Kubernetes生产实践系列之十：安装Kubernetes原生的airflow

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.csdn.net/cloudvtech

二、安装支持Kubernetets的airflow

2.1 获取airflow代码

git clone https://github.com/apache/incubator-airflow.git

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 imageRUN mkdir /root/.kube
COPY config /root/.kube/config2. scripts/ci/kubernetes/kube/airflow.yaml
-    namespace: default
+    namespace: air-job-        image: airflow
-        imagePullPolicy: IfNotPresent
+        image: 172.222.22.11:5000/airflow
+        imagePullPolicy: Always3. scripts/ci/kubernetes/kube/configmaps.yaml
-    executor = KubernetesExecutor
+    executor = LocalExecutor-    namespace = default
+    namespace = air-job-    rbac = True
+    rbac = False4. 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-jobecho "------- webserver logs -------"
-kubectl logs $POD webserver
+kubectl logs $POD webserver -n air-jobecho "------- scheduler logs -------"
-kubectl logs $POD scheduler
+kubectl logs $POD scheduler -n air-job5. 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: PersistentVolumeClaimapiVersion: v1metadata:name: airflow-dags
+  annotations:
+    volume.beta.kubernetes.io/storage-class: "nfs-client"spec:accessModes:- ReadWriteManyresources:requests:
-      storage: 2Gi
----
-kind: PersistentVolume
-apiVersion: v1
-metadata:
-  name: airflow-logs
-spec:
-  accessModes:
-    - ReadWriteMany
-  capacity:
-    storage: 2Gi
-  hostPath:
-    path: /airflow-logs/
+      storage: 100Gi---kind: PersistentVolumeClaimapiVersion: v1metadata:name: airflow-logs
+  annotations:
+    volume.beta.kubernetes.io/storage-class: "nfs-client"spec:accessModes:- ReadWriteManyresources:requests:
-      storage: 2Gi
----
-kind: PersistentVolume
-apiVersion: v1
-metadata:
-  name: test-volume
-spec:
-  accessModes:
-    - ReadWriteOnce
-  capacity:
-    storage: 2Gi
-  hostPath:
-    path: /airflow-dags/
+      storage: 100Gi---kind: PersistentVolumeClaimapiVersion: v1metadata:name: test-volume
+  annotations:
+    volume.beta.kubernetes.io/storage-class: "nfs-client"spec:accessModes:- ReadWriteManyresources:requests:
-      storage: 2Gi
+      storage: 10Gi

2.3 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/airflow

2.4 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.pyc

2.5 访问GUI

转载自https://blog.csdn.net/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.csdn.net/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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