一、前言
Airflow是Airbnb的基于DAG(有向无环图)的任务管理系统，是进行任务分割、调度处理的利器。在生产实践中，有业务部门需要使用airflow来进行大批量数据的分多阶段、阶段内高并发的处理；结合airflow的任务分割调度能力和Kubernetes的集群资源动态调配能力，就可以快速达到业务目标。
转载自https://blog.youkuaiyun.com/cloudvtech
二、安装支持Kubernetets的airflow
2.1 获取airflow代码
2.2 修改airflow文件支持本地Kubernetes环境
- patch Dockerfile
- scripts/ci/kubernetes/kube/airflow.yaml
- scripts/ci/kubernetes/kube/configmaps.yaml
- scripts/ci/kubernetes/kube/deploy.sh
- scripts/ci/kubernetes/kube/volumes.yaml
1.patch Dockerfile
Add above kubeconfig file as /root/.kube/config for airflow image
RUN mkdir /root/.kube
COPY config /root/.kube/config
2. scripts/ci/kubernetes/kube/airflow.yaml
- namespace: default
+ namespace: air-job
- image: airflow
- imagePullPolicy: IfNotPresent
+ image: 172.222.22.11:5000/airflow
+ imagePullPolicy: Always
3. scripts/ci/kubernetes/kube/configmaps.yaml
- executor = KubernetesExecutor
+ executor = LocalExecutor
- namespace = default
+ namespace = air-job
- rbac = True
+ rbac = False
4. scripts/ci/kubernetes/kube/deploy.sh
-kubectl delete -f $DIRNAME/postgres.yaml
-kubectl delete -f $DIRNAME/airflow.yaml
-kubectl delete -f $DIRNAME/secrets.yaml
+kubectl delete -f $DIRNAME/postgres.yaml -n air-job
+kubectl delete -f $DIRNAME/airflow.yaml -n air-job
+kubectl delete -f $DIRNAME/secrets.yaml -n air-job
-kubectl apply -f $DIRNAME/secrets.yaml
-kubectl apply -f $DIRNAME/configmaps.yaml
-kubectl apply -f $DIRNAME/postgres.yaml
-kubectl apply -f $DIRNAME/volumes.yaml
-kubectl apply -f $DIRNAME/airflow.yaml
+kubectl apply -f $DIRNAME/secrets.yaml -n air-job
+kubectl apply -f $DIRNAME/configmaps.yaml -n air-job
+kubectl apply -f $DIRNAME/postgres.yaml -n air-job
+kubectl apply -f $DIRNAME/volumes.yaml -n air-job
+kubectl apply -f $DIRNAME/airflow.yaml -n air-job
- PODS=$(kubectl get pods | awk 'NR>1 {print $0}')
+ PODS=$(kubectl get pods -n air-job | awk 'NR>1 {print $0}')
-POD=$(kubectl get pods -o go-template --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}' | grep airflow | head -1)
+POD=$(kubectl get pods -n air-job -o go-template --template '{{range .items}}{{.metadata.name}}{{"\n"}}{{end}}' | grep airflow | head -1)
echo "------- pod description -------"
-kubectl describe pod $POD
+kubectl describe pod $POD -n air-job
echo "------- webserver logs -------"
-kubectl logs $POD webserver
+kubectl logs $POD webserver -n air-job
echo "------- scheduler logs -------"
-kubectl logs $POD scheduler
+kubectl logs $POD scheduler -n air-job
5. scripts/ci/kubernetes/kube/volumes.yaml
+ annotations:
+ volume.beta.kubernetes.io/storage-class: "nfs-client"
Also change volume size and remove PersistentVolume
-kind: PersistentVolume
-apiVersion: v1
-metadata:
- name: airflow-dags
-spec:
- accessModes:
- - ReadWriteOnce
- capacity:
- storage: 2Gi
- hostPath:
- path: /airflow-dags/
----
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
name: airflow-dags
+ annotations:
+ volume.beta.kubernetes.io/storage-class: "nfs-client"
spec:
accessModes:
- ReadWriteMany
resources:
requests:
- storage: 2Gi
----
-kind: PersistentVolume
-apiVersion: v1
-metadata:
- name: airflow-logs
-spec:
- accessModes:
- - ReadWriteMany
- capacity:
- storage: 2Gi
- hostPath:
- path: /airflow-logs/
+ storage: 100Gi
---
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
name: airflow-logs
+ annotations:
+ volume.beta.kubernetes.io/storage-class: "nfs-client"
spec:
accessModes:
- ReadWriteMany
resources:
requests:
- storage: 2Gi
----
-kind: PersistentVolume
-apiVersion: v1
-metadata:
- name: test-volume
-spec:
- accessModes:
- - ReadWriteOnce
- capacity:
- storage: 2Gi
- hostPath:
- path: /airflow-dags/
+ storage: 100Gi
---
kind: PersistentVolumeClaim
apiVersion: v1
metadata:
name: test-volume
+ annotations:
+ volume.beta.kubernetes.io/storage-class: "nfs-client"
spec:
accessModes:
- ReadWriteMany
resources:
requests:
- storage: 2Gi
+ storage: 10Gi2.3 构建镜像
export AIRFLOW_GPL_UNIDECODE=yes
./scripts/ci/kubernetes/docker/build.sh
docker tag airflow 172.222.22.11:5000/airflow
docker push 172.222.22.11:5000/airflow2.4 部署
#运行部署脚本
./scripts/ci/kubernetes/kube/deploy.sh
#查看部署结果
kubectl get all
NAME READY STATUS RESTARTS AGE
pod/airflow-5cff4ccbb9-4qvxq 2/2 Running 0 30m
pod/postgres-airflow-bbb79b866-wgrcr 1/1 Running 0 32m
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/airflow NodePort 10.96.100.122 <none> 8080:30809/TCP 32m
service/postgres-airflow ClusterIP 10.96.177.122 <none> 5432/TCP 32m
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
deployment.apps/airflow 1 1 1 1 32m
deployment.apps/postgres-airflow 1 1 1 1 32m
NAME DESIRED CURRENT READY AGE
replicaset.apps/airflow-5cff4ccbb9 1 1 1 32m
replicaset.apps/postgres-airflow-bbb79b866 1 1 1 32m
#查看PVC
kubectl get pvc
NAME STATUS VOLUME CAPACITY ACCESS MODES STORAGECLASS AGE
airflow-dags Bound pvc-b522f935-d6a6-11e8-9a48-fa163ebda1b8 100Gi RWX nfs-client 59m
airflow-logs Bound pvc-b5243605-d6a6-11e8-9a48-fa163ebda1b8 100Gi RWX nfs-client 59m
test-volume Bound pvc-b526f36b-d6a6-11e8-9a48-fa163ebda1b8 10Gi RWX nfs-client 59m
#查看NFS存储
ls /mnt/nfs/air-job-airflow-dags-pvc-b522f935-d6a6-11e8-9a48-fa163ebda1b8/
docker_copy_data.py example_kubernetes_operator.pyc example_trigger_target_dag.py
docker_copy_data.pyc example_latest_only.py example_trigger_target_dag.pyc
example_bash_operator.py example_latest_only.pyc example_xcom.py
example_bash_operator.pyc example_latest_only_with_trigger.py example_xcom.pyc
example_branch_operator.py example_latest_only_with_trigger.pyc __init__.py
example_branch_operator.pyc example_passing_params_via_test_command.py __init__.pyc
example_branch_python_dop_operator_3.py example_passing_params_via_test_command.pyc mydag-fail.py
example_branch_python_dop_operator_3.pyc example_python_operator.py mydag-fail.pyc
example_docker_operator.py example_python_operator.pyc mydag.py
example_docker_operator.pyc example_short_circuit_operator.py mydag.pyc
example_http_operator.py example_short_circuit_operator.pyc subdags
example_http_operator.pyc example_skip_dag.py test_utils.py
example_kubernetes_annotation.py example_skip_dag.pyc test_utils.pyc
example_kubernetes_annotation.pyc example_subdag_operator.py tutorial.py
example_kubernetes_executor.py example_subdag_operator.pyc tutorial.pyc
example_kubernetes_executor.pyc example_trigger_controller_dag.py
example_kubernetes_operator.py example_trigger_controller_dag.pyc2.5 访问GUI
转载自https://blog.youkuaiyun.com/cloudvtech
三、运行自定义的任务
定义两个任务一个会成功一个会失败，使用kubernetes_pod_operator，这个任务会成功mydag.py
from airflow import DAG
from datetime import datetime, timedelta
from airflow.contrib.operators.kubernetes_pod_operator import KubernetesPodOperator
from airflow.operators.dummy_operator import DummyOperator
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': '20181023',
'email': ['airflow@example.com'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5)
}
dag = DAG(
'kubernetes_sample_pass', default_args=default_args, schedule_interval=timedelta(minutes=600))
start = DummyOperator(task_id='run_this_first', dag=dag)
passing = KubernetesPodOperator(namespace='air-job',
image="python:3.6",
cmds=["python","-c"],
arguments=["print('hello world')"],
labels={"foo": "bar"},
name="passing-test",
task_id="passing-task",
get_logs=True,
dag=dag
)
success = KubernetesPodOperator(namespace='air-job',
image="ubuntu:16.04",
cmds=["echo","hello world"],
labels={"foo": "bar"},
name="success",
task_id="success-task",
get_logs=True,
dag=dag
)
passing.set_upstream(start)
success.set_upstream(start)再定义一个会失败的任务进行对比，mydag-fail.py
from airflow import DAG
from datetime import datetime, timedelta
from airflow.contrib.operators.kubernetes_pod_operator import KubernetesPodOperator
from airflow.operators.dummy_operator import DummyOperator
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': '20181023',
'email': ['airflow@example.com'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5)
}
dag = DAG(
'kubernetes_sample_fail', default_args=default_args, schedule_interval=timedelta(minutes=600))
start = DummyOperator(task_id='run_this_first', dag=dag)
passing = KubernetesPodOperator(namespace='air-job',
image="python:3.6",
cmds=["python","-c"],
arguments=["print('hello world')"],
labels={"foo": "bar"},
name="passing-test",
task_id="passing-task",
get_logs=True,
dag=dag
)
failing = KubernetesPodOperator(namespace='air-job',
image="ubuntu:16.04",
cmds=["python","-c"],
arguments=["print('hello world')"],
labels={"foo": "bar"},
name="fail",
task_id="failing-task",
get_logs=True,
dag=dag
)
passing.set_upstream(start)
failing.set_upstream(start)将任务倒入：
kubectl cp mydag.py air-job/airflow-5cff4ccbb9-4qvxq:/root/airflow/dags -c scheduler
kubectl cp mydag-fail.py air-job/airflow-5cff4ccbb9-4qvxq:/root/airflow/dags -c scheduler
分别运行成功和失败的任务：
通过以下方式可以调整POD资源配置
# Limit resources on this operator/task with node affinity & tolerations
three_task = PythonOperator(
task_id="three_task", python_callable=print_stuff, dag=dag,
executor_config={
"KubernetesExecutor": {"request_memory": "128Mi",
"limit_memory": "128Mi",
"tolerations": tolerations,
"affinity": affinity}}
)
转载自https://blog.youkuaiyun.com/cloudvtech
四、业务系统多阶段多并发任务
业务使用airflow和Kubernetes进行业务处理
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