Applying Custom Resources

AIRFLOW_CONN_KUBERNETES_IN_CLUSTER: "kubernetes://?__extra__=%7B%22extra__kubernetes__in_cluster%22%3A+true%2C+%22extra__kubernetes__kube_config%22%3A+%22%22%2C+%22extra__kubernetes__kube_config_path%22%3A+%22%22%2C+%22extra__kubernetes__namespace%22%3A+%22%22%7D"

---
apiVersion: airflow.stackable.tech/v1alpha1
kind: AirflowCluster
metadata:
  name: airflow
spec:
  image:
    productVersion: 2.9.2
  clusterConfig:
    loadExamples: false
    exposeConfig: false
    credentialsSecret: simple-airflow-credentials
  webservers:
    roleGroups:
      default:
        envOverrides:
          AIRFLOW_CONN_KUBERNETES_IN_CLUSTER: "kubernetes://?__extra__=%7B%22extra__kubernetes__in_cluster%22%3A+true%2C+%22extra__kubernetes__kube_config%22%3A+%22%22%2C+%22extra__kubernetes__kube_config_path%22%3A+%22%22%2C+%22extra__kubernetes__namespace%22%3A+%22%22%7D"
        replicas: 1
  celeryExecutors:
    roleGroups:
      default:
        envOverrides:
          AIRFLOW_CONN_KUBERNETES_IN_CLUSTER: "kubernetes://?__extra__=%7B%22extra__kubernetes__in_cluster%22%3A+true%2C+%22extra__kubernetes__kube_config%22%3A+%22%22%2C+%22extra__kubernetes__kube_config_path%22%3A+%22%22%2C+%22extra__kubernetes__namespace%22%3A+%22%22%7D"
        replicas: 1
  schedulers:
    roleGroups:
      default:
        envOverrides:
          AIRFLOW_CONN_KUBERNETES_IN_CLUSTER: "kubernetes://?__extra__=%7B%22extra__kubernetes__in_cluster%22%3A+true%2C+%22extra__kubernetes__kube_config%22%3A+%22%22%2C+%22extra__kubernetes__kube_config_path%22%3A+%22%22%2C+%22extra__kubernetes__namespace%22%3A+%22%22%7D"
        replicas: 1

---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: airflow-spark-clusterrole
rules:
- apiGroups:
  - spark.stackable.tech
  resources:
  - sparkapplications
  verbs:
  - create
  - get

---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: airflow-spark-clusterrole-binding
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: airflow-spark-clusterrole
subjects:
- apiGroup: rbac.authorization.k8s.io
  kind: Group
  name: system:serviceaccounts

dags
|_ stackable
  |_ __init__.py
  |_ spark_kubernetes_operator.py
  |_ spark_kubernetes_sensor.py
|_ pyspark_pi.py
|_ pyspark_pi.yaml

---
apiVersion: spark.stackable.tech/v1alpha1
kind: SparkApplication
metadata:
  name: pyspark-pi
spec:
  sparkImage:
    productVersion: 3.5.1
  mode: cluster
  mainApplicationFile: local:///stackable/spark/examples/src/main/python/pi.py
  executor:
    replicas: 1

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

from typing import TYPE_CHECKING, Optional, Sequence
from airflow.models import BaseOperator
from airflow.providers.cncf.kubernetes.hooks.kubernetes import KubernetesHook
import json

if TYPE_CHECKING:
    from airflow.utils.context import Context


class SparkKubernetesOperator(BaseOperator):  (1)
    template_fields: Sequence[str] = ("application_file", "namespace")
    template_ext: Sequence[str] = (".yaml", ".yml", ".json")
    ui_color = "#f4a460"

    def __init__(
        self,
        *,
        application_file: str,
        namespace: Optional[str] = None,
        kubernetes_conn_id: str = "kubernetes_in_cluster",  (2)
        api_group: str = "spark.stackable.tech",
        api_version: str = "v1alpha1",
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        self.application_file = application_file
        self.namespace = namespace
        self.kubernetes_conn_id = kubernetes_conn_id
        self.api_group = api_group
        self.api_version = api_version
        self.plural = "sparkapplications"

    def execute(self, context: "Context"):
        hook = KubernetesHook(conn_id=self.kubernetes_conn_id)
        self.log.info("Creating SparkApplication...")
        self.log.info(json.dumps(self.application_file, indent=4))
        response = hook.create_custom_object(
            group=self.api_group,
            version=self.api_version,
            plural=self.plural,
            body=self.application_file,
            namespace=self.namespace,
        )
        return response

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

from typing import Optional, Dict
from kubernetes import client
from airflow.exceptions import AirflowException
from airflow.sensors.base import BaseSensorOperator
from airflow.providers.cncf.kubernetes.hooks.kubernetes import KubernetesHook


class SparkKubernetesSensor(BaseSensorOperator):  (3)
    template_fields = ("application_name", "namespace")
    FAILURE_STATES = ("Failed", "Unknown")
    SUCCESS_STATES = "Succeeded"

    def __init__(
        self,
        *,
        application_name: str,
        attach_log: bool = False,
        namespace: Optional[str] = None,
        kubernetes_conn_id: str = "kubernetes_in_cluster",  (2)
        api_group: str = "spark.stackable.tech",
        api_version: str = "v1alpha1",
        poke_interval: float = 60,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        self.application_name = application_name
        self.attach_log = attach_log
        self.namespace = namespace
        self.kubernetes_conn_id = kubernetes_conn_id
        self.hook = KubernetesHook(conn_id=self.kubernetes_conn_id)
        self.api_group = api_group
        self.api_version = api_version
        self.poke_interval = poke_interval

    def _log_driver(self, application_state: str, response: dict) -> None:
        if not self.attach_log:
            return
        status_info = response["status"]
        if "driverInfo" not in status_info:
            return
        driver_info = status_info["driverInfo"]
        if "podName" not in driver_info:
            return
        driver_pod_name = driver_info["podName"]
        namespace = response["metadata"]["namespace"]
        log_method = (
            self.log.error
            if application_state in self.FAILURE_STATES
            else self.log.info
        )
        try:
            log = ""
            for line in self.hook.get_pod_logs(driver_pod_name, namespace=namespace):
                log += line.decode()
            log_method(log)
        except client.rest.ApiException as e:
            self.log.warning(
                "Could not read logs for pod %s. It may have been disposed.\n"
                "Make sure timeToLiveSeconds is set on your SparkApplication spec.\n"
                "underlying exception: %s",
                driver_pod_name,
                e,
            )

    def poke(self, context: Dict) -> bool:
        self.log.info("Poking: %s", self.application_name)
        response = self.hook.get_custom_object(
            group=self.api_group,
            version=self.api_version,
            plural="sparkapplications",
            name=self.application_name,
            namespace=self.namespace,
        )
        try:
            application_state = response["status"]["phase"]
        except KeyError:
            self.log.debug(
                f"SparkApplication status could not be established: {response}"
            )
            return False
        if (
            self.attach_log
            and application_state in self.FAILURE_STATES + self.SUCCESS_STATES
        ):
            self._log_driver(application_state, response)
        if application_state in self.FAILURE_STATES:
            raise AirflowException(
                f"SparkApplication failed with state: {application_state}"
            )
        elif application_state in self.SUCCESS_STATES:
            self.log.info("SparkApplication ended successfully")
            return True
        else:
            self.log.info("SparkApplication is still in state: %s", application_state)
            return False

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.

"""Example DAG demonstrating how to apply a Kubernetes Resource from Airflow running in-cluster"""

from datetime import datetime, timedelta, timezone
from airflow import DAG
from airflow.exceptions import AirflowException
from airflow.utils import yaml
import os
from stackable.spark_kubernetes_sensor import SparkKubernetesSensor
from stackable.spark_kubernetes_operator import SparkKubernetesOperator


with DAG(  (4)
    dag_id="sparkapp_dag",
    schedule_interval=None,
    start_date=datetime(2022, 1, 1),
    catchup=False,
    dagrun_timeout=timedelta(minutes=60),
    tags=["example"],
    params={},
) as dag:

    def load_body_to_dict(body):
        try:
            body_dict = yaml.safe_load(body)
        except yaml.YAMLError as e:
            raise AirflowException(f"Exception when loading resource definition: {e}\n")
        return body_dict

    yaml_path = os.path.join(
        os.environ.get("AIRFLOW__CORE__DAGS_FOLDER", ""), "pyspark_pi.yaml"
    )

    with open(yaml_path, "r") as file:
        crd = file.read()
    with open("/run/secrets/kubernetes.io/serviceaccount/namespace", "r") as file:
        ns = file.read()

    document = load_body_to_dict(crd)
    application_name = "pyspark-pi-" + datetime.now(timezone.utc).strftime(
        "%Y%m%d%H%M%S"
    )
    document.update({"metadata": {"name": application_name, "namespace": ns}})

    t1 = SparkKubernetesOperator(  (5)
        task_id="spark_pi_submit",
        namespace=ns,
        application_file=document,
        do_xcom_push=True,
        dag=dag,
    )

    t2 = SparkKubernetesSensor(  (6)
        task_id="spark_pi_monitor",
        namespace=ns,
        application_name="{{ task_instance.xcom_pull(task_ids='spark_pi_submit')['metadata']['name'] }}",
        poke_interval=5,
        dag=dag,
    )

    t1 >> t2  (7)