Per promuovere con maggiore forza il successo e la crescente preferenza dei clienti per le soluzioni OSS, Cloud Composer si sta evolvendo in Managed Service per Apache Airflow . Questo cambio di nome migliora la comprensione del nostro portfolio da parte dei clienti e rafforza il nostro impegno a essere l'ecosistema cloud più aperto.
Managed Airflow (terza generazione) | Managed Airflow (seconda generazione) | Managed Airflow (prima generazione legacy)
Questa pagina descrive come utilizzare gli operatori di Google Kubernetes Engine per creare
cluster in Google Kubernetes Engine e avviare pod Kubernetes in questi
cluster.
Gli operatori di Google Kubernetes Engine eseguono i pod Kubernetes in un cluster specificato, che può essere un cluster separato non correlato al tuo ambiente.
Al contrario, KubernetesPodOperator esegue i pod Kubernetes
nel cluster del tuo ambiente.
Questa pagina ti guida attraverso un DAG di esempio che crea un cluster Google Kubernetes Engine con GKECreateClusterOperator, utilizza GKEStartPodOperator con le seguenti configurazioni e poi lo elimina con GKEDeleteClusterOperator:
Prima di iniziare
Ti consigliamo di utilizzare la versione più recente di Managed Airflow. Come
minimo, questa versione deve essere supportata nell'ambito
delle norme relative al ritiro e all'assistenza .
Configurazione dell'operatore GKE
Per seguire questo esempio, inserisci l'intero gke_operator.py
file nella cartella dags/ del tuo ambiente o
aggiungi il codice pertinente a un DAG.
Crea un cluster
Il codice mostrato qui crea un cluster Google Kubernetes Engine con due pool di nodi, pool-0 e pool-1, ognuno dei quali ha un nodo. Se necessario, puoi impostare
altri parametri dell'API Google Kubernetes Engine come parte del body.
Ti consigliamo di utilizzare i cluster regionali. I cluster a livello di zona sono più esposti a errori a livello di zona. Ad esempio, potresti voler utilizzare la regione us-central1 per il tuo
cluster anziché la zona us-central1-a.
Per saperne di più sulle considerazioni specifiche per le regioni, consulta
Area geografica e regioni .
Prima del rilascio della versione 5.1.0 di apache-airflow-providers-google, non era possibile passare l'oggetto node_pools in GKECreateClusterOperator. Se utilizzi Airflow 2, assicurati che il tuo ambiente utilizzi la versione 5.1.0 o successive di apache-airflow-providers-google. Puoi
installare una versione più recente di questo pacchetto PyPI
specificando apache-airflow-providers-google e >=5.1.0 come versione
richiesta.
Come soluzione alternativa per gli utenti di Airflow 1, utilizziamo
BashOperator e gcloud per creare questi pool di nodi .
Avvia carichi di lavoro nel cluster
Le sezioni seguenti spiegano ogni configurazione di GKEStartPodOperator nell'esempio. Per informazioni su ogni variabile di configurazione, consulta
il riferimento di Airflow per gli operatori GKE .
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
Configurazione minima
Per avviare un pod nel cluster GKE con
il GKEStartPodOperator, sono necessarie solo le opzioni project_id, location, cluster_name,
name, namespace, image e task_id.
Quando inserisci il seguente snippet di codice in un DAG, l'attività pod-ex-minimum ha esito positivo a condizione che i parametri elencati in precedenza siano definiti e validi.
Configurazione del modello
Airflow supporta l'utilizzo dei
modelli Jinja .
Devi dichiarare le variabili richieste (task_id, name, namespace,
e image) con l'operatore. Come mostrato nell'esempio seguente, puoi creare tutti gli altri parametri con Jinja, inclusi cmds, arguments e env_vars.
Senza modificare il DAG o l'ambiente, l'attività ex-kube-templates non riesce. Imposta una variabile Airflow denominata my_value per fare in modo che questo DAG abbia esito positivo.
Per impostare my_value con gcloud o l'UI di Airflow:
gcloud
Per Airflow 2, inserisci il seguente comando:
gcloud composer environments run ENVIRONMENT \
--location LOCATION \
variables set -- \
my_value example_value
Per Airflow 1, inserisci il seguente comando:
gcloud composer environments run ENVIRONMENT \
--location LOCATION \
variables -- \
--set my_value example_value
Sostituisci:
ENVIRONMENT con il nome dell'ambiente.LOCATION con la regione in cui si trova l'ambiente. UI di Airflow
Nell'UI di Airflow 2:
Sulla barra degli strumenti, seleziona Admin (Amministratore) > Variables (Variabili) .
Nella pagina List Variable (Elenca variabili), fai clic su Add a new record (Aggiungi un nuovo record).
Nella pagina Add Variable (Aggiungi variabile), inserisci le seguenti informazioni:
Chiave:my_value
Valore: example_value
Fai clic su Save (Salva).
Nell'UI di Airflow 1:
Sulla barra degli strumenti, seleziona Admin (Amministratore) > Variables (Variabili) .
Nella pagina Variables (Variabili), fai clic sulla scheda Create (Crea).
Nella pagina Variable (Variabile), inserisci le seguenti informazioni:
Chiave:my_value
Valore: example_value
Fai clic su Save (Salva).
Configurazione del modello:
Configurazione dell'affinità dei pod
Quando configuri il parametro affinity in GKEStartPodOperator, controlli i nodi su cui pianificare i pod, ad esempio i nodi solo in un determinato pool di nodi. Quando hai creato il cluster, hai creato due pool di nodi denominati pool-0 e pool-1. Questo operatore impone che i pod vengano eseguiti solo in pool-1.
Posizione di avvio del pod Kubernetes di Managed Airflow con affinità dei pod (fai clic per ingrandire)
Configurazione completa
Questo esempio mostra tutte le variabili che puoi configurare in GKEStartPodOperator. Non devi modificare il codice per l'attività ex-all-configs per avere esito positivo.
Per informazioni dettagliate su ogni variabile, consulta
il riferimento di Airflow per gli operatori GKE .
Elimina il cluster
Il codice mostrato qui elimina il cluster creato all'inizio della guida.
Passaggi successivi
