is recommended to use regional clusters for increased reliability
# though passing a zone in the location parameter is also valid
CLUSTER_REGION = "us-west1"
CLUSTER_NAME = "example-cluster"
kubernetes_template_ex = GKEStartPodOperator(
    task_id="ex-kube-templates",
    name="ex-kube-templates",
    project_id=PROJECT_ID,
    location=CLUSTER_REGION,
    cluster_name=CLUSTER_NAME,
    namespace="default",
    image="bash",
    # All parameters below are able to be templated with jinja -- cmds,
    # arguments, env_vars, and config_file. For more information visit:
    # https://airflow.apache.org/docs/apache-airflow/stable/macros-ref.html
    # Entrypoint of the container, if not specified the Docker container's
    # entrypoint is used. The cmds parameter is templated.
    cmds=["echo"],
    # DS in jinja is the execution date as YYYY-MM-DD, this docker image
    # will echo the execution date. Arguments to the entrypoint. The docker
    # image's CMD is used if this is not provided. The arguments parameter
    # is templated.
    arguments=["{{ ds }}"],
    # The var template variable allows you to access variables defined in
    # Airflow UI. In this case we are getting the value of my_value and
    # setting the environment variable `MY_VALUE`. The pod will fail if
    # `my_value` is not set in the Airflow UI.
    env_vars={"MY_VALUE": "{{ var.value.my_value }}"},
)

Airflow 1

# TODO(developer): update with your values
PROJECT_ID = "my-project-id"
CLUSTER_ZONE = "us-west1-a"
CLUSTER_NAME = "example-cluster"
kubernetes_template_ex = GKEStartPodOperator(
    task_id="ex-kube-templates",
    name="ex-kube-templates",
    project_id=PROJECT_ID,
    location=CLUSTER_ZONE,
    cluster_name=CLUSTER_NAME,
    namespace="default",
    image="bash",
    # All parameters below are able to be templated with jinja -- cmds,
    # arguments, env_vars, and config_file. For more information visit:
    # https://airflow.apache.org/docs/apache-airflow/stable/macros-ref.html
    # Entrypoint of the container, if not specified the Docker container's
    # entrypoint is used. The cmds parameter is templated.
    cmds=["echo"],
    # DS in jinja is the execution date as YYYY-MM-DD, this docker image
    # will echo the execution date. Arguments to the entrypoint. The docker
    # image's CMD is used if this is not provided. The arguments parameter
    # is templated.
    arguments=["{{ ds }}"],
    # The var template variable allows you to access variables defined in
    # Airflow UI. In this case we are getting the value of my_value and
    # setting the environment variable `MY_VALUE`. The pod will fail if
    # `my_value` is not set in the Airflow UI.
    env_vars={"MY_VALUE": "{{ var.value.my_value }}"},
)

Pod-Affinitätskonfiguration

Mit der Konfiguration des Parameters affinity in GKEStartPodOperator können Sie steuern, auf welchen Knoten Pods geplant werden sollen, beispielsweise nur auf Knoten in einem bestimmten Knotenpool. Beim Erstellen des Clusters haben Sie zwei Knotenpools mit den Namen pool-0 und pool-1 erstellt. Dieser Operator legt fest, dass Pods nur in pool-1 ausgeführt werden dürfen.

Pfeil der Managed Airflow-Umgebung, der zeigt, dass sich die gestarteten Pods in einem flüchtigen GKE-Cluster in Pool-1 befinden werden, wobei auch ein separates Feld pool-0 in der Kubernetes Engine-Gruppe gezeigt wird. — Managed Airflow Kubernetes Pod-Startort mit Pod-Affinität (zum Vergrößern klicken)

Airflow 2

# TODO(developer): update with your values
PROJECT_ID = "my-project-id"
# It is recommended to use regional clusters for increased reliability
# though passing a zone in the location parameter is also valid
CLUSTER_REGION = "us-west1"
CLUSTER_NAME = "example-cluster"
kubernetes_affinity_ex = GKEStartPodOperator(
    task_id="ex-pod-affinity",
    project_id=PROJECT_ID,
    location=CLUSTER_REGION,
    cluster_name=CLUSTER_NAME,
    name="ex-pod-affinity",
    namespace="default",
    image="perl",
    cmds=["perl"],
    arguments=["-Mbignum=bpi", "-wle", "print bpi(2000)"],
    # affinity allows you to constrain which nodes your pod is eligible to
    # be scheduled on, based on labels on the node. In this case, if the
    # label 'cloud.google.com/gke-nodepool' with value
    # 'nodepool-label-value' or 'nodepool-label-value2' is not found on any
    # nodes, it will fail to schedule.
    affinity={
        "nodeAffinity": {
            # requiredDuringSchedulingIgnoredDuringExecution means in order
            # for a pod to be scheduled on a node, the node must have the
            # specified labels. However, if labels on a node change at
            # runtime such that the affinity rules on a pod are no longer
            # met, the pod will still continue to run on the node.
            "requiredDuringSchedulingIgnoredDuringExecution": {
                "nodeSelectorTerms": [
                    {
                        "matchExpressions": [
                            {
                                # When nodepools are created in Google Kubernetes
                                # Engine, the nodes inside of that nodepool are
                                # automatically assigned the label
                                # 'cloud.google.com/gke-nodepool' with the value of
                                # the nodepool's name.
                                "key": "cloud.google.com/gke-nodepool",
                                "operator": "In",
                                # The label key's value that pods can be scheduled
                                # on.
                                "values": [
                                    "pool-1",
                                ],
                            }
                        ]
                    }
                ]
            }
        }
    },
)

Airflow 1

# TODO(developer): update with your values
PROJECT_ID = "my-project-id"
CLUSTER_ZONE = "us-west1-a"
CLUSTER_NAME = "example-cluster"
kubernetes_affinity_ex = GKEStartPodOperator(
    task_id="ex-pod-affinity",
    project_id=PROJECT_ID,
    location=CLUSTER_ZONE,
    cluster_name=CLUSTER_NAME,
    name="ex-pod-affinity",
    namespace="default",
    image="perl",
    cmds=["perl"],
    arguments=["-Mbignum=bpi", "-wle", "print bpi(2000)"],
    # affinity allows you to constrain which nodes your pod is eligible to
    # be scheduled on, based on labels on the node. In this case, if the
    # label 'cloud.google.com/gke-nodepool' with value
    # 'nodepool-label-value' or 'nodepool-label-value2' is not found on any
    # nodes, it will fail to schedule.
    affinity={
        "nodeAffinity": {
            # requiredDuringSchedulingIgnoredDuringExecution means in order
            # for a pod to be scheduled on a node, the node must have the
            # specified labels. However, if labels on a node change at
            # runtime such that the affinity rules on a pod are no longer
            # met, the pod will still continue to run on the node.
            "requiredDuringSchedulingIgnoredDuringExecution": {
                "nodeSelectorTerms": [
                    {
                        "matchExpressions": [
                            {
                                # When nodepools are created in Google Kubernetes
                                # Engine, the nodes inside of that nodepool are
                                # automatically assigned the label
                                # 'cloud.google.com/gke-nodepool' with the value of
                                # the nodepool's name.
                                "key": "cloud.google.com/gke-nodepool",
                                "operator": "In",
                                # The label key's value that pods can be scheduled
                                # on.
                                "values": [
                                    "pool-1",
                                ],
                            }
                        ]
                    }
                ]
            }
        }
    },
)

Vollständige Konfiguration

In diesem Beispiel werden alle Variablen gezeigt, die Sie in GKEStartPodOperator konfigurieren können. Sie müssen den Code nicht ändern, damit die Aufgabe ex-all-configs erfolgreich ausgeführt wird.

Weitere Informationen zu den einzelnen Variablen finden Sie in der Airflow-Referenz für GKE-Operatoren.

Airflow 2

# TODO(developer): update with your values
PROJECT_ID = "my-project-id"
# It is recommended to use regional clusters for increased reliability
# though passing a zone in the location parameter is also valid
CLUSTER_REGION = "us-west1"
CLUSTER_NAME = "example-cluster"
kubernetes_full_pod = GKEStartPodOperator(
    task_id="ex-all-configs",
    name="full",
    project_id=PROJECT_ID,
    location=CLUSTER_REGION,
    cluster_name=CLUSTER_NAME,
    namespace="default",
    image="perl:5.34.0",
    # Entrypoint of the container, if not specified the Docker container's
    # entrypoint is used. The cmds parameter is templated.
    cmds=["perl"],
    # Arguments to the entrypoint. The docker image's CMD is used if this
    # is not provided. The arguments parameter is templated.
    arguments=["-Mbignum=bpi", "-wle", "print bpi(2000)"],
    # The secrets to pass to Pod, the Pod will fail to create if the
    # secrets you specify in a Secret object do not exist in Kubernetes.
    secrets=[],
    # Labels to apply to the Pod.
    labels={"pod-label": "label-name"},
    # Timeout to start up the Pod, default is 120.
    startup_timeout_seconds=120,
    # The environment variables to be initialized in the container
    # env_vars are templated.
    env_vars={"EXAMPLE_VAR": "/example/value"},
    # If true, logs stdout output of container. Defaults to True.
    get_logs=True,
    # Determines when to pull a fresh image, if 'IfNotPresent' will cause
    # the Kubelet to skip pulling an image if it already exists. If you
    # want to always pull a new image, set it to 'Always'.
    image_pull_policy="Always",
    # Annotations are non-identifying metadata you can attach to the Pod.
    # Can be a large range of data, and can include characters that are not
    # permitted by labels.
    annotations={"key1": "value1"},
    # Optional resource specifications for Pod, this will allow you to
    # set both cpu and memory limits and requirements.
    # Prior to Airflow 2.3 and the cncf providers package 5.0.0
    # resources were passed as a dictionary. This change was made in
    # https://github.com/apache/airflow/pull/27197
    # Additionally, "memory" and "cpu" were previously named
    # "limit_memory" and "limit_cpu"
    # resources={'limit_memory': "250M", 'limit_cpu': "100m"},
    container_resources=k8s_models.V1ResourceRequirements(
        limits={"memory": "250M", "cpu": "100m"},
    ),
    # If true, the content of /airflow/xcom/return.json from container will
    # also be pushed to an XCom when the container ends.
    do_xcom_push=False,
    # List of Volume objects to pass to the Pod.
    volumes=[],
    # List of VolumeMount objects to pass to the Pod.
    volume_mounts=[],
    # Affinity determines which nodes the Pod can run on based on the
    # config. For more information see:
    # https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
    affinity={},
)

Airflow 1

# TODO(developer): update with your values
PROJECT_ID = "my-project-id"
CLUSTER_ZONE = "us-west1-a"
CLUSTER_NAME = "example-cluster"
kubernetes_full_pod = GKEStartPodOperator(
    task_id="ex-all-configs",
    name="full",
    project_id=PROJECT_ID,
    location=CLUSTER_ZONE,
    cluster_name=CLUSTER_NAME,
    namespace="default",
    image="perl",
    # Entrypoint of the container, if not specified the Docker container's
    # entrypoint is used. The cmds parameter is templated.
    cmds=["perl"],
    # Arguments to the entrypoint. The docker image's CMD is used if this
    # is not provided. The arguments parameter is templated.
    arguments=["-Mbignum=bpi", "-wle", "print bpi(2000)"],
    # The secrets to pass to Pod, the Pod will fail to create if the
    # secrets you specify in a Secret object do not exist in Kubernetes.
    secrets=[],
    # Labels to apply to the Pod.
    labels={"pod-label": "label-name"},
    # Timeout to start up the Pod, default is 120.
    startup_timeout_seconds=120,
    # The environment variables to be initialized in the container
    # env_vars are templated.
    env_vars={"EXAMPLE_VAR": "/example/value"},
    # If true, logs stdout output of container. Defaults to True.
    get_logs=True,
    # Determines when to pull a fresh image, if 'IfNotPresent' will cause
    # the Kubelet to skip pulling an image if it already exists. If you
    # want to always pull a new image, set it to 'Always'.
    image_pull_policy="Always",
    # Annotations are non-identifying metadata you can attach to the Pod.
    # Can be a large range of data, and can include characters that are not
    # permitted by labels.
    annotations={"key1": "value1"},
    # Resource specifications for Pod, this will allow you to set both cpu
    # and memory limits and requirements.
    # Prior to Airflow 1.10.4, resource specifications were
    # passed as a Pod Resources Class object,
    # If using this example on a version of Airflow prior to 1.10.4,
    # import the "pod" package from airflow.contrib.kubernetes and use
    # resources = pod.Resources() instead passing a dict
    # For more info see:
    # https://github.com/apache/airflow/pull/4551
    resources={"limit_memory": "250M", "limit_cpu": "100m"},
    # If true, the content of /airflow/xcom/return.json from container will
    # also be pushed to an XCom when the container ends.
    do_xcom_push=False,
    # List of Volume objects to pass to the Pod.
    volumes=[],
    # List of VolumeMount objects to pass to the Pod.
    volume_mounts=[],
    # Affinity determines which nodes the Pod can run on based on the
    # config. For more information see:
    # https://kubernetes.io/docs/concepts/configuration/assign-pod-node/
    affinity={},
)

Cluster löschen

Der hier gezeigte Code löscht den Cluster, der zu Beginn der Anleitung erstellt wurde.

Airflow 2

delete_cluster = GKEDeleteClusterOperator(
    task_id="delete_cluster",
    name=CLUSTER_NAME,
    project_id=PROJECT_ID,
    location=CLUSTER_REGION,
)

Airflow 1

delete_cluster = GKEDeleteClusterOperator(
    task_id="delete_cluster",
    name=CLUSTER_NAME,
    project_id=PROJECT_ID,
    location=CLUSTER_ZONE,
)

Nächste Schritte

Den KubernetesPodOperator verwenden
DAGs verwalten

Google Kubernetes Engine-Operatoren verwenden Mit Sammlungen den Überblick behalten Sie können Inhalte basierend auf Ihren Einstellungen speichern und kategorisieren.

Hinweis

GKE-Operatorkonfiguration

Cluster erstellen

Airflow 2

Airflow 1

Arbeitslasten im Cluster starten

Airflow 2

Airflow 1

Minimalkonfiguration

Airflow 2

Airflow 1

Vorlagenkonfiguration

gcloud

Airflow-UI

Airflow 2

Airflow 1

Pod-Affinitätskonfiguration

Airflow 2

Airflow 1

Vollständige Konfiguration

Airflow 2

Airflow 1

Cluster löschen

Airflow 2

Airflow 1

Nächste Schritte

Google Kubernetes Engine-Operatoren verwenden